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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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Related Experiment Video

Updated: Jan 15, 2026

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
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A computational approach to understanding effort-based decision-making in depression.

Vincent Valton1, Anahit Mkrtchian2, Madeleine Moses-Payne3

  • 1Institute of Cognitive Neuroscience, https://ror.org/02jx3x895University College London, London, UK.

Psychological Medicine
|October 8, 2025
PubMed
Summary

Depression impairs motivation, but computational analysis reveals reduced effort-acceptance bias, not altered reward sensitivity, underlies this in depressed individuals. This bias may be a trait-like factor for treatment targets.

Keywords:
anhedoniacomputational psychiatrydepressioneffort-based decision-makingmotivation

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry
  • Affective Neuroscience

Background:

  • Motivational dysfunction is a key feature of depression, impacting daily functioning.
  • The cognitive underpinnings of impaired motivation and their persistence post-remission require further investigation.
  • Effort-based decision-making, analyzed computationally, offers a framework for understanding motivation.

Purpose of the Study:

  • To computationally analyze decision-making mechanisms related to effort and reward in depression.
  • To investigate whether cognitive impairments in motivation persist after depression remission.
  • To identify specific computational drivers of motivational deficits in depression.

Main Methods:

  • Utilized the Apple Gathering Task to assess effort-based decision-making with calibrated effort and reward levels.
  • Performed comprehensive computational analysis of decision-making patterns.
  • Compared current depressed, remitted depressed, and healthy control groups (with and without family history of depression).

Main Results:

  • Identified four core computational mechanisms: effort acceptance bias, reward sensitivity, and linear/quadratic effort sensitivity.
  • Depressed groups exhibited lower willingness to exert effort.
  • Computational analysis indicated lower effort-acceptance bias, not altered effort or reward sensitivity, drove this difference.

Conclusions:

  • Provided insight into computational mechanisms of motivational dysfunction in depression.
  • Lower willingness to exert effort, driven by reduced effort-acceptance bias, may be a trait-like factor.
  • This bias presents a potential target for depression treatment and prevention strategies.