A learning mechanism shaping risk preferences and a preliminary test of its relationship with psychopathic traits

Takeyuki Oba,Kentaro Katahira,Hideki Ohira

doi:10.1038/s41598-021-00358-8

Abstract

People tend to avoid risk in the domain of gains but take risks in the domain of losses; this is called the reflection effect. Formal theories of decision-making have provided important perspectives on risk preferences, but how individuals acquire risk preferences through experiences remains unknown. In the present study, we used reinforcement learning (RL) models to examine the learning processes that can shape attitudes toward risk in both domains. In addition, relationships between learning parameters and personality traits were investigated. Fifty-one participants performed a learning task, and we examined learning parameters and risk preference in each domain. Our results revealed that an RL model that included a nonlinear subjective utility parameter and differential learning rates for positive and negative prediction errors exhibited better fit than other models and that these parameters independently predicted risk preferences and the reflection effect. Regarding personality traits, although the sample sizes may be too small to test personality traits, increased primary psychopathy scores could be linked with decreased learning rates for positive prediction error in loss conditions among participants who had low anxiety traits. The present findings not only contribute to understanding how decision-making in risky conditions is influenced by past experiences but also provide insights into certain psychiatric problems.

Highlights

People tend to avoid risk in the domain of gains but take risks in the domain of losses; this is called the reflection effect
This study aimed to examine the learning processes of acquiring risk preferences under conditions of gain and loss and to investigate relationships between reinforcement learning (RL) parameters and psychopathic traits
Our data revealed that participants were likely to learn the values of risky options from the effect of different prediction error (PE) based on nonlinear subjective values

Summary

Introduction

People tend to avoid risk in the domain of gains but take risks in the domain of losses; this is called the reflection effect. In situations where possible outcomes and their probabilities are explicitly given (i.e., a descriptive choice), people generally tend to avoid risky options in the domain of gains but take the risky option in the domain of losses[1,2] This preference pattern is called the reflection effect, and prospect theory, which is the most influential theory for decision-making under risks, uses nonlinear subjective value to account for it1. Several theories proposed to explain risk preference have assumed that the amount of change in the subjective value of outcomes is nonlinear[1,8], Niv, Edlund, Dayan & O’Doherty[9] reported that in a reward-based learning task, an RL model explained risk sensitivities by the contrast between the magnitude of learning rates for positive and negative PEs rather than by nonlinear subjective value. Niv et al.[9] tested RL models that contained either the learning rates for signed PEs or the subjective utility parameter; they did not examine whether these two sets of learning parameters can together influence risk sensitivities

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