IX.  Categorical Knowledge

    A.  The Importance Of Categorization

        1.  Efficiency via Abstraction

        2.  Recognition of Novelty

        3.  Embodying Similarity Relationships

     B.  Classical View

        1.  Simple Categories & H-Theory

            a.  Types

            b.  Blank Trials Procedure

            c.  Learning Strategies

        2.  Complex Conjunctive Categories

            a.  Selection vs. Reception

            b.  Learning Strategies

            c.  Factors Affecting Acquisition

     C.  Other Types Of Well-Defined Categories

        1.  Types

        2.  Bourne's Work on Learning Difficulty

     D.  Probabilistic (Fuzzy) Categories

        1.  Reasons for Claiming Ill-Defined Categories

            a.  Unclear Cases (Fuzzy Boundaries)

            b.  Graded Structure

            c.  Context Effects (Labov)

            d.  Defining Feature Problem (Wittgenstein)

        2.  Prototype Categories (Rosch)

            a.  Finding the Prototype:  Family Resemblance

            b.  Creating Prototypes:  Posner & Keele

            c.  Prototypes & Categorization

            d.  Levels of Categories & Primacy of the Basic Level

        3.  Exemplar Models (Medin & Schaffer;  Estes;  Reed)

            a.  Classification Rules

                ii.  Average Similarity

                iii.  Feature Frequency

                iv.  Prototype (non-exemplar)

            b.  Estes' Model

               ii.  Computing SimToCat (Similarity of an Exemplar to the Category:  Typicality)

     E.  Context & Categorization

        1.  Barsalou & Sewell:  Points of View

        2.  Roth & Shoben:  Restructuring versus Refocusing

        3.  Medin & Shoben:  Changing Similarity Relationships:  A Theory-Based Approach to Categorization

        4.  Ahn & Kim:  Testing a Theory View Against a Prototype View By Presenting/Omitting Central Causal Features