Neural representations of social valence bias economic interpersonal choices

Abstract

<jats:title>Abstract</jats:title><jats:p>Prior personal information is highly relevant during social interactions. Such knowledge aids in the prediction of others, and it affects choices even when it is unrelated to actual behaviour. In this investigation, we aimed to study the neural representation of positive and negative personal expectations, how these impact subsequent choices, and the effect of mismatches between expectations and encountered behaviour. We employed functional Magnetic Resonance Imaging in combination with a version of the Ultimatum Game (UG) where participants were provided with information about their partners’ moral traits previous to their fair or unfair offers. Univariate and multivariate analyses revealed the implication of the supplementary motor area (SMA) and inferior frontal gyrus (IFG) in the representation of expectations about the partners in the game. Further, these regions also represented the valence of expectations, together with the ventromedial prefrontal cortex (vmPFC). Importantly, the performance of multivariate classifiers in these clusters correlated with a behavioural choice bias to accept more offers following positive descriptions, highlighting the impact of the valence on the expectations on participants’ economic decisions. Altogether, our results suggest that expectations based on social information guide future interpersonal decisions and that the neural representation of such expectations in the vmPFC is related to their influence on behaviour.</jats:p>

Multivariate Pattern Analysis Techniques for Electroencephalography Data to Study Flanker Interference Effects

“…Applying t-test statistics on multivariate results is an unsuitable approach to draw statistical inferences at the group level. 43 For that reason, the use of cluster-based non-parametric permutation methods is widespread, not only in fMRI [44][45][46][47] but more recently also in M/EEG studies. [48][49][50][51] In our study, a nonparametric cluster-based permutation approach, proposed in 43 for fMRI data, was adapted and implemented for the statistical analysis.…”