UC San Diego

Background

Real-time control of goal-directed actions requires continuous adjustments in response to both current error (i.e., distance from goal state) and predicted future error. Proportion-integral-derivative control models, which are extensively used in the automated control of industrial processes, formalize this intuition. Previous computational approaches to anxiety have separately addressed behavioral inhibition and exaggerated error processing, but a proportion-integral-derivative control approach that decomposes error processing into current and anticipated error could integrate these accounts and extend them to a real-time sensorimotor control domain.

Methods

We applied a simplified proportion-derivative control model to a virtual driving task in a transdiagnostic psychiatric sample of 317 individuals and computed a drive parameter (weighting of current error) and a damping parameter (weighting of the rate of change of error, enabling adjustment based on future error).

Results

Self-reported fear, but not negative affect, was selectively associated with lower drive and lower damping. Those individuals that were characterized by lower drive and damping also exhibited lower caudal anterior cingulate cortex, but not insula, volume in a structural magnetic resonance imaging analysis.

Conclusions

The proportion-derivative control approach reveals that fear is specifically associated with reduced weighting of current error and overestimation of future error, resulting in both approach inhibition and overcorrecting overshoots around a goal state.