A novel approach-avoidance task to study decision making under outcome uncertainty.

Area of Science:

• Cognitive Neuroscience
• Decision Science
• Computational Psychiatry

Background:

• Adaptive behavior requires integrating probabilistic outcomes and balancing approach-avoidance tendencies.
• The influence of outcome uncertainty on approach-avoidance decision-making dynamics remains incompletely understood.

Purpose of the Study:

• To investigate how uncertainty in positive and negative outcomes affects approach-avoid decision-making.
• To develop and validate a novel task and computational models for studying these dynamics.

Main Methods:

• Developed the Probabilistic Approach-Avoidance Task (PAAT) with variable reward and aversive outcome probabilities.
• Collected behavioral data from clinical (N=34) and non-psychiatric (N=58) samples.
• Applied sequential sampling models to characterize decision-making behavior and information accumulation.

Main Results:

• Participants prioritized options maximizing reward likelihood and minimizing aversive outcome probability.
• Decision weights for rewards and risks varied based on outcome alignment (congruent vs. incongruent trials).
• Computational modeling indicated differences in information accumulation rates underlie the influence of outcome valence and uncertainty.

Conclusions:

• Sequential sampling models effectively capture approach-avoidance decision-making under uncertainty.
• Findings elucidate the role of motivational conflict, outcome type, and uncertainty in decision processes.
• This research advances understanding of adaptive behavior and decision-making mechanisms.