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Predicting affective choice.

Gaurav Suri¹, Gal Sheppes, James J Gross

¹Department of Psychology, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA. rsuri@stanford.edu

Journal of Experimental Psychology. General

|August 29, 2012

Summary

This summary is machine-generated.

This study developed a model to predict choices using affect, specifically valence and arousal. The model successfully predicted decisions involving images and news headlines, demonstrating its broad applicability in understanding decision-making processes.

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Area of Science:

Cognitive Psychology
Neuroeconomics
Decision Science

Background:

Affective states are increasingly acknowledged as crucial for decision-making.
The predictive capacity of affect for specific choices remains an open question in research.

Purpose of the Study:

To develop and validate a model that predicts choice based on affective dimensions.
To assess the model's performance against established theoretical frameworks.

Main Methods:

Developed a predictive model using an image rating task, quantifying valence and arousal.
Tested the model on preference-based selections and risk-reward trade-off decisions.
Validated the model's ecological validity by predicting choices between news articles.

Main Results:

The developed model accurately predicted choices in image-based decisions and risk-reward scenarios.
The affective model outperformed alternative theoretical models in predictive accuracy.
The model demonstrated ecological validity by predicting real-world choices.

Conclusions:

Affect, characterized by valence and arousal, can reliably predict human choice.
The developed model offers a powerful tool for understanding and predicting decision-making across various contexts.
Findings have significant implications for neuroeconomics and judgment and decision-making research.