How are Concepts Defined?  Classical Theory: define necessary and sufficient conditions  Grandmother: a female who has a child who has a child  Likely properties are neglected: grey hair, old  Difficulties:  Not so realistic perhaps…  We don’t have a clear idea of conditions for most concepts  Old woman with adopted son who has children  Usually consider her a grandmother  Introspection suggests…  We often classify using unnecessary features  Dogs: 4 legs and barks  Even though a dog who has lost a leg, and lost his voice, is still a dog!  To be sure of dog we should …  have a careful assessment of its morphology, or chromosomes  … but this is not how we work

How are Concepts Defined?  Deficiencies in classical theory …  Prototype approach  Cognitive Scientists move to a “likelihood” theory  Likelihood that a concept will have some characteristics  Likelihood that something is categorised as that concept  Members of a concept have “Family Resemblance”  “Family Resemblance” idea picks very typical features  Bird:  Robin has very typical features –Flight, size, tendency to perch in branches, sing  Penguin does not  Methods to implement:  Likelihood schema:  Set up a schema with likely features,  and weights on importance  Or use: Average of known examples

Evidence for Prototype Concepts  Experiment by Rosch and Mervis  Took categories:  Fruit  Vegetables  Clothing  Furniture  Vehicles  Weapons  Subjects given 20 items that were instances of a category  Asked to list typical features  From subjects’ responses each item was given a family resemblance score  For each item: One point for each feature also in another item  E.g. furniture: chair scored highest, telephone lowest

Evidence for Prototype Concepts  Another experiment:  Subjects given typical and atypical instances of a category  e.g. furniture: chair, rug, table, telephone  Asked to rate them on a 1-7 “typicality” scale  Items with highest “family resemblance” score (from previous) given highest rating  Shows:  having features in common with other members means: more typical

Evidence for Prototype Concepts  Another experiment:  Subjects given a category, and then instances  Asked if instance belongs: “yes” or “no”  e.g.  Bird: robin  yes  Bird: rabbit  no  Items with highest “family resemblance” score had faster response  e.g.  Bird: robin  fast  Bird: pigeon  medium  Bird: eagle  medium  Bird: chicken  slow  Shows:  having features in common with other members means: more typical

How do we Choose Concepts  Rosch analysed features we use  Typical use  Visual shape  Suggests these characteristics constrain categories  Culture (use)  Visual system

How to Represent Concepts  Can use propositions as before  Proposition represents both the item and the concept  Example:  Vegetable:plantplant: green bean edibleedible fibrousfibrous greengreen main dishmain dish long/thin  Put a “weight” on each link  to indicate how important it is to distinguish that concept  Check:  how many overlapped paths  And how strong  …To decide in green bean is a vegetable

Dynamic Theory of Concepts  Proposed by Barsalou 1993  When concepts retrieved in a certain context  Certain features are given prominence  Example: thinking of concept cucumber 1.During Spring planting 2.During August dinner  Different features given prominence  Experimental evidence  Subjects were given a context with a sentence (priming)  Then asked if a feature was part of the concept  Results showed low-weight features could be boosted  Dynamic concepts  Means that your notion of the concept is changing  Depends on your current context

How to Learn the Concepts  For a prototype concept:  Train a network with the examples that have been seen  Adjust the weights on the features on the concept  End up with a good average prototype  Problem:  What about features like colour of a cow?  Seem to be set of possible colours  Not just any colour, but certain options

How to Learn the Concepts  “Exemplar Approach”  Alternative to prototype approach  Store all the examples  e.g. all known example of “dog”  When a new one comes along, see how well it matches known ones  “dog-similarity value”  Approach works well in lab tests  Better than prototype approach  Concern:  Need to store so many examples,  and compare a new instance with each stored one  Could compare in parallel by neural network  …but still a lot of storage

How to Learn the Concepts  Top down and bottom up processes  Seeing a fat man in a foreign country  You would not conclude that all men in that country are fat  Seeing a coin in a foreign country  You would conclude that all those coins would have that size  This is using some higher level knowledge  People seem to have “theories of domains”  Concepts seem to incorporate high level knowledge as well as low level “likely” features  Proper theory of concepts may take some time…

Cognitive Science Concepts and AI?  Sometimes the devil is in the details…  It is easy to describe for some simple concepts and features  Describe a handful, and how they link in an associative network  …but does not scale up for a great number of concepts  Number of features seems infeasible  Example: Barsalou has “can be walked on” as a feature of “roof”  Imagine how many features roof has if we want to go to this level of explanation  When it comes to connecting to the world…  Not clear how to do it  Even recognising the most basic things is beyond vision systems  A chair  Unless constrained to particular types/lighting etc.  Recognising most basic concepts from language also problematic  Concepts most interesting in toy demonstrations  Conclusion: Cognitive Science Concepts interesting  Clearly reveals some insight on how mind works  …But still a big gap between them and AI systems

Memory  We will focus on “declarative” memory  i.e. think of declare some fact to be true  We already talked about “procedural” memory  Skill acquisition: play musical instrument, ride a bicycle  Psychologists consider three stages for memory 1.Acquisition 2.Retention interval  Seconds, minutes, years 3.Retrieval  Short-term / long-term  Think of difference between your own phone number  And one you remember just long enough to dial  Experiment  Subjects asked to try “rehearsal” or “elaboration”  Rehearsal was good for short-term recall  Elaboration was good for long-term recall  Why?

Memory  If subjects do “deeper” processing  have better long-term memory recall  Experiment:  Is the word in capital letters?tableTABLE  Does the word rhyme with weight?crateMARKET  Is the word a type of fish?SHARKheaven  Would the word fit this sentence:FRIENDcloud He met a _____ in the street  Subjects answered 40 questions on different words  Result: words where the question required deeper processing were remembered better  Also experimented with higher complexity “sentence questions”  Even better memory  Interesting: intention to remember does not help!  Another experiment:  Some subjects told they need to remember  Others told they just need to answer quickly, then given surprise memory test at end

Memory - Elaborations  In terms of propositional associative networks  Elaboration activates more connected nodes  If you forget the main part, the associated activations might activate it  Some elaborations produce better memory effects than others  Bradshaw and Anderson showed “cause” and “effect” effective  “Mozart made a long journey from Munich to Paris”  Cause: “Mozart wanted to leave Munich to avoid a romantic entanglement”  Effect: “Mozart was inspired by Parisian musical life”  Downside of elaborations  Subjects often remember things that weren’t there  After 24 hours  Subjects recalled 1 incorrect elaboration for every 2 propositions in the story  Relevant to witness testimony…  Watergate: John Dean misattributed statements to people  Subjects shown film of car crash  Asked:“how fast were they going when they smashed into each other?” “how fast were they going when they hit each other?”  First group more likely to have “seen” broken glass

Reasoning  Remember deduction from the AI part on logic?  IF a guy is tall THEN Mary likes the guy  John is a tall guy  Mary must like John  Do humans really use logical deduction?  Experiment: Four cards EK47EK47  IF a vowel on one side THEN an even number on other side  High rate of error  But performed better if detecting cheating involved…  Deductive model should not depend on content  Why are humans so bad at logical reasoning?  Human thought more heuristic – works most of the time