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S. Dehaene, INSERM, France

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Y. Dudai, Weizmann Institute, Israel

A.K. Engel, Hamburg University, Germany

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A.D. Friederici, MPI, Leipzig, Germany

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February 2011 Volume 15, Number 2 pp. 47–94

Cover: Although vision holds a central role in social interactions, the social perception of actions also relies on auditory and

olfactory information. On pages 47–55, Salvatore M. Aglioti and Mariella Pazzaglia review recent evidence showing how

actions can be guided by sounds and smells both independently as well as within the context of the multimodal perceptions

and representations that characterize real world experiences. Crucially, non-visual information appears to have a crucial

role not only in guiding actions, but also in anticipating others' actions and thus in shaping social interactions more generally.

Forthcoming articles

Cognitive neuroscience of self-regulation failure

Todd Heatherton and Dylan D. Wagner

Representing multiple objects as an ensemble enhances visual cognition

George A. Alvarez

Songs to syntax: The linguistics of birdsong

Robert C Berwick, Kazuo Okanoya, Gabriel J Beckers and Johan J. Bolhuis

Connectivity constrains the organization of object knowledge

Bradford Zack Mahon and Alfonso Caramazza

Specifying the self for cognitive neuroscience

Kalina Christoff, Diego Cosmelli, Dorothée Legrand and Evan Thompson

Review

47 Sounds and scents in (social) action

56 Value, pleasure and choice in the ventral

prefrontal cortex

68 Cognitive culture: theoretical and empirical

insights into social learning strategies

77 Visual search in scenes involves selective

and nonselective pathways

85 Emotional processing in anterior cingulate

and medial prefrontal cortex

Salvatore M. Aglioti and

Mariella Pazzaglia

Fabian Grabenhorst and

Edmund T. Rolls

Luke Rendell, Laurel Fogarty,

William J.E. Hoppitt, Thomas J.H. Morgan,

Mike M. Webster and Kevin N. Laland

Jeremy M. Wolfe, Melissa L.-H. Võ,

Karla K. Evans and Michelle R. Greene

Amit Etkin, Tobias Egner and

Raffael Kalisch

Sounds and scents in (social) actionSalvatore M. Aglioti1,2 and Mariella Pazzaglia1,2

1Dipartimento di Psicologia, Sapienza University of Rome, Via dei Marsi 78, Rome I-00185, Italy2 IRCCS Fondazione Santa Lucia, Via Ardeatina 306, Rome I-00179, Italy

Although vision seems to predominate in triggering the

simulation of the behaviour and mental states of others,

the social perception of actions might rely on auditory

and olfactory information not onlywhen vision is lacking

(e.g. in congenitally blind individuals), but also in daily

life (e.g. hearing footsteps along a dark street prompts

an appropriate fight-or-fly reaction and smelling the

scent of coffee prompts the act of grasping amug). Here,

we review recent evidence showing that non-visual,

telereceptor-mediated motor mapping might occur as

an autonomous process, as well as within the context of

the multimodal perceptions and representations that

characterize real-world experiences. Moreover, we dis-

cuss the role of auditory and olfactory resonance in

anticipating the actions of others and, therefore, in

shaping social interactions.

Telereceptive senses, namely vision, audition and

olfaction

Perceiving and interacting with the world and with other

individuals might appear to be guided largely by vision,

which, according to classical views, leads over audition,

olfaction and touch, and commands, at least in human

and non-human primates, most types of cross-modal and

perceptuo-motor interactions [1]. However, in sundry daily

life circumstances, our experience with the world is inher-

ently cross-modal [2]. For example, inputs from all sensory

channels combine to increase the efficiency of our actions

and reactions. Seeing flames, smelling smoke or hearing a

fire alarmmight each be sufficient to create an awareness of

a fire. However, the combination of all these signals ensures

that our response to danger is more effective. The multi-

modal processing of visual, acoustic and olfactory informa-

tion is even more important for our social perception of the

actions of other individuals [3]. Indeed, vision, audition and

olfaction are the telereceptive senses that process informa-

tion coming from both the near and the distant external

environment, onwhich the brain then defines the self–other

border and the surrounding social world [4,5].

Behavioural studies suggest that action observation and

execution are coded according to a common representa-

tional medium [6]. Moreover, neural studies indicate that

seeing actions activates a fronto-parietal neural network

that is also active when performing those same actions

[7,8]. Thus, the notion that one understands the actions of

others by simulating them motorically is based mainly on

visual studies (Box 1). Vision is also the channel used for

studying the social nature of somatic experiences (e.g.

touch and pain) [9–11] and emotions (e.g. anger, disgust

and happiness) [12]. In spite of the notion that seeing

might be informed by what one hears or smells, less is

known about the possible mapping of actions through the

sound and the odour associated with them, either in the

absence of vision or within the context of clear cross-modal

perception. In this review, we question the exclusive

supremacy of vision in action mapping, not to promote a

democracy of the senses, but to highlight the crucial role of

the other two telereceptive channels in modulating our

actions and our understanding of the world in general, and

of the social world in particular.

The sound and flavour of actions

Classic cross-modal illusions, such as ventriloquism or the

McGurk effect, indicate that vision is a key sense in several

circumstances [13,14]. Therefore, when multisensory cues

are simultaneously available, humans display a robust

tendency to rely more on visual than on other forms of

sensory information, particularlywhen dealingwith spatial

tasks (a phenomenon referred to as the ‘Colavita visual

dominance effect’) [15]. However, our knowledge is some-

times dominated by sound and is filtered through a predom-

inantly auditory context. Auditory stimuli might, for

example, capture visual stimuli in temporal localization

tasks [16]. Moreover, the presentation of two beeps and a

single flash induces the perception of two visual stimuli [17].

Thus, sound-induced flash illusions create the mistaken

belief that we are seeing what we are, in fact, only hearing.

This pattern of results might be in keeping with the

notion that multisensory processing reflects ‘modality ap-

propriateness’ rules, whereby vision dominates in spatial

tasks, and audition in temporal ones [18]. However, psy-

chophysical studies indicate that the degradation of visual

inputs enables auditory inputs to modulate spatial locali-

zation [19]. This result is in keeping with the principle of

inverse effectiveness [20], according to which multisensory

integration is more probable or stronger for the unisensory

stimuli that evoke relatively weak responses when pre-

sented in isolation. Notably, the recording of neural activi-

ty from the auditory cortex of alert monkeys watching

naturalistic audiovisual stimuli indicates that not only

do congruent bimodal events provide more information

than do unimodal ones, but also that suppressed responses

are also less variable and, thus, more informative than are

enhanced responses [21].

Relevant to the present review is that action sounds

might be crucial for signalling socially dangerous or un-

pleasant events. Efficient mechanisms for matching audi-

tion with action might be important, even at basic levels,

because they might ensure the survival of all hearing

Review

Corresponding author: Aglioti, S.M. (salvatoremaria.aglioti@uniroma1.it).

1364-6613/$ – see front matter � 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tics.2010.12.003 Trends in Cognitive Sciences, February 2011, Vol. 15, No. 2 47

mailto:salvatoremaria.aglioti@uniroma1.it

http://dx.doi.org/10.1016/j.tics.2010.12.003

individuals. For example, in the dark of the primordial

nights, ancestral humans probably detected potential dan-

gers (e.g. the footsteps of enemies) mainly by audition and,

therefore, implemented effective fight-or-flight behaviour.

However, action–sound mediated inferences about others

might also occur in several daily life circumstances in

present times. Imagine, for example, your reaction to

the approach of heavy footsteps when you are walking

along a dark street. Furthermore, listening to footsteps

of known individuals might enable one to not only recog-

nize the identity [22], but also determine the disposition of

these individuals (e.g. bad mood).

Although olfaction in some mammals mediates sophisti-

cated social functions, such as mating, and might facilitate

the recognition of ‘who is who’ [23], this sense is considered

somewhat reductional in humans. However, even in

humans, olfaction is closely related to not only neurovege-

tative and emotional reactivity, but also higher-order func-

tions, suchasmemory.Moreover, olfaction inhumans isalso

linked to empathic reactivity [24], kin recognition [25],

cross-modal processing of the faces of others and the con-

struction of the semantic representation of objects [26].

Behavioural studies indicate that the grasping of small

(e.g. an almond) or large (e.g. an apple) objects with charac-

teristic odours is influenced by the delivery of the same or of

different smells. In particular, a clear interference with the

kinematics of grasping [27] and reaching [28] movements

was found in conditions of mismatch between the observed

objects (e.g. a strawberry) and the odour delivered during

the task (e.g. the scent of an orange).

Mapping sound- and odour-related actions in the

human brain

Inspired by single-cell recording in monkeys [7], many

neuroimaging and neurophysiological studies suggest that

the adult human brain is equipped with neural systems

and mechanisms that represent visual perception and the

execution of action in common formats. Moreover, studies

indicate that a large network, centred on the inferior

frontal gyrus (IFG) and the inferior parietal lobe (IPL),

and referred to as the action observation network (AON)

[29,30], underpins action viewing and action execution.

Less information is available about whether the AON

[31] is also activated by the auditory and olfactory coding

of actions.

The phenomena, mechanisms and neural structures

involved in processing action-related sounds have been

explored in healthy subjects (Figure 1) and in brain-dam-

aged individuals (Box 2) using correlational [32–36] and

causative approaches [37]. At least two important conclu-

sions can be drawn from these studies. The first is that

listening to the sound produced by human body parts (e.g.

two hands clapping) activates the fronto-parietal AON.

The second is that such activation might be somatotopi-

cally organized, with the left dorsal premotor cortex and

the IPL being more responsive to the execution and hear-

ing of hand movements than to mouth actions or to sounds

that are not associated with human actions (e.g. environ-

mental sounds, a phase-scrambled version of the same

sound, or a silent event). Conversely, the more ventral

regions of the left premotor cortex are more involved in

processing sounds performed by the mouth (Figure 1 and

Box 2).

The social importance of olfaction in humans has been

demonstrated in a positron emission tomography (PET)

study [38], showing that body odours activate a set of

cortical regions that differed from those activated by

non-body odours. In addition, smelling the body odour of

a friend activates different neural regions (e.g. Extrastri-

ate body area (EBA)) from smelling the odour of strangers

(e.g. amygdala and insula). However, interest in the olfac-

tory coding of actions and its neural underpinnings is very

recent, and only two correlational studies have addressed

this topic thus far (Figure 2). In particular,mere perception

of smelling food objects induced both a specific facilitation

of the corticospinal system [39] and specific neural activity

in the AON [40].

Multimodal coding of actions evoked by auditory and

olfactory cues

The inherently cross-modal nature of action perception is

supported by evidence showing that a combination of

multiple sensory channels might enable individuals to

interpret actions better. The merging of visual and audito-

ry information, for example, enables individuals to opti-

mize their perceptual and motor behaviour [41]. Moreover,

a combination of olfactive and visual inputs facilitates the

selection of goal-directed movements [42]. Importantly,

although auditory or olfactory cues might increase neural

activity in action-related brain regions, such effects

might be higher when the two modalities are combined.

It has been demonstrated, for example, that the blood

Box 1. Beyond the visuomotor mirror system

Mirror neurons (MNs), originally discovered in the monkey ventral

premotor cortex (F5) and inferior parietal lobe (PFG), increase their

activity during action execution as well as during viewing of the

same action [68,69]. Single-cell recording from the ventral premotor

cortex showed that MNs fired also when sight of the hand–object

interaction was temporarily occluded [70]. In a similar way, the

activity in the parietal MNs of the onlooking monkey was modulated

differentially when the model exhibited different intentions (e.g.

grasping the same object to eat or to place it) [71]. Taken together,

these results suggest that MNs represent the observed actions

according to anticipatory codes. Relevant to the present review is

the existence of audio-motor MNs specifically activated when the

monkey hears the sound of a motor act without seeing or feeling it

[72]. In addition, the multimodal response of visuo-audio-motor

neurons might be superadditive; that is, stronger than the sum of

the unimodal responses [73]. Whereas audio MNs might underpin

an independent and selective mapping modality [72], triple-duty

neurons are likely to constitute the neural substrate of the complex

multimodal mapping of actions [73]. Therefore, the physiological

properties of these resonant neurons suggest they constitute a core

mechanism for representing the actions of others.

In a recent study, single-cell recording was conducted in human

patients who observed and performed emotional and non-emo-

tional actions. The study provides direct evidence of double-duty

visuo-motor neurons, possibly coding for resonant emotion and

action [74]. Importantly, the human ‘see-do’ neurons were found in

the medial frontal and temporal cortices (where the patients, for

therapeutic reasons, had electrodes implanted). These two regions

are not part of the classic mirror system, suggesting that the

onlooker-model resonance extends beyond action mirroring and the

premotor-parietal network. Direct information relating to the non-

visual and anticipatory properties of the human mirror system is,

however, still lacking.

Review Trends in Cognitive Sciences February 2011, Vol. 15, No. 2

48

oxygenation level-dependent (BOLD) signal in the left

ventral premotor cortex is enhanced when seeing and

hearing another individual tearing paper as compared

with viewing a silent video depicting the same scene or

only hearing the sound associated with the observed action

[43]. No such dissociation was found for the parietal

regions, indicating that cross-modal modulation might

differentially impact on the different nodes of the AON.

Similarly, corticospinal motor activity in response to the

acoustic presentation of the sound of a hand crushing a

small bottle was lower than to the presentation of congru-

ent visuo-acoustic input (e.g. the same sound and the

corresponding visual scene), and higher than to incongru-

ent visuo-acoustic information (e.g. the same sound and a

hand pouring water from the same bottle) [35] (Figure 1c).

This pattern of results hints at a genuine, cross-modal

modulation of audiomotor resonance [31]. Neurophysiolog-

ical studies have identified multisensory neurons in the

superior temporal sulcus (STS) that code both seen and

heard actions [21]. When driven by audiovisual bimodal

[()TD$FIG]

BA 6 IPL

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Key: Key:Key:

Key:

TRENDS in Cognitive Sciences

Water

CrushCrush

Crush

Figure 1. The sound of actions. Representative studies on the auditory mapping of actions, performed by using different state-of-the-art cognitive neuroscience techniques.

(a) Left panel: cortical activity evoked by listening to sounds associated with human finger (red line) and tongue (blue line) movements that were used as deviant stimuli to

evoke a potential known as mismatch negativity (MMN). Sounds not associated with any actions were used as control stimuli. Deviant stimuli produced larger MMNs than

did sounds not already associated with actions 100 ms after the stimulus presentation. Furthermore, the source estimation of MMN indicates that finger and tongue sounds

activated distinct regions in the left pre-motor areas, suggesting an early automatic, somatotopic mapping of action-related sounds in these regions. Right panel: auditory-

evoked potentials in response to context-related sounds that typically cue a responsive action by the listener (e.g. a ringing telephone; red line) and to context-free sounds

that do not elicit responsive actions (e.g. a ringing tower bell; blue line). Responses were higher for action-evoking sounds than for non-action-evoking sounds 300 ms after

the stimulus, mainly in the left premotor and inferior frontal and prefrontal regions [32,33]. (b) Hearing sounds related to human actions increases neural activity in left

perisylvian fronto-parietal areas relative to hearing environmental sounds, a phase-scrambled version of the same sound, or a silent event. In the frontal cortex, the pattern

of neural activity induced by action-related sounds reflected the body part evoked by the sound heard. A dorsal cluster was more involved during listening to and executing

hand actions, whereas a ventral cluster was more involved during listening to and executing mouth actions. Thus, audio-motor mapping might occur according to

somatotopic rules. The audio-motor mirror network was also activated by the sight of the heard actions, thus hinting at the multimodal nature of action mapping [34]. (c)

Single pulse TMS enables the exploration of the functional modulation of the corticospinal motor system during visual or acoustic perception of actions. During unimodal

presentations, participants observed a silent video of a right hand crushing a small plastic bottle or heard the sound of a bottle being crushed. During bimodal conditions,

vision and auditory stimuli were congruent (seeing and hearing a hand crushing a bottle; blue lines and bars) or incongruent (e.g. seeing a hand crushing a bottle but

hearing the sound of water being poured in a glass and hearing the sound of a hand crushing a bottle but seeing a foot crushing a bottle; red lines and bars). Compared with

incongruent bimodal stimulation, unimodal and congruent bimodal stimulation induced an increase of amplitude of the motor potentials evoked by the magnetic pulse.

Thus, corticospinal reactivity is a marker of both unimodal and cross-modal mapping of actions [35]. Data adapted, with permission, from [32–35].


49

input, the firing rate of a proportion of these cells was

higher with respect to the sum of auditory or visual input

alone. This superadditive response occurred when the seen

action matched the heard action [44]. The STS is heavily

connected to the frontal and parietal regions [45], thus

hinting at the important role of temporal structures in the

simulation of actions triggered by audiovisual inputs.

A clear multimodal contribution to action mapping was

demonstrated in a functional magnetic resonance imaging

(fMRI) study where subjects observed hand-grasping

actions directed to odourant objects (e.g. a fruit, such as

a strawberry or an orange) that were only smelt, only seen,

or both smelt and seen [40]. Grasping directed towards

objects perceived only through smell activated not only the

olfactory cortex, but also the AON (Figure 2). Moreover,

perceiving the action towards an object coded via both

olfaction and vision (visuo-olfacto-motor mapping) induced

further increase in activity in the temporo-parietal cortex

[40]. A clear increase of corticospinal motor facilitation

during the observation of unseen but smelt objects, and

the visual observation of the grasping of the same objects,

has also been reported, further confirming the presence of

visuo-olfacto-motor resonance [39]. It is also relevant that

the neural activity in response to visual–olfactory action-

related cues in the right middle temporal cortex and left

superior parietal cortex might be superadditive; that is,

higher than the sum of visual and olfactory cues presented

in isolation [40]. Accordingly, although unimodal input

might trigger action representation, congruent bimodal

input is more appropriate because it provides an enriched

sensory representation, which, ultimately, enables full-

blown action simulation. In human and non-human pri-

mates, the orbitofrontal cortex (OFC) receives input from

both the primary olfactory cortex and the higher-order

visual areas [46], making it a prominent region for the

multisensory integration of olfactory and visual signals.

When the integration concerns visual–olfactory represen-

tations related to the simulation of a given action, the

product of such computation has to be sent to motor

regions. The OFC is heavily connected to brain regions

Box 2. Audio-motor resonance in patients with apraxia

Crucially, causative information on the auditory mapping of actions

has been provided by a study on patients with apraxia [37], where a

clear association was identified between deficits in performing hand-

or mouth-related actions and the ability to recognize the associated

sounds. Moreover, using state-of-the-art lesion-mapping proce-

dures, it was shown that, whereas both frontal and parietal

structures are involved in executing actions and discriminating the

sounds produced by the actions of others, the ability to recognize

specifically sounds arising from non-human actions appears to be

linked to the temporal regions (Figure Ia). This finding supports the

notion that different neural substrates underpin the auditory

mapping of actions and the perception of non-human action-related

cues. Because this study was based on a sound-picture matching

task, it is, in principle, possible that the audio-motor mapping deficit

reflects a deficit in visual motor mapping, in keeping with the

reported visual action recognition impairment of patients with

apraxia [75].

To determine whether deficits in audio-motor and visuo-motor

action mapping reflect a common supramodal representation, or

were driven by visual deficits, results from the lesion mapping study

[37] were compared with those from neuroimaging studies in healthy

subjects where visual or auditory action recognition was required.

Distinct neural regions in the left hemisphere were identified that

were specifically related to observing or hearing hand- and mouth-

related actions. In particular, a somatotopic arrangement along the

motor strip seems to be distinctive of visual- and auditory-related

actions (Figure Ib) [31]. Thus, although multimodal perception might

optimize action mapping, an independent contribution to this process

could be provided by vision and audition. Olfactory mapping of

actions has not yet been performed in patients with brain damage.[()TD$FIG]

Hand Mouth

Body-related action sounds in fmri

Limb related actions

Overlap

Mouth-related action sounds in VLSM

Body-related action observation in fmri

Mouth related actions

(b)

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Mouth sounds Limb sounds Non-human sounds

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Key:


Figure I. Visual and auditory action mapping in brain damaged patients. (a) Direct evidence for the anatomical and functional association between action execution and

discrimination during matching of specific visual pictures to previously presented sounds in patients with brain damage and with or without apraxia. Voxel-based lesion

symptom mapping (VLSM) analysis demonstrated a clear association between deficits in performing hand- or mouth-related actions, the ability to recognize the same

sounds acoustically, and frontal and parietal lesions [37]. (b) Cortical rendering shows the voxel clusters selectively associated with deficits (VLSM study) or activation

(fMRI studies) when processing limb- or mouth-related action sounds [31]. Data adapted, with permission, from [31,37].


50

involved in movement control. In particular, direct con-

nections between the OFC and the motor part of the

cingulate area, the supplementary and the pre-supplemen-

tary motor areas, the ventral premotor area and even the

primary motor cortex, have been described [47,48].

The possible functional gain of multimodal over unim-

odal coding of actions deserves further discussion. Motor

resonance does not only involve the commands associated

with motor execution, but also a variety of sensory signals

that trigger or modulate the action simulation process.

Such modulation might be more effective when mediated

by more than one sensory modality. Indeed, multimodal

integration seems to enhance perceptual accuracy and

saliency by providing redundant cues that might help to

characterize actions fully. Importantly, multisensory inte-

gration appears more effective when weak and sparse

stimuli are involved [49]. Thus, it might be that multisen-

sory integration in the service of action simulation provides

precise dynamic representations of complex sensory

actions. Moreover, the functional gain derived from multi-

modal integration might help robust and detailed simula-

tion of the perceived action.

Can the social mapping of an action occur

independently from vision or audition?

Inferences about the sensory andmotor states of others can

be drawn via mental imagery that involves specific neural

systems (e.g. the somatic or the visual cortex for tactile and

[()TD$FIG]

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No-smell Smelling

Figure 2. The flavour of actions. The effects of unimodal and cross-modal olfactory stimulation have been investigated in subjects who smelled the odours of graspable objects

and observed a model grasping ‘odourant’ foods. Unimodal presentation consisted of either visual (see a model while reaching to grasp food) or olfactory (smelling the

graspable foodwith no concurrent visual stimulation) stimuli. In the bimodal presentation, visual and olfactory stimuli occurred together. (a) Single-pulse TMSwas delivered to

the primary motor cortex of healthy subjects. Sniffing alimentary odourants induced an increase of corticospinal reactivity of the samemuscles that would be activated during

actual grasping of the presented food. Moreover, cross-modal facilitation was observed during concurrent visual and olfactory stimulation [39]. (b) The observation of a hand

grasping an object that was smelt but not seen activated the frontal, parietal and temporal cortical regions. No such activity was found during observation of a mimed grasp.

Additive activity in this action observation networkwas observedwhen the object to be graspedwas both seen and smelt [40]. Importantly,maximalmodulation of corticospinal

reactivity (TMS) and of BOLD signal (fMRI) was observed when both visuo-motor and olfacto-motor information were presented. This result suggests that, although olfactory

stimuli might unimodally modulate the action system, its optimal tuning is achieved through cross-modal stimulation. Data adapted, with permission, from [39,40].


51

visual imagery, respectively) [9,10,50]. However, only the

telereceptive senses allow the social perception of touch

and pain. Perceiving the touch of others, for example, can

occur only through vision [11], whereas perceiving pain in

others can occur through vision (e.g. direct observation of

needles penetrating skin) [10,51–53], audition (e.g. hearing

another’s cry) [54], or even smell (e.g. the odour of burning

flesh) [55]. Although the telereceptive senses can map the

actions of others unimodally, cross-modalmapping is likely

to be the norm. However, whether vision or audition is

more dominant in modulating this process in humans is

still an open question. The study of blind or deaf individu-

als provides an excellent opportunity for addressing this

issue (Figure 3). A recent fMRI study demonstrated that

the auditory presentation of hand-executed actions in

congenitally blind individuals activated the AON, al-

though to a lesser extent compared with healthy, blind-

folded participants [56]. However, a clear lack of

corticospinal motor reactivity to vision and the sound of

actions in individuals with congenital deafness and blind-

ness, respectively, was found [57]. This pattern of results

suggests that, despite the plastic potential of the develop-

ing brain, action mapping remains an inherently cross-

modal process.

Anticipatory coding of the actions of others based on

auditory and olfactory cues

Influential theoretical models suggest that the human

motor system is designed to function as an anticipation

device [58] and that humans predict forthcoming actions by

using their own motor system as an internal forward

model. Action prediction implies the involvement of specif-

ic forms of anticipatory, embodied simulation that triggers

neural activity in perceptual [59] and motor [60] systems.

Evidence in support of this notion comes from a study in

which merely waiting to observe a forthcoming movement

made by another individual was found to trigger (uncon-

sciously) a readiness potential in the motor system of an[()TD$FIG]

-25-20-15-10-505

10152025

ME

P a

mp

litu

de

(% v

s. b

as

eli

ne

)

Visual/auditory

dPM

vPMvPM

vPM

IFIF

IF

IPL

SPLSPL

SPL

dPM

dPM

aMF IPL

IPL

MT/ST MT/ST

MT/ST

IPL

vPM

Blind Deaf

-25-20-15-10-505

10152025

ME

P a

mp

litu

de

(% v

s. b

as

eli

ne

)

ControlsAuditoryVisual

6 s

0 s

4.1 s

4.8 s

TMS

(b)

(a)

BlindControls

AuditoryVisual

Scissors

Scissors

-8

+8

-2.3+2.3

t sco

res

Motor pantomime

Action sound

Mirror overlap


Figure 3. The auditory and visual responsiveness of the action observation network in individuals with congenital blindness or deafness. (a) The modulation of resonant

action systems was investigated by using fMRI while congenitally blind or sighted individuals listened and recognized hand-related action sounds or environmental sounds

and executed motor pantomimes upon verbal utterance of the name of a specific tool. The sighted individuals were also requested to perform a visual action recognition

task. Listening to action sounds activated a premotor-temporoparietal cortical network in the congenitally blind individuals. This network largely overlapped with that

activated in the sighted individuals while they listened to an action sound and observed and executed an action. Importantly, however, the activity was lower in blind than

in sighted individuals, suggesting that multimodal input is necessary for the optimal tuning of action representation systems [56]. (b) Corticospinal reactivity to TMS was

assessed in congenitally blind (blue bars) or congenitally deaf (green bars) individuals during the aural or visual presentation of a right-hand action or a non-human action

(the flowing of a small stream of water in a natural environment). All videos were aurally presented to the blind and sighted control subjects and visually presented with

muted sound to the deaf and hearing control individuals (grey bars = control subjects). Amplitudes of the motor evoked potentials (MEPs) recorded from the thumb (OP)

and wrist (FCR) muscles during action perception in the deaf versus the hearing control group and the blind versus the sighted control group indicated that somatotopically,

muscle-specific modulation was absent in individuals with a loss of a sensory modality (either vision or hearing). The reduction of resonant audio- or visuo-motor

facilitation in individuals with congenital blindness or deafness suggests that the optimal tuning of the action system is necessarily multimodal [57]. Data adapted, with

permission, from [56,57].


52

onlooker [61]. In a similar vein, by using single-pulse

transcranial magnetic stimulation (TMS), it was demon-

strated that the mere observation of static pictures, repre-

senting implied human actions, induced body part-specific,

corticospinal facilitation [62–64]. Moreover, it was also

demonstrated that the superior perceptual ability of elite

basketball players in anticipating the fate of successful

versus unsuccessful basket throws is instantiated in a

time-specific increase in corticospinal activity during the

observation of erroneous throws [65]. Although cross-mod-

al perception studies indicate that auditory [17] or olfacto-

ry [40] inputs might predominate over visual perception in

certain circumstances, information on the phenomenology

and neural underpinnings of the anticipatory process that

enables individuals to predict upcoming actions on the

basis of auditory and olfactory information remains mea-

gre (Figure 4).

Anticipation of sound sequences typically occurs when

repeatedly listening to a music album in which different

tracks are played in the same order. Indeed, it is a

common experience that hearing the end of a given track

evokes, in total silence, the anticipatory image of the

subsequent track on the same album. Interestingly, the

creation of this association brings about an increase of

neural activity in premotor and basal ganglia regions,

suggesting that analogous predictive mechanisms are

involved in both sound sequence and motor learning

[66,67]. The prediction of an upcoming movement and

the anticipation of forthcoming actions might be even

stronger when dealing with precise sound–action order

association. It is relevant that hearing sounds typically

associated with a responsive action (e.g. a doorbell) brings

about an increase in neural activity in the frontal regions,

mainly on the left hemisphere, which is not found in

response to sounds that do not elicit automatic motor

responses (e.g. piano notes that have not been heard

before) [33]. Thus, social action learning triggered by

auditory cues might imply the acquisition of a temporal

contingency between the perception of a particular sound

and the movement associated with a subsequent action.

This experience-related, top-downmodulation of auditory

perception might be used to predict and anticipate forth-

coming movements and to create a representation of

events that should occur in the near future. The grasping

actions triggered by smelling fruits or sandwiches, indi-

cate that olfactory cues might trigger anticipatory action

planning [40]. Therefore, the sensory consequences of an

odour are integrated and become part of the cognitive

representation of the related action. Unfortunately, stud-

ies on the role of social odours in triggering anticipatory

[()TD$FIG]

(b)

TMS0.02 s

10 s

10.5 sTMS

1° trials0 s

ME

P a

mplit

ude

Start Middle EndStart Middle End

0

0.4

0.8

1.2

1.60.5 s

% B

old

ch

an

ge

Action--perception connectionNo action--perception connection

midPMC(54, 2, 48)

SMA(0, -6, 58)

0

0.4

0.8

1.2

0

0.4

0.8

1.2

midPMC

Key:

(-50, -6, 52)

Cerebellum(24, -72, -50)

Passive listen

Listen with anticipation

Tap Passivelisten


Tap

(a)

2° trials

4 12


FLICKGRASPFLICKGRASP

Sta

rtM

idd

leE

nd


Figure 4. Prospective coding of actions. (a) A single-pulse TMS study demonstrated that the observation of the start and middle phases of grasp and flick actions induces a

significantly higher motor facilitation than does observation of final posture. Higher resonance with upcoming than with past action phases supports the notion that the

coding of observed actions is inherently anticipatory [63]. (b) An fMRI study demonstrated that neural activity in the ventral premotor cortex (which is part of the motor

resonance network) and cerebellum is higher when subjects listen to a specific rhythm in anticipation of an overt reaction to it than when they listen to the same sound

passively, without expecting an action to follow. This result sheds light on the nature of action–perception processes and suggests an inherent link between auditory and

motor systems in the context of rhythm [66]. Data adapted, with permission, from [63,66].


53

representations of the actions of others are currently

lacking (see Box 3).

Conclusions and future directions

Hearing and smelling stimuli that evoke, or are associated

with, actions activate their representation, thus indicating

that not only vision, but also the other two telereceptive

senses (i.e. audition and olfaction) might trigger the social

mapping of actions somewhat independently from one

another. Although the mapping process might be triggered

by unimodal stimulation, the action representation process

elicited by auditory and olfactory cues typically occurs

within the context of multimodal perception, as indicated

by the defective resonance in blind or deaf individuals. The

results expand current knowledge by suggesting that

cross-modal processing optimizes not only perceptual,

but also motor performance. The analysis of how these

two sensory channels contribute to the perspective coding

of the actions of others remains a fundamental topic for

future research.

AcknowledgementsFunded by the Istituto Italiano di Tecnologia (SEED Project Prot. Num.

21538), by EU Information and Communication Technologies Grant

(VERE project, FP7-ICT-2009-5, Prot. Num. 257695) and the Italian

Ministry of Health.

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� Does action anticipation occur in the complete absence of visual

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� Does subconscious perception of odours have a role in the

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� What is the exact role of olfactory cues in action anticipation?

� Based on the notion that multimodal modulation is essential for

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that the presence of odors during virtual interactions with avatars,

would trigger greater embodiment of their sensorimotor states.


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55

http://dx.doi.org/10.1162/jocn.2010.21517

http://dx.doi.org/10.1016/j.cub.2010.02.045

http://dx.doi.org/10.1016/j.cub.2010.02.045

Value, pleasure and choice in theventral prefrontal cortexFabian Grabenhorst1 and Edmund T. Rolls2

1University of Cambridge, Department of Physiology, Development and Neuroscience, Cambridge, UK2Oxford Centre for Computational Neuroscience, Oxford, UK

Rapid advances have recently been made in understand-

ing how value-based decision-making processes are

implemented in the brain. We integrate neuroeconomic

and computational approaches with evidence on the

neural correlates of value and experienced pleasure to

describe how systems for valuation and decision-mak-

ing are organized in the prefrontal cortex of humans and

other primates. We show that the orbitofrontal and

ventromedial prefrontal (VMPFC) cortices compute

expected value, reward outcome and experienced plea-

sure for different stimuli on a common value scale.

Attractor networks in VMPFC area 10 then implement

categorical decision processes that transform value sig-

nals into a choice between the values, thereby guiding

action. This synthesis of findings across fields provides a

unifying perspective for the study of decision-making

processes in the brain.

Integrating different approaches to valuation and

decision-making

Consider a situation where a choice has to be made be-

tween consuming an attractive food and seeking a source of

warm, pleasant touch. To decide between these fundamen-

tally different rewards, the brain needs to compute the

values and costs associated with two multisensory stimuli,

integrate this informationwithmotivational, cognitive and

contextual variables and then use these signals as inputs

for a stimulus-based choice process. Rapid advances have

been made in understanding how these key component

processes for value-based, economic decision-making are

implemented in the brain. Here, we review recent findings

from functional neuroimaging, single neuron recordings

and computational neuroscience to describe how systems

for stimulus-based (goal-based) valuation and choice deci-

sion-making are organized and operate in the primate,

including human, prefrontal cortex.

When considering the neural basis of value-based deci-

sion-making, the sensory nature of rewards is often

neglected, and the focus is on action-based valuation and

choice. However, many choices are between different senso-

ry and, indeed, multisensory rewards, and can be action

independent [1–3]. Here, we bring together evidence from

investigations of the neural correlates of the experienced

pleasureproducedby sensory rewardsand fromstudies that

have used neuroeconomic and computational approaches,

thereby linking different strands of research that have

largely been considered separately so far.

Neural systems for reward value and its subjective

correlate, pleasure

Reward and emotion: a Darwinian perspective

The valuation of rewards is a key component process of

decision-making. The neurobiological and evolutionary con-

text is as follows [3]. Primary rewards, such as sweet taste

andwarm touch, are gene-specified (i.e. unlearned) goals for

action built into us during evolution by natural selection to

direct behavior to stimuli that are important for survival

and reproduction. Specification of rewards, the goals for

action, by selfish genes is an efficient and adaptiveDarwini-

an way for genes to control behavior for their own reproduc-

tive success [3]. Emotions are states elicited when these

gene-specified rewardsare received, omitted, or terminated,

and by other stimuli that become linked with them by

associative learning [3]. The same approach leads to under-

standing motivations or ‘wantings’ as states in which one of

these goals is being sought [3]. (This approach suggests that

whenanimalsperformresponses for rewards thathavebeen

devalued, which have been described as ‘wantings’ [4], such

behavior is habit or stimulus-response based after over-

training, and is not goal directed.) Neuronal recordings in

macaques, used as amodel for these systems in humans [3],

and functional neuroimaging studies in humans have led to

the concept of three tiers of cortical processing [1], illustrat-

ed in Figure 1 and described in this review.

Object representations independent of reward valuation:

Tier 1

The first processing stage is for the representation of what

object or stimulus is present, independently of its reward

value and subjective pleasantness. In this first tier, the

identity and intensity of stimuli are represented, as exem-

plified by correlations of activations in imaging studies with

the subjective intensity but not pleasantness of taste in the

primary taste cortex [5,6], and neuronal activity that is

independent of reward value, investigated, for example,

when food value is reduced to zero by feeding to satiety

[1,3].AsshowninFigure1, thisfirst tier includes theprimary

taste cortex in the anterior insula, the pyriform olfactory

cortex and the inferior temporal visual cortex, where objects

and faces are represented relatively invariantlywith respect

to position on the retina, size, view and so on, where this

invariant representation is ideal for association with a re-

ward [1,3,7]. Part of the utility of a ‘what’ representation

Review

Corresponding authors: Rolls, E.T. (Edmund.Rolls@oxcns.org).

URL: www.oxcns.org

56 1364-6613/$ – see front matter � 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tics.2010.12.004 Trends in Cognitive Sciences, February 2011, Vol. 15, No. 2

mailto:Edmund.Rolls@oxcns.org

www.oxcns.org


independent of reward value is that one can learn about an

object, for example about its location and properties, even

when it is not rewarding, for example when satiated.

Reward value and pleasure: Tier 2

The orbitofrontal cortex: the value and pleasure of stimuli

Receiving inputs from Tier 1, the primate, including

human, orbitofrontal cortex (OFC) in Tier 2 (Figure 1) is

the first stage of cortical processing inwhich reward value is

made explicit in the representation. This is supported by

discoveries that: (i) OFC neurons decrease their responses

to a food or to water to zero when the reward values of food

and water are reduced to zero by feeding to satiety; (ii) OFC

neurons with visual responses learn rapidly and reverse

their responses to visual stimuli depending on whether the

stimulus is associated with a reward or punisher; and (iii)

activations in humans are related to the reward value of

taste, olfactory, oral texture, somatosensory, visual, social

and monetary stimuli [1,3] (Table 1 and the supplementary

material online for references). Subjective pleasure is the

[()TD$FIG]

Behavior:Habit

Autonomicand endocrineresponses

Cingulate cortex

Behavior:Action-outcome

Dopamine

V1 V2 V4

Thalamusreceptors solitary tract VPMpc nucleus

Vision

Taste

Taste

bulb

Frontal operculum/Insula

visual cortexInferior temporal

(Primary taste cortex)

Nucleus of the

Amygdala

Gate

Lateral

function

by e.g. glucose utilization,stomach distension or bodyweight

Gate

OrbitofrontalCortex

hypothalamus

Hunger neuron controlled

Touch

Olfaction

Thalamus VPL

Olfactory

Primary somatosensory cortex (1,2,3)

Olfactory (Pyriform)cortex

Insula

Striatum

Pregencing

Medial PFC area 10Choice valuedecision-making

'What' Decision-making

/ Output

Reward / Affective

value

top-down affective modulation

Tier 1 Tier 2 Tier 3

Lateral PFC


Figure 1. Organization of cortical processing for computing value (in Tier 2) and making value-based decisions (in Tier 3) and interfacing to action systems. The Tier 1 brain

regions up to and including the columnheaded by the inferior temporal visual cortex compute and represent neuronally ‘what’ stimulus or object is present, but not its reward or

affective value. Tier 2 represents, by its neuronal firing, the reward or affective value, and includes theOFC, amygdala, and anterior including pregenual cingulate cortex. Tier 3 is

involved in choices based on reward value (in particular VMPFC area 10), and in different types of output to behavior. The secondary taste cortex and the secondary olfactory

cortex are within the orbitofrontal cortex. Abbreviations: lateral PFC, lateral prefrontal cortex, a source for top-down attentional and cognitive modulation of affective value [50];

PreGen Cing, pregenual cingulate cortex; V1, primary visual cortex; V4, visual cortical area V4. ‘Gate’ refers to the finding that inputs such as the taste, smell and sight of food in

regions where reward value is represented only produce effects when an appetite for the stimulus (modulated e.g. by hunger) is present [3]. Adapted, with permission, from [1].


57

consciously experienced affective state produced by

rewarding stimuli [3]. In imaging studies, neural

activations in the OFC and adjacent anterior cingulate

cortex (ACC) are correlated with the subjective pleasure

produced by many different stimuli (Figure 2a). For

example, the subjective pleasantness of the oral texture

of fat, an indicator for high energy density in foods, is

represented on a continuous scale by neural activity in

the OFC and ACC (Figure 2b) [8].

Neuroeconomic approaches focus largely on subjective

value as inferred from choices (revealed preferences). By

contrast, pleasure is a consciously experienced state. The

conscious route to choice and action may be needed for

rational (i.e. reasoning) thought about multistep plans

[3,9]. Primary rewards would become conscious by virtue

of entering a reasoning processing system, for example

when reasoning about whether an experienced reward,

such as a pleasant touch, should be sought in future

[3,9,10]. Because pleasure may reflect processing by a

reasoning, conscious system when decision-making is per-

formed by goal-directed explicit decision systems involving

the prefrontal cortex (as opposed to implicit habit systems

involving the basal ganglia) [1,3,11], pleasure may provide

insight into what guides decision-making beyond what can

be inferred from observed choices [12].

The ACC: the reward value of stimuli; and an interface to

goal-directed action The pleasure map in Figure 2

indicates that the ACC, which receives inputs from the

OFC (Figure 1), also has value-based representations,

consistent with evidence from single neuron studies [13–

17]. These value representations provide the goal

representation in an ‘action to goal outcome’ associative

learning system in the mid-cingulate cortex (Box 1), and

also provide an output for autonomic responses to affective

stimuli [18].

Key principles of value representations in the OFC and

ACC

Key principles of operation of the OFC and ACC in reward

and punishment valuation are summarized in Table 1. We

examine some of these principles, focusing on recent devel-

opments in understanding how valuation signals in the

OFC and ACC are scaled, how they adapt to contexts and

how they are modulated by top-down processes.

Table 1. Principles of operation of the OFC and ACC in reward processing, and their adaptive valuea

Operational principle Adaptive value

1. Neural activity in the OFC and ACC represents reward value and

pleasure on a continuous scale.

This type of representation provides useful inputs for neural attractor

networks involved in choice decision-making.

2. The identity and intensity of stimuli are represented at earlier

cortical stages that send inputs to the OFC and ACC: stimuli

and objects are first represented, then their reward and

affective value is computed in the OFC.

This separation of sensory from affective processing is highly adaptive

for it enables one to identify and learn about stimuli independently of

whether one currently wants them and finds them rewarding.

3. Many different rewards are represented close together in the OFC,

including taste, olfactory, oral texture, temperature, touch, visual,

social, amphetamine-induced and monetary rewards.

This organization facilitates comparison and common scaling of

different rewards by lateral inhibition, and thus provides appropriately

scaled inputs for a choice decision-making process.

4. Spatially separate representations of pleasant stimuli (rewards)

and unpleasant stimuli (punishers) exist in the OFC and ACC.

This type of organization provides separate and partly independent

inputs into brain systems for cost–benefit analysis and decision-making.

5. The value of specific rewards is represented in the OFC: different

single neurons respond to different combinations of specific taste,

olfactory, fat texture, oral viscosity, visual, and face and vocal

expression rewards.

This type of encoding provides a reward window on the world that

allows not only selection of specific rewards, but also for sensory-

specific satiety, a specific reduction in the value of a stimulus after

it has been received continuously for a period of time.

6. Both absolute and relative value signals are present in the OFC. Absolute value is necessary for stable long-term preferences and

transitivity. Being sensitive to relative value might be useful in

climbing local reward gradients as in positive contrast effects.

7. Top-down cognitive and attentional factors, originating in lateral

prefrontal cortex, modulate reward value and pleasantness in the

OFC and ACC through biased competition and biased activation.

These top-down effects allow cognition and attention to modulate the

first cortical stage of reward processing to influence valuation and

economic decision-making.aReferences to the investigations that provide the evidence for this summary are provided in the supplementary material online.

Box 1. Reward representations in the ACC

If activations in both the OFC and ACC reflect the value of rewards,

what might be the difference in function between these two areas

[1,18,89]? We suggest that the information about the value of

rewards is projected from the OFC to ACC (its pregenual and dorsal

anterior parts). The pregenual and dorsal ACC parts can be

conceptualized as a relay that allows information about rewards

and outcomes to be linked, via longitudinal connections running in

the cingulum fiber bundle, to information about actions represented

in the mid-cingulate cortex.

Bringing together information about specific rewards with

information about actions, and the costs associated with actions,

is important for associating actions with the value of their outcomes

and for selecting the correct action that will lead to a desired reward

[89,90]. Indeed, consistent with its strong connections to motor

areas [91], lesions of ACC impair reward-guided action selection

[92,93], neuroimaging studies have shown that the ACC is active

when outcome information guides choices [94], and single neurons

in the ACC encode information about both actions and outcomes,

including reward prediction errors for actions [14,15]. For example,

Luk and Wallis [14] found that, in a task where information about

three potential outcomes (three types of juice) had to be associated

on a trial-by-trial basis with two different responses (two lever

movements), many neurons in the ACC encoded information about

both specific outcomes and specific actions. In a different study, Seo

and Lee [17] found that dorsal ACC neurons encoded a signal related

to the history of rewards received in previous trials, consistent with

a role for this region in learning the value of actions. Interestingly, in

both of these studies, there was little evidence for encoding of

choices, indicating that a choice mechanism between rewards might

not be implemented in the ACC.


58

Reward-specific value representations on a common

scale, but not in a common currency

Reward-specific representations Single neurons in the

OFC encode different specific rewards [1,3] by responding

to different combinations of taste, olfactory, somatosensory,

visual and auditory stimuli, including socially relevant

stimuli such as face expression [1,3,19]. Part of the

adaptive utility of this reward-specific representation is

that it provides for sensory-specific satiety as implemented

by a decrease in the responsiveness of reward-specific

neurons [1]. This is a fundamental property of every

reward system that helps to ensure that a variety of

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Pleasant

Unpleasant

1615

314 52120

18 2822

19

Common scaling and adaptive encoding of value in orbitofrontal cortex

Pleasure maps in orbitofrontal and anterior cingulate cortex

Neural representation of the subjective pleasantness of fat texture

50

25

00.1 0.3 0.5

Narrow distribution

Wide distribution

Neuro

nal re

sponses im

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es/s

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Temperature

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rag

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0 2 4 6 8 100

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DV=2DV=3DV=4DV=6DV=10

(a)

(b)

(c)

FlavorKey:

Key:

Key:


Figure 2. Pleasure and value in the brain. (a) Maps of subjective pleasure in the OFC (ventral view) and ACC (sagittal view). Yellow font indicates sites where activations

correlate with subjective pleasantness; whereas white font indicates sites where activations correlate with subjective unpleasantness. The numbers refer to effects found in

specific studies: taste: 1, 2; odor: 3–10; flavor: 11–16; oral texture: 17, 18; chocolate: 19; water: 20; wine: 21; oral temperature: 22, 23; somatosensory temperature: 24, 25; the

sight of touch: 26, 27; facial attractiveness: 28, 29; erotic pictures: 30; and laser-induced pain: 31. (See the supplementary material online for references to the original

studies.) (b) How the brain represents the reward value of the oral texture (i.e. the mouth feel) of food stimuli [8]. Oral texture is a prototypical primary reward important for

detecting the presence of fat in foods and is thus an indicator of high energy density in foods. Subjective pleasantness (+2 = very pleasant, -2 = very unpleasant) of the oral

texture of liquid food stimuli that differed in flavor and fat content tracked neural activity (% BOLD signal change) in the OFC (left) and ACC (right). (c) Common scaling and

adaptive encoding of value in the OFC. (left) A common scale for the subjective pleasure for different primary rewards: neural activity in the OFC correlates with the

subjective pleasantness ratings for flavor stimuli in the mouth and somatosensory temperature stimuli delivered to the hand. The regression lines describing the

relationship between neural activity (% BOLD signal) and subjective pleasantness ratings were indistinguishable for both types of reward. (middle) Padoa-Schioppa [43]

found that neurons in the OFC that encode the offer value of different types of juice adapt their sensitivity to the value range of juice rewards available in a given session,

while keeping their neuronal activity range constant. Each line shows the average neuronal response for a given value range. (right) Kobayashi et al. [44] found that neurons

in the OFC adapt their sensitivity of value coding to the statistical distribution of reward values, in that the reward sensitivity slope adapted to the standard deviation of the

probability distribution of juice volumes. These findings indicate that the range of the value scale in the OFC can be adjusted to reflect the range of rewards that are available

at a given time. Reproduced, with permission, from [30] (c left), [43] (c middle) and [44] (c right).


59

different rewards is selected over time [3]. Representations of

both reward outcome and expected value are specific for

the particular reward: not only do different neurons

respond to different primary reinforcers, but different

neurons also encode the conditioned stimuli for different

outcomes, with different neurons responding, for example,

to the sight or odor of stimuli based on the outcome that is

expected [20,21].

Topology of reward and punishment systems Different

types of reward tend to be represented in the humanmedial

OFC and pregenual ACC, and different types of punisher

tend to be represented in the lateral OFC and the dorsal

part of the ACC (Figure 2). The punishers include negative

reward prediction error encoded by neurons that fire only

when an expected reward is not received [20]. To compute

this OFC signal, inputs are required from neurons that

respond to the expected value of a stimulus (exemplified in

the OFC by neurons that respond to the sight of food), and

from other neurons that respond to the magnitude of the

reward outcome (exemplified in the OFC by neurons that

respond to the taste of food) [3,22]. All these signals are

reflected in activations found for expected value and for

reward outcome in the human medial OFC [23,24], and for

monetary loss and negative reward prediction error for

social reinforcers in the human lateral OFC [25]. This

topological organization with different types of specific

reward represented close together in the OFC may allow

for comparison between different rewards implemented by

lateral inhibition as part of a process of scaling different

specific rewards to the same range [3]. A topological

organization of reward and punishment systems is also

important to provide partly separate inputs into systems

for learning, choice and cost–benefit analysis (Box 2).

A common scale for different specific rewards A classic

view of economic decision theory [26] implies that decision-

makers convert the value of different goods into a common

scale of utility. Ecological [27], psychological [28] and

neuroeconomic approaches [29] similarly suggest that

the values of different types of reward are converted

into a common currency. Rolls and Grabenhorst [1,3]

have argued that different specific rewards must be

represented on the same scale, but not converted into a

common currency, as the specific goal selected must be the

output of the decision process so that the appropriate action

for that particular goal can then be chosen [1,3]. The key

difference between the two concepts of common currency

and common scaling lies in the specificity with which

rewards are represented at the level of single neurons.

Whereas a common currency view implies convergence of

different types of reward onto the sameneurons (aprocess in

which information about reward identity is lost), a common

scaling view implies that different rewards are represented

by different neurons (thereby retaining reward identity in

information processing), with the activity of the different

neurons scaled to be in the same value range.

A recent functional magnetic resonance imaging (fMRI)

study demonstrated the existence of a region in the human

OFC where activations are scaled to the same range as a

function of pleasantness for even fundamentally different

primary rewards: taste in the mouth and warmth on the

hand [30] (Figure 2c). A different study found that the

decision value for different categories of goods (food, non-

food consumables and monetary gambles) during purchas-

ing decisions correlated with activity in the adjacent ven-

tromedial prefrontal cortex [VMPFC (the term ‘VMPFC’ is

used to describe a large region of the medial prefrontal

cortex that includes parts of the medial OFC, ACC and the

medial prefrontal cortex area 10)] [31]. Importantly, be-

cause of the limited spatial resolution of fMRI, these

studies are unable to determine whether it is the same

or different neurons in these areas that encode the value of

different rewards. However, as shown most clearly by

single-neuron recording studies, the representations in

the OFC provide evidence about the exact nature of each

reward [1,3,22] (see the supplementary material online).

Moreover, in economic decision-making, neurons in the

macaque OFC encode the economic value of the specific

choice options on offer, for example different juice rewards

[2]. For many of these ‘offer value’ neurons, the relation-

ship between neuronal impulse rate and value was invari-

ant with respect to the different types of juice that were

available [32], suggesting that different types of juice are

evaluated on a common value scale.

Box 2. Cost–benefit analysis for decision-making: extrinsic

and intrinsic costs

If the OFC and ACC encode the value of sensory stimuli, does neural

activity in these structures also reflect the cost of rewards? We

propose that, when considering this, it is important to distinguish

two types of cost. Extrinsic costs are properties of the actions

required to obtain rewards or goals, for example physical effort and

hard work, and are not properties of the rewards themselves (which

are stimuli). By contrast, intrinsic costs are properties of stimuli. For

example, many rewards encountered in the world are hedonically

complex stimuli containing both pleasant and unpleasant compo-

nents at the same time, for example: natural jasmine odor contains

up to 6% of the unpleasant chemical indole; red wines and leaves

contain bitter and astringent tannin components; and dessert wines

and fruits can contain unpleasant sulfur components. Furthermore,

cognitive factors can influence intrinsic costs, for example when

knowledge of the energy content of foods modulates their reward

value. Intrinsic costs can also arise because of the inherent delay or

low probability/high uncertainty in obtaining them.

We suggest that intrinsic costs are represented in the reward–

pleasure systems in the brain, including the OFC, where the values

of stimuli are represented, and that extrinsic costs are represented in

brain systems involved in linking actions to rewards, such as the

cingulate cortex. Evaluation of stimulus-intrinsic benefits and costs

appears to engage the OFC [55,95,96]. For example, in a recent fMRI

study, it was found that the medial OFC, which represents the

pleasantness of odors, was sensitive to the pleasant components in

a naturally complex jasmine olfactory mixture, whereas the lateral

OFC, which represents the unpleasantness of odors, was sensitive

to the unpleasant component (indole) in the mixture [95]. A recent

neurophysiological study found that reward risk and value are

encoded by largely separate neuronal populations in the OFC [97].

The implication is that both reward value and intrinsic cost stimuli

are represented separately in the OFC. This might provide a neural

basis for processing related to cognitive reasoning about reward

value and its intrinsic cost, and for differential sensitivity to rewards

and aversion to losses. By contrast, a role for the cingulate cortex in

evaluating the physical effort associated with actions has been

demonstrated in studies in rats, monkeys [98] and humans [99].

Interestingly, single neurons in the lateral prefrontal cortex encode

the temporally discounted values of choice options, suggesting that

reward and delay costs are integrated in this region [100].


60

With current computational understanding of how deci-

sions are made in attractor neural networks [33–36] (see

below), it is important that different rewards are expressed

on a similar scale for decision-making networks to operate

correctly but retain information about the identity of the

specific reward. The computational reason is that one type

of reward (e.g. food reward) should not dominate all other

types of reward and always win in the competition, as this

would be maladaptive. Making different rewards approxi-

mately equally rewardingmakes it probable that a range of

different rewards will be selected over time (and depending

on factors such as motivational state), which is adaptive

and essential for survival [3]. The exact scaling into a

decision-making attractor network will be set by the num-

ber of inputs from each source, their firing rates and the

strengths of the synapses that introduce the different

inputs into the decision-making network [7,33,35,36]. Im-

portantly, common scaling need not imply conversion into

a new representation that is of a common currency of

general reward [1]. In the decision process itself, it is

important to know which reward has won, and the mecha-

nism is likely to involve competition between different

rewards represented close together in the cerebral cortex,

with one of the types of reward winning the competition,

rather than convergence of different rewards onto the same

neuron [3,7,33,35,36].

The OFC and ACC represent value on a continuous scale,

and not choice decisions between different value signals

To test whether the OFC and ACC represent the value

of stimuli on a continuous scale and, thus, provide the

evidence for decision-making, or instead are implicated

themselves in making choices, Grabenhorst, Rolls et al.

performed a series of investigations in which the valuation

of thermal and olfactory stimuli in the absence of choice

was compared with choice decision-making about the same

stimuli. Whereas activation in parts of the OFC and ACC

represented the value of the rewards on a continuous scale

[10,37], the next connected area in the system, VMPFC

area 10 (Figure 1), had greater activations when choices

were made, and showed other neural signatures of deci-

sion-making indicative of an attractor-based decision pro-

cess, as described below for Tier 3 processing [38,39]

(Figure 3d).

Absolute value and relative value are both represented

in the OFC

For economic decision-making, both absolute and relative

valuation signals have to be neurally represented. A re-

presentation of the absolute value of rewards is important

for stable long-term preferences and consistent economic

choices [32,40]. Such a representation should not be influ-

enced by the value of other available rewards. By contrast,[()TD$FIG]

6

215

22

1

2

2

7

810

10

1 113

1718

19

9

23

14

16

15324

12

(a) Decision-making map of the ventromedial prefrontal cortex

(b) Relative value of the chosen option

(c) Chosen stimulus value (prior to action)

(d) Decision easiness (prior to action)20

4


Figure 3. From value to choice in the VMPFC. (a)Activations associatedwith 1: (economic) subjective value during intertemporal choice; 2: immediate versus delayed choices; 3

immediate versus delayed primary rewards; 4: expected value during probabilistic decision-making; 5: expected value based on social and experience-based information; 6:

expected value of the chosen option; 7: price differential during purchasing decisions; 8: willingness to pay; 9: goal value during decisions about food cues; 10: choice probability

during exploitative choices; 11: conjunction of stimulus- and action-based value signals; 12: goal value during decisions about food stimuli; 13: willingness to pay for different

goods; 14: willingness to pay for lottery tickets; 15: subjective value of charitable donations; 16: decision value for exchangingmonetary against social rewards; 17: binary choice

versus valuation of thermal stimuli; 18: binary choice versus valuation of olfactory stimuli; 19: easy versus difficult binary choices about thermal stimuli; 20: easy versus difficult

binary choices about olfactory stimuli; 21: value of chosen action; 22: difference in value between choices; 23: prior correct signal during probabilistic reversal learning; and 24:

free versus forced charitable donation choices. It is notable that some of the most anterior activations in VMPFC area 10 (activations 17–19) were associated with binary choice

beyond valuation during decision-making. (See supplementary material online for references to the original studies.) (b) VMPFC correlates of the relative value of the chosen

option during probabilistic decision-making. (c)VMPFC correlates of the chosen stimulus value are present even before action information is available [72]. (d) VMPFC correlates

of value difference, and thus decision easiness and confidence, during olfactory and thermal value-based choices. Effects in this study were found in the far anterior VMPFC,

medial area 10, but not in the OFC or ACC. Reproduced, with permission, from [70] (b), [72] (c), and [38] (d).


61

to select the option with the highest subjective value in a

specific choice situation, the relative value of each option

needs to be represented. A recent study provided evidence

for absolute value coding in the OFC, in that neuronal

responses that encoded the value of a specific stimulus did

not depend on what other stimuli were available at the

same time [32]. It was suggested that transitivity, a fun-

damental trait of economic choice, is reflected by the

neuronal activity in the OFC [32]. This type of encoding

contrasts with value-related signals found in the parietal

cortex, where neurons encode the subjective value associ-

ated with specific eye movements in a way that is relative

to the value of the other options that are available [41]. The

apparent difference in value coding between the OFC and

parietal cortex has led to the suggestion that absolute

value signals encoded in the OFC are subsequently

rescaled in the parietal cortex to encode relative value to

maximize the difference between the choice options for

action selection [41]. However, there is also evidence for

the relative encoding of value in the OFC, in that neuronal

responses to a food reward can depend on the value of the

other reward that is available in a block of trials [42]. Two

recent studies demonstrated that neurons in the OFC

adapt the sensitivity with which reward value is encoded

to the range of values that are available at a given time

[43,44] (Figure 2c). This reflects an adaptive scaling of

reward value, evident also in positive and negative con-

trast effects, that makes the system optimally sensitive to

the local reward gradient, by dynamically altering the

sensitivity of the reward system so that small changes

can be detected [3]. The same underlying mechanism may

contribute to the adjustment of different types of reward to

the same scale described in the preceding section.

Given that representations of both absolute value and

relative value are needed for economic decision-making,

Grabenhorst and Rolls [45] tested explicitly whether both

types of representation are present simultaneously in the

human OFC. In a task in which two odors were successive-

ly delivered on each trial, they found that blood oxygen-

ation level-dependent (BOLD) activations to the second

odor in the antero-lateral OFC tracked the relative subjec-

tive pleasantness, whereas activations in the medial and

mid-OFC tracked the absolute pleasantness of the second

odor. Thus, both relative and absolute subjective value

signals, both of which provide important inputs to deci-

sion-making processes, are separately and simultaneously

represented in the human OFC [45].

Cognitive and attentional influences on value: a biased

activation theory of top-down attention

How do cognition and attention affect valuation and neural

representations of value?Onepossibility is that value repre-

sentations ascend from the OFC and ACC to higher lan-

guage-related cortical systems, and there become entwined

with cognitive representations. In fact, there is amoredirect

mechanism.Cognitive descriptions at the highest, linguistic

level of processing (e.g. ‘rich delicious flavor’) or attentional

instructions at the same, linguistic level (e.g. ‘pay attention

to and rate pleasantness’ vs ‘pay attention to and rate

intensity’) have a top-down modulatory influence on value

representations in the OFC and ACC of odor [46], taste and

flavor [6], and touch [47] stimuli by increasing or decreasing

neural responses to these rewards. Thus, cognition and

attention have top-down influences on the first part of the

cortex in which value is represented (Tier 2), and modulate

the effects of the bottom-up sensory inputs.

Recent studies have identified the lateral prefrontal

cortex (LPFC, a region implicated in attentional control;

Figure 1 [7,48]) as a site of origin for these top-down

influences. In one study, activity in the LPFC correlated

with value signals in the ventral ACC during self-con-

trolled choices about food consumption [49]. Grabenhorst

and Rolls have shown recently with fMRI connectivity

analyses that activity in different parts of the LPFC dif-

ferentially correlated with activations to a taste stimulus

in the OFC or anterior insula, depending on whether

attention was focused on the pleasantness or intensity of

the taste, respectively [50]. Because activations of con-

nected structures in whole cortical processing streams

were modulated, in this case the affective stream (Tier 2

of Figure 1, including the OFC and ACC) versus the

discriminative (object) stream (Tier 1 of Figure 1, including

the insula), Grabenhorst and Rolls extended the concept of

biased competition [51] and its underlying neuronal

mechanisms [52] in which top-down signals operate to

influence competition within an area implemented

through a set of local inhibitory interneurons, to a biased

activation theory of top-down attention [50], in which

activations in whole processing streams can be modulated

by top-down signals (Figure 4c).

These insights have implications for several areas re-

lated to neuroeconomics and decision-making, including

the design of studies in which attentional instructions

might influence which brain systems become engaged, as

well as situations in which affective processing might be

usefully modulated (e.g. in the control of the effects of the

reward value of food and its role in obesity and addiction)

[3,7,53].

From valuation to choice in the ventromedial prefrontal

cortex

The operational principles described above enable the OFC

and ACC (Tier 2 in Figure 1) to provide value representa-

tions that are appropriately scaled to act as inputs into

neural systems for economic decision-making, and to pro-

mote a progression through the reward space in the envi-

ronment to find the range of rewards necessary for survival

and reproduction [3]. We next consider how neural value

representations are transformed into choices in the

VMPFC. We describe evidence that choices are made in

attractor networks with nonlinear dynamics, in which one

of the possible attractor states, each biased by a different

value signal, wins the competition implemented through

inhibitory interneurons [36].

Neural activity in the VMPFC in neuroeconomic tasks

Studies based on neuroeconomic and computational

approaches have revealed that neural activity in the

VMPFC correlates with the expected value of choice

options during decision-making (Figure 3) [41,54]. For

example, subject-specific measures of the expected ‘goal

value’ of choice options can be derived from observed


62

choices between different rewards, such as when subjects

bidmoney for goods they wish to acquire (i.e. willingness to

pay), and these can be used as regressors for fMRI activity

[31,49,55–57]. Using this approach, neural correlates of the

goal value for different types of expected reward, including

food items, non-food consumables, monetary gambles and

lottery tickets, have been found in the VMPFC (Figure 3).

Decision-related activity in the VMPFC is also found for

choices about primary rewards, such as a pleasant warm or

unpleasant cold touch to the hand, and between olfactory

stimuli [10].

As can be seen from Figure 3a, there is considerable

variability in the exact anatomical location of decision-

related effects in the VMPFC. Moreover, VMPFC activity

has been linked to a wide range of valuation and choice

signals that incorporates information about temporal de-

lay [58–60], uncertainty [61], price or value differential

[62,63], social advice [64], and monetary expected value

[()TD$FIG]

hi = dendritic activation

yi = output firing

Output axons

(AMPA,NMDA)

(AMPA,NMDA)

(AMPA)(AMPA)

(GABA)

(GABA)

yj

Recurrentcollateral

axonsCell bodies

Dendrites

Recurrentcollateralsynapses wij

S D2

Spontaneous state

Decision state attractor

D1 D2 Nonspecific

neuronsInhibitory

pool

D1

Decision state attractor

“Pote

ntial”

Firing rate

(a)

RC

Confidence decision networkDecision network

RC

Output:

Decision

Inputs

Output:

Decision about

confidence in the

decision

(b)

Biased activation of Biased activation ofcortical stream 2 cortical stream 1

cortical stream 1

Bottom Upinput 1

Bottom Upinput 2

Output of Output of

cortical stream 2

cortical stream 1 Short term memory bias source for

cortical stream 2

Short term memory bias source for(c)

λ1

λA

λB

λ1 λ2

λ1 λ2

λext

λext

λ2

wij


Figure 4. Decision-making and attentional mechanisms in the brain. (a) (top) Attractor or autoassociation single network architecture for decision-making. The evidence for

decision 1 is applied via the l1, and for decision 2 via the l2 inputs. The synaptic weights wij have been associatively modified during training in the presence of l1 and at a

different time of l2. When l1 and l2 are applied, each attractor competes through the inhibitory interneurons (not shown), until one wins the competition, and the network

falls into one of the high firing rate attractors that represents the decision. The noise in the network caused by the random spiking times of the neurons (for a given mean

rate) means that, on some trials, for given inputs, the neurons in the decision 1 (D1) attractor are more likely to win and, on other trials, the neurons in the decision 2 (D2)

attractor are more likely to win. This makes the decision-making probabilistic, for, as shown in (bottom), the noise influences when the system will jump out of the

spontaneous firing stable (low energy) state S, and whether it jumps into the high firing state for decision 1 (D1) or decision 2 (D2). (middle) The architecture of the

integrate-and-fire network used to model decision-making. (bottom) A multistable ‘effective energy landscape’ for decision-making with stable states shown as low

‘potential’ basins. Even when the inputs are being applied to the network, the spontaneous firing rate state is stable, and noise provokes transitions into the high firing rate

decision attractor state D1 or D2. (b) A network for making confidence-based decisions. Given that decisions made in a first decision-making network have firing rates in the

winning attractor that reflect the confidence in the first decision, a second ‘monitoring’ decision network can take confidence-related decisions based on the inputs received

from the first decision-making network. The inputs to the decision-making network are lA and lB. A fixed reference firing rate input to the second, confidence decision,

network is not shown. (c) A biased activation theory of attention. The short-term memory systems that provide the source of the top-down activations may be separate (as

shown), or could be a single network with different attractor states for the different selective attention conditions. The top-down short-term memory systems hold what is

being paid attention to active by continuing firing in an attractor state, and bias separately either cortical processing system 1, or cortical processing system 2. This weak

top-down bias interacts with the bottom-up input to the cortical stream and produces an increase of activity that can be supralinear [52]. Thus, the selective activation of

separate cortical processing streams can occur. In the example, stream 1 might process the affective value of a stimulus, and stream 2 might process the intensity and

physical properties of the stimulus. The outputs of these separate processing streams must then enter a competition system, which could be, for example, a cortical

attractor decision-making network that makes choices between the two streams, with the choice biased by the activations in the separate streams. (After Grabenhorst and

Rolls 2010 [50].) Adapted, with permission, from [38] (aiii), [36] (b) and [50] (c).


63

and reward outcome [24]. This heterogeneity of findings

raises the question of whether a common denominator for

the functional role of VMPFC in value-based decision-

making can be identified or, alternatively, whether differ-

ent VMPFC subregions make functionally distinct contri-

butions to the decision-making process. A common theme

that has emerged from the different strands of research is

that the VMPFC provides a system for choices about

different types of reward and for different types of decision,

including in the social domain [64–67]. For example, Beh-

rens and colleagues found that the VMPFC encoded the

expected value of the chosen option based on the subjects’

own experiences as well as on social advice [64].

On the basis of these findings, it has been suggested that

the VMPFC represents a common valuation signal that

underlies different types of decision as well as decisions

about different types of goods [31,41,59,68]. A related

account [69] suggests that, whereas the OFC is involved

in encoding the value of specific rewards, the VMPFC plays

a specific role in value-guided decision-making about

which of several options to pursue by encoding the expected

value of the chosen option [64,70,71]. Indeed, VMPFC

activity measured with fMRI correlates with the value

difference between chosen and unchosen options (i.e. rela-

tive chosen value), and this signal can be further dissected

into separate value signals for chosen and unchosen

options [70] (Figure 3b). However, with the temporal reso-

lution of fMRI, it is difficult to distinguish input signals to a

choice process (the expected or offer value, or value differ-

ence between options) from output signals of a choice

process (the value of the chosen or unchosen option) and

from those that represent the categorical choice outcome

(the identity of the chosen option).

Value in the OFC and choice in VMPFC area 10

Rolls, Grabenhorst and colleagues have proposed an alter-

native account [1,10,36,38,39] that suggests that, whereas

the OFC and ACC parts of the VMPFC are involved in

representing reward value as inputs for a value-based

choice process, the anterior VMPFC area 10 is involved

in choice decision-making beyond valuation, as has been

found in studies that have contrasted choice with valuation

[10,37] (Figure 3d). Part of this proposal is that area 10 is

involved in decision-making beyond valuation by imple-

menting a competition between different rewards, with the

computational mechanism described below. This choice

process operates on the representation of rewarding sti-

muli (or goods, in economic terms) and, thus, occurs before

the process of action selection. This is based, in part, on the

evidence that neuronal activity in the OFC is related to the

reward value of stimuli, and that actions such as whether

any response should be made, or a lick response, or a touch

response [3,7], or a right versus left response [2], are not

represented in the OFC [3]. Indeed, using an experimental

design that dissociated stimulus and action information in

a value-based choice task, Wunderlich et al. demonstrated

that correlates of the value of the chosen stimulus can be

found in the VMPFC even before action information is

available [72] (Figure 3c). Thus, we suggest that the role

of the anterior VMPFC area 10 is to transform a continu-

ously scaled representation of expected value (or offer

value) of the stimulus choice options into a categorical

representation of reward stimulus choice. This process

uses a mechanism in which the winner in the choice

competition is the chosen stimulus, which can then be

used as the goal for action to guide action selection.

This computational view on the role of the VMPFC in

decision-making is fundamentally different from the pro-

posal made by Damasio and colleagues, in which the

VMPFC is involved in generating somatic markers

(changes in the autonomic, endocrine and skeletomotor

responses), which are then sensed in the insular and

somatosensory cortices and thereby reflect the value of

choice options and ‘weigh in’ on the decision process [73], as

has been discussed in detail elsewhere [3].

Computational mechanisms for choice and their neural

signatures

Phenomenological approaches

Byexamining computationalmodels of decision-making,we

now consider the processes by which the brain may make

choices between rewards. One approach, which has been

used mainly in the domain of sensory decision-making, can

be described as phenomenological, in that a mathematical

model is formulated without specifying the underlying neu-

ral mechanisms. The main such approach is the accumu-

lator or race model, in which the noisy (variable) incoming

evidence is accumulated or integrated until some decision

threshold is reached [74]. This provides a good account of

many behavioral aspects of decision-making, but does not

specify howamechanism for choice could be implemented in

a biologically realistic way in the brain.

Choice implemented by competition between attractor

states in cortical networks

A different approach is to formulate a theory at the mech-

anistic level of the operation of populations of neurons with

biologically plausible dynamics of how choices are made in

the brain (Figure 4) [33–36,75]. In this scenario, the param-

eters are given by the time constants and strengths of the

synapses and the architecture of the networks; neuronal

spiking occurring in the simulations provides a source of

noise that contributes to the decision-making being prob-

abilistic and can be directly compared with neuronal activ-

ity recorded in the brain; and predictions can be made

about the neuronal and fMRI signals associated with

decision-making, which can be used to test the theory.

Interestingly, the theory implements a type of nonlinear

diffusion process that can be related to the linear diffusion

process implemented by accumulator or race models [76].

Furthermore, the degree of confidence in one’s decisions

and other important properties of a decision-making pro-

cess, such as reaction times and Weber’s Law, arise as

emergent properties of the integrate-and-fire attractor

model summarized in Figure 4 [33,36].

Predictions of the noisy attractor theory of decision-

making

The attractor-based integrate-and-fire model of decision-

making makes specific predictions about the neuronal sig-

nature of a choice system in the brain, including higher

neuronal firing, and correspondingly larger fMRI BOLD


64

signals, on correct than error trials. The reason for this is

that the winning attractor on a given trial (say attractor 1

selected as a consequence of a larger l1 thanl2 and thenoise

in the system caused by the randomness in the neuronal

spiking times for a given mean rate) receives additional

support from the external evidence that is received via l1 on

correct trials [36,39,75]. For the same reason, on correct

trials, as the difference Dl between l1 and l2 increases, so

the firing rates and the predicted fMRI BOLD signal in-

crease.Rolls etal.haverecently confirmed thisprediction for

VMPFCarea 10when choiceswere beingmade between the

pleasantnessof successiveodors [39].Conversely, but for the

same reason, on error trials, as Dl increases, so the firing

rates and the predicted fMRI BOLD signal decrease [39].

Thispredictionhasalsobeenconfirmed forarea10 [39]. If all

trials, both correct and error, are considered together, then

themodel predicts an increase in the BOLD signal in choice

decision-making areas, and this prediction has been con-

firmed for area 10 [38,39]. (Indeed, this particular signature

has beenused to identify decision-makingareas of the brain,

even though there was no account of why this was an

appropriate signature [77].) The confirmation of these pre-

dictions for area 10, but not for the OFCwhere the evidence

described above indicates that value is represented, pro-

vides strong support for this neuronal mechanism of deci-

sion-making in the brain [38,39].

The same neuronal cortical architecture for decision-

making (Figure 4) is, Rolls and Deco propose [36], involved

in many different decision-making systems in the brain,

including vibrotactile flutter frequency discrimination in

the ventral premotor cortex [35], optic flow in the parietal

cortex and the confidence associated with these decisions

[78], olfactory confidence-related decisions in the rat pre-

frontal cortex [79,80] and perceptual detection [36]. A

useful property of this model of decision-making is that

it maintains as active the representation of the goal or

state that has been selected in the short-term memory

implemented by the recurrent collateral connections, pro-

viding a representation for guiding action and other be-

havior that occurs subsequent to the decision [36]. In a

unifying computational approach, Rolls and Deco [36]

argue that the same noise-influenced categorization pro-

cess also accounts for memory recall, for the maintenance

of short-term memory and therefore attention, and for the

way in which noise affects signal detection. Furthermore,

disorders in the stability of these stochastic dynamical

cortical systems implemented by the recurrent collateral

excitatory connections between nearby cortical pyramidal

cells, contribute to a new approach to understanding

schizophrenia (in which there is too little stability)

[81,82] and obsessive-compulsive disorder (in which it is

hypothesized that there is too much stability) [83].

Confidence in decisions

As the evidence for a decision becomes stronger, confidence

in the decision being correct increases. More formally,

before the outcome of the decision is known, confidence

in a correct decision increases withDl on correct trials, and

decreases on trials when an error has in fact been made

[84]. The model just described accounts for confidence in

decisions as an emergent property of the attractor network

processes just described, with the firing rates and pre-

dicted BOLD signals reflecting confidence, just as they

do Dl on correct than error trials.

If one does not have confidence in an earlier decision

then, even before the outcome is known, one might abort

the strategy and try the decision-making again [79]. The

second decision can be modeled by a second decision-

making network that receives the outputs from the first

decision-making network [36,80] (see Figure 4b). If the

first network in its winning attractor has relatively high

firing rates reflecting high confidence in a correct deci-

sion, then the second network can use these high firing

rates to send it into a decision state reflecting ‘confidence

in the first decision’. If the first network in its winning

attractor has relatively lower firing rates reflecting low

confidence in a correct decision, then the second network

can use these lower firing rates to send it into a decision

state reflecting ‘lack of confidence in the first decision’

[80].

This two-decision network system (Figure 4b) provides a

simple model of monitoring processes in the brain, and

makes clear predictions of the neuronal activity that

reflects this monitoring process [36,80]. Part of the interest

is that ‘self-monitoring’ is an important aspect of some

approaches to consciousness [85,86]. However, we think

that it is unlikely that the two attractor network architec-

ture would be conscious [36].

Concluding remarks and future priorities

We have linked neurophysiological and neuroimaging to

computational approaches to decision-making and have

shown that representations of specific rewards on a con-

tinuous and similar scale of value in the OFC and ACC

(Tier 2) are followed by a noisy attractor-based system for

making choices between rewards in VMPFC area 10 (Tier

3). Subjective pleasure is the state associated with the

activation of representations in Tier 2, and confidence is

an emergent property of the decision-making process in

Tier 3. Similar neuronal choice mechanisms in other brain

areas are suggested to underlie different types of decision,

memory recall, short-term memory and attention, and

signal detection processes, and for some disorders in these

processes.

In future research, it will be important to examine how

well this stochastic dynamical approach to decision-mak-

ing, memory recall, and so on, can account for findings in

many brain systems at the neuronal level; how subjective

reports of confidence before the outcome is known are

related to neural processing in these different brain sys-

tems; how this stochastic dynamic approach to decision-

making may be relevant to economic decision-making

[87,88]; and whether this approach helps to understand

and treat patients, for example those with damage to the

brain that affects decision-making, and those with schizo-

phrenia and obsessive-compulsive disorder.

AcknowledgmentsSome of the research described in this paper was supported by the

Medical Research Council and the Oxford Centre for Computational

Neuroscience. F.G. was supported by the Gottlieb-Daimler- and Karl

Benz-Foundation, and by the Oxford Centre for Computational

Neuroscience.


65

Appendix A. Supplementary data

Supplementary data associated with this article can

be found, in the online version, at doi:10.1016/j.tics.

2010.12.004.

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67

Cognitive culture: theoretical andempirical insights into social learningstrategiesLuke Rendell, Laurel Fogarty, William J.E. Hoppitt, Thomas J.H. Morgan,Mike M. Webster and Kevin N. Laland

Centre for Social Learning and Cognitive Evolution, School of Biology, University of St. Andrews, Bute Medical Building,

St. Andrews, Fife KY16 9TS, UK

Research into social learning (learning from others) has

expanded significantly in recent years, not least because

of productive interactions between theoretical and em-

pirical approaches. This has been coupled with a new

emphasis on learning strategies, which places social

learning within a cognitive decision-making framework.

Understanding when, how and why individuals learn

from others is a significant challenge, but one that is

critical to numerous fields in multiple academic disci-

plines, including the study of social cognition.

The strategic nature of copying

Social learning, defined as learning that is influenced by

observation of or interaction with another individual, or its

products [1], and frequently contrasted with asocial learn-

ing (e.g. trial and error), is a potentially cheap way of

acquiring valuable information. However, copying comes

with pitfalls [2] – the acquired information might be out-

dated, misleading or inappropriate. Nevertheless, social

learning is widespread in animals [3,4] and reaches a

zenith in the unique cumulative culture of humans. Un-

derstanding how to take advantage of social information,

while managing the risks associated with its use, has

become a focus for research on social learning strategies

[5–7], which explores how natural selection has shaped

learning strategies in humans and other animals.

Research on this topic has expanded rapidly in recent

years, in part by building on a more detailed understand-

ing of social learning and teaching mechanisms (Box 1).

However, the expansion has primarily been fuelled by a

strong link between theory and empirical work, as well as

the often surprising parallels between the social decision-

making of humans and that of other animals (Box 2). Thus,

the field has moved beyond asking which psychological

mechanisms individuals use to copy each other toward an

exploration of the cognitive decision-making framework

that individuals use to balance the competing demands

of accuracy and economy in knowledge gain [8]. The mar-

riage between the economics of information use and evolu-

tionary theory has generated a rich research program that

spans multiple disciplines, including biology, psychology,

anthropology, archaeology, economics, computer science

and robotics. Researchers are now starting to gain an

understanding of the functional rules that underlie the

decision to copy others, and are beginning to appreciate

that the rules deployed at the individual level profoundly

affect the dynamics of cultural evolution over larger tem-

poral and social scales.

Theoretical insights

Research into social learning strategies is supported by a

rich and interdisciplinary theoretical background (Box 3)

[5–18], with active ongoing debates, such as on the impor-

tance of conformity [5,16,17,19–21], whether the decision

to copy is more dependent on the content of the acquired

information or the social context [5,22,23], and whether,

and under what circumstances, social learning can lead to

maladaptive information transmission [2,5,13,24].

An important starting point was a simple thought ex-

periment that became one of the most productive ideas to

date related to the evolution of social learning, known as

Rogers’ paradox [10]. Anthropologist Alan Rogers con-

structed a simple mathematical model to explore how best

to learn in a changing environment. The analysis sug-

gested, somewhat surprisingly, that social learning does

not increasemean population fitness, because its efficacy is

highly frequency-dependent. Copying is advantageous at

low frequency because social learners acquire their infor-

mation primarily from asocial learners who have directly

sampled the environment, but avoid the costs of asocial

learning. However, copying becomes disadvantageous as it

increases in frequency, because social learners find them-

selves increasingly copying other copiers. The information

acquired is then rendered outdated by environmental

change, giving a fitness advantage to asocial learningwhen

the latter is rare. At equilibrium, both social and asocial

learners persist with the same average fitness. Rogers’

Review

Glossary

Conformist bias: positive frequency-dependent social learning for which the

probability of acquiring a trait increases disproportionately with the number of

demonstrators performing it.

Cultural drift: random, or unbiased, copying in which individuals acquire

variants according to the frequency at which they are practiced.

Social learning strategy: evolved psychological rule specifying under what

circumstances an individual learns from others and/or from whom they learn.Corresponding author: Rendell, L. (ler4@st-andrews.ac.uk).

68 1364-6613/$ – see front matter � 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tics.2010.12.002 Trends in Cognitive Sciences, February 2011, Vol. 15, No. 2

mailto:ler4@st-andrews.ac.uk


finding, although not paradoxical in any strict sense, was

viewed as counterintuitive because culture, and thus social

learning, is widely thought to be the basis of human

population growth [25], which implies an increase in abso-

lute fitness. More recently, spatially explicit models have

exacerbated this challenge by suggesting that with certain

kinds of population structure and realistic patterns of

ecological change, social learning could drive asocial learn-

ing to extinction, with disastrous consequences for fitness

when environments change [12,13].

This thought experiment vastly simplifies the choices

available to individuals. Several studies have shown that a

way out of this ‘paradox’ is through the selective use of

asocial and social learning [5,12,14,15,18,26]. For example,

a strategy termed critical social learning, which uses social

learning initially but switches to asocial learning if it fails

to acquire an adaptive behaviour, outcompetes pure social

learners and, under most circumstances, asocial learners,

while also increasing fitness across a broad range of con-

ditions [12,15]. However, there are also relatively narrow

circumstances in which pure social learning outcompetes

both individual learning and conditional strategies, while

also increasing fitness [12]. The conditions for this exist

when individual learning is challenging (e.g. very costly in

time) but there are a range of viable alternatives available

to copy, any of which might produce a reasonably effective,

if not globally optimal, solution. Interestingly, these con-

ditions seem to fit well to some examples of human cultural

evolution that are best described by the kind of drift

dynamics expected under unbiased (or random) copying,

Box 1. Social learning and teaching processes

A large amount of research has focused on determining the

psychological mechanisms underlying social learning in animals.

This was initially driven by the question of which non-human animals

are capable of imitation, a process assumed to involve sophisticated

cognition, requiring an observer to extract the motor program for an

action from the experience of observing another individual perform

that action [74]. The recognition of alternative processes through

which animals could come to acquire similar behaviour following

social interaction, not all of which implied complex mechanisms,

eventually spawned a number of classifications of different social

learning processes that can result in the transmission of behaviour

between individuals [1,75]. Simpler mechanisms, such as local and

stimulus enhancement (see Table I) were usually seen as explana-

tions that should be ruled out before imitation could be inferred [76].

This enabled researchers to devise the two-action test, a laboratory

procedure for inferring imitation [77]. The two-action method requires

experimental subjects to solve a task with two alternative solutions,

with half observing one solution and the other half the alternative; if

subjects disproportionately use the method that they observed, this is

taken as evidence of imitation.

In recent years, interest has shifted away from the question of ‘do

animals imitate?’ towards the more general question of ‘how do

animals (including humans) copy others?’ [78–81]. This approach

includes recreation of the movements of objects in the environment,

copying the goals of observed behaviour, learning about the

affordance of objects and imitation at a number of levels of copying

fidelity [78,79]. Other researchers aim to elucidate the neural

mechanisms and developmental processes underpinning imitation

[80,81]. Collectively, this work has revealed an extensive repertoire

of copying processes, all of which are probably exhibited by humans,

but only some of which are observed in other species. Advances in

both experimental and statistical methods [3,82,83] mean that

specific learning processes can now be identified, which will

potentially facilitate mapping of the taxonomic distribution of these

processes.

Historically, teaching has been viewed as a contributor of additional

and separate mechanisms to the list of social learning processes.

However, recent findings on simple forms of teaching in ants, bees,

pied babblers and meerkats [84] have led to the detection of

correspondences between teaching and social learning processes.

Social learning mechanisms relate primarily to psychological pro-

cesses in the observer (pupil), whereas teaching processes relate

specifically to activities of the demonstrator (tutor). Accordingly,

alternative forms of teaching can be viewed as special cases of

established social learning processes, in which the demonstrator

actively facilitates information transmission. For instance, while many

species, including ants, teach through local enhancement, humans

might be unique in teaching through imitation.

Table I. A classification of social learning mechanisms.

Social learning mechanism Definition

Stimulus enhancement A demonstrator exposes an observer to a single stimulus, which leads to a change in the

probability that the observer will respond to stimuli of that type

Local enhancement A demonstrator attracts an observer to a specific location, which can lead to the observer

learning about objects at that location

Observational conditioning The behaviour of the demonstrator exposes an observer to a relationship between stimuli,

enabling the observer to form an association between them

Social enhancement of food preferences Exposure to a demonstrator carrying cues associated with a particular diet causes the

observer to become more likely to consume that diet

Response facilitation A demonstrator performing an act increases the probability that an animal that sees it will

do the same. This can result in the observer learning about the context in which to perform

the act and the consequences of doing so

Social facilitation Social facilitation occurs when the mere presence of a demonstrator affects the observer’s

behaviour, which can influence the observer’s learning

Contextual imitation Observing a demonstrator performing an action in a specific context directly causes an

observer to learn to perform that action in the same context

Production imitation Observing a demonstrator performing a novel action, or action sequence, that is not in its

own repertoire causes an observer to be more likely to perform that action or sequence

Observational R-S learning Observation of a demonstrator exposes the observer to a relationship between a response

and a reinforcer, causing the observer to form an association between them

Emulation Observation of a demonstrator interacting with objects in its environment causes an observer

becomes more likely to perform any actions that bring about a similar effect on those objects

Note that these definitions relate to psychological processes in the observer. The presence or absence of active demonstration or teaching (behaviourwhose function is to

facilitate learning in others) can be regarded as orthogonal to mechanisms in the observer. Hence, it is possible to categorize instances of teaching as, for example,

teaching through local enhancement. For the original sources of these definitions, see Hoppitt and Laland [3] and Hoppitt et al. [84].


69

such as choice of pet breeds, baby names and aesthetic craft

production [27].

One challenge for the developing field is that the poten-

tial diversity of strategies is huge, and only a small number

of plausible strategies have been subject to formal analy-

ses. Nonetheless, many of these have received theoretical

support, backed up in several cases by empirical evidence

from humans or other animals (Figure 1). Strategies relate

Box 2. Functional parallels in the social learning of humans and non-human animals

Experimental studies in non-human animals have explored both

when animals copy and from whom they do so, and revealed

surprising parallels with the social learning of humans [85]. Although

the social learning mechanisms used can vary across species (Box 1),

this does not mean we cannot learn a lot about the functional

consequences of various strategies from comparative studies.

Studies of sticklebacks (Pungitius spp.) have revealed evidence that

these fish disproportionately copy when uncertain [86], when the

demonstrator receives a higher payoff than they do [87,88] and when

asocial learning would be costly [89,90]. Sticklebacks are disproportio-

nately more likely to use social information that conflicts with their own

experience as the number of demonstrators increases, which provides

evidence of conformist bias in this species [91]. It has also been found

that small fish are sensitive to a range of attributes in their tutors,

including age [92], size [93], boldness [94] and familiarity [95], and adjust

their social information use with reproductive state, with gravid females

much more likely to use social information than other individuals [90].

A similar set of studies investigated the contexts that promote the

social enhancement of food preferences in rats (Rattus norvegicus)

and provide evidence of the use of various strategies, including

copy if dissatisfied, copy when uncertain, and copy in a stable

environment [96]. As yet, however, there is no evidence that rats

copy selectively with respect to demonstrator age, familiarity,

relatedness or success [96]. By contrast, chimpanzees (Pan troglo-

dytes) disproportionately adopt the behaviour of the oldest and

highest-ranking of two demonstrators [97], and vervet monkeys

(Chlorocebus aethiops) preferentially copy dominant female mod-

els over dominant males (females are the philopatric sex in this

species) [98].

These studies imply that even relatively simple animals are capable

of flexibly using a range of social learning strategies. Although there

is clearly scope for further comparative experiments, it is apparent

from existing research that strategic learning behaviour has evolved

in a range of taxa, with strikingly similar context-specific patterns of

copying to those observed in humans clearly evident [58,59,61]. This

suggests that the evolution of copying behaviour is best regarded as a

convergent response to specific selection pressures, and might not be

well predicted by the relatedness of a species to humans.

Box 3. Modelling social learning from individuals to populations

A variety of theoretical approaches has been used to model the

evolution of social learning strategies, commonly known as cultural

evolution, gene–culture co-evolution and dual inheritance theory

[5,9,10,14,16,18–21]. Typically, models are based on systems of

recursions that track the frequencies of cultural and genetic variants

in a population, often with fitness defined by the match between a

behavioural phenotype and the environment. These systems range

from those containing only two possible discrete behavioural variants

through to traits that vary continuously along one or more dimen-

sions, with evolutionarily stable strategy (ESS) and population-

genetic analyses applied to these models [15,18,21].

Other approaches include multi-armed bandits (in which a number

of discrete choices with different expected payoffs are available to

players [8,11,32]), reaction-diffusion models (in which differential

equations describe the change in frequency of cultural traits over

time and incorporate individual learning biases [17]) and informa-

tion-cascade games (in which individuals choose from a limited set

of options after receiving private information and observing the

decisions of previous actors [50,52]), all of which have been

influential in identifying adaptive social learning strategies. The

complexities of tracking genetic and cultural parameters over time,

and the need to incorporate increasingly complex learning strate-

gies, have led to greater use of simulation modelling in recent years

[12–14,19,26], which has enabled researchers to build models that

are spatially explicit [12] and to separately track knowledge and

behaviour [32].

Here we illustrate the methods using a classic model of unbiased,

directly biased and frequency-dependent biased cultural transmis-

sion, introduced by Boyd and Richerson [5]. Consider a cultural trait

with two alternative variants, denoted c and d, acquired through

social learning. The model tracks the spread of c in the population; the

proportion of the population with c is denoted by p. Each individual in

the population is exposed to three randomly selected cultural role

models: thus, the probability of having i role models with trait c, given

p, is MðijpÞ ¼ 3i

� �

pið1� pÞ3�i . To model cultural transmission with

frequency-dependent bias, the strength of which is D, expressions for

the probability that an individual acquires c when i role models have c

are given in Table I (note that when D=0, then transmission is

unbiased). This gives a recursion for the frequency of c in the

population: p0 = p + Dp(1 � p)(2p � 1). A direct learning bias can be

modelled by assuming that some feature of trait c renders it

inherently more likely to be copied. B is the strength of this direct

bias and the recursion expression is p0 = p + Bp(1 � p). These

equations can be used to compare the fate of trait c over time under

different transmission biases, and show that the different individual-

level learning strategies produce different outcomes at the population

level (Figure I).

[()TD$FIG]

2 4 6 8 100.4

0.5

0.6

0.7

0.8

0.9

1

Cultural generations

Fre

quency o

f tr

ait

Unbiased transmission (D=0)

Frequency−dependent bias (D=0.5)

Directly biased transmission (B=0.3)

Key:


Figure I. Individual-level transmission biases produce different outcomes at the

population level. The figure shows the time course of trait c when different

biases are operating.

Table I. Probability that an individual acquires trait c given its

frequency in the set of cultural role models

Number of role

models with c

Probability that

a focal individual

acquires c

0 0

1 13� D

3

2 23þ D

3

3 1


70

to both when it is best to choose social sources to acquire

information and from whom one should learn. These latter

class are often referred to as learning biases [5]. These can

be based on content (such as a preference for social infor-

mation [28], attractive information [29], or content that

evokes a strong emotion such as disgust [30]) as well as

context, such as the frequency of a trait in a population (e.g.

a conformist bias towards adopting the majority behav-

iour), the payoff associated with it (e.g. copy the most

successful individual), or some property of the individuals

from whom one learns (model-based biases such as copy

familiar individuals).

Many studies have focussed on establishing the theoreti-

cal viability of a given strategy or a small number of strate-

gies, and explored the conditions under which each is

expected to prosper [5,11,12,15,16,18–21,31]. A different

approach is to establish a framework within which the

relativemerits of awide range of strategies can beevaluated

[11,32]. A recent example is the social learning strategies

tournament [32], an open competition in which entrants

submitted strategies specifying how agents should learn in

order to prosper in a simulated environment (Box 4). This

study relaxed some assumptions prevalent in the field, such

as thatasocial learning ismorecostly thansocial learning, to

surprising effect. It revealed that copying pays under a far

greater range of conditions than ever previously thought,

even when extremely error-prone. In any given simulation

involving the top-performing strategies, very little of the

learningperformedwasasocial and learning for thewinning

strategy was almost exclusively social. The strength of this

result depends in part on the tournament assumption that

individuals build up a repertoire of multiple behaviour

patterns, rather than focussing on a single acquired behav-

iour, as in most analytical theory. This meant that when a

copied behaviour turned out to confer low fitness, agents

could switch rapidly to an alternative behaviour in the

[()TD$FIG]

Social

learning

strategies

Guided variation [5] (trial−and−errorlearning combined with unbiasedtransmission)

Contentdependent

Contextdependent

Unbiased or random

copying [9,66]

Bias formemorable orattractive

variants [29]

Bias for social

information [28]

Bias derived fromemotional reaction

(e.g. disgust [30])

Modelbased

Frequencydependent

Statebased

Number of

demonstrators [39]

Copy variants thatare increasing

in frequency [47]

Copy the majority,

conformist bias [5,91]

Copy if demonstrators

consistent [53]

Copy rare

behaviour [54]

Copy depending on

reproductive state [90]

Copy ifdissatisfied [11]

Copy if personal

information outdated [86]

Copy if

uncertain [96]

Gender−based [98]

Age−based [92]

Size−based [93]

Success−based

Based on

model’s knowledge [43]

Prestige−based [31]

Dominance rank

based [97]

Kin−based [62]

Familiarity−based [48,59,95]

Copy most successful

individual [35]

Copy in proportion

to payoff [88]

Copy if payoff

better [87]


Figure 1. Social learning strategies for which there is significant theoretical or empirical support. The tree structure is purely conceptual and not based on any empirical data

on homology or similarity of cognition. The sources given are not necessarily the first descriptions or the strongest evidence, but are intended as literature entry points for

readers.


71

repertoire, thereby removing one of the drawbacks to copy-

ing identified in the analytical literature.

The tournament also highlighted the role of copied

individuals as filters of information. Previous theory had

placed the onus on learners to perform this adaptive

filtering [15], demanding selectivity, and therefore specific

cognitive capabilities, on the part of the copier. However,

the tournament established that even nonselective copying

is beneficial relative to asocial learning, because copied

individuals typically perform the highest payoff behaviour

in their repertoire generating a non-random sample of

high-performance behaviour for others to copy. These

insights go some way to explaining the discrepancy be-

tween Rogers’ analysis and the empirical fact of human

reliance on social information. They also help to explain

why social learning is so widespread in nature, observed

not just in primates and birds [3], but even in fruit flies and

crickets [4]: even indiscriminate copying is generally more

efficient than trial-and-error learning. However, because of

its design, the tournament provided no information on the

issue of from whom one should learn. A similar study

incorporating individual identities would be potentially

informative, and we suspect that selectivity here would

confer additional fitness benefits.

Conclusions as to which strategies are likely to prosper

depend inevitably on the assumptions built into the mod-

els. For example, the conditional strategies described

above depend on individuals knowing immediately the

payoff of a behavioural option, but this information is

not always available. If everyone else is planting potatoes,

should you plant potatoes or another crop? Information on

the relative payoffs will not be available for months, so a

simple conditional strategy is not viable. An influential

view is that under such circumstances, it pays to conform to

the local traditions [4,16]. Indeed, theoretical models sug-

gest that natural selection should favour such a conformist

bias over most conditions that favour social learning [16],

which brings us closer to an evolutionary understanding of

the behavioural alignment prevalent in human herding

behaviour [33]. However, this view has been challenged

by subsequent analyses pointing out that conformity can

hinder the adoption of good new ideas (and, by inference,

cumulative cultural evolution), and therefore can be

expected toperformrelativelypoorly insomecircumstances,

Box 4. The social learning strategies tournament

The social learning strategies tournament was a computer-based

competition in which entrants submitted a strategy specifying the

best way for agents living in a simulated environment to learn [32].

The simulation environment was characterized as a multi-armed

bandit [11] with, in this case, 100 possible arms or behaviour patterns

that an agent could learn and subsequently exploit. Each behaviour

had a payoff, drawn from an exponential distribution, and the payoff

could change over time (the rate of change was a model parameter).

This simulated environment contained a population of 100 agents,

each controlled by one of the strategies entered into the tournament.

In each model iteration, agents selected one of three moves, as

specified by the strategy. The first, INNOVATE, resulted in an agent

learning the identity and payoff of one new behaviour, selected at

random. The second, EXPLOIT, represented an agent choosing to

perform a behaviour it already knew and receiving the payoff

associated with that behaviour (which might have changed from

when the agent learned about it). The third, OBSERVE, represented an

agent observing one or more of those agents who chose to play

EXPLOIT, and learning the identity and payoff of the behaviour the

observed agent was performing. Agents could only receive payoffs by

playing EXPLOIT, and the fitness of agents was determined by the

total payoff received divided by the number of iterations through

which they had lived. Evolution occurred through a death–birth

process, with dying agents replaced by the offspring of survivors; the

probability of reproduction was proportional to fitness. Offspring

would carry the same strategy as their parents with probability 0.98,

such that successful strategies tended to increase in frequency, and

another strategy with probability 0.02, so that strategies could invade

and re-invade the population.

The most important finding was the success of strategies that relied

almost entirely on copying (i.e. OBSERVE) to learn behaviour (Figure

Ia). Social learning in this context proved an extremely robustly

successful strategy because the exploited behaviour patterns avail-

able to copy constituted a select subset that had already been chosen

for their high payoff (see the main text). The results also highlighted

the parasitic nature of social learning, because successful strategies

did worse when fixed in the population than when other strategies

were present and providing information (Figure Ib).[()TD$FIG]

0 0.2 0.4 0.6 0.8 10

0.3

0.6

0.9(a)

Mean s

core

Proportion of OBSERVE when learning

1 2 3 4 5 6 7 8 9 100

0.2

0.4

Mean lifetim

e p

ayoff w

hen a

lone

0

20

40

Mean s

core

in m

ele

e

Tournament rank

(b)


Figure I. Social learning strategies tournament results [32]. (a) Strategy score plotted against the proportion of the learning moves that were OBSERVE for that strategy.

(b) Final score for the top ten strategies when competing simultaneously with other strategies (black) and individual fitness, measured as mean lifetime payoff, in

populations containing only single strategies (red).


72

particularly in changing environments [19,20]. More recent

analysessuggest, however, that the strengthof conformity is

expected to vary with environmental stability and learning

costs [18,21]. One way through this debate stems from the

suggestion that conformity is only widely favoured when

weak, becauseweak conformity acts to increase the frequen-

cy of beneficial variants when they are common, but its

action is insufficient to prevent their spread when rare [17].

Such debates, and the formal theory in general, have stim-

ulated an increase in empirical research on the strategic

nature of human social learning (Figure 1) that sets out to

determine whether copying behaviour fits with the theoret-

ical predictions.

Empirical studies

Empirical investigations of social learning strategies in

humans span a range of scales, from laboratory studies

that pick apart the factors affecting minute-by-minute

decisions at the individual level [34,35] through to obser-

vational work that seeks to explain the population-level

frequencies of socially transmitted traits in historical and

archaeological data [36–38].

Laboratory-based experiments have been successful in

revealing the variety and subtlety of human social infor-

mation use. Although there is a long tradition of these

studies in social psychology [39], the new wave of re-

search that we review here is different because it is rooted

in the formal evolutionary theory described above [40].

Thus, whereas social psychology can provide immediate

descriptions of the way in which people use social infor-

mation, more recent research on social learning strate-

gies seeks to link such observations with functional

evolutionary explanations [40]. The use of micro-societies

[41] and transmission chains [28], in which social learn-

ing is studied experimentally in small groups or chains of

subjects that change composition, has been very produc-

tive. Such experiments have provided evidence of many of

the biases explored in the theoretical literature. Exam-

ples include a bias for copying successful [35,42] or

knowledgeable [43] models, a tendency to conform to

route choices [44] and increased reliance on social infor-

mation when payoff information is delayed [45] or at low

rates of environmental change [46]. These experiments

have also provided new insights not anticipated by theo-

ry; for example, it has been shown that people prefer

variants that are increasing in frequency [47] and that in

some circumstances people pay more attention to social

information that originates outside their own sociocultur-

al group [48].

Recently, some researchers in economics have started to

introduce social learning into the experimental study of

strategic games. Studies have shown that introduction of

intergenerational social information can establish long-

term social conventions that do not necessarily represent

the predicted optimal strategy for any player [49,50], can

drive up contributions in public-goods games [51], and can

reveal unexpected biases in people’s valuation of informa-

tion sources, such as an over-weighting of private informa-

tion in some conditions [52]. However, this research has yet

to overlap with research on social learning strategies,

which can potentially provide explanations for this appar-

ently suboptimal behaviour in terms of the inherent biases

people have about using social information.

Importantly, these studies can also throw up significant

challenges to existing theory, such as individual variation

in people’s responses to social information, which has not

yet been considered in the theoretical literature. Some

subjects show a greater propensity to use social informa-

tion than others, and those who do use social information

can do so in different ways [34,47,53]. In a recent study

using a simple binary choice task (choose the red or the

blue technology), only a subset of subjects behaved as

predicted by the conformist learning model, with the

remaining ‘maverick’ subjects apparently ignoring social

information altogether [34]. In another example, reading

positive reviews of a piece of music caused some subjects to

increase their valuation of that tune, whereas a significant

minority actually decreased their evaluations [53]. Social

psychology studies suggest that people will switch between

conformity and anti-conformity depending on the social

context, and are more or less likely to use social informa-

tion depending on their mood [54]. Such flexibility is not

inconsistent with an evolutionary explanation, but rather

implies context-specific use of strategies [7]. The extent to

which current theory needs to incorporate state-dependent

and contextual cues requires exploration, and new formal

methods are becoming available that facilitate such exten-

sions [55].

Another area inwhich empirical and theoretical studies

can inform each other is the ontogeny of learning strate-

gies. Early in life, a child is surrounded by adultswho have

presumably engaged in decades of the kind of knowledge

filtering that can make social learning adaptive. Young

children have a tendency to imitate even irrelevant

actions indiscriminately [56], which might reflect this

informational imbalance. Evidence from attention studies

suggests that very young infants have evolved mechan-

isms to focus attention on subtle cues given by their carers

that indicate when important information is being made

available [57]. As they grow and interact with a wider

range of people, the challenge becomes less a problem of

when andmore of fromwhom to learn. This iswhenmodel-

based, payoff-based, or frequency-dependent biases would

become more pertinent.

There is ample evidence of model-based learning biases

in young children [58–60] and in a surprising number of

instances these echo similar patterns observed in other

animals (Box 2). For example, preschool-age children (�3

years) tend to trust information presented to them by

familiar teachers more strongly than that given by unfa-

miliar teachers [59]. In a follow-on study, older children

(�5 years) further increased their trust in the information

supplied by a familiar teacher who presented information

that the children knew to be accurate, but reduced trust

when the teacher provided inaccurate information, where-

as the trust of younger children in familiar teachers was

unaffected by the accuracy of the information provided

[61], an example of the way we might expect adaptive

social learning strategies to vary ontogenetically. More

studies of how learning biases change during life, extend-

ing into adolescence and adult life, would be highly instruc-

tive in both humans and other animals.


73

Recent empirical work on social learning has also es-

caped the laboratory, which is vital for external validity.

For instance, studies in traditional Fijian populations have

found that food taboos that lead pregnant and lactating

females to avoid consumption of toxic fish are initially

transmitted through families, but as individuals get older

they preferentially seek out local prestigious individuals to

refine their knowledge [62]. Formal theory suggests that

such learning strategies are highly adaptive [5]. Another

study used the two-technology choice task in the subsis-

tence pastoralist population of the Bolivian Altiplano,

where a comparative lack of reliance on social information

demonstrated that subtle effects of setting and cultural

background probably play an important role in human

social learning [63]. These results emphasize flexibility

in the use of social information.

The combination of novel theory with empirical data has

also been successful in understanding the spread of cul-

tural traits across populations. Different social learning

strategies lead to different transmission dynamics at the

population level, generating detectable signatures in the

frequency distributions and temporal dynamics of cultural

traits. Comparison of real data with expected distributions

can therefore indicate the processes behind the spread of

ideas, trends and interests. This approach has been suc-

cessful in highlighting several cultural domains where

unbiased, or random, copying seems to dominate, such

as the popularity of baby names, music and choice of

dog breeds [37], and of the use of complementary and

traditional medicines [64]. It has also illustrated the inter-

actions between independent decisions and social trans-

mission in the spread of interest in disease pandemics such

as H5N1 and bird flu virus [65]. Here, random copying

refers to unbiased copying in direct proportion to the rate a

trait is observed, and does not imply that individual deci-

sion-making is random. For instance, in spite of all of the

thought and care that individual parents put into choosing

their child’s name, parents as a group behave in a manner

that is identical to the case in which they choose names at

random [37]. The reason for this is nothing more than that

common names are more likely to be observed and consid-

ered by parents than obscure names, and the likelihood

that a name is chosen is approximately proportional to its

frequency at the time. These studies also reveal how the

drift-like dynamics that result from random copying can be

perturbed by the influence of key events, such as a spike in

popularity of the Dalmatian dog breed after the re-release

of 101 Dalmatians, a film that artificially inflated the

number of Dalmatians observed [37]. This work is impor-

tant because it provides potential tools for interpreting

more ancient data when we have much less knowledge of

the social context at the time [38,66,67].

Concluding remarks

The work we have reviewed here opens up a rich seam of

opportunities for future development in several disci-

plines, from anthropology and cultural evolution through

to economics and artificial life. Here we focus on just three.

The first is related to the study of cooperation. One of the

more intriguing results from the social learning strategies

tournament was the parasitic effect of strategies that used

only social learning. The way that a population learns can

be viewed as a cooperation problem: innovators who en-

gage in asocial learning are altruistic cooperators who

introduce new information, whereas copiers are defectors

who exploit that information. The tournament showed

how, at the individual level, the temptation to defect

(i.e. copy) is very powerful, but also that populations of

defectors do worse than more mixed populations, which

creates a classical cooperation dilemma. Although some

have recognized the link [5,25,68], there is much to be done

before the interactions between social learning strategies,

cultural evolution and the evolution of cooperation are fully

understood [69,70].

Second, we highlight the way in which computer scien-

tists are now starting to use the concept of strategic social

learning, and its interactions with individual learning and

genetic evolution, to develop novel algorithms for evolu-

tionary computing [71,72]. These studies show that social

learning using a fixed strategy of copying from the most

successful individuals significantly increases the success of

agents exploring a complex fitness landscape (specifically

the NK landscape widely adopted as a test bed for evolu-

tionary computation), a result that striking parallels an-

thropological research on human social learning [35]. The

prospect that research on social learning strategies can

simultaneously provide inspiration for those working at

the cutting edge of technology while benefiting from the

novel insights such a dynamic field can produce is tremen-

dously exciting.

Finally, we see open fields for research into the neuro-

biological basis of social learning. Hitherto, most experi-

mental neuroscience concernedwith learning and decision-

making has focused largely on asocial learning, in spite of

the important role of social influences on human learning.

Research exploring the brain pathways and structures

used in social learning and socially biased decision-making

is needed. One pressing question is to what extent different

social learning processes and strategies map onto different

neural circuits. A pioneering study exploring how the

opinion of others affects the valuation of objects has

revealed that the human anterior insula cortex or lateral

orbitofrontal cortex uniquely responds to the unanimous

opinions of others [53]. This finding is suggestive of an

evolved neural sensitivity to consistency in demonstrator

behaviour, and is consistent with an economics experiment

that suggests that people are more reinforced by following

social information than otherwise expected by payoff alone

[8]. Another key issue is whether our brains contain cir-

cuitry specific to social information processing, or whether

these processes piggyback on established reinforcement

learning circuitry. Recent evidence is suggestive of the

latter [73], but our general lack of knowledge in this area

is profound.

Clearly, the study of social learning strategies is a

rapidly growing field with implications for multiple fields

of research (Box 5). The empirical studies reviewed here

reveal the subtlety and complexity of the learning strate-

gies used by humans. An important contribution of this

work, in parallel with studies on non-humans, is to chal-

lenge the notion of a single best strategy, or a strategy

associated with a particular type of individual, or species.


74

Rather, recent work emphasizes instead the way in which

the flexible context-dependent use of a range of subtle

biases is a general feature of social learning, in both

humans and other animals. In future, this should inspire

theoretical researchers in turn to take on the challenge of

incorporating meta-strategies into their models.

AcknowledgementsThis work was funded by an ERC Advanced Fellowship to K.N.L.

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Box 5. Questions for future research

� How are the performances of various learning strategies general-

ized across different learning environments?

� Can social learning be studied as a cooperation game? Innovators

who engage in asocial learning could be viewed as altruistic

cooperators who introduce new information, whereas copiers are

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social learning strategies affect the establishment and mainte-

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evolutionary computing and robotics?

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processing, or do these processes piggyback on established

reinforcement learning circuitry?


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http://dx.doi.org/10.1016/j.evolhumbehav.2010.08.001

http://dx.doi.org/10.1016/j.evolhumbehav.2010.08.001

http://dx.doi.org/10.1098/rspb.2010.1562

Visual search in scenes involvesselective and nonselective pathwaysJeremy M. Wolfe, Melissa L.-H. Vo, Karla K. Evans and Michelle R. Greene

Brigham & Women’s Hospital, Harvard Medical School, 64 Sidney St. Suite 170, Cambridge, MA 02139, USA

How does one find objects in scenes? For decades, visual

search models have been built on experiments in which

observers search for targets, presented among distractor

items, isolated and randomly arranged on blank back-

grounds. Are these models relevant to search in continu-

ous scenes? This article argues that themechanisms that

govern artificial, laboratory search tasks do play a role in

visual search in scenes. However, scene-based informa-

tion is used to guide search in ways that had no place in

earlier models. Search in scenes might be best explained

byadual-pathmodel: a ‘selective’path inwhichcandidate

objectsmustbe individuallyselected for recognitionanda

‘nonselective’ path inwhich information can be extracted

from global and/or statistical information.

Searching and experiencing a scene

It is an interesting aspect of visual experience that you can

look for an object that is, literally, right in front of your

eyes, yet not find it for an appreciable period of time. It is

clear that you are seeing something at the location of the

object before you find it. What is that something and how

do you go about finding that desired object? These ques-

tions have occupied visual search researchers for decades.

Whereas visual search papers have conventionally de-

scribed search as an important real-world task, the bulk

of research had observers looking for targets among some

number of distractor items, all presented in random con-

figurations on otherwise blank backgrounds. During the

past decade, there has been a surge of work using more

naturalistic scenes as stimuli and this has raised the issue

of the relationship of the search to the structure of the

scene. In this article, we briefly summarize some of the

models and solutions developed with artificial stimuli and

then describe what happens when these ideas confront

search in real-world scenes. We argue that the process of

object recognition, required for most search tasks, involves

the selection of individual candidate objects because all

objects cannot be recognized at once. At the same time, the

experience of a continuous visual field tells you that some

aspects of a scene reach awareness without being limited

by the selection bottleneck in object recognition. Work in

the past decade has revealed how this nonselective proces-

sing is put to use when you search in real scenes.

Classic guided search

One approach to search, developed from studies of simple

stimuli randomly placed on blank backgrounds, can be

called ‘classic guided search’ [1]. It has roots in Treisman’s

Feature Integration Theory [2]. As we briefly review below,

it holds that search is necessary because object recognition

processes are limited to one or, perhaps, a few objects at

one time. The selection of candidate objects for subsequent

recognition is guided by preattentively acquired informa-

tion about a limited set of attributes, such as color, orien-

tation and size.

Object recognition is capacity limited

You need to search because, although you are good at

recognizing objects, you cannot recognize multiple objects

simultaneously. For example, all of the objects in Figure 1

are simple in construction, but if you are asked to find ‘T’s

that are both purple and green, you will find that you need

to scrutinize each item until you stumble upon the targets

(there are four). It is introspectively obvious that you can

see a set of items and could give reasonable estimates for

their number, color, and so forth. However, recognition of a

specific type of item requires another step of binding the

visual features together [3]. That step is capacity limited

and, often, attention demanding [4] (however, see [5]).

In the case of Figure 1, the ability to recognize one object

is also going to be limited by the proximity of other, similar

items. These ‘crowding’ phenomena have attracted in-

creasing interest in the past few years ([6,7]). However,

although it would be a less compelling demonstration, it

would still be necessary to attend to item after item to bind

their features and recognize them even if there were only a

few items and even if those were widely spaced [8].

The selection mechanism is a serial–parallel hybrid

Whereas it is clear that object recognition is capacity

limited, the nature of that limitation has been less clear

(for an earlier discussion of this issue, see [9]). The classic

debate has been between ‘serial’ models that propose that

items are processed one after the other [2] and ‘parallel’

models that hold that multiple objects, perhaps all objects,

are processed simultaneously but that the efficiency of

processing of any one item decreases as the number of

items increases [10,11]. The debate has been complicated

by the fact that the classic reaction time data, used inmany

experiments, are ambiguous in the sense that variants of

serial and parallel models can produce the same patterns

of data [12]. Neural evidence has been found in support of

both types of process (Box 1).

Similar to many cognitive science debates, the correct

answer to the serial–parallel debate is probably ‘both’.

Consider the timing parameters of search. One can esti-

Review

Corresponding author: Wolfe, J.M. (wolfe@search.bwh.harvard.edu).

1364-6613/$ – see front matter � 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tics.2010.12.001 Trends in Cognitive Sciences, February 2011, Vol. 15, No. 2 77

mailto:wolfe@search.bwh.harvard.edu


mate the rate at which items are processed from the slopes

of the reaction time (RT) by set size functions. Although the

estimate depends on assumptions about factors such as

memory for rejected distractors (Box 2), it is in the range of

20–50 msec/item for easily identified objects that do not

need to be individually fixated [13]. This estimate is sig-

nificantly faster than any estimate of the total amount of

time required to recognize an object [14]. Even on the short

end, object recognition seems to require more than 100

msec/item (<10 items/sec). Note that we are speaking

about the time required to identify an object, not the

minimum time that an observer must be exposed to an

object, which can be very short indeed [15].

As a solution to thismismatch of times,Moore andWolfe

[16] proposed a metaphorical ‘carwash’ (also called ‘pipe-

line’ in computer science). Items might enter the binding

and recognition carwash one after another every 50msec or

so. Each itemmight remain in the process of recognition for

several hundred milliseconds. As a consequence, if an

experimenter looked at the metaphorical front or the back

of the carwash, serial processing would dominate, but if

one looked at the carwash as a whole, one would see

multiple items in the process of recognition in parallel.

Other recent models also have a serial–parallel hybrid

aspect, although they are often different from the carwash

in detail [17,18]. Consider, for example, models of search

with a primary focus on eye movements [19–21]. Here, the

repeated fixations impose a form of serial selection every

250 msec or so. If one proposes that five or six items are

processed in parallel at each fixation, one can produce the

throughput of 20–30 items/second found in search experi-

ments. Interestingly, with large stimuli that can be re-

[()TD$FIG]


Figure 1. Find the four purple-and-green Ts. Even though it is easy to identify such targets, this task requires search.

Box 1. Neural signatures of parallel and serial processing

What would parallel and serial processing look like at a neuronal

level? One type of parallel processing in visual search is the

simultaneous enhancement of all items with a preferred feature (e.g.

all the red items). Several studies have shown that, for cells

demonstrating a preference for a specific feature, the preference is

stronger when the task is to find items with that feature [77]. For

serial processing, one would like to see the ‘spotlight’ of attention

moving around from location to location. Buschman and Miller [78]

saw something similar to this when it turned out that monkeys in

their experiment liked to search a circular array of items in the same

sequence on every trial. As a result, with multiple electrodes in

place, the authors could see an attentional enhancement rise at the 3

o’clock position, then fall at 3 and rise at 6, as attention swept

around in a serial manner to find a target that might be at the 9

o’clock position in that particular trial.

Similar shifts of attention can be seen in human evoked potential

recordings [79]. Bichot et al. [80] produced an attractive illustration

of both processes at work in visual area, V4. When the monkey was

searching for ‘red’, a cell that liked red would be more active, no

matter where the monkey was looking and/or attending. If the next

eye movement was going to take the target item into the receptive

field of the cell, the cell showed another burst of activity as serial

attention reached it in advance of the eyes.

Box 2. Memory in visual search

There is a body of seemingly contradictory findings about the role of

memory in search. First, there is the question of memory during a

search. Do observers keep track of where they have been, for

example, by inhibiting rejected distractors? There is some evidence

for inhibition of return in visual search [81,82], although it seems

clear that observers cannot use inhibition to mark every rejected

distractor [16,83]. Plausibly, memory during search serves to

prevent perseveration on single salient items [82,84].

What about memory for completed searches? If you find a target

once, are you more efficient when you search for it again? A body of

work on ‘repeated search’ finds that search efficiency does not

improve even over hundreds of trials of repetition [85,86]. By contrast,

observers can remember objects that have been seen during search

[87] and implicitmemory for the arbitrary layout of displays can speed

their response [88]. How can all of these facts be true? Of course,

observers remember some results of search. (Where did I find those

scissors last time?). The degree to which these memories aid

subsequent search depends on whether it is faster to retrieve the

relevant memory or to repeat the visual search. In many simple tasks

(e.g. with arrays of letters; [86]), memory access is slower than is

visual search [85]. In many more commonplace searches (those

scissors), memory will serve to speed the search.


78

solved in the periphery, the pattern of response time data

is similar with and without eye movements [22]. Given the

close relationship of eye movements and attention [23], it

could be proposed that search is accomplished by selecting

successive small groups of items, whether the eyes move or

not. Note that all of these versions are hybrids of some

serial selection and parallel processing.

A set of basic stimulus attributes guide search

Object recognition might require attention to an object

[24], but not every search requires individual scrutiny of

random items before the target is attended. For example,

in Figure 1, it is trivial to find the one tilted ‘T’. Orientation

is one of the basic attributes that can guide the deployment

of attention. A limited set of attributes can be used to

reduce the number of possible target items in a display. If

you are looking for the big, red, moving vertical line, you

can guide your attention toward the target size, color,

motion and orientation. We label the idea of guidance by

a limited set of basic attributes as ‘classic guided search’

[25]. The set of basic attributes is not perfectly defined but

there are probably between one and two dozen [26]. In the

search for the green-and-purple Ts of Figure 1, guidance

fails. Ts and Ls both contain a vertical and a horizontal

line, so orientation information is not useful. The nature of

the T or L intersection is also not helpful [27]; neither can

guidance help by narrowing the search to the items that

are both green and purple. When you specify two features

(here two colors) of the same attribute, attention is guided

to the set of items that contain either purple or green. In

Figure 1, this is the set of all items [28] so no useful

guidance is possible.

The internal representation of guiding attributes is

different from the perceptual representation of the same

attributes. What you see is not necessarily what guides

your search. Consider color as an example. An item of

unique color ‘pops out’. Youwould have no problemfinding

the one red thing among yellow things [29]. The red thing

looks salient and it attracts attention. It is natural to

assume that the ability to guide attention is basically

the same as the perceived salience of the item [30,31].

However, look for the desaturated, pale targets in Figure 2

(there are two in each panel). In each case, the target lies

halfway between the saturated and white distractors in a

perceptual color space. In the lab, although not in this

figure, the colors can be precisely controlled so that the

perceived difference between red and pale red is the same

as the difference betweenpale greenandgreen or pale blue

and blue. Nevertheless, the desaturated red target will be

found more quickly [32], a clear dissociation between

guidance and perception. Similar effects occur for other

guiding attributes, such as orientation [33]. The represen-

tation guiding attention should be seen as a control device,

managing access to the binding and recognition bottle-

neck. It does not reveal itself directly in conscious percep-

tion.

Visual search in natural(istic) scenes

The failure of classic guided search

To this point, we have described what could be called

‘classic guided search’ [1,25]. Now, suppose that we wanted

to apply this classic guided search theory to the real world.

Find the bread in Figure 3a. Guided search, and similar

models, would say that the one to two dozen guiding

attributes define a high-dimensional space in which objects

would be quite sparsely represented. That is, ‘bread’ would

be defined by some set of features [21]. If attention were

guided to objects lying in the portion of the high-dimen-

sional feature space specified by those features, few other

objects would be found in the neighborhood [34]. Using a

picture of the actual bread would produce better guidance

than its abstract label (‘bread’) because more features of

the specific target would be precisely described [35]. So in

the real world, attention would be efficiently guided to the

few bread-like objects. Guidance would reduce the ‘func-

tional set size’ [36].

It is a good story, but it is wrong or, at least, incomplete.

The story should be just as applicable to search for the loaf

of bread in Figure 3b; maybe more applicable as these

objects are clearly defined on a blank background. Howev-

er, searches for isolated objects are inefficient [37], whereas

searches such as the kitchen search are efficient (given

some estimate of ‘set size’ in real scenes) [38]. Models such

as guided search, based on bottom-up and top-down pro-

cessing of a set of ‘preattentive’ attributes, seem to fail

when it comes to explaining the apparent efficiency of

search in the real world. Guiding attributes do some work

[21,39], but not enough.[()TD$FIG]


Figure 2. Find the desaturated color dots. Colors are only an approximation of the colors that would be used in a carefully calibrated experiment. The empirical result is that

it is easier to find the pale-red (pink) targets than to find the pale-green or -blue targets.


79

The way forward: expanding the concept of guidance for

search in scenes

Part of the answer is that real scenes are complex, but never

random. Elements are arranged in a rule-governedmanner:

people generally appear on horizontal surfaces [40,41],

chimneys appear on roofs [42] and pots on stoves [43]. Those

and other regularities of scenes can provide scene-based

guidance.Borrowing fromthememory literature,werefer to

‘semantic’ and ‘episodic’ guidance. Semantic guidance

includes knowledge of the probability of the presence of

an object in a scene [43] and of its probable location in that

scene given the layout of the space [40,44], as well as inter-

object relations (e.g. knives tend to be near forks, [45]).

Violations of these expectations impede object recognition

[46] and increase allocation of attention [43]. It can take

longer to find a target that is semantically misplaced, (e.g.

searching for the bread in the sink [47]). Episodic guidance,

which we will merely mention here, refers to memory for a

specific, previously encountered scene that comprises infor-

mation about specific locations of specific objects [48]. Hav-

ing looked several times, you know that the bread is on the

counter to the left, not in all scenes, but in this one. The role

ofmemory in search is complex (Box2), but it is the case that

you will be faster, on average, to find bread in your kitchen

than in another’s kitchen.

When searching for objects in scenes, classic sources of

guidance combine with episodic and semantic sources of

guidance to direct your attention efficiently to those parts

of the scene that have the highest probability of containing

targets [40,49–51]. In naturalistic scenes, guidance of eye

movements by bottom-up salience seems to play a minor

role compared with guidance by more knowledge-based

factors [51,52]. A short glimpse of a scene is sufficient to

narrow down search space and efficiently guide gaze [53] as

long as enough time is available to apply semantic knowl-

edge to the initial scene representation [44]. However,

semantic guidance cannot be too generic. Presenting a

word prime (e.g. ‘kitchen’) instead of a preview of the scene

does not produce much guidance [35]. Rather, the combi-

nation of semantic scene knowledge (kitchens) with infor-

mation about the structure of the specific scene (this

kitchen) seems to be crucial for effective guidance of search

in real-world scenes [44,51].

A problem: where is information about the scene

coming from?

It seems reasonable to propose that semantic and episodic

information about a scene guides search for objects in the

scene, but where does that information come from? For

scene information to guide attention to probable locations

of ‘bread’ in Figure 3a, youmust know that the figure shows

something like a kitchen. One might propose that the

information about the scene develops as object after object

is identified. A ‘kitchen’ hypothesis might emerge quickly if

you were lucky enough to attend first to the microwave and

then to the stove, but if you were less fortunate and

attended to a lamp and a window, your kitchen hypothesis

might come too late to be useful.

A nonselective pathway to gist processing

Fortunately, there is another route to semantic scene

information. Humans are able to categorize a scene as a

forest without selecting individual trees for recognition

[54]. A single, brief fixation on the kitchen of Figure 3a

would be enough to get the ‘gist’ of that scene. ‘Gist’ is an

imperfectly defined term but, in this context, it includes the

basic-level category of the scene, an estimate of the dis-

tributions of basic attributes, such as color and texture

[55], and the spatial layout [54,56–58]. These statistical

and structural cues allow brief exposures to support above-

chance categorization of scenes into, for example, natural

or urban [54,59,60] or containing an animal [15,61]. Within

a single fixation, an observer would know that Figure 3a

was a kitchen without the need to segment and identify its

component objects. At 20–50 objects/second, that observer

will have collected a few object identities as well but, on

average, these would not be sufficient to produce categori-

zation [54,62].

How is this possible? The answer appears to be a two-

pathway architecture somewhat different from, but per-

[()TD$FIG]


(a) (b)

Figure 3. Find the loaf of bread in each of (a) and (b).


80

haps related to, previous two-pathway proposals [63,64],

and somewhat different from classic two-stage, preatten-

tive-attentivemodels (Box 3). The basic idea is cartooned in

Figure 4. Visual input feeds a capacity-limited ‘selective

pathway’. As described earlier, selection into the bottle-

neck ismediated by classic guidance and, when possible, by

semantic and episodic guidance. In this two-pathway view,

the rawmaterial for semantic guidance could be generated

in a nonselective pathway that is not subject to the same

capacity limits. Episodic guidance would be based on the

results of selective and nonselective processing.

What is a ‘nonselective pathway’? It is important not to

invest a nonselective pathwaywith toomany capabilities. If

all processing could be done without selection and fewer

capacity limits, one would not need a selective pathway.

Global nonselective image processing allows observers to

extract statistical information rapidly from the entire im-

age. Observers can assess the mean and distribution of a

variety of basic visual feature dimensions: size [65], orien-

tation [66], some contrast texture descriptors [67], velocity

and direction of motion [68], magnitude estimation [69],

center of mass for a set of objects [70] and center of area

[71]. Furthermore, summary statistics can be calculated for

more complex attributes, such as emotion [72] or the pres-

ence of classes of objects (e.g. animal) in a scene [73].

Using these image statistics, models and (presumably)

humans, can categorize scenes [54,56,57] and extract basic

Box 3. Old and new dichotomies in theories of visual search

The dichotomy between selective and nonselective pathways,

proposed here, is part of a long tradition of proposing dichotomies

between processes with strong capacity limits that restrict their

work to one or a few objects or locations and processes that are able

to operate across the entire image. It is worth briefly noting the

similarities and differences with some earlier formulations.

Preattentive and attentive processing

Preattentive processing is parallel processing over the entire image.

Similar to nonselective processing, it is limited in its capabilities. In

older formulations such as Feature Integration Theory [2], it handled

only basic features, such as color and orientation, but it could be

expanded to include the gist and statistical-processing abilities of a

nonselective pathway. The crucial difference is embodied in the

term ‘preattentive’. In its usual sense, preattentive processing refers

to processing that occurs before the arrival in time or space of

attentive processing [89]. Nonselective processing, by contrast, is

proposed to occur in parallel with selective processing, with the

outputs of both giving rise to visual experience.

Early and late selection

The nonselective pathway could be seen as a form of late selection

in which processing proceeds to an advanced state before any

bottleneck in processing [90]. The selective pathway embodies early

selection with only minimal processing before the bottleneck.

Traditionally, these have been seen as competing alternatives that

coexist here. However, traditional late selection would permit object

recognition (e.g. word recognition) before a bottleneck. The

nonselective pathway, although able to extract some semantic

information from scenes, is not proposed to have the ability to

recognize either objects or letters.[()TD$FIG]

Nonselecti

ve p

athway

Sele

ctive

path

way

Bottleneck

Features

Semantic

Guidance

EpisodicEarly vision

Binding &

Recognition

ColorOrientation

SizeDepthMotion

Etc.


Figure 4. A two-pathway architecture for visual processing. A selective pathway can bind features and recognize objects, but it is capacity limited. The limit is shown as a

‘bottleneck’ in the pathway. Access to the bottleneck is controlled by guidance mechanisms that allow items that are more likely to be targets preferential access to feature

binding and object recognition. Classic guidance, cartooned in the box above the bottleneck, gives preference to items with basic target features (e.g. color). This article

posits scene guidance (semantic and episodic), with semantic guidance derived from a nonselective pathway. This nonselective pathway can extract statistics from the

entire scene, enabling a certain amount of semantic processing, but not precise object recognition.


81

spatial structure [54,59]. This nonselective information

could then provide the basis for scene-based guidance of

search. Thus, nonselective categorical information, per-

haps combined with the identification of an object or two

by the selective pathway, could strongly and rapidly sug-

gest that Figure 3a depicts a kitchen. Nonselective struc-

tural information could give the rough layout of surfaces in

the space. In principle, these sources of information could

be used to direct the resources of the selective pathway

intelligently so that attention and the eyes can be deployed

to probable locations of bread.

Your conscious experience of the visual world is com-

prised of the products of both pathways. Returning to the

example at the outset of this article, when you have not yet

found the object that is ‘right in front of your eyes’, your

visual experience at that location must be derived primar-

ily from the nonselective pathway. You cannot choose to

see a nonselective representation in isolation, but you can

gain some insight into the contributions of the two path-

ways from Figure 5. The nonselective pathway would ‘see’

the forest [54] and could provide some information about

the flock of odd birds moving through it. However, identi-

fication of a tree with both green and brown boughs or of a

bird heading to the right would require the work of the

selective path [61].

Expert searchers, such as radiologists hunting for signs

of cancer or airport security officers searching for threats,

might have learned to make specific use of nonselective

signals. With some regularity, such experts will tell you

that they sometimes sense the presence of a target before

finding it. Indeed, this ‘Gestalt process’ is a component of a

leading theory of search in radiology [74]. Doctors and

technicians screening for cancer can detect abnormal

cases at above-chance levels in a single fixation [75].

The abilities of a nonselective pathway might underpin

this experience. Understanding how nonselective proces-

sing guides capacity-limited visual search could lead to

improvements in search tasks that are, literally, a matter

of life and death.

Concluding remarks

What is next in the study of search in scenes? It is still not

understood how scenes are divided up into searchable

objects or proto-objects [76]. There is much work to be

done to describe fully the capabilities of nonselective pro-

cessing and even more to document its impact on selective

processes. Finally, we would like to know if there is a

neurophysiological reality to the two pathways proposed

here. Suppose one ‘lesioned’ the hypothetical selective

pathway. The result might be an agnosic who could see

something throughout the visual field but could not iden-

tify objects. A lesion of the nonselective pathway might

produce a simultagnosic or Balint’s patient, able to identify

the current object of attention but otherwise unable to see.

This sounds similar to the consequences of lesioning the

ventral and dorsal streams, respectively [64], but more

research will be required before ‘selective’ and ‘nonselec-

tive’ can be properly related to ‘what’ and ‘where’.

[()TD$FIG]


Figure 5. What do you see? How does that change when you are asked to look for an untilted bird or trees with brown trunks and green boughs? It is proposed that a

nonselective pathway would ‘see’ image statistics, such as average color or orientation, in a region. It could get the ‘gist’ of forest and, perhaps, the presence of animals.

However, it would not know which trees had brown trunks or which birds were tilted.


82

AcknowledgmentsThis work was supported by NIH EY017001 and ONR MURI

N000141010278 to J.M.W. K.K.E. was supported by NIH/NEI

1F32EY019819-01, M.R.G. by NIH/NEI F32EY019815-01and M.L-H.V.

by DFG 1683/1-1.

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84

Emotional processing in anteriorcingulate and medial prefrontal cortexAmit Etkin1,2, Tobias Egner3 and Raffael Kalisch4

1Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA2Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC) at the Veterans Affairs Palo Alto Health Care

System, Palo Alto, CA, USA3Department of Psychology & Neuroscience and Center for Cognitive Neuroscience, Duke University, Durham, NC, USA4 Institute for Systems Neuroscience and NeuroImage Nord, University Medical Center Hamburg-Eppendorf (UKE), Hamburg,

Germany

Negative emotional stimuli activate a broad network of

brain regions, including the medial prefrontal (mPFC)

and anterior cingulate (ACC) cortices. An early influential

view dichotomized these regions into dorsal–caudal

cognitive and ventral–rostral affective subdivisions. In

this review, we examine a wealth of recent research on

negative emotions in animals and humans, using the

example of fear or anxiety, and conclude that, contrary to

the traditional dichotomy, both subdivisions make key

contributions to emotional processing. Specifically, dor-

sal–caudal regions of the ACC and mPFC are involved in

appraisal and expression of negative emotion, whereas

ventral–rostral portions of the ACC and mPFC have a

regulatory role with respect to limbic regions involved in

generating emotional responses. Moreover, this new

framework is broadly consistent with emerging data

on other negative and positive emotions.

Controversies about anterior cingulate and medial

prefrontal functions

Although the medial walls of the frontal lobes, comprising

the anterior cingulate cortex (ACC) and the medial pre-

frontal cortex (mPFC), have long been thought to play a

critical role in emotional processing [1], it remains uncer-

tain what exactly their functional contributions might be.

Some investigators have described evaluative (appraisal)

functions of the ACC and mPFC, such as representation of

the value of stimuli or actions [2–4] and the monitoring of

somatic states [5]. Others hold that the ACC is primarily a

generator of physiological or behavioral responses [6,7].

Still others have described a regulatory role for these

regions, such as in the top-down modulation of limbic

and endocrine systems for the purpose of emotion regula-

tion [3,8–11]. An additional source of uncertainty lies in the

way in which any one of these proposed functions might

map onto distinct subregions of the ACC or mPFC (Box 1).

Undoubtedly the most influential functional parcella-

tion of this type has been the proposal that there exists a

principal dichotomy between caudal–dorsal midline

regions that serve a variety of cognitive functions and

rostral–ventral midline regions that are involved in some

form of emotional processing [12]. However, even this

broadly and long-held view of basic functional specializa-

tion in these regions has been shaken by considerable

evidence over the past decade indicating that many types

of emotional processes reliably recruit caudal–dorsal ACC

and mPFC regions [13,14].

Here, we review recent human neuroimaging, animal

electrophysiology, and human and animal lesion studies

that have produced a wealth of data on the role of the ACC

andmPFC in the processing of anxiety and fear.We chose to

focus primarily on the negative emotions of anxiety and fear

because they are by far the most experimentally tractable

and most heavily studied, and they afford the closest link

between animal and human data. We subsequently briefly

examinewhether a conceptual framework derived from fear

and anxiety can be generalized to other emotions.

Given the complexity [15] andmultidimensional nature

[16] of emotional responses, we address the specific func-

tions or processes that constitute an emotional reaction,

regardless of whether they are classically seen as emo-

tional (e.g. a withdrawal response or a feeling) or cognitive

Review

Glossary

Appraisal: evaluation of the meaning of an internal or external stimulus to the

organism. Only stimuli that are appraised as motivationally significant will

induce an emotional reaction, and the magnitude, duration and quality of the

emotional reaction are a direct result of the appraisal process. Moreover,

appraisal can be automatic and focus on basic affective stimulus dimensions

such as novelty, valence or value, or expectation discrepancy, or may be

slower and sometimes even require controlled conscious processing, which

permits a more sophisticated context-dependent analysis.

Fear conditioning: learning paradigm in which a previously neutral stimulus,

termed the conditioned stimulus (CS), is temporally paired with a non-learned

aversive stimulus, termed the unconditioned stimulus (US). After pairing, the

CS predicts the US and hence elicits a conditioned response (CR). For example,

pairing of a tone with a foot shock results in elicitation of fear behavior during

subsequent responses to a non-paired tone.

Extinction: learning process created by repeatedly presenting a CS without

pairing with an US (i.e. teaching the animal that the CS no longer predicts the

US) after fear conditioning has been established. This results in formation of an

extinction memory, which inhibits expression of, but does not erase, the

original fear memory.

Reappraisal: specific method for explicit emotion regulation whereby a

conscious deliberate effort is engaged to alter the meaning (appraisal) of an

emotional stimulus. For example, a picture of a woman crying can be

reappraised from a negative meaning to a positive one by favoring an

interpretation that she is crying tears of joy.

Regulation: general process by which conflicting appraisals and response

tendencies are arbitrated between to allow selection of a course of action.

Typically, regulation is thought to have an element of inhibition and/or

enhancement for managing competing appraisals and response tendencies.Corresponding author: Etkin, A. (amitetkin@stanford.edu).

1364-6613/$ – see front matter . Published by Elsevier Ltd. doi:10.1016/j.tics.2010.11.004 Trends in Cognitive Sciences, February 2011, Vol. 15, No. 2 85

mailto:amitetkin@stanford.edu


(e.g. attentional focusing on a relevant stimulus).

We also distinguish between processes involved in emo-

tional stimulus appraisal and consequential response ex-

pression [17] and those involved in emotion regulation.

Regulation occurs when stimuli induce conflicting apprai-

sals and hence incompatible response tendencies or when

goal-directed activity requires suppression of interference

from a single, emotionally salient, task-irrelevant stimu-

lus source. We found that an appraisal or expression

versus regulation contrast provides a robust framework

for understanding ACC and mPFC function in negative

emotion.

Fear conditioning and extinction in humans

The paradigms used in the acquisition and extinction of

learned fear are particularly valuable for isolating the

neural substrates of fear processing because the anticipa-

tory fear or anxiety triggered by the previously neutral

conditioned stimulus (CS) can be dissociated from the

reaction to the aversive unconditioned stimulus (US) per

se. This is not possible in studies that, for example, use

aversive images to evoke emotional responses. Further-

more, comparison between fear conditioning and fear ex-

tinction facilitates an initial coarse distinction between

regions associated with either the appraisal of fear-rele-

vant stimuli and generation of fear responses (fear condi-

tioning), or the inhibitory regulation of these processes

(extinction).

Several recent quantitative meta-analyses of human

neuroimaging studies examined activations associated

with fear CS presentation compared to a control CS never

paired with the US [13,14,18]. In Figure 1a we present

Box 1. Anatomy of the ACC and mPFC

Within the ACC, a subdivision can be made between a more ventral

portion, comprising areas 24a, 24b, 24c, 25, 32 and 33 [pregenual

(pgACC) and subgenual ACC (sgACC) in Figure I] and a more dorsal

portion, comprising areas 24a0, 24b0, 24c0, 24d, 320 and 33 [dorsal ACC

(dACC) in Figure 1]. This distinction is consistent with that of Vogt and

colleagues between an anterior and a midcingulate cortex [63]. In the

dACC, a further distinction exists between anterior and posterior

portions of the dACC (adACC and pdACC), similar to partitioning of

the midcingulate into anterior and posterior portions by Vogt et al.

[64] and consistent with partitioning between rostral and caudal

cingulate zones [65].

These subdivisions are also reflected in patterns of connectivity.

Connectivity with core emotion-processing regions such as the

amygdala, PAG and hypothalamus is strong throughout the sgACC,

pgACC and adACC, but very limited in the pdACC [46,66–70]. In

general, cingulo–amygdalar connectivity is focused on the basolateral

complex of the amygdala.

ACC subregions can also be distinguished based on connectivity

with premotor and lateral prefrontal cortices, which are heaviest in

the pdACC and adACC [67,71]. In summary, the pattern of anatomical

connectivity supports an important role for the sgACC, pgACC and

adACC in interacting with the limbic system, including its effector

regions, and for the adACC and pdACC in communicating with other

dorsal and lateral frontal areas that are important for top-down forms

of regulation [72].

Like the ACC (Figure I), the mPFC can be divided into several

functionally distinct subregions, although borders between these

subregions are generally less clear, and differential anatomical

connectivity is less well described. Amygdalar, hypothalamic and

PAG connectivity with mPFC subregions is considerably lighter than

the connectivity of adjacent ACC subregions, with the strongest

connections observed for the ventromedial (vmPFC) and dorsomedial

PFC (dmPFC) [46,68–70].

Much like the nearby ACC subregions, the supplementarymotor area

(SMA) is heavily interconnected with primary motor cortex and is the

origin for direct corticospinal projections [65,73]. The pre-SMA, by

contrast, is connected with lateral prefrontal cortices, but not with

primary motor cortex [65,73]. Premotor and lateral prefrontal connec-

tions are also present, albeit to a lesser degree, in the dmPFC [71]. Thus,

the patterns of connectivity are similar between abutting ACC and

mPFC subregions, with the difference being primarily in the density of

limbic connectivity, which is substantially greater in the ACC.[()TD$FIG]

dmPFCpdACC

adACC

pgACC

rmPFC

vmPFC

sgACC

SMA/preSMA


Figure I. Parcellation of ACC and mPFC subregions. Abbreviations: sg, subgenual; pg, pregenual; vm, ventromedial; rm, rostromedial; dm, dorsomedial; ad, anterior

dorsal; pd, posterior dorsal.


86

plots of the location of each activation peak reported in the

ACC or mPFC in the relevant fear conditioning studies,

collapsing across left and right hemispheres. It is readily

apparent that activations in fear conditioning studies are

not evenly distributed throughout the ACC andmPFC, but

rather are clustered heavily within the dorsal ACC (dACC),

dorsomedial PFC (dmPFC), supplementary motor area

(SMA) and pre-SMA. These activations, however, might

reflect a variety of different processes that occur simulta-

neously or in rapid temporal succession, for example CS

appraisal and expression of conditioned responses (CRs).

These processes are intermixed with, and supported by,

learning processes, namely, acquisition, consolidation and

storage of a fear memory (CS–US association), and retriev-

al of the fear memory on subsequent CS presentations.

The acquisition component of fear conditioning can, to

some extent, be circumvented by instructing subjects about

CS–US contingencies at the beginning of an experiment.

Such instructed fear experiments nevertheless also consis-

tently activate the dorsal ACC and mPFC (Figure 1b)

[14,19]. Similarly, recalling and generating fear in the

absence of reinforcement several days after conditioning

activate dorsal midline areas, and are not confounded by

fear learning [20]. Rostral parts of the dorsal ACC/mPFC

are specifically involved in the (conscious) appraisal, but

not direct expression, of fear responses, as shown by re-

duction of rostral dACC and dmPFC activity to threat by

high working memory load in the context of unchanged

physiological reactivity [2,14], and correlations of rostral

dACC and dmPFC activity with explicit threat evaluations

but not physiological threat reactions [21].

Response expression, conversely, seems to involve more

caudal dorsal areas in SMA, pre-SMA and pdACC, and

caudal parts of dmPFC and adACC, although some of the

evidence for this contention is indirect and based on stud-

ies of the arousal component inherent to most fear and

anxiety responses. For example, Figure 1c shows clusters

that correlate with sympathetic nervous system activity,

irrespective of whether the context was fear-related or not.

Positive correlations are found throughout the mPFC, but

are again primarily clustered in mid-to-caudal dorsal

mPFC areas. Lesion [22] and electrical stimulation studies

[23] confirmed this anatomical distribution.

Considering these data in conjunction with observations

that dACC activity correlates with fear-conditioned skin

conductance responses [24] and with increases in heart rate

induced by a socially threatening situation [25], as well as

findings that direct electrical stimulation of the dACC can

elicit subjective states of fear [26], strongly suggests that the

dorsal ACC and mPFC are involved in generating fear

responses. Neuroimaging studies of autonomic nervous sys-

tem activity also indirectly suggest that the same areas do

not exclusively function in response expression, but might

also support appraisal processes. For example, the dorsal

ACC and mPFC are associated with interoceptive aware-

ness of heart beats [27], and, importantly, recruitment of the

dorsal ACC and mPFC during interoceptive perception is

positively correlated with subjects’ trait anxiety levels [27].

Thus, the dorsal ACCandmPFC seem to function generally

in the appraisal and expression of fear or anxiety. These

studies leave uncertain the role that the dorsal ACC and

mPFC might play in the acquisition of conditioned fear,

[()TD$FIG]

(a) (b)

Learned fear

Extinction (d1) Extinction (d2)

(d) (e)

Fear appraisal/

expression

Positive

Negative

Sympathetic

activity

(c)


Figure 1. Activation foci associated with fear and its regulation. Predominantly dorsal ACC and mPFC activations are observed during classical (Pavlovian) fear conditioning

(a), as well as during instructed fear paradigms, which circumvent fear learning (b). Likewise, sympathetic nervous system activity correlates positively primarily with dorsal

ACC and mPFC regions and negatively primarily with ventral ACC and mPFC regions, which supports a role for the dorsal ACC and mPFC in fear expression (c). During

within-session extinction, activation is observed in both the dorsal and ventral ACC and mPFC (d), whereas during subsequent delayed recall and expression of the

extinction memory, when the imaging data are less confounded by residual expression of fear responses, activation is primarily in the ventral ACC and mPFC (e).

Information on the studies selected for this and all following peak voxel plots can be found in the online supplemental material.


87

although converging evidence from studies in rodents (Box

2) suggests only a minor role in acquisition.

To elucidate how fear is regulated, we next discuss

activations associated with extinction of learned fear. In

extinction, the CS is repeatedly presented in the absence

of reinforcement, leading to the formation of a CS–no US

association (or extinction memory) that competes with

the original fear memory for control over behavior [28–

30]. Hence, extinction induces conflicting appraisals of,

and response tendencies to, the CS because it now signals

both threat and safety, a situation that requires regula-

tion, as outlined above. We further distinguish between

within-session extinction (Figure 1d, day 1) and extinc-

tion recall, as tested by CS presentation on a subsequent

day (Figure 1e, day 2). Within-session extinction is asso-

ciated with activation in both the dorsal ACC and mPFC

(dACC, dmPFC, SMA and pre-SMA) and the ventral ACC

and mPFC (pgACC and vmPFC; Figure 1d). Given the

close association of dorsal ACC and mPFC with fear

conditioning responses, it should be noted that the acti-

vations observed within these regions during fear extinc-

tion might in fact reflect remnants of fear conditioning,

because in early extinction trials the CS continues to

elicit a residual CR. Activation within the ventral ACC

and mPFC is thus a candidate neural correlate of the fear

inhibition that occurs during extinction (for convergent

rodent data, see Box 2). Accordingly, acute reversal of a

fear conditioning contingency, during which a neutral,

non-reinforced, CS is paired with an aversive stimulus,

whereas the previously reinforced CS is not and now

inhibits fear, is associated with activation in the pgACC

[31]. Likewise, exposure to distant threat is associated

with ventral ACC and mPFC activation, presumably

acting in a regulatory capacity to facilitate planning of

adaptive responses, whereas more imminent threat is

associated with dorsal ACC and mPFC activation, which

is consistent with greater expression of fear responses

[32]. Along with ventral ACC and mPFC activation dur-

ing extinction, decreases in amygdalar responses have

also been reported [33,34], consistent with the idea that

amygdalar inhibition is an important component of ex-

tinction.

In support of this conclusion, recall of extinction more

than 24 h after conditioning, a process that is less con-

founded by residual CRs, yields primarily ventral ACC and

mPFC activations (pgACC, sgACC, vmPFC; Figure 1e). It

should be stressed, however, that extinction, like condi-

tioning, involves multiple component processes, including

acquisition, consolidation, storage and retrieval of the

extinction memory, and the related appraisal of the CS

as safe, of which CR inhibition is only the endpoint. The

limited number of human neuroimaging studies of extinc-

tion do not allow a reliable parcellation of these processes,

although a rich literature on rodents suggests that, like for

fear conditioning, the role of the mPFC is primarily in

expression rather than acquisition of inhibitory fear mem-

ories (Box 2). Moreover, our conclusions are also supported

by findings of negative correlations primarily between

ventral areas (pgACC and vmPFC) and sympathetic activ-

ity (Figure 1c), and with activation in an area consistent

with the periaqueductal gray matter (PAG), which med-

iates heart rate increases under social threat [25,35].

In summary, neuroimaging studies of the learning and

extinction of fear in humans reveal evidence of an impor-

tant differentiation between dorsal ACC and mPFC sub-

regions, which are implicated in threat appraisal and the

expression of fear, and ventral ACC andmPFC subregions,

which are involved in the inhibition of conditioned fear

through extinction.

Emotional conflict regulation

Convergent evidence of the functional differentiation be-

tween dorsal and ventral ACC andmPFC comes fromwork

on emotional conflict. Two recent studies used a task that

required subjects to categorize face stimuli according to

their emotional expression (fearful vs happy) while

Box 2. Studies of fear conditioning and extinction in rodents

A rich literature has examined the role of the rodent medial frontal

cortex in the acquisition and extinction of conditioned fear, as well as

the expression of conditioned and unconditioned fear [74]. These

studies facilitate a greater degree of causal inference than imaging

studies.Much like the human dorsal ACC andmPFC, the rodentmPFC is

strongly activated during fear conditioning [75,76]. Lesion or acute

inactivation studies have revealed a role for the ventrally located

infralimbic (IL) and dorsally located prelimbic (PL) subregions in

conditioned fear expression when recall tests are performed within a

few days after initial conditioning [77–81]. Interestingly, the mPFC does

not seem to be required during fear acquisition itself, as evidenced by

intact initial fear learning after disruption of IL or PL prior to

conditioning [82–85]. As with expression of fear memories, activity in

the rodent mPFC is also required for expression of unconditioned fear

[86,87].

In terms of extinction, the recall and expression of an extinction

memory more than 24 h after learning requires activity in IL

[80,82,84,88] and to some degree PL [85,89]. By contrast, within-session

extinction of CRs during repeated non-reinforced presentations of the

CS does not require activity in IL or PL [80,82,84,88]. Thus, the role of the

mPFC during extinction closely follows its role during fear conditioning:

it is required for recall or expression, but not for initial acquisition.

Electrical microstimulation of the rodent mPFC generally does not

directly elicit fear behavior or produce overt anxiolysis, but rather

exerts a modulatory function, gating behavioral output elicited by

external fear-eliciting stimuli or by direct subcortical stimulation [90–

93]. Curiously, given the role of the mPFC in fear expression, it has

been found that these effects are generally, but not exclusively, fear-

inhibitory and occur with stimulation in all mPFC subregions [90–93].

Of note, however, one recent study found a fear-enhancing effect of

PL stimulation, but a fear-inhibiting effect of IL stimulation [92].

Together, these findings suggest that a model of mPFC function in

fear or extinction must account for interactions of the mPFC with

other elements of the fear circuit, because the mPFC itself functions

primarily by modifying activity in other brain areas.

With respect to one important interacting partner, the amygdala, it

has been reported that stimulation in the IL or PL inhibits the activity

of output neurons in the central amygdalar nucleus (CEA) [94], as well

as the basolateral amygdalar complex (BLA) [95]. IL and PL

stimulation can also directly activate BLA neurons [96]. Thus, the

mPFC can promote fear expression through BLA activation and can

inhibit amygdala output through CEA inhibition. CEA inhibition,

however, is achieved through the action of excitatory glutamatergic

mPFC projections onto inhibitory interneurons in the amygdala,

probably through the intercalated cell masses [97,98]. Innervation of

the intercalated cell masses originates predominantly from IL rather

than PL [99,100], which supports a preferential role for IL in inhibitory

regulation of the amygdala.


88

attempting to ignore emotionally congruent or incongruent

word labels (Happy, Fear) superimposed over the faces.

Emotional conflict, created by a word label incongruent

with the facial expression, substantially slowed reaction

times [8,36]. Moreover, when incongruent trials were pre-

ceded by an incongruent trial, reaction times were faster

than if incongruent trials were preceded by a congruent

trial [8,36], an effect that has previously been observed in

traditional, non-emotional conflict tasks, such as the

Stroop and flanker protocols [37]. According to the con-

flict-monitoring model [38], this data pattern stems from a

conflict-driven regulatory mechanism, whereby conflict

from an incongruent trial triggers an upregulation of

top-down control, reflected in reduced conflict in the sub-

sequent trial. This model can distinguish brain regions

involved in conflict evaluation and those involved in con-

flict regulation [38,39]. In studies of emotional conflict,

regions that activated more to post-congruent incongruent

trials than post-incongruent incongruent trials, inter-

preted as being involved in conflict evaluation, included

the amygdala, dACC, dmPFC and dorsolateral PFC [8,36].

The role of dorsal ACC and mPFC areas in detecting

emotional conflict is further echoed by other studies of

various forms of emotional conflict or interference, the

findings of which we plot in Figure 2a.

By contrast, regions more active in post-incongruent

incongruent trials are interpreted as being involved in

conflict regulation, and prominently include the pgACC

[8,36]. Regulation-related activation in the pgACC was

accompanied by a simultaneous and correlated reduction

in conflict-related amygdalar activity and does not seem to

involve biasing of early sensory processing streams [39],

but rather the regulation of affective processing itself [36].

These data echo the dorsal–ventral dissociation discussed

above with respect to fear expression and extinction in the

ACC and mPFC.

The circuitry we find to be specific for regulation of

emotional conflict (ventral ACC and mPFC and amygdala)

is very similar to that involved in extinction. Although

these two processes are unlikely to be isomorphic, and each

can be understood without reference to the other, we

consider the striking similarity between extinction and

emotional conflict regulation to be potentially important.

Much like the relationship between improved emotional

conflict regulation and decreased conflict evaluation-relat-

ed activation in the dorsal ACC and mPFC, more success-

ful extinction is associated with decreased CS-driven

activation in the dorsal ACC and mPFC of humans and

rodents [40,41]. Thus, the most parsimonious explanation

for these data is that emotional conflict evaluation-related

functions involve overlapping neural mechanisms with

appraisal and expression of fear, and that regulation of

emotional conflict also involves circuitry that overlaps with

fear extinction. These conceptual and functional–anatomi-

cal similarities between evaluation and regulation of emo-

tional conflict and fear also support the generalizability of

our account of ACC and mPFC functional subdivisions

beyond simply fear-related processing, but more generally

to negative emotional processing. Of note, although the

intensity of the negative emotions elicited during fear

conditioning and evoked by emotional conflict differ signif-

icantly, they nonetheless engage a similar neural circuitry,

probably because both fear and emotional conflict reflect

biologically salient events.

[()TD$FIG]

(a)

Emotional conflict

(c)

Appraisal/expression Regulation

(d)

(b)

Reappraisal

Positive

Negative

Connectivity:

Positive

Negative

Connectivity:


Figure 2. (a) Emotional conflict across a variety of experimental paradigms is associated with activation in the dorsal ACC and mPFC. (b) Decreasing negative emotion

through reappraisal is associated with preferential activation of the dorsal ACC and mPFC. Targets of amygdalar connectivity during tasks involving appraisal or expression

(c) or regulation (d) of negative emotion. Positive connectivity is observed primarily during appraisal or expression tasks, and most heavily in the dorsal ACC and mPFC. By

contrast, negative connectivity is observed primarily in the ventral ACC and mPFC across both appraisal or expression and regulation tasks. These connectivity findings are

therefore consistent with the dorsoventral functional–anatomical parcellation of the ACC and mPFC derived from activation analyses.


89

Top-down control of emotion

During emotional conflict regulation, emotional processing

is spontaneously modulated in the absence of an explicit

instruction to regulate emotion. Emotional processing can

also be modulated through deliberate and conscious appli-

cation of top-down executive control over processing of an

emotional stimulus. The best-studied strategy for the lat-

ter type of regulation is reappraisal, a cognitive technique

whereby appraisal of a stimulus is modified to change its

ability to elicit an emotional reaction [42]. Reappraisal

involves both the initial emotional appraisal process and

the reappraisal process proper, whereby an additional

positive appraisal is created that competes with the initial

negative emotional appraisal. Thus, we would expect re-

appraisal to involve the dorsal ACC and mPFC regions

that we observed to be important for emotional conflict

detection (Figure 2a). Consistent with this prediction, a

meta-analysis found that reappraisal was reliably associ-

ated with activation in the dorsal ACC and mPFC

(Figure 2b) [43].

This reappraisal meta-analysis, interestingly, did not

implicate a consistent role for the ventral ACC and mPFC

[43], which suggests that reappraisal does not primarily

work by suppressing the processing of an undesired emo-

tional stimulus. Nevertheless, activity in the ventral ACC

and mPFC in some instances is negatively correlated with

activity in the amygdala in paradigms inwhich reappraisal

resulted in downregulation of amygdalar activity in re-

sponse to negative pictures [44,45]. Thus, the ventral ACC

and mPFC might be mediators between activation in

dorsal medial and lateral prefrontal areas, involved in

reappraisal [43], and the amygdala, with which lateral

prefrontal structures in particular have little or no direct

connectivity [46]. Consistent with this idea, the ventral

ACC and mPFC are also engaged when subjects perform

affect labeling of emotional faces [47] or when they self-

distract from a fear-conditioned stimulus [48], two other

emotion regulation strategies that result in downregula-

tion of amygdalar activity.

These data suggest that controlled top-down regulation,

like emotional conflict regulation, uses ventral ACC and

mPFC areas to inhibit negative emotional processing in

the amygdala, thus dampening task interference. The

ventral ACC and mPFC might thus perform a generic

negative emotion inhibitory function that can be recruited

by other regions (e.g. dorsal ACC and mPFC and lateral

PFC) when there is a need to suppress limbic reactivity

[10]. This would be a prime example of parsimonious use of

a basic emotional circuitry, conserved between rodents and

humans (Box 2), for the purpose of higher-level cognitive

functions possible only in humans.

Amygdala–ACC and –mPFC functional connectivity

Our analysis of the neuroimaging data has emphasized

task-based activation studies. Complementary evidence

can be found in analyses of functional connectivity, because

ACC and mPFC subregions can be distinguished through

their differential anatomical connectivity (Box 1). In some

ways, psychological context-specific temporal covariation

(i.e. task-dependent connectivity) between regions might

provide an even stronger test of the nature of inter-regional

relationships than consistency with regions that simply

coactivate in a task. Figure 2c,d shows the ACC and mPFC

connectivity peaks for all such connectivity studies, irre-

spective of the specific paradigm or instructions used

(primarily general negative stimuli), as long as the task

facilitated discrimination between appraisal or expression

(Figure 2c) and regulation (Figure 2d). The spatial distri-

bution of peaks during appraisal/expression tasks shows a

relative preponderance of positive connectivity peaks in

the dorsal ACC and mPFC and of negative connectivity

peaks in the ventral ACC and mPFC. In addition, during

regulation tasks, connectivity was restricted to the ventral

ACC and mPFC and was primarily negative (Figure 2d).

These data thus lend further support to our proposal of a

dorso–ventral separation in terms of negative emotion

generation (appraisal and expression) and inhibition (reg-

ulation).

Integration with other perspectives on ACC and mPFC

function and other emotions

Although less developed than the literature on fear and

anxiety, studies on other emotions are broadly consistent

with our formulation of ACC and mPFC function. On the

negative emotion appraisal and expression side, direct

experience of pain, or empathy for others experiencing

pain, activates the dorsal ACC and mPFC [49], and lesions

of the dACC also serve as treatment for chronic pain [50].

Similarly, increased sensitivity to a range of negative

emotions is associated with greater engagement of the

dorsal ACC andmPFC, including disgust [51] and rejection

[52], and transcranial-magnetic-stimulation-induced dis-

ruption of the dmPFC interferes with anger processing

[53]. Uncertainty or ambiguity, which can induce anxiety

and relates to emotional conflict, leads to activation in the

dACC and dmPFC [54]. On the regulation side, endoge-

nously driven analgesia by means of the placebo effect has

been closely tied to the pgACC, which is thought to engage

in top-down modulation of regions that generate opioid-

mediated anti-nociceptive responses, such as the amygdala

and PAG [55,56]. It remains unclear how sadness is evalu-

ated and regulated, andwhat role the sgACC plays in these

processes, because it is a common activation site in re-

sponse to sad stimuli [57].

Positive emotion, which can serve to regulate and di-

minish negative emotion, has been associated in a meta-

analysis with activation in the sgACC, vmPFC and pgACC

[58]. Extinction of appetitive learning activates the vmPFC

[59], much as extinction of learned fear does. The evalua-

tion of positive stimuli and reward is more complicated.

For instance, Rushworth and co-workers proposed that the

processes carried out by the adACC aremirrored by similar

contributions to reinforcement-guided decision-making

from the orbitofrontal cortex, with the distinction that

the adACC is concerned with computing reinforcement

value of actions whereas the orbitofrontal cortex is con-

cerned with gauging the reinforcement values of stimuli

[60].

Taken together, these data broadly support our dorsal–

ventral distinction along appraisal–expression versus reg-

ulation lines, with respect specifically to negative emotion.

Conversely, it is not obvious how to accommodate our


90

analysis with the suggestion that the vmPFC specifically

assesses stimulus values [10], but not action values, with

the opposite being the case for the dACC [60]. Thus, this

should be seen as an early attempt to integrate these and

other models of ACC and mPFC function and can serve to

stimulate further research in this area.

It is also worth examining why the conceptualization

proposed in this review differs significantly from the earli-

er view of a cognitive–affective division [12]. Although the

meta-analysis reported in the earlier paper did not indicate

which specific studies were included, it seems that much of

the support for this scheme comes from studies of patients

with affective disorders, in whom ventral ACC and mPFC

dysfunction can be more readily observed in the context of

deficits in regulation [40,61]. Moreover, the dorsal–ventral

dissociation between dACC activation in a counting Stroop

and pgACC in an emotional counting Stroop [12] has not

held up to subsequent evidence (Figure 2a) or direct con-

trasts between emotional and non-emotional conflict pro-

cessing [36], nor does the emotional counting Stroop

involve a true Stroop conflict effect in the way that the

counting Stroop does [62].

Concluding remarks

This review has highlighted several important themes.

First, the empirical data do not support the long-held

popular view that dorsal ACC and mPFC regions are

involved in cognitive but not emotional functions, whereas

ventral regions do the reverse [12]. Rather, the key func-

tional distinction between these regions relates to evalua-

tive function on the one hand, and regulatory function on

the other hand for the dorsal and ventral ACC and mPFC,

respectively (Figure 3). This new framework can also be

broadly generalized to other negative and positive emo-

tions, and points to multiple exciting lines of future re-

search (Box 3).

Disclosure statement

Amit Etkin receives consulting fees from NeoStim. The

other authors report no financial conflicts.

AcknowledgementsWe would like to thank Gregory Quirk, Kevin LaBar, James Gross and

Carsten Wotjak for their helpful comments and criticisms of this

manuscript. This work was supported by NIH grants P30MH089888

and R01MH091860, and the Sierra-Pacific Mental Illness Research

Education and Clinical Center at the Palo Alto VA Health Care System.

Appendix A. Supplementary data

Supplementary data associated with this article can

be found, in the online version, at doi:10.1016/j.tics.

2010.11.004.

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[()TD$FIG]

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Figure 3. Graphical depiction of the ACC and mPFC functional model aligned

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Box 3. Future directions

� Further work is needed, in particular in exploring the neurophy-

siological basis for appraisal and expression versus regulation-

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Specifically, how does this coding take place at the single-cell

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studies?

� We have left out discussion of other regions, such as the insula

and brainstem, that are probably important partners of the ACC

and mPFC, although far less is known about these interactions.

Additional work is required to bring to these interactions the

depth of understanding currently available for interactions with

the amygdala. Moreover, a better systems-level understanding of

how ACC and mPFC activity is shaped by its input regions, such as

the amygdala, hippocampus and thalamus, is necessary.

� Although we hint at levels of similarity between our model of ACC

and mPFC in negative emotion and other models of the roles of

this region in other functions, additional work is required to

directly contrast and harmonize other conceptualizations of ACC

and mPFC functions to create a more comprehensive framework

capable of making predictions about a wide range of task

contexts.

� With a few notable exceptions [40,61], the sophisticated cognitive

neuroscience models described above have not been extended to

populations with anxiety-related disorders. A great deal of work

will be needed to translate our increasingly nuanced descriptions

of ACC and mPFC functions into a better understanding of

psychopathology.


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