Clarifying what Functionalism is… Categorisation Attention Memory Knowledge representation Numerical cognition Thinking Learning Language Sight Hearing.

Clarifying what Functionalism is…

Categorisation

Attention

Memory

Knowledge representation

Numerical cognition

Thinking

Learning

Language

Sight

Hearing

Taste

Smell

Touch

Balance

Heat/cold

…

Voice

Limbs

Fingers

Head

…sensory

input

Motor output

Sensory systems

Central systems

Motor systems

Physical Implementation

Functionalism says we can study the

information processing tasks

(and algorithms for doing them)

independently from the physical level

This means they are “multiply realisable”

= able to be manifested in various systems,

even perhaps computers,

so long as the system performs the appropriate

functions

(Wikipedia definition)

Clarifying what Functionalism is…

Categorisation

Attention

Memory

Knowledge representation

Numerical cognition

Thinking

Learning

Language

Sight

Hearing

Taste

Smell

Touch

Balance

Heat/cold

…

Voice

Limbs

Fingers

Head

…sensory

input

Motor output

Sensory systems

Central systems

Motor systems

Physical Implementation

What about Brooks? (remember tutorial article)

Is he a functionalist?

Yes! Otherwise he wouldn’t be trying to use computers to implement the

processing in his robots.

He would instead be trying to use some organic system,

as a non-functionalist would believe that the processing happening in an

animals’ neurons could not be performed by a computer

Clarifying what Functionalism is…

Categorisation

Attention

Memory

Knowledge representation

Numerical cognition

Thinking

Learning

Language

Sight

Hearing

Taste

Smell

Touch

Balance

Heat/cold

…

Voice

Limbs

Fingers

Head

…sensory

input

Motor output

Sensory systems

Central systems

Motor systems

Physical Implementation

So what was it Brooks was saying about the real world?

He said this side needs to be connected to the

real world, not a simulation

e.g. digital camera getting data from real world, with noise, and

messy lighting conditions, etc.

Clarifying what Functionalism is…

Categorisation

Attention

Memory

Knowledge representation

Numerical cognition

Thinking

Learning

Language

Sight

Hearing

Taste

Smell

Touch

Balance

Heat/cold

…

Voice

Limbs

Fingers

Head

…sensory

input

Motor output

Sensory systems

Central systems

Motor systems

Physical Implementation

So what was it Brooks was saying about the real world?

He said this side needs to be connected to the

real world, not a simulation

e.g. wheels on the robot, which might slip on the ground or stick on the

carpet, etc.i.e. messy

Clarifying what Functionalism is…

Categorisation

Attention

Memory

Knowledge representation

Numerical cognition

Thinking

Learning

Language

Sight

Hearing

Taste

Smell

Touch

Balance

Heat/cold

…

Voice

Limbs

Fingers

Head

…sensory

input

Motor output

Sensory systems

Central systems

Motor systems

Physical Implementation

So what was it Brooks was saying about the real world?

He didn’t say he had any problem

with the algorithms being implemented on a

computer

The Physical Symbol System

Some sort of Physical Symbol System seems to be needed to explain human abilities Humans are “programmable” We can take on new information and instructions We can learn to follow new procedures

e.g. a new mathematical procedure

Human mind is very flexible …But not true of other animals, even apes

Animals have special solutions for specific tasks Frog prey location

Human flexible Physical Symbol System must have evolved from animals’ processing systems Details of physical implementation are unknown

Let’s stick with Physical Symbol System for now… See can we flesh out more details

The Language of Thought What is the language we “think in”? Is it our natural language, e.g. English, or mentalese? Some introspective arguments against natural language

Word is “on the tip of my tongue”, but can’t find it Difficult to define concepts in natural language, e.g. dog, anger We have a feeling of knowing something, but hard to translate to

language

Some observable evidence against natural language Children reason with concepts before they can speak

We often remember gist of what is said, not exact words Cognitive science experiment: (recall after 20 second delay) He sent a letter about it to Galileo, the great Italian Scientist. He sent Galileo, the great Italian Scientist, a letter about it. A letter about it was sent to Galileo, the great Italian Scientist. Galileo, the great Italian Scientist, sent him a letter about it.

Represent as Propositions Just like the logic we had for AI

likes(john,mary)

likes

johnmary

rela

tion

objectsubject

isa

applea

rela

tion

objectsubject

gives

john

mary

rela

tion

objectsubjectre

cipi

ent

a

Evidence for Propositions A cognitive Science experiment (Kintsch and Glass)

Consider two different sentences, but both with three “content words”

The settler built the cabin by hand. One 3-place relation

The crowded passengers squirmed uncomfortably. Three 1-place relations

Subjects recalled first sentence better Suggests it was simpler in the representation

(Cognitive Science involves a fair bit of guessing!)

Associative Networks Idea: put together the bits of the propositions that are similar

likes

johnmary

isa

apple

gives

a

Associative Networks Idea: put together the bits of the propositions that are similar Each node has some level of activation Activation spreads in parallel to connecting nodes Activation fades rapidly with time A node’s total activation is divided among its links

These rules make sure it doesn’t spread everywhere

Nodes and links can have different capacities Important ones are activated very often Have higher capacity

These ideas seem to match our intuition from introspection One thought links to another connected one

Associative NetworksCognitive Science experiment (McKoon and Ratcliff) Made short paragraphs of connected propositions Subjects viewed 2 paragraphs for a short time Subjects were shown 36 test words in sequence

and asked if those words occurred in one of the stories For some of the 36 words, they were preceded by a word from

same story For some of the 36 words, they were preceded by a word from

other story Word from same story helped them remember …Suggests it is because they were linked in a network They also showed recall was better if closer in the network …Suggests activation weakens as it spreads

Schemas Propositional networks can represent specific knowledge

John gave the apple to Mary

…but what about general knowledge, or commonsense? Apple is edible fruit Grows on a tree Roundish shape Often red when ripe…

Could augment our proposition network Add more propositions to the node for apple Apple then becomes a concept The connections to apple are a schema for the concept

What about more advanced concepts/schemas like a trip to a restaurant?...

ScriptsElements of a script… Identifying name or theme

Eating in a restaurant Visiting the doctor

Typical roles Customer Waiter Cook

Entry conditions Customer hungry, has money

Scripts Sequence of goal directed scenes

Enter Get a table Order Eat Pay bill Leave

Sequence of actions within scene Get menu Read menu Decide order Give order to waiter

Scripts How to represent a script? Could use proposition network for all the parts … but maybe whole script should be a unit Introspection suggests that it is activated as a unit

without interference from associated propositions Experimental evidence (Bower, Black, Turner 1979)…

Got subjects to read a short story Story followed a script, but didn’t fill in all details They were then presented various sentences Some from story, and some not Some trick sentences were included:

Not from the story, but part of the script

Subjects were asked to rate 1(sure I didn’t read it) -7(sure I did read it) Subjects had a tendency to think they read the trick sentences Suggests that they activate the script and fill in the blanks in memory

…Starting to get a Model of the Mind Propositional-schema representations stored in long-term

memory Associative activation used to retrieve relevant memories …but many details unspecified Need more machinery to account for

Assess retrieved information, see does it relate to current goals Decompose goals into subgoals Draw conclusions, make decisions, solve problems

More importantly: How to get new propositions and schemas into memory Schemas are often generalised from examples, not taught

What about working memory?

Working Memory Most long-term memory not “active” most of the time Just keep a few things in working memory for current

processing Very limited: try multiplying 3-digit numbers without paper Working memory holds 3-4 chunks at a time Why so limited? (it seems useful to have more nowadays)

Maybe complex circuitry required Maybe costly in energy Maybe tasks were less complex in environment of early humans Or maybe more working memory would cause too many clashes,

or be too hard to manage

However limits can be overcome by skill formation

Note also: limit of 3-4 does not mean other “propositions” inactive Could be a lot more going on subconsciously

Skill Acquisition With a lot of practice we can “automate” many tasks We distinguish this from “controlled processing” – using working memory Once automated:

Takes little attention or working memory(these are “freed up”)

Hard not to perform the task – cannot control it well

Most advanced skills use a combination Automatic processes under direction of controlled processes, to meet goals Examples: martial arts expert, or musician

Is Skill Acquisition Separate? Evidence from Neuropsychology:

People with severe “anterograde amnesia” Cannot learn new facts

i.e. can’t get them into long-term propositional memory

…but can learn new skills Example:

Can learn to solve towers of Hanoi with practice But cannot remember any occasion when they practised it

Suggests that a different part of the brain handles each Skill may reside in visual and motor systems, rather than central systems Maybe because of evolution:

Animals often have good skill acquisition Maybe humans evolved a specific new module for high level functions

Mental Images

Sometimes we seem to evoke visual images in “mind’s eye” Subjective experience suggests visual image is separate from propositions

…but need experimental evidence

In imagining a scene: Example: search a box of blocks for 3cm cube with two adjacent blue sides Properties are added to a description But not so many properties as would be present in a real visual scene

Support, illumination, shading, shadows on near surfaces

Image does not include properties not available to visual perception Other side of cube

Intuition suggests that “mind’s eye” mimics visual perception Maybe it uses the same hardware? Would mean that “central system” sends information to vision system

Mental Images

Hypothesis: there is a human “visual buffer” Short-term memory structure Used in both visual perception and “mind’s eye” Special features/procedures:

Can load it, refresh it, perform transformations Has a centre with high resolution Focus of attention can be moved around

Assuming it exists… what good is it? Allows you to pull things out of your visual long term memory Use it to build a scene, with all spatial details filled in Useful to plan a route, or a rearrangement of objects

Experiment: how many edges on a cube? (Assuming answer is not in long term memory)

Experiments to show Mental ImagesTest a special procedure: mental rotation

Experiments to show Mental ImagesTime taken depended on how much rotation was needed

Suggests that we really rotate in the “visual buffer”

Experiments to show Mental Images

Experiments to show Mental Images However… just because we rotate stuff doesn’t necessarily mean that we do

it in the “visual buffer” …Need more evidence

PET brain scans have shown that the “occipital cortex” is used “occipital cortex” is known to be involved in visual processing

So far…The “Symbolic” Approach to

explaining cognition

an alternative…the “Connectionist” approach…

Connectionist Approach What is connectionism?

Concepts are not stored as clean “propositions” They are spread throughout a large network “Apple” activates thousands of microfeatures Activation of apple depends on context, no single dedicated unit

Neural plausibility Graceful degradation, unlike logical representations

Cognitive plausibility Could explain entire system, rather than some task in central system

(symbolic accounts can be quite fragmented) Could explain the “pattern matching” that seems to happen everywhere

(for example in retrieval of memories) Explain how human concepts/categories do not have clear cut definitions

Certain attributes increase likelihood (ANN handles this well) But not hard and fast rules

Explains how concepts are learned Adjust weights with experience

Another Perspective on Cognitive Science / AI We have seen multiple models for the mind,

and each has an “AI version” too Propositions AI’s logic statements Scripts AI’s case based reasoning Mental images AI: some work, but not much Connectionist models AI’s neural networks

This gives us another perspective on Cognitive Science / AI Both are working in different directions

AI person starts with a computer and says How can I make this do something that a mind does? May take some inspiration from what/how a mind does it

Cognitive Science person starts with a mind and says How can I explain something this does, using the “computer metaphor”? May take some inspiration from how computers can do it Especially from how AI people have shown certain things can be done

Another Perspective on Cognitive Science / AI We have seen multiple models for the mind,

and each has an “AI version” too Propositions AI’s logic statements Scripts AI’s case based reasoning Mental images AI: some work, but not much Connectionist models AI’s neural networks

This gives us another perspective on Cognitive Science / AI Both are working in different directions

AI person starts with a computer and says How can I make this do something that a mind does? May take some inspiration from what/how a mind does it

Cognitive Science person starts with a mind and says How can I explain something this does, using the “computer metaphor”? May take some inspiration from how computers can do it Especially from how AI people have shown certain things can be done

Which model is correct?

…possibly… all of them

i.e. all working together

e.g. we have seen that logic could be implemented on top of Neurons

(need not be in “clean” symbolic way)

This would give opportunity for logical reasoning,

while still having “scruffy” intuitions going on in the background.