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Computational Mechanisms of Approach-Avoidance Conflict Predictively Differentiate Between Affective and Substance

Marishka M Mehta1,2, Navid Hakimi1, Orestes Pena1

  • 1Laureate Institute for Brain Research, Tulsa, OK, US.

Computational Psychiatry (Cambridge, Mass.)
|September 12, 2025
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Summary

Computational modeling of decision-making under approach-avoidance conflict (AAC) can differentiate psychiatric disorders. These findings highlight computational mechanisms as potential targets for novel treatments in psychopathology.

Keywords:
AnxietyApproach-Avoidance ConflictComputational ModelingDepressionPredictive ClassificationSubstance Use Disorders

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Area of Science:

  • Computational psychiatry
  • Neuroscience of decision-making
  • Machine learning in clinical diagnosis

Background:

  • Psychiatric disorders exhibit significant heterogeneity and comorbidity, complicating treatment.
  • Computational modeling offers a promising avenue for understanding and differentiating these conditions.
  • Approach-avoidance conflict (AAC) decision-making involves navigating choices with both positive and negative consequences.

Purpose of the Study:

  • To investigate the utility of computational mechanisms from AAC tasks in differentiating psychiatric disorders.
  • To replicate and extend previous findings on decision uncertainty (DU) and emotion conflict (EC) in clinical populations.
  • To assess the classification performance of computational measures using machine learning.

Main Methods:

  • Pre-registered computational modeling analyses of AAC tasks in 480 individuals.
  • Replication of cross-sectional and longitudinal findings from a prior dataset (N=478).
  • Stacked machine learning approach for out-of-sample classification between diagnostic groups.

Main Results:

  • Replicated findings that decision uncertainty (DU) and emotion conflict (EC) differentiate depression, anxiety, substance use disorders, and healthy controls.
  • Achieved above-chance classification accuracy (>0.688) between affective and substance use disorders using computational measures.
  • Demonstrated classification utility irrespective of comorbid conditions.

Conclusions:

  • Computational measures derived from AAC tasks possess predictive utility for clinical differentiation.
  • These findings suggest distinct computational mechanisms underlying different psychopathological conditions.
  • Computational psychiatry may reveal novel therapeutic targets for psychiatric disorders.