Common and distinct neural substrates of the money illusion in win and loss domains

Valence precedes value in neural encoding of prediction error

Event-related potentials that follow feedback in reinforcement learning tasks have been proposed to reflect neural encoding of prediction errors. Prior research has shown that in the interval of 240-340 ms multiple different prediction error encodings appear to co-occur, including a value signal carrying signed quantitative prediction error and a valence signal merely carrying sign. The effects used to identify these two encoders, respectively a sign main effect and a sign × size interaction, do not reliably discriminate them. A full discrimination is made possible by comparing tasks in which the reinforcer available on a given trial is set to be either appetitive or aversive against tasks where a trial allows the possibility of either. This study presents a meta-analysis of reinforcement learning experiments, the majority of which presented the possibility of winning or losing money. Value and valence encodings were identified by conventional difference wave methodology but additionally by an analysis of their predicted behavior using a Bayesian analysis that incorporated nulls into the evidence for each encoder. The results suggest that a valence encoding, sensitive only to the available outcomes on the trial at hand precedes a later value encoding sensitive to the outcomes available in the wider experimental context. The implications of this for modeling computational processes of reinforcement learning in humans are discussed.

Data Triangulation in Consumer Neuroscience: Integrating Functional Neuroimaging With Meta-Analyses, Psychometrics, and Behavioral Data

This article reviews a wide range of functional magnetic resonance imaging (fMRI) studies conducted in the field of consumer neuroscience to (1) highlight common interpretative approaches of neuroimaging data (i.e., forward inference and reverse inference), (2) discuss potential interpretative issues associated with these approaches, and (3) provide a framework that employs a multi-method approach aimed to possibly raise the explanatory power and, thus, the validity of functional neuroimaging research in consumer neuroscience. Based on this framework, we argue that the validity of fMRI studies can be improved by the triangulation of (1) careful design of neuroimaging studies and analyses of data, (2) meta-analyses, and (3) the integration of psychometric and behavioral data with neuroimaging data. Guidelines on when and how to employ triangulation methods on neuroimaging data are included. Moreover, we also included discussions on practices and research directions that validate fMRI studies in consumer neuroscience beyond data triangulation.