Interactions between emotion and action in the brain

Abstract

A growing literature supports the existence of interactions between emotion and action in the brain, and the central participation of the anterior midcingulate cortex (aMCC) in this regard. In the present functional magnetic resonance imaging study, we sought to investigate the role of self-relevance during such interactions by varying the context in which threating pictures were presented (with guns pointed towards or away from the observer). Participants performed a simple visual detection task following exposure to such stimuli. Except for voxelwise tests, we adopted a Bayesian analysis framework which evaluated evidence for the hypotheses of interest, given the data, in a continuous fashion. Behaviorally, our results demonstrated a valence by context interaction such that there was a tendency of speeding up responses to targets after viewing threat pictures directed towards the participant. In the brain, interaction patterns that paralleled those observed behaviorally were observed most notably in the middle temporal gyrus, supplementary motor area, precentral gyrus, and anterior insula. In these regions, activity was overall greater during threat conditions relative to neutral ones, and this effect was enhanced in the directed towards context. A valence by context interaction was observed in the aMCC too, where we also observed a correlation (across participants) of evoked responses and reaction time data. Taken together, our study revealed the context-sensitive engagement of motor-related areas during emotional perception, thus supporting the idea that emotion and action interact in important ways in the brain. ______________________________________________________________________________________________________

Can the early visual processing of others’ actions be related to social power and dominance?

“…As a general strategy to analyze our data, we opted for the Bayesian statistical analysis framework (for an introduction, see McElreath, 2020). Compared to null hypothesis significance testing, Bayesian analyses do not rely on p-values and statistical significance (Benjamin et al, 2018;Lima Portugal et al, 2020;McShane et al, 2019), but report P(θ | data), the probability distribution of the model's parameters (or quantities of interest derived from them) that are consistent with the model, observed data and prior information. Here, we summarize the uncertainty in our inference results by reporting the 95% credible intervals (95% CrI; 2.5%-97.5% quantiles) of the quantities of interest as well as the probability (P+ or P-) of the quantities of interest θ being greater or lower than 0, P+ = P(θ > 0 | data) or P-= P(θ < 0 | data).…”