COURSE SUMMARY

1. What is cognitive science?

   • ~def the interdisciplinary study of cognition
     • reasoning
       
     • remembering
       
     • language understanding & generation
       
     • perception
       
     • learning
       
     • consciousness
       
     • emotions
       
     • etc.

   • computationalism: cognition is computable

     • working hypothesis:
       • how much of cognition is computable?

     • "computable" = classical symbolic computation
                              as well as connectionism

     • methodology: express theories of cognition as computer programs

       • can test theory by running the program
         • philosophical problem:
           • Do computers running such programs actually exhibit cognition?
           • if not, why not?

       • contrast with behaviorism:
         • algorithms show how to get from stimulus/input to response/output

   • 3 major underlying ideas of computational cognitive science:

     • Newell & Simon's PSSH:
       • a physical system can exhibit cognition
         iff
         it is a physical symbol system (= TM)

     • Fodor's LOT & RTM:
       • cognitive states/processes are computations over cognitive representations
       • cognitive representations are a language,
         with a syntax (& maybe a semantics)

     • Dennett's Intentional Stance:
       • best to treat complex systems as if they were intentional (i.e., cognitive)

2. Philosophy

   • looked at relevant history of philosophy:
     • rationalism (e.g., Descartes, Leibniz)
     • empiricism (e.g., Locke, Berkeley, Hume)
     • Kant's synthesis:
       our minds impose structure on sensory impressions

   • mind/body problem:
     • functionalism:
       • the kind of cognitive state/process that a cognitive state/process is
         is determined by its causal role in the individual's cognitive life
       • problems: (absent/inverted) qualia

3. Psychology

   • concepts & categories:
     • problems with classical theories of categories
       defined by necessary & sufficient conditions
     • Rosch's theory of basic-level categories

   • reasoning:
     • logical puzzles in "natural" reasoning
     • Johnson-Laird's theory of mental models

   • folk psychology, Theory of Mind theory, simulation theory

   • guest lecture: David Smith
     • animal metacognition:
       • thought about thought
       • knowing what you (don't) know

4. Artificial Intelligence

   • computational philosophy, computational psychology

   • algorithms, Turing Machines
     4 great insights:
     • 2 NPs (0,1)
     • 5 VPs (TM actions)
     • 3 grammar rules (sequence, selection, repetition)
     • Church-Turing Thesis:
       • A problem is computable
         iff
         there is a TM program that computes its solution

   • AIQ Test

   • SNePS

5. Linguistics

   • Chomsky:
     • tranformational grammar
     • deep vs. surface structure + T-rules

   • Lakoff's theory of conceptual metaphors

   • guest lectures:
     • Len Talmy (LIN): how language structures concepts
     • Jeff Higginbotham (CDS): augmentative communication

6. Anthropology
   • guest lecture: Chuck Frake: navigation as a cognitive problem

7. Neuroscience
   • guest lecture: Susan Udin: vision

8. Geography
   • guest lecture: David Mark: cognitive geography

9. Interdisciplinary projects:

   • deixis in narrative

   • contextual vocabulary acquisition

   • there are others! E.g.:
     • LIN + Neurosci: PET studes of linguistic processing (Jaeger, Lockwood)
     • LIN + PSY: lexical semantics (Koenig, Mauner)
     • LIN + AI: computational linguistics (Koenig, Shapiro, Rapaport, Pierce)
     • PHI + GEO: spatial ontology (Smith, Mark)

10. Turing Test & Chinese-Room Argument

    • Turing Test:
      a computer that can convince a human that it can think
      can (be said to) think

    • Chinese-Room Argument (Searle):
      it's possible to pass a TT without really thinking

    • ultimately, might be a moral choice!