clarifying what functionalism is… categorisation attention memory knowledge representation...
TRANSCRIPT
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.