1. Clarifying what Functionalism is…
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)
Central systems
Motor systems
Sensory systems
Sight
Hearing
Taste
Smell
Touch
Balance
Heat/cold …
Categorisation
Attention
Memory
Knowledge representation
Numerical cognition
Thinking
Learning
Language
Voice
Limbs
Fingers
Head …
Functionalism says we can study the information processing tasks (and algorithms for doing them) independently from the physical level
Motor output
sensoryinput
Physical Implementation

2. Clarifying what Functionalism is…
Central systems
Motor systems
Sensory systems
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
Sight
Hearing
Taste
Smell
Touch
Balance
Heat/cold …
Categorisation
Attention
Memory
Knowledge representation
Numerical cognition
Thinking
Learning
Language
Voice
Limbs
Fingers
Head …
Motor output
sensoryinput
Physical Implementation

3. Clarifying what Functionalism is…
So what was it Brooks was saying about the real world?
Central systems
Motor systems
Sensory systems
Sight
Hearing
Taste
Smell
Touch
Balance
Heat/cold …
Categorisation
Attention
Memory
Knowledge representation
Numerical cognition
Thinking
Learning
Language
Voice
Limbs
Fingers
Head …
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.
Motor output
sensoryinput
Physical Implementation

4. Clarifying what Functionalism is…
So what was it Brooks was saying about the real world?
Central systems
Motor systems
Sensory systems
Sight
Hearing
Taste
Smell
Touch
Balance
Heat/cold …
Categorisation
Attention
Memory
Knowledge representation
Numerical cognition
Thinking
Learning
Language
Voice
Limbs
Fingers
Head …
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
Motor output
sensoryinput
Physical Implementation

5. Clarifying what Functionalism is…
So what was it Brooks was saying about the real world?
Central systems
Motor systems
Sensory systems
Sight
Hearing
Taste
Smell
Touch
Balance
Heat/cold …
Categorisation
Attention
Memory
Knowledge representation
Numerical cognition
Thinking
Learning
Language
Voice
Limbs
Fingers
Head …
He didn’t say he had any problem with the algorithms being implemented on a computer
Motor output
sensoryinput
Physical Implementation

6. 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

7. 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.

8. Represent as Propositions
Just like the logic we had for AI
likes(john,mary)
isa
relation
likes
subject
relation
object a
apple
subject
object
gives
mary
john
relation
subject
object
john a
recipient
mary

9. 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!)

10. Associative Networks
Idea: put together the bits of the propositions that are similar
likes
isa
mary
john a
apple
gives

11. 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

12. Associative Networks
Cognitive 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

13. 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?...

14. Scripts Elements of a script… Identifying name or theme Typical roles
Eating in a restaurant
Visiting the doctor
Typical roles
Customer
Waiter
Cook
Entry conditions
Customer hungry, has money

15. 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

16. 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

17. …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?

18. 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

19. 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

20. 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

21. 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

22. 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)

23. Experiments to show Mental Images
Test a special procedure: mental rotation

24. Experiments to show Mental Images
Time taken depended on how much rotation was needed
Suggests that we really rotate in the “visual buffer”

25. Experiments to show Mental Images

26. 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

27. So far… The “Symbolic” Approach to explaining cognition an alternative… the “Connectionist” approach…

28. 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

29. 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

30. 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.