Value-based decision-making battery: A Bayesian adaptive approach to assess impulsive and risky behavior

Area of Science:

• Behavioral Economics
• Computational Neuroscience
• Decision Science

Background:

• Accurate assessment of impulsive and risky decision-making is vital for understanding behavior and health.
• Existing tools for measuring discounting rates (temporal and probability) and loss aversion have limitations.
• There is a need for improved, adaptive algorithms to assess these decision-making constructs.

Purpose of the Study:

• To present a novel Bayesian adaptive algorithm for assessing impulsive and risky decision making.
• To improve the measurement of discounting rates and loss aversion.
• To provide a more informative and efficient method for behavioral assessment.

Main Methods:

• Developed a Bayesian adaptive algorithm based on trial-by-trial choice observations between immediate and delayed/risky options.
• Algorithm updates parameter estimates and generates informative offers from the indifference point.
• Simulated experiments and implemented the algorithm as an experimental battery for temporal and probability discounting and loss aversion.

Main Results:

• The algorithm demonstrated reproducibility of parameters for individual assessments in simulations.
• Group-level analysis showed reliability of the estimation procedure.
• Initial testing on a healthy participant sample successfully measured temporal and probability discounting rates and loss aversion.

Conclusions:

• The Bayesian adaptive algorithm offers a robust and efficient method for assessing decision-making constructs.
• This tool can advance research in behavioral economics, neuroscience, and clinical psychology.
• Further application of this algorithm can refine our understanding of individual differences in decision making.