IV.  Illusion of Knowledge

    A.  The Illusion of Knowledge

       1.  Some Initial Examples

            a.  Draw a Bicycle

            b.  Simple Science Knowledge: Seasons

            c.  Simple Science Knowledge: Phases of the Moon

            d.  Probability Estimates, Problem 1

             e.  Probability Estimates, Problem 2

            f.  Probability Estimates, Problem 3

            g.  Probability Estimates, Problem 4

             h.  Probability Estimates, Problem 5-7

            f.  Probability Estimates, Problem 3

      2.  Mechanisms & Research

            a.  Mistaking Coarse-Grained Understanding for Fine-Grained Understanding

                  i.  Lawson's (2006) Draw-A-Bike Task

                  ii.  Schneps (1989): Some Scientific Misunderstandings:  Seasons & Moon Phases

                  iii.  Čech:  Discourse Constraints?

                  iv.  Vincente & Brewer (1993)

                  vi.  Rozenblit: Why oh why?

             b.  Attributing Knowledge Based On External Events

                  i.  Valins (1966):  My Heart Beats For You

                  ii.  Wolf & McFall (2006):  And You're Not So Bad Yourself

                  iii.  Moore (2005): Which Are The Good Computer Games?

            c.  Just-So Stories: Rationalization

                  i.  Gazzaniga:  Split-Brain Patients

                  ii.  Sachs: Korsakoff's syndrome

                  iii.  Nisbett & Wilson


    B.  Kahneman & Tversky

      1.  Base Rate Neglect

            a.  Back to Tversky & Kahneman's (1982) Taxi Cab Example

            b.  Taking Base Rate into Account:  Bayes' Theorem

            c.  Are Experts Immune?  Casscells, Schoenberger, & Grayboys (1978)

            d.  Moderating Base Rate:  Frequencies vs. Probabilities (Cosmides & Tooby)

             e.  Some (of many) Explanations

                  i.  Frequentist/Evolutionary Approach (e.g., Cosmides & Tooby)

                  ii. Relevance Theory (Sperber & Wilson); Cooperative Principle (Grice)

      2.  Representativeness Heuristic

            a.  Back to Kahneman & Tversky's (1972) Example

             b.  Representativeness & The Conjunction Fallacy:  Tversky & Kahneman's (1983) Example

            c.  Representativeness & Base Rate Neglect

            d.  Relevance Theory & The Cooperative Principle 

      3.  Availability Heuristic

            a.  Back to Kahneman & Tversky's (1972) Example

             b.  Lichtenstein et al. (1978):  Mortality Likelihoods

            c.  Support Theory:  Tversky & Koehler (1994)

            d.  Decision Frames (Tversky & Kahneman, 1981) 

                  i.  Dunegan (1983)

                  ii. Possible Explanations of Framing & Support Effects?

     C.  And Two Final Practical Points

       1.  Kahneman & Tversky's Planning Fallacy (e.g., Buehler, Griffin, & MacDonald, 1997)

       2.  Implications for Juries

            a.  Using Schemas to Make Sense of Events (e.g., Pennington & Hastie's (1991) Story Model)

             b.  Calculating Probabilities That Something Happened

            c.  Relying on Availability

            d.  etc.