Computational Modeling of Uncertainty and Volatility Beliefs in Escape-Avoidance Learning: Comparing Individuals with...

Area of Science:

• Cognitive neuroscience
• Computational psychiatry
• Suicidology

Background:

• Individuals with suicidal ideation (SI) tend to actively escape negative emotional states.
• Previous computational models suggest this active escape tendency.
• This study investigates belief updating and uncertainty in individuals with SI during escape tasks.

Purpose of the Study:

• To examine how individuals with a history of SI update beliefs about action-outcome contingencies and uncertainty.
• To compare belief updating strategies between individuals with and without SI history in an escape task.
• To identify cognitive mechanisms underlying escape behavior in SI.

Main Methods:

• A negative reinforcement task was administered to undergraduate students with (n=58) and without (n=62) a history of SI.
• A Hierarchical Gaussian Filter (HGF) modeled trial-by-trial belief updating, uncertainty, and learning rates.
• Bayesian mixed-effects models analyzed the impact of SI history and trial type on HGF parameters.

Main Results:

• Participants with SI history showed increased rigidity in contingency beliefs and perceived environmental stability.
• Despite rigidity, individuals with SI history maintained higher uncertainty during escape trials.
• Higher dynamic learning rates were observed in participants with SI history during escape trials.

Conclusions:

• Individuals with SI history demonstrate cognitive inflexibility and hyper-reactivity to prediction errors in escape contexts.
• This cognitive profile may impair adaptation to environmental changes and stress regulation.
• These findings offer insights into the cognitive mechanisms relevant to suicide risk.