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Peter R Killeen1, Thomas J Taylor2, Mario Treviño3

  • 1Department of Psychology, Arizona State University.

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Summary
This summary is machine-generated.

Optimal decision-making involves setting a criterion C to distinguish signal from noise. Deviating from the optimal criterion C* results in a loss of expected value (EV), with the severity depending on discrimination ability.

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Area of Science:

  • Cognitive Psychology
  • Decision Theory
  • Signal Detection Theory

Background:

  • The standard model in detection theory posits an optimal criterion (C*) for partitioning decisions into signal and noise.
  • Deviations from C* lead to a reduction in expected value (EV) over repeated trials.

Purpose of the Study:

  • To derive an explicit equation quantifying the loss in EV when the criterion deviates from C*.
  • To investigate how discrimination ability influences the gradient of EV loss and criterion adjustment.
  • To explore the impact of unequal signal probabilities or distribution variances on criterion dynamics and potential bias.

Main Methods:

  • Mathematical modeling of decision processes within signal detection theory.
  • Analysis of the relationship between criterion placement, expected value, and discrimination performance.
  • Development of an error correction model to explain empirical deviations from optimal criteria.

Main Results:

  • The rate of EV loss is steep with good discrimination and shallow with poor discrimination.
  • Gradients of EV loss theoretically drive the criterion (C) towards C*, with stronger influence in good discrimination contexts.
  • Asymmetric signal probabilities or variances create asymmetric "basins of attraction," leading to biased criterion adjustments.

Conclusions:

  • The proposed model offers a framework for understanding criterion dynamics and potential biases in decision-making.
  • The model accounts for empirically observed deviations from C* that are not explained by the standard signal detection model.
  • Insights are provided into factors influencing acquisition speed, response variability, and discrimination reversal.