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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Modelling decision-making biases.

Ettore Cerracchio1, Steven Miletić1, Birte U Forstmann1

  • 1Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.

Frontiers in Computational Neuroscience
|November 6, 2023
PubMed
Summary
This summary is machine-generated.

Mathematical cognitive models, specifically evidence accumulation models, offer a more comprehensive understanding of decision-making biases than statistical models. These models incorporate response time, providing deeper insights into attention and expectation biases.

Keywords:
DDMEAMSDTattentioncognitive modellingdecision-making biasprior probability

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Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Decision science

Background:

  • Decision-making biases are integral to human behavior.
  • Statistical models offer descriptive insights but lack predictive power and can be ambiguously interpreted.
  • Mathematical cognitive models provide formal assumptions for predicting and simulating decision processes.

Purpose of the Study:

  • To compare the efficacy of signal detection theory and evidence accumulation models in capturing decision-making biases.
  • To investigate the influence of attention and expectation on cognitive processes.
  • To challenge existing findings regarding the impact of attention and prior probability on decision parameters.

Main Methods:

  • Comparative analysis of studies employing signal detection theory and evidence accumulation models.
  • Review of recent research on attention and expectation biases using evidence accumulation models.
  • Examination of novel findings challenging established parameter influences.

Main Results:

  • Evidence accumulation models provide a more complete account of decision-making biases by incorporating response time data.
  • Attention influences not only the speed of evidence accumulation but also non-decision time.
  • Prior probability affects not only the starting point but also the drift rate of evidence accumulation.

Conclusions:

  • Evidence accumulation models are superior to statistical models for understanding decision-making biases.
  • Novel findings necessitate a revised understanding of how attention and prior probability modulate cognitive decision parameters.