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Decision by sampling implements efficient coding of psychoeconomic functions.

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Decision by sampling (DbS) theory explains subjective value using memory recall ranks. A new generalization, grounded in efficient coding principles, broadens its scope to include cognitive noise and context sensitivity in decision-making.

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Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Behavioral Economics

Background:

  • Classic decision theories assume stable preferences, limiting explanations for context-dependent choices.
  • The theory of decision by sampling (DbS) posits subjective value is determined by an attribute's rank in a memory-retrieved sample.
  • DbS accounts for context dependence beyond traditional models.

Purpose of the Study:

  • Provide a normative justification for the decision by sampling (DbS) theory.
  • Develop a generalized version of DbS based on efficient coding principles.
  • Explain how cognitive limitations and noise affect decision-making processes.

Main Methods:

  • Applied the principle of efficient coding to information representation.
  • Derived a generalized DbS model from efficient coding in noisy channels.
  • Connected the generalized DbS to range-frequency theory.

Main Results:

  • A normative justification for DbS was established using efficient coding.
  • A generalized DbS model was developed, incorporating signal smoothing for cognitive noise.
  • The generalized model explains context sensitivity variations with response category numbers.

Conclusions:

  • Efficient coding provides a normative basis for decision by sampling.
  • A generalized DbS model offers a more comprehensive account of context-dependent preferences.
  • This framework enhances understanding of cognitive limitations in decision-making and its relation to range-frequency effects.