Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a...

Area of Science:

• Neuroscience
• Computational Psychiatry
• Decision Science

Background:

• Imbalances in approach-avoidance conflict (AAC) decision-making are central to psychiatric disorders.
• Distinguishing decision uncertainty from emotional conflict in AAC is crucial for understanding these disorders.

Purpose of the Study:

• To use computational modeling to examine decision uncertainty and emotional conflict in AAC.
• To investigate if these factors differ between healthy controls and individuals with psychiatric disorders.

Main Methods:

• Utilized a validated AAC task with 478 participants, including healthy controls and individuals with substance use or depression/anxiety disorders.
• Employed an active inference model to estimate individual decision uncertainty and emotional conflict parameters.
• Conducted analyses on the full sample and a propensity-matched subsample for age and intelligence.

Main Results:

• The computational model demonstrated high accuracy (72%) and correlated with behavioral and self-report measures.
• Individuals with depression/anxiety and substance use disorders exhibited higher decision uncertainty compared to controls.
• Individuals with substance use disorders showed lower emotional conflict in the full sample.

Conclusions:

• Reduced confidence in decision-making (decision uncertainty) may be a key factor in maladaptive AAC behaviors in psychiatric populations.
• These findings differentiate between uncertainty and emotional conflict, offering a more nuanced understanding of AAC deficits.