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Confidence control for efficient behaviour in dynamic environments.

Tarryn Balsdon1,2, Marios G Philiastides3

  • 1School of Psychology and Neuroscience, University of Glasgow, Glasgow, United Kingdom. tarryn.balsdon@ens.fr.

Nature Communications
|October 21, 2024
PubMed
Summary
This summary is machine-generated.

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Confidence actively guides decision-making by moderating evidence accumulation. This new model explains human behavior in dynamic environments and links brain activity to faster, more accurate choices.

Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • Decision-making models traditionally overlook the online role of confidence.
  • Confidence is observed during decision processes, suggesting functional importance.
  • Sequential sampling models are foundational but lack dynamic adaptability.

Purpose of the Study:

  • To formulate a novel decision-making model where confidence actively moderates evidence accumulation.
  • To investigate the model's ability to adapt to dynamic sensory evidence quality.
  • To explore the neurobiological underpinnings of confidence-modulated decision-making.

Main Methods:

  • Extension of sequential sampling models incorporating online confidence control.
  • Design of a dynamic sensory environment with adaptive evidence quality.

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  • Multivariate decoding of electroencephalography (EEG) data to identify neural correlates.
  • Behavioral analysis comparing the novel model against traditional models.

    • Main Results:

    • The confidence-controlled model significantly outperforms models without confidence control in dynamic environments.
    • Human behavior in adaptive sensory environments is better described by the confidence-modulated model.
    • EEG correlates of latent model processes were successfully decoded.
    • Stronger EEG-derived confidence control correlated with enhanced decision speed and accuracy.

    Conclusions:

    • Confidence serves as an active control mechanism, enhancing decision-making efficiency.
    • The proposed model provides a neurobiologically plausible framework for confidence in decision processes.
    • Online confidence modulation is crucial for adapting to changing evidence quality.
    • This research bridges computational modeling, behavior, and neurophysiology.