Univeristy of Alberta Cognitive Science Dictionary (Entries
Page)
The University of Alberta's Cognitive Science
DictionaryDictionary Entries As Of February 24,
1997|A|B|C|D|E|F|G|H|I|J|K|L|M|N|O|P|Q|R|S|T|U|V|W|X|Y|Z|
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
Adaptation Alzheimer's Disease Analogy Apparent Motion
Articulatory Loop Artificial Intelligence Associative Memory
Attention Attention Getting Attention Holding Attention Releasing [
dictionary | letter index | top | bottom ]
12. 13. 14. 15.
Behavioural Indeterminacy Biological Naturalism Bottom-Up
Processing Broca's Area
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Univeristy of Alberta Cognitive Science Dictionary (Entries
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[ dictionary | letter index | top | bottom ]
16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.
Cascade Processing Central Executive Cognitive Development
Cognitive Mapping Cognitive Penetrability Cognitive Psychology
Cognitive Science Connectionism Consciousness Content Addressable
Memory Crystallized Intelligence Cued Recall [ dictionary | letter
index | top | bottom ]
28. 29. 30. 31.
Deductive Inference Dementia Discrete Processing The Disjunction
Problem [ dictionary | letter index | top | bottom ]
32. Elaborative Rehearsal 33. Enactment
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Univeristy of Alberta Cognitive Science Dictionary (Entries
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34. 35. 36. 37. 38.
Encoding Encoding Specificity Equilibration Error Analysis
Extension [ dictionary | letter index | top | bottom ]
39. 40. 41. 42. 43.
Fluid Intelligence The Formality Condition Free Recall
Functional Analysis Functional Architecture [ dictionary | letter
index | top | bottom ]
44. Generalization 45. Graceful Degradation [ dictionary |
letter index | top | bottom ]
46. Hebbian Learning Rule 47. Humor [ dictionary | letter index
| top | bottom ]
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Univeristy of Alberta Cognitive Science Dictionary (Entries
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48. 49. 50. 51. 52. 53. 54. 55. 56.
Imagery Debate Incidental Learning Induction Learning Inductive
Inference Intension Intention Intentional Stance Intermediate State
Evidence Intrusion Errors [ dictionary | letter index | top |
bottom ]
NO CURRENT ENTRIES
[ dictionary| letter index| top| bottom]
NO CURRENT ENTRIES
[ dictionary| letter index| top| bottom]
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Univeristy of Alberta Cognitive Science Dictionary (Entries
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57. 58. 59. 60.
Learning Rule Levels of Processing Linguistic Determination Long
Term Potentiation [ dictionary | letter index | top | bottom ]
61. 62. 63. 64. 65. 66. 67.
Machine Learning Maintenance Rehearsal Mandelbrot Set Memory
Span Metaphor Misrepresentation Modularity [ dictionary | letter
index | top | bottom ]
68. Neurocognition 69. Neuron 70. Neuroscience [ dictionary |
letter index | top | bottom ]
71. Occam's Razor [ dictionary | letter index | top | bottom
]
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72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84.
Paradigm Parallel Distributed Processing Models Parallel Search
Perseveration Errors Philosophy of Mind Piaget's Stage Theory of
Development Primacy Effect Priming Primitive Production Production
System Proposition Protocol Analysis [ dictionary | letter index |
top | bottom ]
NO CURRENT ENTRIES
[ dictionary| letter index| top| bottom]
85. 86. 87. 88. 89. 90.
Recency Effect Recognition Recall Recursive Decomposition
Relative Complexity Evidence Retrieval Ryle's Regress
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91. 92. 93. 94. 95. 96. 97. 98. 99. 100.
Sapir-Whorf Hypothesis Schemas Semantics Serial Position Curve
Serial Search Short Term Memory Spontaneous Generalization Strong
Equivalence Sustained Attention Symbolic Architecture [ dictionary
| letter index | top | bottom ]
101. Top-Down Processing 102. Turing Equivalence 103. Turing
Test [ dictionary | letter index | top | bottom ]
NO CURRENT ENTRIES
[ dictionary| letter index| top| bottom]
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104. Veridicality 105. Visuospatial Perception 106. Visuospatial
Sketchpad [ dictionary | letter index | top | bottom ]
107. 108. 109. 110.
WAIS Weak Equivalence Wernicke's Area Working Memory [
dictionary | letter index | top | bottom ]
NO CURRENT ENTRIES
[ dictionary| letter index| top| bottom]
NO CURRENT ENTRIES
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Univeristy of Alberta Cognitive Science Dictionary (Entries
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111. Z Lens [ dictionary | letter index | top | bottom ]
Dictionary Home Page |
Maintained by M.R.W. Dawson
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U of A Cog Sci Dictionary (Adaptation)
AdaptationIn Piaget's Theory of Development, there are two
cognitive processes that are crucial for progressing from stage to
stage: assimilation, accommodation. These two concepts are
described below.
AssimilationThis refers to the way in which a child transforms
new information so that it makes sense within their existing
knowledge base. That is, a child tries to understand new knowledge
in terms of their existing knowledge. For example, a baby who is
given a new knowledge may grasp or suck on that object in the same
way that he or she grasped or sucked other objects.
AccomodationThis happens when a child changes his or her
cognitive structure in an attempt to understand new information.
For example, the child learns to grasp a new object in a different
way, or learns that the new object should not be sucked. In that
way, the child has adapted his or her way of thinking to a new
experience. Taken together, assimilation and accomodation make up
adaptation, which refers to the child's ability to adapt to his or
her environment. References: 1. Siegler, R. (1991). Children's
thinking. Englewood Cliffs, NJ: Prentice-Hall. 2. Vasta, R., Haith,
M. M., & Miller, S. A. (1995). Child psychology: The modern
science. New York, NY: Wiley.
See Also:Equilibration | Piaget's Stage Theory of
Development
Contributed by J.
Sandwellhttp://web.psych.ualberta.ca/%7emike/Pearl_Street/Dictionary/contents/A/adaptation.html
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U of A Cog Sci Dictionary (Adaptation)
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U of A Cog Sci Dictionary (Alzheimer's Disease)
Alzheimer's DiseaseAlzheimer's Disease (AD), a term coined by
Alois Alzheimer in 1907, is a relentlessly progressive disease
characterized by cognitive decline, behavioural disturbances, and
changes in personality. Current estimates of prevalence of AD in
Canada suggest that 5.1% of all Canadians 65 and over meet the
criteria for the clinical diagnosis of AD, which translates into
approximately 161,000 cases. AD prevalence is slightly higher in
women than in men. It may be that this difference is due to the
longer life expectancy of women although other factors have not
been ruled out. The prevalence of dementia is strongly associated
with age, affecting 1% of the Canadian population aged 65 to 74,
6.9% of individuals 75-84 and 26% of individuals 85 years and older
(Canadian Study of Health and Aging, 1994). The diagnostic criteria
for dementia of the Alzheimer's Type (DAT) are as follows:q
q
q q
q q
(A) The development of multiple cognitive deficits manifested by
both: 1. Memory impairment (impaired ability to learn new
information or to recall previously learned information) 2. One or
more of the following cognitive disturbances: s aphasia (language
disturbance) s apraxia (impaired ability to carry out motor
activities despite intact motor function) s agnosia (failure to
recognize or identify objects despite intact sensory function) s
disturbances in executive functioning (i.e., planning, organizing,
sequencing, abstracting) (B) The cognitive deficits in Criteria A1
and A2 each cause significant impairment in social and occupational
functioning and represent a significant decline from a previous
level of functioning. (C) The course is characterized by gradual
onset and continuing cognitive decline (D) The cognitive deficits
in Criteria A1 and A2 are not due to any of the following: 1. other
central nervous system conditions that cause progressive deficits
in memory and cognition (e.g., cerebrovascular disease, Parkinson's
Disease, Huntington's Disease, subdural hematoma, normal pressure
hydrocephalus, brain tumor). 2. systemic conditions that are known
to cause a dementia (e.g., hypothyroidism, vitamin B12 or folic
acid deficiency, hypercalcemia, neurosyphilis, HIV infection) 3.
substance-induced conditions (E) The deficits do not occur
exclusively during the course of a delirium (F) The disturbance is
not better accounted for by another Axis 1 disorder (e.g., Major
Depressive Disorder, Schizophrenia)
The diagnosis of AD is based on exclusionary criteria (i.e., the
absence of an identifiable cause) with diagnosis confirmed at
autopsy. Treatment strategies to date have been largely
ineffective, with experimental treatments mainly directed toward
overcoming the cholinergic deficit.
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U of A Cog Sci Dictionary (Alzheimer's Disease)
References: 1. American Psychiatric Association (1994).
Diagnostic and statistical manual of mental disorders (4th ed.).
Washington, DC: Author. 2. Canadian study of health and aging:
Study methods and prevalence of dementia. (1994). Canadian Medical
Association Journal, 150(6). 3. Whitehouse, P.J. (1993) Dementia.
Philadelphia: F.A. Davis.
See Also:Dementia
Contributed by Bonnie M. French
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U of A Cog Sci Dictionary (Analogy)
AnalogyIn cognitive psychology, analogy is considered an
important method of problem solving. The problem solver attempts to
use his or her knolwedge of one problem to solve another problem
about which she or he has very little or no information. Barsalou
(1992) provides the following example of problem solving by
analogy: "...someone who has worked at the complex for a while
could simply explain to you that the layout is analogous to a
starfish. On hearing this analogy you might transfer knowledge
about starfish to the office complex. Thus the knowledge that a
starfish has a circular body, with five legs extending from it
radially and symetrically would lead to the belief that the office
complex contains a center circular body, with five tapered
buildings extending from it in a radially symmetric pattern."
(p.110) Obviously people do not use all of their knowledge about
one problem to solve another problem. In the context of his
starfish example Barsalou points out that we would not begin to
think that the office complex is alive, or that it lives
underwater. One problem facing cogntive psychologists is to
determine how people decide upon the extent to which an analogy
applies. Determining how this may be done is more difficult than it
may seem. Consider that, given enough time people can find
analogies between any two phenomena. We might want to say that,
like the starfish, the office complex is alive--its heating ducts
are like blood vessels, its doors are like mouths eating the people
who enter the office complex every day. As a cognitive process
analogy seems limitless. In a science that strives for regularity
and lawfulness the limitlessness of analogical thinking poses a
serious problem. References: 1. Barsalou, L. (1992). Cognitive
psychology: An overview for cognitive psychologists. Hillsdale, NJ:
Lawrence Erlbaum Associates.
Contributed by Jeff Stepnisky
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[06.07.2003 21:59:22]
U of A Cog Sci Dictionary (Apparent Motion)
Apparent MotionThis is a perceptual phenomenon that occurs when
we perceive motion in two or more static images that are presented
in succession with appropriate spatial and temporal displacements.
The ability to perceive this phenomenon is mediated by the
visuospatial pathway of the visual association regions of the
brain. We see examples of this phenomenon almost everyday when we
view television or movies. This is an example of a cognitively
impenetrable perception. That is, even though we know that the
images are not moving, we still perceive motion. References: 1.
Marr, D. (1982). Vision. Freeman: San Francisco, pp.159-182. 2.
Zeki, S. (1992). The visual image in mind & brain. Scientific
American, 241(3), 150-162.
See Also:Cognitive Penetrability | Visuospatial Perception
Contributed by M. Kincade
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[06.07.2003 21:59:22]
U of A Cog Sci Dictionary (Articulatory Loop)
Articulatory LoopThe articulatory loop (AL) is one of two
passive slave systems within Baddeley's (1986) tripartite model of
working memory. The AL, responsible for storing speech based
information, is comprised of two components. The first component is
a phonological memory store which can hold traces of acoustic or
speech based material. Material in this short term store lasts
about two seconds unless it is maintained through the use of the
second subcomponent, articulatory subvocal rehearsal. Prevention of
articulatory rehearsal results in very rapid forgetting. Try this
experiment with a friend. Present your friend with three consonants
(e.g., C-X-Q) and ask them to recall the consonants after a 10
second delay. During the 10 second interval, prevent your friend
from rehearsing the consonants by having them count 'backwards by
threes' starting at 100. You will find that your friend's recall is
significantly impaired! See Murdoch (1961) and Baddeley (1986) for
a complete review. References: 1. Baddeley, A. (1986). Working
memory. Oxford: Clarendon Press. 2. Murdock, B.B. Jr. (1961). The
retention of individual items. Journal of Experimental Psychology,
62, 618-625.
See Also:Working Memory | Visuospatial Sketchpad | Central
Executive
Contributed by Bonnie M. French
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[06.07.2003 21:59:23]
U of A Cog Sci Dictionary (Artificial Intelligence)
Artificial IntelligenceArtificial intelligence is concerned with
the attempt to develop complex computer programs that will be
capable of performing difficult cognitive tasks. Some of those who
work in artificial intelligence are relatively unconcerned as to
whether the programs they devise mimic human cognitive functioning,
while others have the explicit goal of simulating human cognition
on the computer. The artificial intelligence approach has been
applied to several different areas within cognitive psychology,
including perception, memory, imagery, thinking, and problem
solving. There are a number of advantages of the artificial
intelligence approach to cognition. Computer programming requires
that every process be specified in detail, unlike cognitive
psychology which often relies on vague descriptions. AI also tends
to be highly theoretical, which leads to general theoretical
orientations having wide applicability. The main disadvantage of AI
is that there is a lot of controversy about the ultimate similarity
between human cognitive functioning and computer functioning. Some
of the major differences between brains and computers were spelled
out in the following terms by Churchland (1989, p.100): "The brain
seems to be a computer with a radically different style. For
example, the brain changes as it learns, it appears to store and
process information in the same places...Most obviously, the brain
is a parallel machine, in which many interactions occur at the same
time in many different channels." This contrasts with most computer
functions which involves serial processing and relatively few
interactions. References: 1. Churchland, P.S. (1989). From
Descartes to neural networks. Scientific American , July, 100. 2.
Eysenck, M.W. (Ed.). (1990). The Blackwell Dictionary of Cognitive
Psychology. Cambridge, MA: Basil Blackwell.
See Also:Cognitive Science | Cognitive Psychology
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U of A Cog Sci Dictionary (Artificial Intelligence)
Contributed by L.A. Keple
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U of A Cog Sci Dictionary (Associative Memory)
Associative MemoryAt its simplest, an associative memory is a
system which stores mappings of specific input representations to
specific output representations. That is to say, a system that
"associates" two patterns such that when one is encountered
subsequently, the other can be reliably recalled. Kohonen draws an
analogy between associative memory and an adaptive filter function
[2]. The filter can be viewed as taking an ordered set of input
signals, and transforming them into another set of signals---the
output of the filter. It is the notion of adaptation, allowing its
internal structure to be altered by the transmitted signals, which
introduces the concept of memory to the system. A further
refinement in terminology is possible with regard to the
associative memory concept, and is ubiquitous in connectionist
(neural network) literature in particular. A memory that reproduces
its input pattern as output is referred to as autoassociative (i.e.
associating patterns with themselves). One that produces output
patterns dissimilar to its inputs is termed heteroassociative (i.e.
associating patterns with other patterns). Most associative memory
implementations are realized as connectionist networks. Hopfield's
collective computation network [1] serves as an excellent example
of an autoassociative memory, whereas Rosenblatt's perceptron [3]
is often utilized as a heteroassociator. There are many practical
problems implementing effective associative memories however, most
notably their inefficiency; the tendency is for them to fill up and
become unreliable rather quickly. This is a long running open
problem for both connectionism and adaptive filter theory---one
that Kohonen refers to as the "problem of infinite state memory"
[2]. References: 1. J.J. Hopfield. Neural networks and physical
systems with emergent collective computation abilities. Proceedings
of the National Academy of Science. 79:2554-2558, 1982. 2. T.
Kohonen. Self-Organization and Associative Memory. Springer Series
In Information Sciences, Vol.8. Springer-Verlag, Berlin,
Heidelberg, New York, Tokyo, 1984. 3. F. Rosenblatt. Principles of
Neurodynamics. Spartan, New York, 1962.
See AlsoConnectionism| Content Addressable Memory
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U of A Cog Sci Dictionary (Associative Memory)
Contributed by David B. McCaughan Dictionary Home Page| Letter
Index| Search Index
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U of A Cog Sci Dictionary (Attention)
Attention"Attention" is a term commonly used in education,
psychiatry and psychology. The definition is often vague. Attention
can be defined as an internal cognitive process by which one
actively selects environmental information (ie. sensation) or
actively processes information from internal sources (ie. visceral
cues or other thought processes). In more general terms, attention
can be defined as an ability to focus and maintain interest in a
given task or idea, including managing distractions. William James,
a 19th century psychologist, explains attention as follows:
"Everyone knows what attention is. It is the taking possession by
the mind in clear and vivid form, of one out of what seem several
simultaneously possible objects or trains of thought...It implies
withdrawl from some things in order to deal effectively with
others, and is a condition which has a real opposite in the
confused, dazed, scatterbrained state." (1890, p. 403) Attention is
important to psychologists because it is often considered a core
cognitive process, a basis on which to study other cognitive
processes; most importantly learning. DeGangi and Porges (1990)
illustrate only "when a person is actively engaged in voluntary
attention, functional purposeful activity and learning can occur."
(p. 6) Poor attention is often a key symptom of behaviour disorders
such as hyperactivity and learning disorders. References: 1.
DeGangi, G., & Porges, S. (1990). Neuroscience foundations of
human performance. Rockville, MD: American Occupational Therapy
Association. 2. James, W. (1890). Principles of psychology. New
York: Holt.
See Also:Attention Getting | Attention Holding | Sustained
Attention
Contributed by Cassie Jacknicke
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[06.07.2003 21:59:24]
U of A Cog Sci Dictionary (Attention Getting)
Attention GettingAttention getting is more than just the
orienting reflex, it is the "initial orientation or alerting to a
stimulus." Though this may be considered an automatic act, in fact
it requires complex active thought processing. Attention getting is
reliant on the qualitative nature of the stimulus. The stimulus
must be stong enough to elicit a response. DeGangi and Porges
(1990) explain the types of stimuli that are attention getting vary
according to past experiences of the individual, what they already
know, individual reactivity to sensory stimuli, and what an
individual has determined to be important to them. A hungry person
may be more apt to pay attention to the smell of food than the
sounds surrounding them in a traffic jam! Attention getting is
important to psychologists, particularily developmental
psychologists because of its role in learning. A child's chosen
attention getting stimuli can guide his/her learning abilities. "A
child who learns better through the auditory channel will orient
more readily to a song about body parts than a picture of a body."
References: 1. DeGangi, G., & Porges, S. (1990). Neuroscience
foundations of human performance. Rockville, MD: American
Occupational Therapy Association.
See Also:Attention Holding | Attention Releasing | Sustained
Attention
Contributed by Cassie Jacknicke
Dictionary Home Page |
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[06.07.2003 21:59:25]
U of A Cog Sci Dictionary (Attention Holding)
Attention HoldingAttention holding is the "maintenance of
attention when a stimulus is intricate or novel." Stimuli that hold
our attention must be both novel and complex in order to encourage
information processing. Attention holding is measured by how long
one engages in a cognitive activity involving that stimulus.
Attention holding is important because of its role in learning. If
an activity or stimulus is moderately complex, the person will
expend energy in information processing. In other words, the person
will expend energy in learning. Unfortunately, this can be
complicated by poor motivation. Low motivation may present a
challenge as the psychologist (or other professional) must
determine if the decreased motivation is due to sensory processing
problems, cognitive impairment, or other learning-related problems
(of which poor attention holding may be identified). References: 1.
DeGangi, G., & Porges, S. (1990). Neuroscience foundations of
human performance. Rockville, MD: American Occupational Therapy
Association.
See Also:Attention Getting | Attention Releasing | Sustained
Attention
Contributed by Cassie Jacknicke
Dictionary Home Page |
Letter Index |
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[06.07.2003 21:59:26]
U of A Cog Sci Dictionary (Attention Releasing)
Attention ReleasingAttention releasing is the final stage in
DeGangi and Porges' (1990) process of sustained attention.
Attention releasing can simply be defined as the "releasing or
turning off of attention from a stimulus." Attention releasing can
occur for a variety of reasons. A person can fatigue physically or
mentally requiring release of attention. Arousal level can
decrease, therefore a different type/strength of stimuli becomes
required to maintain an alert and active state. Attention releasing
provides a person with a method to reach closure on a given
activity, task, or event thereby allowing that person to switch
attention to something new. As with attention getting and holding,
attention releasing (the ability to shift focus) plays an important
role in the learning process. References: 1. DeGangi, G., &
Porges, S. (1990). Neuroscience foundations of human performance.
Rockville, MD: American Occupational Therapy Association.
See Also:Attention Holding | Attention Getting | Sustained
Attention
Contributed by Cassie Jacknicke
Dictionary Home Page |
Letter Index |
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[06.07.2003 21:59:26]
University of Alberta Cognitive Science Dictionary (Home
Page)
The University of Alberta's Cognitive Science DictionaryThis
site is edited and maintained by Dr. Michael R.W. Dawson and David
A. Medler.. On January 21, 1997 this dictionary received an
Editor's Choice Award from:
"It often does more harm than good to force definitions on
things we don't understand. Besides, only in logic and mathematics
do definitions ever capture concepts perfectly. The things we deal
with in practical life are usually too complicated to be
represented by neat, compact expressions. Especially when it comes
to understanding minds, we still know so little that we can't be
sure our ideas about psychology are even aimed in the right
directions. In any case, one must not mistake defining things for
knowing what they are." -- Marvin Minsky, from The Society Of Mind,
1985 With this warning from Professor Minsky keenly in mind, feel
free to explore the dictionary entries below.
As of February 24, 1997 111 entries have been made to the
Dictionary. To find an entry in the dictionary, you can...
q
index by first letter, or search for a specific term.
q
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University of Alberta Cognitive Science Dictionary (Home
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You can also
submit a new entry to the Cognitive Science Dictionary.
Note: Currently, only students registered within PSYCO 560 and
INTD 554 at the University of Alberta may submit terms.
This dictionary of cognitive science terms was initiated by Dr.
Michael Dawson, and introduced as a class project for Psychology
560, a graduate course in memory and cognition, and
Interdisciplinary Studies 554, a graduate course in cogntive
science (both are offered at the University of Alberta). The
project was designed to give students the opportunity to learn more
about the basic concepts of cognitive science, and also to learn
about the delivery of information via the world wide web. This page
is maintained by Dr. Michael Dawson, and is protected by
copyright.
Pearl Street |
Dawson Home Page
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U of A Cog Sci Dictionary (Behavioural Indeterminacy)
Behavioural IndeterminacyThe claim that in principle psychology
is restricted to establishing weak equivalence. Weak equivalence is
equivalence with respect to input/output behaviour. Therefore,
measuring behavioural data is unable to establish equivalence at
the level of functional architecture. Behavioural studies are
indeterminate with respect to strong equivalence. This issue is of
importance to cognitive psychology because, if true, it implies
that cognitive psychology cannot generate insight into cognition
without importing knowledge based on non-behavioural observations
from other disciplines. References: 1. Pylyshyn, Z. W. (1989).
Computing in cognitive science. In M. I. Posner (Ed.), Foundations
of cognitive science, Cambridge MA: MIT Press.
See Also:Functional Architecture | Strong Equivalence | Weak
Equivalence
Contributed by J. Andrews
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U of A Cog Sci Dictionary (Biological Naturalism)
Bilogical NaturalismPromoted by John Searle, Biological
Naturalism states that consciousness is a higher level function of
the brain's physical capabilities. The neurophysiological processes
in the brain cause mental phenomena, which are also a feature of
the brain. However, such features as consciousness are not
reducible to neurophysiological systems. Not all brains produce
this higher level functioning, and there are many questions still
open in Biological Naturalism, which Searle himself points out, for
example: how does neurophysiology account for the range of mental
phenomena? how does consciousness come about? how advanced does a
neurophysiological system have to be to produce consciousness?
References: 1. Searle, John. The Rediscovery of the Mind. MIT
Press, Massachusetts. 1994
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[06.07.2003 21:59:29]
U of A Cog Sci Dictionary (Bottom-Up Processing)
Bottom-Up ProcessingThe cognitive system is organized
hierarchically. The most basic perceptual systems are located at
the bottom of the hierarchy, and the most complex cogntive (e.g.
memory, problem solving) systems are located at the top of the
hierarchy. Information can flow both from the bottom of the system
to the top of the system and from the top of the system to the
bottom of the system. When information flows from the bottom of the
sytstem to the top of the system this is called "bottom-up"
processing. Lower level systems categorize and describe incoming
perceptual information and pass this descriptive information onto
hiher levels for more complex processing.
See Also:Top-Down Processing
Contributed by Jeff Stepnisky
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U of A Cog Sci Dictionary (Broca's Area)
Broca's AreaNamed for Paul Broca who first described it in 1861,
Broca's area is the section of the brain which is involved in
speech production, specifically assessing syntax of words while
listening, and comprehending structural complexity. People
suffering from neurophysiological damage to this area (called
Broca's aphasia or nonfluent aphasia) are unable to understand and
make grammatically complex sentences. Speech will consist almost
entirely of content words. Auditory and speech information is
transported from the auditory area to Wernicke's area for
evaluation of significance of content words, then to Broca's area
for analysis of syntax. In speech production, content words are
selected by neural systems in Wernicke's area, grammatical
refinements are added by neural systems in Broca's area, and then
the information is sent to the motor cortex, which sets up the
muscle movements for speaking. References: 1. Gray, Peter. (1994).
Psychology. New York, NY: Worth Publishing.
See Also:Wernicke's Area
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[06.07.2003 21:59:30]
U of A Cog Sci Dictionary (Cascade Processing)
Cascade ProcessingUnder the assumption that a cpmplex task can
be broken down into distinct stages of information processing, and
that these stages can be sequentially ordered, the complex task can
be performed by completing each distinct stage. Unlike discrete
processing, with cascade models the latter stages of information
processing can begin operating before the completion of earlier
information processing stages. Connectionist models of information
processing operate in a cascade manner and are important for the
way in which these models can learn relationships between stimule
and responses. Depending on the complexity of the information being
processed, it may be transmitted between some processing stages in
a cascade manner, but in other stages it may be processed in a
discrete manner. References: 1. Eysenck, M.W. (Ed.). (1990). The
Blackwell Dictionary of Cognitive Psychology. Cambridge, MA: Basil
Blackwell.
See Also:Discrete Processsing
Contributed by Valerie Trifts
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[06.07.2003 21:59:30]
U of A Cog Sci Dictionary (Central Executive)
Central ExecutiveThe central executive, the most important yet
least well understood component of Baddeley's (1986) working memory
model, is postulated to be responsible for the selection,
initiation, and termination of processing routines (e.g., encoding,
storing, retrieving). Baddeley (1986, 1990) equates the central
executive with the supervisory attentional system (SAS) described
by Norman and Shallice (1980) and by Shallice (1982). According to
Shallice (1982), the supervisory attentional system is a limited
capacity system and is used for a variety of purposes, including:q
q q q q
tasks involving planning or decision making trouble shooting in
situations in which the automatic processes appear to be running
into difficulty novel situations dangerous or technically difficult
situations situations where strong habitual responses or
temptations are involved
Extensive damage to the frontal lobes may result in impairments
in central executive functioning. Baddeley (1986) coined the term
dysexecutive syndrome (DES) to describe dysfunctions of the central
executive. The classic frontal syndrome is characterized by
disturbed attention, increased distractibility, a difficulty in
grasping the whole of a copmlicated state of affairs ... well able
to work along old routines ... (but) ... cannot learn to master new
types of task, in new situations ... [the patient is] at a loss.
(Rylander, 1939, p.20) In other words, patients suffering from
frontal lobe syndrome lack flexibility and the ability to control
their processing resources, functions attributed to the central
executive. References: 1. Baddeley, A.D. (1990). Human memory:
Theory and practice,. Oxford: Oxford University Press. 2. Baddeley,
A.D. (1986). Working memory. Oxford: Clarendon Press. 3. Norman,
D.A., & Shallice, T. (1980). Attention to action. Willed and
automatic control of behavior. University of California San Diego
CHIP Report 99. 4. Shallice, T. (1982). Specific impairments of
planning. Philosophical Transactions of the Royal Society London B
298, 199-209. 5. Rylander, G. (1939). Personality changes after
operations on the frontal lobes. Acta Psychiatrica Neurologica,
Supplement No. 30.
See Also:Articulatory Loop | Visuospatial Sketchpad | Working
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(1 of 2) [06.07.2003 21:59:31]
U of A Cog Sci Dictionary (Central Executive)
Contributed by Bonnie M. French
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U of A Cog Sci Dictionary (Cognitive Development)
Cognitive Development (In Children)Generally it is referred to
the changes which occur to a person's cognitive structures,
abilities, and processes. The most widely known theory of childhood
cognitive development was proposed by Jean Piaget in 1969. He
proposed the idea that cognitive development consisted of the
development of logical competence, and that the development of this
competence consists of four major stages: 1. 2. 3. 4. sensori-motor
preoperational concrete operational formal operational
He also argued that a child's cognitive performance depended
more on the stage of development he was in than on the specific
task being performed. More recent studies have cast some doubt on
Piaget's theory of homogeneous performance within a given stage.
Instead, it is now believed that performance varies greatly within
each stage and depends more on the acquisition and development of
language, perception, decision rules, and real-world knowledge for
any individual child.
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[06.07.2003 21:59:32]
U of A Cog Sci Dictionary (Cognitive Mapping)
Cognitive MappingCognitive mapping is a general term that
applies to a series of methods for measuring mental
representations. These techniques attempt to describe mental images
that subjects use to encode knowledge and information. Most
researchers treat cognitive maps as a tool that can usefully
summarise and communicate information rather than as a literal
description of mental images. References: 1. Huff, A.S. (1990).
Mapping Strategic Thought Chichester, John Wiley & Sons
See Also:Contributed by J.P. Andrews
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[06.07.2003 21:59:32]
U of A Cog Sci Dictionary (Cognitive Penetrability)
Cognitive PenetrabilityAn approach to testing strong
equivalence. The cognitive penetrability approach seeks to
establish whether phenomena are equivalent at the level of
functional architecture by investigating whether phenomena are
independent of beliefs and goals, that is if they are primitive. If
manipulation of beliefs and goals systematically alters the
empirical phenomenon then the phenomenon is not describing
functional architecture and is cognitively penetrable. The
cognitive penetrability approach was used in the imagary debate in
cognitive science in the 1980's. References: 1. Pylyshyn, Z. W.
(1989). Computing in cognitive science. In M. I. Posner (Ed.),
Foundations of cognitive science. Cambridge, MA: MIT Press.
See Also:Strong Equivalence | Weak Equivalence
Contributed by J. Andrews
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[06.07.2003 21:59:32]
U of A Cog Sci Dictionary (Cognitive Psychology)
Cognitive PsychologyCognitive psychology is concerned with
information processing, and includes a variety of processes such as
attention, perception, learning, and memory. It is also concerned
with the structures and representations involved in cognition. The
greatest difference between the approach adopted by cognitive
psychologists and by the Behaviorists is that cognitive
psychologists are interested in identifying in detail what happens
between stimulus and response. Some of the ingredients of the
information processing approach to cognition were spelled out by
Lachman, Lachman, and Butterfield (1979). In essence, it is assumed
that the mind can be regarded as a general purpose, symbol
processing system, and that these symbols are transformed into
other symbols as a result of being acted on by different processes.
The mind has structural and resource limitations, and so should be
thought of as a limited capacity processor. A key issue in the
field is the extent to which human and computer information
processing systems resemble one another. The consensual view is
probably that there are indeed striking similarities between
computer minds, but there are also probably substantial
differences. In recent years, explicitly cognitive approaches have
been adopted in social and developmental psychology, as well as in
occupational and clinical psychology. References: 1. Eysenck, M.W.
(Ed.). (1990). Blackwell Dictionary of Cognitive Psychology.
Cambridge, MA: Basil Blackwell. 2. Lachman, R., Lachman, J.L.,
& Butterfield, E.C., (1979) Cognitive psychology and
information processing. Hillsdale, NJ: Lawrence Erlbaum
Associates.
See Also:Artificial Intelligence | Cognitive Science
Contributed by L.A. Keple
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U of A Cog Sci Dictionary (Cognitive Psychology)
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(2 of 2) [06.07.2003 21:59:33]
U of A Cog Sci Dictionary ()
Cognitive ScienceSeveral students have supplied definitions for
this term: #1 | #2 | #3
Definition 1"the study of intelligence and intelligent systems,
with particular reference to intelligent behaviour as computation"
(Simon & Kaplan, 1989) Simon, H. A. & C. A. Kaplan,
"Foundations of cognitive science", in Posner, M.I. (ed.) 1989,
Foundations of Cognitive Science, MIT Press, Cambridge MA.
Contributed by J. Andrews, November 23, 1995
Definition 2Cognitive science refers to the interdisciplinary
study of the acquisition and use of knowledge. It includes as
contributing disciplines: artificial intelligence, psychology,
linguistics, philosophy, anthropology, neuroscience, and education.
The cognitive science movement is far reaching and diverse,
containing within it several viewpoints. Cognitive science grew out
of three developments: the invention of computers and the attempts
to design programs that could do the kinds of tasks that humans do;
the development of information processing psychology where the goal
was to specify the internal processing involved in perception,
language, memory, and thought; and the development of the theory of
generative grammar and related offshoots in linguistics. Cognitive
science was a synthesis concerned with the kinds of knowledge that
underlie human cognition, the details of human cognitive
processing, and the computational modeling of those processes.
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U of A Cog Sci Dictionary ()
There are five major topic areas in cognitive science: knowledge
representation, language, learning, thinking, and perception.
Eysenck, M.W. ed. (1990). The Blackwell Dictionary of Cognitive
Psychology. Cambridge, Massachusetts: Basil Blackwell Ltd.
See Also:Cognitive Psychology I Artificial Intelligence
Contributed by: L.A. Keple, November 5, 1995
Definition 3Generally stated, this is the study of intelligence
and intelligence systems. It is a relatively new science that
combines knowledge gained from a number of disciplines. These
include: computer science,neuroscience, cognitive psychology,
philosophy, and linguistics. As a result of the collaborative
effort between these disciplines, there have been, and will
continue to be, huge advancements in our understanding of human
cognition.
See Also:Neuroscience Contributed by M. Kincade Dictionary Home
Page
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(2 of 2) [06.07.2003 21:59:34]
U of A Cog Sci Dictionary (Connectionism)
ConnectionismConnectionism is an alternate computational
paradigm to that provided by the von Neumann architecture.
Originally taking its inspiration from the biological neuron and
neurological organization, it emphasizes collections of simple
processing elements in place of the monolithic processors seen more
commonly within computing. These simple processing elements are
typically only capable of rudimentary calculations (such as
summation), however possess a high degree of weighted
inter-connectivity with one another and generally operate in
parallel [2]. A particular organization of inter-connected
processing elements (a network), is paired with a mathematical
basis by which the connection weights are adjusted (or simply
calculated directly). This allows a network to either learn a task
by iterating on training examples (induction learning), or to
provide a system in which solutions to particular problems can be
computed. Arguably the most widely used example of the former is
the multi-layer perceptron trained via error back-propagation (see
[5], for example); whereas the latter is typified by networks such
as the Hopfield and Tank model for combinatorial optimization [3].
To the casual reader, "connectionism", "parallel distributed
processing" (PDP) and "neural networks" may be entirely synonymous.
The term "neural network" is somewhat misleading to begin with as,
aside from the original inspiration coming from biology, there is
nothing particularly "neural" about them and any perceived
biological relevance is often debatable. There is also merit in
making a philosophical distinction between PDP and connectionism.
For example, over time, PDP has been disposed to seek biological
relevance for their models, tended to emphasize learning oriented
tasks and follow a largely empirical approach. The field of neural
networks has become richer than is encompassed by the traditional
view of PDP. Connectionism distinguishes itself by also viewing the
network model as a computational architecture. This encompasses a
wider range of network structures for which biological relevance is
not an issue or for which a learning process per se is not
utilized. Falling into areas such as these include a wealth of
recent work which has sought to establish the formal relationship
between computational power of connectionist networks and abstract
machines (for example [1],[4]), and even harkens back to the
aforementioned Hopfield and Tank model which computes solutions to
problems by minimizing energy within a pre-wired system of weights
[3]. In this respect, connectionism subsumes PDP. That is to say
that PDP researchers are connectionists, however not all
connectionists consider themselves to be PDP researchers. Although
debatable, this point is one that this author, among others, feels
is an important one.
References:http://web.psych.ualberta.ca/%7emike/Pearl_Street/Dictionary/contents/C/connectionism.html
(1 of 2) [06.07.2003 21:59:35]
U of A Cog Sci Dictionary (Connectionism)
1. C.L. Giles, B.G. Horne, T. Lin. Learning a class of large
finite state machines with a recurrent neural network. Neural
Networks. 8(9):1359-1365, 1995. 2. J. Hertz, A. Krogh and R.G.
Palmer. Introduction to the theory of neural computation.
AddisonWesley, Redwood City, 1991. 3. J.J. Hopfield and D.W. Tank.
`Neural' computation of decisions in optimization problems.
Biological Cybernetics. 52:141-152. 4. S.C. Kremer. On the
computational power of Elman-style recurrent networks. IEEE
Transactions on Neural Networks. 6(4):1000-1004, 1995. 5. D.E.
Rumelhart, G.E. Hinton, and R.J. Williams. Learning internal
representations by error propagation. In D.E. Rumelhart and J.L.
McClelland, editors, Parallel Distributed Processing, volume 1. MIT
Press, Cambridge, 1986.
See AlsoAssociative Memory| Content Addressable Memory|
Induction Learning| Learning Rule| Machine Learning| Parallel
Distributed Processing Models
Contributed by David B. McCaughan Dictionary Home Page| Letter
Index| Search Index
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U of A Cog Sci Dictionary (Consciousness)
ConsciousnessConsciousness refers to awareness of our own mental
processes (or of the products of such processes). This awareness
can be made manifest by introspective reports, in which an
individual provides information about his or her mental experience.
There has been a considerable amount of controversy over the
centuries concerning the value of psychology of assessing the
contents of consciousness by means of introspective evidence.
Aristotle claimed that the only way to study thinking was by
introspection. Others, such as Galton (1883), argued that the
position of consciousness "appears to be a helpless spectator of
but a minute fraction of automatic brain work. Behaviorists tend to
agree with Galton that psychologists should not concern themselves
with consciousness and introspection. There are certain
cognitivists who would disagree with these definitions. Marvin
Minsky (1985), maintains that human consciousness can never
represent what is occurring at the present moment, but only a
little of the recent past. This is due both because agencies have
limited capacity to represent what happened recently and partly
because it take time for agencies to communicate with one another.
Consciousness is difficult to describe because each time we attempt
to examine temporary memories, we distort the very record we are
trying to interpret. References: 1. Eysenck, M.W. (Ed.). (1990).
Blackwell Dictionary of Cognitive Psychology . Cambridge, MA: Basil
Blackwell. 2. Galton, F. (1883). Inquiries into human faculty and
its development. London: Macmillan. 3. Minsky, M. (1985). The
society of mind. New York, NY: Simon & Schuster.
See Also:Mandelbrot Set
Contributed by L.A. Keple
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U of A Cog Sci Dictionary (Content Addressable Memory)
Content Addressable MemoryIn a symbolic system information is
stored in an external mechanism. In the example of the computer it
is stored in files on the disks. As the information has been
encoded in some form of file system in order to retrieve that
information one must know the index system of the files. In other
words, data can only be accessed by certain attributes. In a
connectionist system the data is stored in the activation pattern
of the units. Hence, if a processing unit receives excitatory input
from one of its connections, each of its other connections will
either be excited or inhibited. If these connections represent the
attributes of the data then the data may be recalled by any one of
its attributes, not just those that are part of an indexing system.
As these connections represent the content of the data, this type
of memory is called content addressable memory. This type of memory
has the advantage of allowing greater flexibility of recall and is
more robust. This distributed memory is able to work its way around
errors by reconstructing information that may have been lesioned
from the system. References: 1. Bechtel, W., & Abrahamsen, A.
(1991). Connectionism and the mind: An introduction to parallel
processing in networks. Cambridge, MA: Blackwell.
See Also:Functional Architecture | Graceful Degradation |
Parallel Distributed Processing Models | Spontaneous Generalisation
| Symbolic Architecture
Contributed by J. Andrews
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[06.07.2003 21:59:36]
U of A Cog Sci Dictionary (Crystallized Intelligence)
Crystallized IntelligenceCrystallized intelligence can be
defined as "the extent to which a person has absorbed the content
of culture."(Belsky, 1990, p. 125) It is the store of knowledge or
information that a given society has accumulated over time.
Crystallized intelligence is measured by most of the verbal
subtests of the Wechsler Adult Intelligence Scale (WAIS).
Crystallized intelligence is important to psychologists as it
relates to the study of aging. There is ongoing intense debate
among psychologists as to whether or not intelligence declines with
aging. Horn (1970) hypothesized that because crystallized
intelligence is based on learning and experience, it remains
relatively stable over time. He claims it may even increase "as the
rate at which we acquire or learn new information in the course of
living balances out or exceeds the rate at which we forget." (as
cited in Belsky, 1990, p. 125) On the other side of the debate,
Belsky (1990) claims crystallized intelligence in fact declines
with age. Why? Because, "at a certain time of life the cumulative
effect of losses - of job, of health, of relationships - cause
disengagement from the culture, and so forgetting finally exceeds
the rate at which knowledge is acquired." (p. 125) References: 1.
Belsky, J. K. (1990). The psychology of aging theory, research, and
interventions. Pacific Grove, CA: Brooks/Cole. 2. Horn, J. (1970).
Organization of data on life-span development of human abilities.
In R. Goulet and P.B. Baltes (Eds.). Life-span developmental
psychology: Research and theory. New York: Academic Press.
See Also:Fluid Intelligence | WAIS
Contributed by Cassie Jacknicke
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U of A Cog Sci Dictionary (Crystallized Intelligence)
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U of A Cog Sci Dictionary (Cued Recall)
Cued RecallThis is a component of a memory task in which the
subject is asked to recall items that were presented to them on an
intial training, or initial presentation list. However, it is
slightly different than the free recall task because the subject is
given a hint, or a cue, about the items on the original list. For
example, and experimenter may say: "Tell me all the words from the
list that were animals".
See Also:Free Recall | Intrusions | Perseverations
Contributed by M. Kincade
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[06.07.2003 21:59:37]
U of A Cog Sci Dictionary (Deductive Inference)
Deductive (Logical) InferenceInferences are made when a person
(or machine) goes beyond available evidence to form a conclusion.
With a deductive inference, this conclusion always follows the
stated premises. In other words, if the premises are true, then the
conclusion is valid. Studies of human efficiency in deductive
inference involves conditional reasoning problems which follow the
"if A, then B" format. The task of making deductions consists of
three stages. First, a person must understand the meaning of the
premises. Next they must be able to formulate a valid conclusion.
Thirdly, a person should evaluate their conclusion to tests its
validity. Although deductive inference is easy to test or model,
the results of this type of inference never increase the semantic
information above what is already stated in the premises.
References: 1. Eysenck, M.W. (Ed.). (1990). The Blackwell
dictionary of cognitive psychology. Cambridge, MA: Basil Blackwell.
2. Johnson-Laird, P. N. (1993). Human and machine thinking.
Hillsdale, NJ : Lawrence Erlbaum Associates.
See Also:Inductive Inference
Contributed by Valerie Trifts
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[06.07.2003 21:59:37]
U of A Cog Sci Dictionary (Dementia)
DementiaDementia is a clinical state characterized by loss of
function in multiple cognitive domains. The most commonly used
criteria for diagnoses of dementia is the DSM-IV (Diagnostic and
Statistical Manual for Mental Disorders, American Psychiatric
Association). Diagnostic features include :q
q
q q
memory impairment and at least one of the following: aphasia,
apraxia, agnosia, disturbances in executive functioning. In
addition, the cognitive impairments must be severe enough to cause
impairment in social and occupational functioning. Importantly, the
decline must represent a decline from a previously higher level of
functioning. Finally, the diagnosis of dementia should NOT be made
if the cognitive deficits occur exclusively during the course of a
delirium.
There are many different types of dementia (approximately 70 to
80). Some of the major disorders causing dementia are: 1. 2. 3. 4.
5. 6. Degenerative diseases (e.g., Alzheimer's Disease, Pick's
Disease) Vascular Dementia (e.g., Multi-infarct Dementia) Anoxic
Dementia (e.g., Cardiac Arrest) Traumatic Dementia (e.g., Dementia
pugilistica [boxer's dementia]) Infectious Dementia (e.g.,
Creutzfeldt-Jakob Disease) Toxic Dementia (e.g., Alcoholic
Dementia)
7.9 % of all Canadians 65 years and older meet the criteria for
the clinical diagnoses of dementia (Canadian Study on Health and
Aging, 1994). Alzheimer's Disease is the major cause of dementia,
accounting for 64% of all dementias in Canada for persons 65 and
older and 75% of all dementias for persons 85 plus. References: 1.
American Psychiatric Association (1994). Diagnostic and statistical
manual of mental disorders (4th ed.). Washington, DC: Author. 2.
Canadian study of health and aging: Study methods and prevalence of
dementia. (1994). Canadian Medical Association Journal, 150(6).
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U of A Cog Sci Dictionary (Dementia)
See Also:Alzheimer's Disease
Contributed by Bonnie M. French
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U of A Cog Sci Dictionary (Discrete Processing)
Discrete ProcessingA model using discrete processing requires
that information is passed from one stage to another only after the
processing in the first stage is complete. Therefore, the
processing time required in a discrete model is additive and equal
to the sum of the time taken at each level of processing. The
advantage of this type of model is that it provides a convienent
method of understanding the effects of different variables on the
performance of a given task. References: 1. Eysenck, M.W. (Ed.).
(1990). The Blackwell Dictionary of Cognitive Psychology.
Cambridge, MA: Basil Blackwell.
See Also:Cascade Processsing
Contributed by Valerie Trifts
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[06.07.2003 21:59:38]
U of A Cog Sci Dictionary (The Disjunction Problem)
The Disjunction ProblemAny theory of the content of a
representation must be able to explain how a representation can
misrepresent --how it can represent an object as being something it
is not, or as having properties it does not have-- basically how
its content can be false of the object represented. The difficulty
is that we need to explain --in a principled, non-circular way--
how the representation can correctly represent some things which
cause its activation, yet misrepresent other things which cause its
activation. For instance, we9d like to be able to say that my
kangaroo representation represents kangaroos. If so, then if a
wallaby causes the activation of that representation, then the
wallaby is misrepresented; the representation9s content that9s a
kangaroo is false of the wallaby. Unfortunately, to Fodor (1987,
1990) this doesn9t work. The problem is that if the wallaby can
also cause the activation of my kangaroo representation, then we
seem to have no principled reason for saying that the content of
the representation is simply that9s a kangaroo rather than the
disjunctive content that9s either a kangaroo or a wallaby. If this
is so, then when a wallaby activates my kangaroo representation,
this representation doesn9t represent the wallaby as something it
is not. This representation has the (disjunctive) content that9s
either a kangaroo or it9s a wallaby which, of course, is true of
the wallaby. This content might better be described as
3unspecific2, rather than 3disjunctive2. That is, perhaps the
content is something like an unspecific description which applies
correctly to all the things which can activate it, such as that9s a
large animal with a long tail that gets about by hopping on its
hind legs. So to say that some things which activate the
representation are correctly represented and others are
misrepresented doesn9t work. Even if I9ve only ever seen kangaroos,
and have never met a wallaby, the wallaby can be correctly
represented by this representation, because the wallaby is also a
large animal with a long tail that gets about by hopping on its
hind legs. This is especially a problem for theories which explain
content in terms of covariance: some sort of reliable, lawlike,
connection between tokenings of the representation and the
occurrence of certain types of thing in the world. Such theories
have to be able to justify describing the representation9s content
3conservatively2 as Cummins (1990) calls it, rather than
3liberally2; as that9s a kangaroo rather than that9s a large animal
with a long tail that gets about by hopping on its hind legs.
Cummins summarises various attempts to do this, arguing that
covariance theories don9t explain content in a way that allows
representations to misrepresent. Fodor (1990) claims that any
theory which purports to account for the content of a
representation must solve the disjunction problem. Such an account
must be able to explain misrepresentation, by showing what a
representation9s content is--exactly-- and also how a
representation can be caused to be
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by something to which that content does not apply. References:
1. Cummins, R. (1989). Meaning and Mental Representation.
Cambridge, Mass: MIT Press. A Bradford Book. 2. Fodor, J. (1987).
3Meaning and the World Order2. In Psychosemantics (pp. 97-133).
Cambridge Mass.: MIT Press. A Bradford Book. 3. Fodor, J. (1990).
3A Theory of Content I: The Problem2. In A Theory of Content and
Other Essays. (pp. 51-88). Cambridge, Massachusetts: MIT Press. A
Bradford Book.
See Also:Semantics | Misrepresentation | Representation
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U of A Cog Sci Dictionary (Elaborative Rehearsal)
Elaborative RehearsalElaborative rehearsal is a type of
rehearsal proposed by Craik and Lockhart (1972) in their Levels of
Processing model of memory. In contrast to maintenance rehearsal,
which involves simple rote repetition, elaborative rehearsal
involves deep sematic processing of a to-be-remembered item
resulting in the production of durable memories. For example, if
you were presented with a list of digits for later recall
(4920975), grouping the digits together to form a phone number
transforms the stimuli from a meaningless string of digits to
something that has meaning. References: 1. Craik, F.I.M., &
Lockhart, R.S. (1972). Levels of processing. A framework for memory
research. Journal of Verbal Learning and Verbal Behaviour, 11,
671-684.
See Also:Levels of Processing | Maintenance Rehearsal
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U of A Cog Sci Dictionary (Enactment)
EnactmentWeick (1988) describes the term enactment as
representing the notion that when people act they bring structures
and events into existence and set them in action. The process of
enactment involves two steps. First, preconceptions are used to set
aside portions of the field of experience for further attention,
that is, perception is focused on predetermined stimuli. Second,
people act within the context of these portions of experience
guided by preconceptions in such a way as to reinforce these
preconceptions. Hence, attention to certain stimuli will guide
subsequent action so that those stimuli are confirmed as important.
The result of the process of enactment is the enacted environment
(Weick, 1988). This enacted environment comprises "real" objects
but the significance, meaning and content of these objects will
vary. These objects are not significant unless they are acted upon
and incorporated into events, situations and explanations. In this
way the enacted environment is a direct result of the
preconceptions held by the social actor. An enacted environment is
internalised by social actors as the way in which actions have led
to certain consequences; it is therefore analogous to the concept
of schema and is the source of expectations for future action
(Weick, 1988) . An enacted environment is "a map of if-then
assertions in which actions are related outcomes" that in turn
serve as expectations for future action and focus perception in
such way that these preconceived relationships will be supported.
The importance of the notion of enactment is that it provides a
direct link between individual cognitive processes and
environments. By showing how preconceptions can shape the nature of
the environment this concept allows one to argue the importance of
schema in the sensemaking process. Schema guide both perception and
inference (Fiske & Taylor, 1991) and so will 'enact'
environment by assigning significance, meaning and content to
objects perceived in the environment. References: 1. Fiske, S.T.,
& Taylor, S.E. (1991). Social cognition (2nd ed.). New York:
McGraw-Hill. 2. Weick, K. E. (1988). Enacted sensemaking in crisis
situations. Journal of Management Studies, 24(4).
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U of A Cog Sci Dictionary (Encoding)
EncodingEncoding refers to the processess of how items are
placed into memory.
See Also:Working Memory
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U of A Cog Sci Dictionary (Encoding Specificity)
Encoding SpecificityThe encoding specificity principle of memory
(Tulving & Thomson, 1973) provides an general theoretical
framework for understanding how contextual information affects
memory. Specifically, the principle states that memory is improved
when information available at encoding is also available at
retrieval. For example, the encoding specificity principle would
predict that recall for information would be better if subjects
were tested in the same room they had studied in versus having
studied in one room and tested in a different room (see S.M. Smith,
Glenberg, & Bjork, 1978). References: 1. Smith, S.M., Glenberg,
A.M., & Bjork, R.A. (1978). Environmental contest and human
memory. Memory and Cognition, 6, 342-353. 2. Tulving, E., &
Thomson, D.M. (1973). Encoding specificity and retrieval processes
in episodic memory. Psychological Review, 80, 352-373.
See Also:Encoding | Retrieval
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U of A Cog Sci Dictionary (Equilibration)
EquilibrationAccording to Piaget, development is driven by the
process of equilibration. Equilibration encompasses assimilation
(i.e., people transform incoming information so that it fits within
their existing thinking) and accommodation (i.e, people adapt their
thinking to incoming information). Piaget suggested that
equilibration takes place in three phases. First children are
satisfied with their mode of thought and therefore are in a state
of equilibrium. Then, they become aware of the shortcomings in
their existing thinking and are dissatisfied (i.e., are in a state
of disequilibration and experience cognitive conflict). Last, they
adopt a more sophisticated mode of thought that eliminates the
shortcomings of the old one (i.e., reach a more stable
equilibrium).
See Also:Adaptation | Piaget's Stage Theory of Development
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U of A Cog Sci Dictionary (Error Analysis)
Error AnalysisOne of the key goals of cognitive science is to
develop theories that are strongly equivalent with respect to
to-be-explained systems. This requires that evidence be collected
to defend the claim that the model and the to-be-explained system
are carrying out the same procedures to compute a function. One
kind of information that could be used to examine this claim is
called error analysis. In an error analysis, one could (for two
different systems) rank order problems in terms of their
difficulty, as revealed by their likelihood to produce mistakes.
This is an example of relative complexity evidence. A more detailed
approach would be to classify the nature of the errors that each
system made. In either case, if the two systems were strongly
equivalent, then we would expect them to produce the same rank
orderings of difficulty, and to also produce the same qualitative
patterns of errors. References: 1. Pylyshyn, Z.W. (1984).
Computation and cognition. Cambridge, MA: MIT Press.
See Also:Intermediate State Evidence | Protocol Analysis |
Relative Complexity Evidence | Strong Equivalence
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Extension
ExtensionThe extension of the term 'cat' is the class of 'cat'.
What a term means has two components: i) the referent of the
term--this is 'class' talk, and is the component of meaning to
which 'extension' applies; and ii) the sense of the term, i.e., all
of the psychological associations that one has with that term--this
is 'concept' talk. This second sense is referred to as the
'intension' of the term. Examples of the two components follow. The
referent of the term 'cat' is all the cats; the sense of the term
is related to your experience of cats, their history, their
attributes, etc. A classic example is 'the morning star' and 'the
evening star'; both of which refer to the same thing, the planet
'Venus', but the sense of 'morning star' and 'evening star' is not
the same. You cannot change the terms in a statement including one
of them and retain the same truth value. Other words sometimes used
to pick out the distinctions between 'extension' and 'intension'
are 'denotation' and 'connotation', respectively. Note the
following definition by Cohen and Nagel: A term [an element of a
proposition] may be viewed in two ways, either as a class of
objects (which may have only one member), or as a set of attributes
or characteristics which determine the objects. The first phase or
aspect is called the denotation or extension of the term, while the
second is called the connotation or intension. The extension of the
term 'philosopher' is 'Socrates', 'Plato', 'Thales', and the like;
its intension is 'lover of wisdom', 'intelligent', and so on. (31)
The distinctions in the meaning of a term are important to clarify.
Without such distinctions, no discussion of meaning in general can
begin. If we wish to construct models and theories of human
language and thought--and here talk of meaning necessarily
enters--we need to make precise those issues and problems we
specifically want to address. Cohen, M. R. and Nagel, E. (1993). An
Introduction to Logic. Indianapolis, Indiana: Hackett Publishing
Company.
See
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Extension
Contributed by C. P. Watling, February 27, 1996. Dictionary Home
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U of A Cog Sci Dictionary (Fluid Intelligence)
Fluid IntelligenceFluid intelligence is tied to biology. It is
defined as our "on-the-spot reasoning ability, a skill not
basically dependant on our experience." (Belsky, 1990, p. 125)
Belsky (1990) indicates this type of intelligence is active when
the central nervous system (CNS) is at its physiological peak.
Fluid intelligence is measured by the performance subtasks on the
Wechsler Adult Intelligence Scale (WAIS). Fluid intelligence is
important to psychologists as it relates to the study of aging.
There is ongoing intense debate among psychologists as to whether
or not intelligence declines with aging. Belsky (1990) claims fluid
intelligence "reaches a peak in early adulthood and then regularly
declines." (p. 125) This is because of the physiological changes
that accompany aging. "The development of CNS structures is
exceeded by the rate of CNS breakdown." (Horn, 1970 as quoted in
Belsky, 1990, p. 125) References: 1. Belsky, J. K. (1990). The
psychology of aging theory, research, and interventions. Pacific
Grove, CA: Brooks/Cole. 2. Horn, J. (1970). Organization of data on
life-span development of human abilities. In R. Goulet and P.B.
Baltes (Eds.). Life-span developmental psychology: Research and
theory. New York: Academic Press.
See Also:Crystallized Intelligence | WAIS
Contributed by Cassie Jacknicke
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[06.07.2003 21:59:43]
U of A Cog Sci Dictionary (The Formality Condition)
The Formality ConditionThe semantic properties of a
representation are the properties it has due to its relationship
with the world; properties such as being true, of being a
representation of something, of saying something about some object.
On the other hand, the properties that the representation has in
itself, are its formal properties. Fodor (1980) defines a
representation9s formal properties negatively, by specifying what
they are not: 3Formal properties are the ones that can be specified
without reference to such semantic properties as, for example,
truth reference, and meaning.2 (p.227) Fodor stresses that formal
properties are not syntactic properties. A representation can have
formal properties, and a process can operate on those formal
properties, without that representationhaving a syntax (p227);
rotating an image on a screen, for instance this operation is
performed on the image9s formal properties, but the image doesn9t
even have a syntax.. The point for a computational theory of mind,
which takes mental processes to be formal operations on
representations, (and thus, to Fodor, taking the mind to be a 3kind
of computer2) is that such processes only have access to a
representation9s formal properties. Computational processes do not
have any access to semantic properties; that is, to a
representation's relationships with the world. Thus the processes
that operate on representations cannot operate on the basis of what
this is a representation of, or whether it represents that thing
correctly or not, but only on the character of the representation
itself, its 3shape2 as it were. Thus the Formality Condition incurs
what Putnam (1975) calls Methodological Solipsism. 3If mental
processes are formal, then they have access only to the formal
properties of such representations of the environment as the senses
provide. Hence, they have no access to the semantic properties of
such representations, including the property of being true, of
having referents, or, indeed, the property of being representations
of the environment.2 (Fodor (1980), p231, Fodor9s emphasis) The
solution to this methodological solipsism is to pair a
computational psychology with what Fodor calls a naturalistic
psychology: a theory of the relations between representations and
the world, which fix the semantic interpretations of
representations9 formal properties. (p233) That is, a
representation9s formal properties must somehow mirror the
representation9s semantic properties, so that operations can
operate on formal properties which can at least be interpreted as
saying something about some part of the world (whether or not that
interpretation is correct, true, appropriate, etc.). References: 1.
Fodor, J. (1980). Methodological Solipsism Considered as a Research
Strategy in
Cognitivehttp://web.psych.ualberta.ca/%7emike/Pearl_Street/Dictionary/contents/F/formality.html
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U of A Cog Sci Dictionary (The Formality Condition)
Psychology. In Representations (pp. 225-253). Cambridge,
Massachusetts: MIT Press. A Bradford Book. 2. Putnam, H. (1975).
3The Meaning of Meaning2. In K. Gunderson (Ed.), Minnesota Studies
in the Philosophy of Science (pp. 131-193). Minneapolis: University
of Minnesota Press.
See Also:Semantics | Representation
Contributed by Mason Cash
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U of A Cog Sci Dictionary (Free Recall)
Free RecallFree recall is a basic paradigm used to study human
memory. In a free recall task, a subject is presented a list of
to-be-remembered items, one at at time. For example, an
experimenter might read a list of 20 words aloud, presenting a new
word to the subject every 4 seconds. At the end of the presentation
of the list, the subject is asked to recall the items (e.g., by
writing down as many items from the list as possible). It is called
a free recall task because the subject is free to recall the items
in any order that he or she desires. The free recall task is of
interest to cognitive science because it provided some of the basic
information used to decompose the mental state term "memory" into
simpler subfunctions ("primary memory", "secondary memory"). This
is because the results of a free recall task were typically plotted
as a serial position curve. This curve exhibited a recency effect
and a primacy effect. The behavior of these two effects provided
support to the hypothesis that the free recall task called upon
both a short-term and a long-term memory.
See Also:Primacy Effect | Recency Effect | Serial Position Curve
| Short Term Memory
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U of A Cog Sci Dictionary (Functional Analysis)
Functional AnalysisFunctional analysis is a methodology that is
used to explain the workings of a complex system. The basic idea is
that the system is viewed as computing a function (or, more
generally, as solving an information processing problem).
Functional analysis assumes that such processing can be explained
by decomposing this complex function into a set of simpler
functions that are computed by an organized system of
subprocessors. The hope is that when this type of decomposition is
performed, the subfunctions that are defined will be simpler than
the original function, and as a result will be easier to explain. A
very detailed treatment of functional analysis is provided by
Cummins (1983). He proposes a threestage methodology that defines
functional analysis. In the first stage, the to-be-explained
function is defined. In the second stage, analysis is performed.
The to-be-explained function is decomposed into an organized set of
simpler functions. This analysis can proceed recursively by
decomposing some (or all) of the subfunctions into
sub-subfunctions. In the third stage, analysis is stopped by
subsuming the bottom level of functions. This means that the
operation of each of these operation is explained by appealing to
natural laws (e.g., mechanical or biological principles). If
functional analysis is applied to an information processing system,
then the level of subsumed functions defines the functional
architecture for that information processor. Functional analysis is
important to cognitive science because it offers a natural
methodology for explaining how information processing is being
carried out. For instance, any "black box diagram" offered as a
model or theory by a cogntive psychologist represents the result of
carrying out the analytic stage of functional analysis. Any
proposal about what constitutes the cognitive architecture can be
viewed as a hypothesis about the nature of cognitive functions at
the level at which these functions are subsumed. References: 1.
Cummins, R. (1983). The nature of psychological explanation.
Cambridge, MA: MIT Press.
See Also:Functional Architecture | Primitive | Ryle's
Regress
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U of A Cog Sci Dictionary (Functional Architecture)
Functional ArchitectureThe functional architecture can be viewed
as the set of basic information processing capabilities available
to an information processing system. "Specifying the functional
architecture of a system is like providing a manual that defines
some programming language. Indeed, defining a programming language
is equivalent to specifying the functional architecture of a
virtual machine" (Pylyshyn, 1984, p. 92). In other words, if it is
assumed that cognition is the result of the brain's "running of a
program", then the functional architecture is the language in which
that program has been written. The functional architecture is of
interest to cognitive science because if offers an escape from
Ryle's Regress (a.k.a. the homunculus problem). The functional
architecture is comprised of a set of primitive operations or
functions. This means that these basic functions cannot be
explained by being further decomposed into less complex ("smaller")
subfunctions. Instead, they must be explained by appealing to
implementational properties (e.g., for human cognition, properties
of the human brain). As a result, the functional architecture
represents the point at which the decomposition of mental state
terms into other mental state terms via functional analysis can
stop. By specifying the functional architecture, one converts the
black box descriptions that cognitivists create into explanations.
References: 1. Pylyshyn, Z.W. (1984). Computation and cognition.
Cambridge, MA: MIT Press.
See Also:Functional Analysis | Primitive | Ryle's Regress
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U of A Cog Sci Dictionary (Generalization)
GeneralizationKlahr & Wallace (1982) felt that Piaget's
theory of adaptation was not enough to explain cognitive
development. They therefore developed a new theory, and posited
that the mechanism behind development was generalization. Klahr and
Wallace divided generalization into three more specific categories:
the time line, regularity detection, and redundancy elimination
(Siegler, 1991). These three categories are described below.
The Time LineThe time line contains the data on which
generalizations are based. In Klahr and Wallace's theory, whenever
a system encounters a situation, it records the responses to that
situation, the outcomes from those actions, and what new situations
arose as a result. This recording of events ensures that the system
keeps all the information about an even stored so that it can be
referred back to in the future.
Regularity DetectionThis process uses the contents of the time
line to draw generalizations about experience. The system notes
situations that are similar and notes where variations do not
change the outcomes of situations.
Redundancy EliminationThis process improves efficiency by
identifying processeing steps that are unecessary. In this way, it
reaches a generalization that a less-complex sequence can achieve
the same goal (Siegler, 1991). Klahr and Wallace have developed a
self-modifying computer simulation that models findings about
children's thinking, and can demonstrate these processes in
generalization. References: 1. Klahr, D. (1982). Nonmonotone
assessment of monotone development: An information processing
analysis. In S. Strauss (Ed.), U-shaped behavioral growth. New
York: Academic Press. 2. Siegler, R. (1991). Children's thinking.
Englewood Cliffs, NJ: Prentice-Hall. 3. Vasta, R., Haith, M. M.,
& Miller, S. A. (1995). Child psychology: The modern science.
New York, NY: Wiley.
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U of A Cog Sci Dictionary (Generalization)
See Also:Adaptation | Equilibration
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U of A Cog Sci Dictionary (Graceful Degradation)
Graceful DegradationIn a symbolic system removing part of the
system will result in a clear degradation of performance. Removing
a symbol token will result in the loss of the information stored in
that token. The loss of an operating procedure destroys the systems
ability to perform the missing process. The fall in performance is
sudden and clearly defined. In a connect