Clarifying what Functionalism is

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)
Motor systems
Central systems
Sensory systems
Categorisation Attention Memory Knowledge
representation Numerical cognition Thinking Learni
ng Language
Sight Hearing Taste Smell Touch Balance Heat/cold

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
Motor systems
Central systems
Sensory systems
What about Brooks? (remember tutorial
article) Is he a functionalist? Yes! Otherwise
he wouldnt 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
Categorisation Attention Memory Knowledge
representation Numerical cognition Thinking Learni
ng Language
Sight Hearing Taste Smell Touch Balance Heat/cold

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?
Motor systems
Central systems
Sensory systems
Categorisation Attention Memory Knowledge
representation Numerical cognition Thinking Learni
ng Language
Sight Hearing Taste Smell Touch Balance Heat/cold

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?
Motor systems
Central systems
Sensory systems
Categorisation Attention Memory Knowledge
representation Numerical cognition Thinking Learni
ng Language
Sight Hearing Taste Smell Touch Balance Heat/cold

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?
Motor systems
Central systems
Sensory systems
Categorisation Attention Memory Knowledge
representation Numerical cognition Thinking Learni
ng Language
Sight Hearing Taste Smell Touch Balance Heat/cold

Voice Limbs Fingers Head
He didnt 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
• Lets 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 cant 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
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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!)

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Associative Networks

• Idea put together the bits of the propositions
  that are similar

likes
isa
mary
john
a
apple
gives
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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 nodes total activation is divided among its
  links
• These rules make sure it doesnt 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

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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 sequenceand
  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

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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?...

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Scripts

• Elements 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

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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

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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
  unitwithout interference from associated
  propositions
• Experimental evidence (Bower, Black, Turner
  1979)
• Got subjects to read a short story
• Story followed a script, but didnt 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 didnt 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

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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?

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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

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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

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Is Skill Acquisition Separate?

• Evidence from Neuropsychology
• People with severe anterograde amnesia
• Cannot learn new facts
• i.e. cant 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

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Mental Images

• Sometimes we seem to evoke visual images in
  minds 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 minds eye mimics
  visual perception
• Maybe it uses the same hardware?
• Would mean that central system sends
  information to vision system

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Mental Images

• Hypothesis there is a human visual buffer
• Short-term memory structure
• Used in both visual perception and minds 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)

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Experiments to show Mental Images
Test a special procedure mental rotation
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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
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Experiments to show Mental Images

• However just because we rotate stuff doesnt
  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

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So farThe Symbolic Approach to explaining
cognitionan alternativethe 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 ? AIs logic statements
• Scripts ? AIs case based reasoning
• Mental images ? AI some work, but not much
• Connectionist models ? AIs 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 ? AIs logic statements
• Scripts ? AIs case based reasoning
• Mental images ? AI some work, but not much
• Connectionist models ? AIs 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.