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A pattern recognition account of decision making

D W Massaro1

  • 1Program in Experimental Psychology, University of California, Santa Cruz 95064.

Memory & Cognition
|September 1, 1994
PubMed
Summary
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This study suggests decision-making, like pattern recognition, is optimal. The fuzzy logical model of perception (FLMP) accurately describes how people combine information, challenging the idea of non-optimal judgment in tasks like the Linda problem.

Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Pattern Recognition

Background:

  • Perceivers optimally integrate multiple information sources in pattern recognition.
  • Previous research suggests decision-making is often non-optimal, citing the conjunction fallacy (e.g., Linda problem).
  • The conjunction fallacy appears to violate probability theory.

Purpose of the Study:

  • To test if judgment tasks, like the Linda problem, are interpreted as pattern recognition tasks.
  • To determine if the fuzzy logical model of perception (FLMP) can accurately describe these judgments.
  • To investigate similarities between pattern recognition and decision-making processes.

Main Methods:

  • Experiment 1: Extended the Linda task with a factorial design (5 vocations, 5 avocations).

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  • Experiment 2: Used two fictitious people (Linda, Joan) with both ratings and categorization judgments.
  • Applied the fuzzy logical model of perception (FLMP) and a simple probability model for analysis.

    • Main Results:

    • FLMP accurately described probability ratings in Experiment 1, outperforming a simple probability model.
    • In Experiment 2, FLMP and information averaging both described ratings well.
    • FLMP provided a better description of categorization judgments in Experiment 2.

    Conclusions:

    • Judgment tasks may be interpreted as pattern recognition tasks.
    • Probability judgments appear optimal, similar to pattern-recognition judgments.
    • Findings reveal significant overlap between pattern recognition and decision-making mechanisms.