Quantifying the immediate computational effects of preceding outcomes on subsequent risky choices

Hayley R. Brooks,Peter Sokol-Hessner

doi:10.1038/s41598-020-66502-y

Abstract

Forty years ago, prospect theory introduced the notion that risky options are evaluated relative to their recent context, causing a significant shift in the study of risky monetary decision-making in psychology, economics, and neuroscience. Despite the central role of past experiences, it remains unclear whether, how, and how much past experiences quantitatively influence risky monetary choices moment-to-moment in a nominally learning-free setting. We analyzed a large dataset of risky monetary choices with trial-by-trial feedback to quantify how past experiences, or recent events, influence risky choice behavior and the underlying processes. We found larger recent outcomes both negatively influence subsequent risk-taking and positively influence the weight put on potential losses. Using a hierarchical Bayesian framework to fit a modified version of prospect theory, we demonstrated that the same risks will be evaluated differently given different past experiences. The computations underlying risky decision-making are fundamentally dynamic, even if the environment is not.

Highlights

Forty years ago, prospect theory introduced the notion that risky options are evaluated relative to their recent context, causing a significant shift in the study of risky monetary decision-making in psychology, economics, and neuroscience
While this central insight led to two of the most successful components of prospect theory, the reflection effect in risk aversion and loss aversion, it is unclear how recent events quantitatively influence risky monetary decision-making, moment-to-moment
Each model varied only by the type of recent events included as additional predictor variables

Summary

Introduction

Prospect theory introduced the notion that risky options are evaluated relative to their recent context, causing a significant shift in the study of risky monetary decision-making in psychology, economics, and neuroscience. We used a hierarchical Bayesian framework to fit a modified prospect theory model, simultaneously capturing individual differences and group-level effects across a large sample of participants to identify whether and how temporal context influences the processes underlying risky decision-making. This model captures several qualitatively distinct and dissociable processes that contribute to risky choice behavior: loss aversion, risk aversion, choice consistency, and decision bias (see below for more details on each).

Methods

Results

Conclusion