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Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
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Updated: Jun 25, 2025

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Using Drift Diffusion and RL Models to Disentangle Effects of Depression On Decision-Making vs. Learning in the

Daniel G Dillon1,2, Emily L Belleau1,2, Julianne Origlio3

  • 1Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont MA, USA.

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|May 22, 2024
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Summary

Major Depressive Disorder (MDD) affects decision-making in the Probabilistic Reward Task (PRT). Bupropion responders showed distinct pre-treatment biases, suggesting potential for PRT in predicting treatment outcomes.

Keywords:
anhedoniacomputational modelingdecision-makingdepressionplaceboreward

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Psychology

Background:

  • Major Depressive Disorder (MDD) impacts reinforcement learning (RL) and decision-making.
  • The Probabilistic Reward Task (PRT) is a key tool for studying these effects in MDD.

Purpose of the Study:

  • To analyze PRT metrics in unmedicated adults with MDD.
  • To evaluate computational models of decision-making in MDD.
  • To determine if PRT metrics predict treatment response to bupropion or placebo.

Main Methods:

  • Detailed analysis of PRT metrics: response bias and discriminability.
  • Psychometric analysis of two computational models: Hierarchical Drift Diffusion Model (HDDM) and a belief model.
  • Correlation of PRT metrics and model parameters with treatment response.

Main Results:

  • Response bias correlated with response time; reward totals depended on discriminability.
  • The HDDM accurately captured behavior with good psychometric properties.
  • The belief model better predicted response bias evolution but had weaker psychometrics.
  • Limited sample size constrained predictive utility.
  • Bupropion responders exhibited greater pre-treatment HDDM starting point bias.

Conclusions:

  • PRT metrics and computational models offer insights into MDD-related decision-making deficits.
  • Pre-treatment HDDM starting point bias may predict bupropion response in MDD.
  • Further research with larger samples is needed to confirm PRT's predictive utility.