RT Journal Article
SR Electronic
T1 Revisiting the Neural Architecture of Decision-Making: Univariate and Multivariate Evidence for System-Based Models in Adolescence
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.11.26.400416
DO 10.1101/2020.11.26.400416
A1 João F. Guassi Moreira
A1 Adriana S. Méndez Leal
A1 Yael H. Waizman
A1 Natalie Saragosa-Harris
A1 Emilia Ninova
A1 Jennifer A. Silvers
YR 2020
UL http://biorxiv.org/content/early/2020/11/27/2020.11.26.400416.abstract
AB System-based theories are a popular approach to explaining the psychology of human decision making. Such theories posit that decision-making is governed by interactions between different psychological processes that arbitrate amongst each other for control over behavior. To date, system-based theories have received inconsistent support at the neural level, leading some to question their veracity. Here we examine the possibility that prior attempts to evaluate system-based theories have been limited by their reliance on predicting brain activity from behavior, and seek to advance evaluations of system-based models through modeling approaches that predict behavior from brain activity. Using within-subject decision-level modeling of fMRI data from a risk-taking task in a sample of over 2000 decisions across 51 adolescents—a population in which decision-making processes are particularly dynamic and consequential—we find support for system-based theories of decision-making. In particular, neural activity in lateral prefrontal cortex and a multivariate pattern of cognitive control both predicted a reduced likelihood of making a risky decision, whereas increased activity in the ventral striatum—a region typically associated with valuation processes—predicted a greater likelihood of engaging in risk-taking. These results comprise the first formalized within-subjects neuroimaging test of system-based theories, garnering support for the notion that competing systems drive decision behaviors.Significance Statement Decision making is central to adaptive behavior. While dominant psychological theories of decision-making behavior have found empirical support, their neuroscientific implementations have received inconsistent support. This may in part be due to statistical approaches employed by prior neuroimaging studies of system-based theories. Here we use brain modeling—an approach that predicts behavior from brain activity—of univariate and multivariate neural activity metrics to better understand how neural components of psychological systems guide decision behavior. We found broad support for system-based theories such that that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.Competing Interest StatementThe authors have declared no competing interest.