A Novel Approach-Avoidance Task to Study Decision Making Under Outcome Uncertainty.

Area of Science:

• Cognitive Neuroscience
• Decision Science
• Behavioral Economics

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:

• Development of a Probabilistic Approach Avoidance Task (PAAT).
• Behavioral characterization using sequential sampling models.
• Participant (N=34) choices between options with variable reward probabilities and aversive outcome probabilities.

Main Results:

• Participants prioritized options maximizing reward likelihood and minimizing aversive outcome likelihood.
• The weighting of rewards versus aversions differed based on outcome alignment (congruent vs. incongruent trials).
• Computational modeling indicated that information accumulation rates reflected the relative influence of rewards and aversions.

Conclusions:

• Sequential sampling models reliably captured behavioral data, validating their utility.
• Findings enhance understanding of how motivational conflict, outcome type, and uncertainty shape approach-avoid decisions.