Models of Affective Decision Making: How Do Feelings Predict Choice?

Area of Science:

• Cognitive Psychology
• Neuroscience
• Behavioral Economics

Background:

• Prospect theory, a cornerstone of psychology, assumes feelings drive decision-making.
• However, the precise mechanisms transforming feelings into choices remain largely unexplored.
• Understanding this process is crucial for explaining human behavior and choice architecture.

Purpose of the Study:

• To develop and test a computational model that predicts choices based on feelings.
• To differentiate the unique contribution of feelings to decisions beyond value-based models.
• To investigate the role of emotional asymmetry in loss aversion.

Main Methods:

• Development of a novel computational model incorporating 'feelings' as a predictor of choice.
• Comparison of the feeling-based model against established value-based models (e.g., prospect theory).
• Analysis of how feelings about gains and losses are weighted in decision-making.

Main Results:

• The feeling-based model demonstrated superior predictive accuracy compared to value-based models.
• Feelings were found to contribute uniquely to decisions, independent of outcome value.
• Loss aversion in choice stemmed from asymmetric weighting of feelings, not asymmetric feelings themselves.

Conclusions:

• Feelings play a distinct and significant role in guiding choices, beyond their objective value.
• The transformation of feelings into decisions involves an asymmetric weighting process, particularly for losses.
• This research offers novel insights into the computational underpinnings of emotion-guided decision-making.