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Generality of likelihood ratio decisions.

Murray Glanzer1, Andrew Hilford2, Kisok Kim3

  • 1Department of Psychology, New York University, USA; Center for Neural Science, New York University, USA.

Cognition
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PubMed
Summary

Decisions across various domains, including memory and sensory events, consistently follow likelihood ratio principles. This research supports the idea that decision-making is fundamentally optimal, challenging views emphasizing inefficiency.

Keywords:
Bayesian Decision TheoryDecision theoryDecision-makingLikelihood ratioMirror effectRecognition memorySignal Detection Theory

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Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Psychology

Background:

  • Signal Detection Theory, a form of Bayesian Decision Theory, assumes decisions rely on likelihood ratios.
  • Previous work by Glanzer et al. (2009) validated this in recognition memory tasks, identifying three key regularities.

Purpose of the Study:

  • To test the generalizability of likelihood ratio regularities beyond recognition memory.
  • To investigate if these regularities apply to diverse decision-making contexts.

Main Methods:

  • Analysis of existing decision-making studies across various fields.
  • Conducting new experimental studies in semantic memory and mental rotation.

Main Results:

  • Evidence supports the generalization of likelihood ratio regularities to sensory events, reasoning, and forecasting.
  • New experiments in semantic memory and mental rotation further confirm these decision-making regularities.

Conclusions:

  • Decision-making processes generally adhere to optimal likelihood ratio principles.
  • Findings challenge the prevalent view of decision-making inefficiencies, highlighting its inherent optimality.