Lecture19-Induction

# Lecture19-Induction - Lecture 19: Induction Fall 2009 Prof....

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1 Lecture 19: Induction Fall 2009 Prof. Lombrozo Overview of today’s class: 1. Preliminaries: evaluations & concepts 2. Inductive reasoning - Confirmation bias - Representativeness Heuristic - Base-rate neglect Reminder: Midterm on Wed – bring scantron! Evaluation Summary I 18.2 Evaluation Summary II General agreement: – Lectures clear and like demos, but sometimes too fast – Course/website well organized – Textbook not very clear/exciting Some agreement: – Exams difficult: prefer shorter/simpler MC or short answer – Sections could be more structured and involve more review Disagreement: – Value of CogLab – Value of Sections Other issues: – Overlap with CogSci 1 18.3

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2 19.4 Concepts Overview Theory Main idea Problems Classical View Definition with necessary & sufficient conditions Few definitions Graded membership Typicality effects Prototype Theories Statistical average, “summary representation” Problems w/similarity Knowledge effects Prototypical primes Compositionality Exemplar theories Set of actual examples Theory-theory Relationship to other concepts & beliefs Promising, but underspecified 19.5 Concepts Overview: Example “A number evenly divisible only by 1 and itself” Classical View Exemplar Theories 2, 3, 5, 7, 11, 13, 17, 19… Prototype Theories + Exactly 2 factors + Odd number Theory-Theory Prime numbers related to many other concepts in folk mathematics, e.g. division, factorization Representations of concept PRIME NUMBER 19.6 Inductive Reasoning Unlike deduction, induction does not guarantee the truth of conclusions How are possible conclusions (“hypotheses”) evaluated? Hypotheses:
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## This note was uploaded on 10/01/2011 for the course COG SCI 10 taught by Professor Lombrozo during the Fall '09 term at University of California, Berkeley.

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Lecture19-Induction - Lecture 19: Induction Fall 2009 Prof....

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