Brain structure correlates of social information use: an exploratory machine learning approach.

ABSTRACT:

Introduction

Individual differences in social learning impact many important decisions, from voting behavior to polarization. Prior research has found that there are consistent and stable individual differences in social information use. However, the underlying mechanisms of these individual differences are still poorly understood.

Methods

We used two complementary exploratory machine learning approaches to identify brain volumes related to individual differences in social information use.

Results and discussion

Using lasso regression and random forest regression we were able to capture linear and non-linear brain-behavior relationships. Consistent with previous studies, our results suggest there is a robust positive relationship between the volume of the left pars tr

SUBMITTER: de Groot ECS 

PROVIDER: S-EPMC11250561 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature