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Cognitive Control of Working Memory: A Model-Based Approach.

Russell J Boag1, Niek Stevenson1,2, Roel van Dooren2

  • 1Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands.

Brain Sciences
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

Cognitive control processes in working memory (WM) decision-making incur speed and accuracy costs. These costs arise from delays outside decision-making and inhibiting automatic responses, influencing how individuals make choices.

Keywords:
cognitive controldiffusion decision modelreference-back tasksequential samplingworking memory

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Decision Science

Background:

  • Working memory (WM) is crucial for decision-making, relying on cognitive control to manage information flow.
  • Cognitive control processes, while essential, introduce performance costs in WM-based decisions.
  • Understanding these costs is key to explaining WM's role in decision-making.

Purpose of the Study:

  • To investigate the underlying mechanisms of performance costs in WM-based decision-making.
  • To differentiate between various explanations for observed empirical costs in WM control.
  • To model the impact of cognitive control on decision parameters.

Main Methods:

  • Utilized the reference-back task as a standard measure of WM control.
  • Employed evidence accumulation modeling to analyze performance costs.
  • Tested competing theoretical explanations for benchmark empirical costs.

Main Results:

  • Performance costs were attributed to both non-decision time delays and reduced drift rates (inhibited prepotent responses).
  • WM control trials showed longer non-decision times and lower drift rates compared to baseline.
  • Individuals adopted more conservative response thresholds when anticipating WM updates.

Conclusions:

  • Cognitive control in WM decision-making involves trade-offs affecting speed and accuracy.
  • Evidence accumulation modeling provides a framework for dissecting WM control costs.
  • This approach offers insights into cognitive control mechanisms within WM-dependent decision processes.