How mood-related physiological states bias economic decisions.

Area of Science:

• Decision-making science
• Affective neuroscience
• Behavioral economics

Background:

• Human decision-making deviates from rational norms due to various biases.
• Incidental affective states, such as mood variations, are key factors influencing choices.
• Previous computational models linked mood to a predisposition for facing costs and seeking rewards.

Purpose of the Study:

• To generalize a computational model of mood's effect on economic decisions.
• To investigate how induced happiness and sadness influence trade-offs between costs (risk, delay, effort) and financial rewards.
• To validate the model using self-report and physiological measures of mood.

Main Methods:

• Conducted exploratory and confirmatory studies with 94 participants.
• Induced specific mood states (happiness, sadness).
• Assessed mood via self-reports and physiological measures (facial expression valence, arousal).
• Utilized gaze tracking during economic decision-making tasks.
• Applied a computational model to explain choice biases.

Main Results:

• Transitory mood states consistently biased economic decisions.
• A mood-scaled bonus in the computational model explained the choice bias across different cost types (risk, delay, effort).
• Gaze tracking revealed an early, mood-congruent preference driving the choice bias.

Conclusions:

• Objective measures of affective states can predict irrational choices.
• Mood influences economic decisions by altering the perceived value of options.
• The developed computational model provides a robust framework for understanding mood-driven decision biases.