A neurocognitive model of perceptual decision‐making on emotional signals

Abstract

Humans make various kinds of decisions about which emotions they perceive from others. Although it might seem like a split‐second phenomenon, deliberating over which emotions we perceive unfolds across several stages of decisional processing. Neurocognitive models of general perception postulate that our brain first extracts sensory information about the world then integrates these data into a percept and lastly interprets it. The aim of the present study was to build an evidence‐based neurocognitive model of perceptual decision‐making on others' emotions. We conducted a series of meta‐analyses of neuroimaging data spanning 30 years on the explicit evaluations of others' emotional expressions. We find that emotion perception is rather an umbrella term for various perception paradigms, each with distinct neural structures that underline task‐related cognitive demands. Furthermore, the left amygdala was responsive across all classes of decisional paradigms, regardless of task‐related demands. Based on these observations, we propose a neurocognitive model that outlines the information flow in the brain needed for a successful evaluation of and decisions on other individuals' emotions. Highlights Emotion classification involves heterogeneous perception and decision‐making tasks Decision‐making processes on emotions rarely covered by existing emotions theories We propose an evidence‐based neuro‐cognitive model of decision‐making on emotions Bilateral brain processes for nonverbal decisions, left brain processes for verbal decisions Left amygdala involved in any kind of decision on emotions

Toward a hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind.

“…Next, we overlaid our three cluster solution to maps from related meta-analyses on more 'affective' topics. For a meta-analysis on Emotion Matching (Dricu & Frühholz, 2020), that is, matching expressions of a target face and a number of other faces, overlap was clearly highest for our affective cluster. For an additional meta-analysis on Emotion Labeling (Dricu & Frühholz, 2020), we found considerable overlaps with both the affective and the intermediate cluster.…”

“…For a meta-analysis on Emotion Matching (Dricu & Frühholz, 2020), that is, matching expressions of a target face and a number of other faces, overlap was clearly highest for our affective cluster. For an additional meta-analysis on Emotion Labeling (Dricu & Frühholz, 2020), we found considerable overlaps with both the affective and the intermediate cluster. In this meta-analysis, tasks required participants to match facial, vocal or bodily expressed emotions with one label out of several alternatives.…”

Disrupting inferior frontal cortex activity alters affect decoding efficiency from clear but not from ambiguous affective speech

“…2D for each group. [54,34,10]), decided after the CG was scanned in whole. Wholebrain activations are reported at p<.005 uncorrected with k>59 voxels, equivalent to a FWE cluster correction for multiple comparisons of p<.05.…”

“…For the CG, continuous theta burst stimulation (cTBS) was administered for 40 seconds over the vertex (MNI xyz [0, -20, 80]) because it was previously successfully used as a good control site for TMS protocols (34). For the EG, cTBS was administered to the right inferior frontal cortex (rIFC; MNI xyz [54,34,10]), for 40 seconds as well (see detailed TMS procedure below). As soon as the TMS procedure ended (for both groups), necessary care was taken to relocate the participant as fast and as smoothly as possible inside the scanner magnet.…”

“…Within this broad auditory-frontal-limbic network for voice signals processing and voice information decoding (Dricu, Ceravolo et al 2017, Dricu and Frühholz 2020, Frühholz and Schweinberger 2020), the involvement of the IFC in decoding emotions from affective speech has been consistently reported (Frühholz and Schweinberger 2020, Roswandowitz, Swanborough et al 2020, Swanborough, Staib et al 2020), but its functional role in the neural network and its functional contribution to voice signal processing and especially in vocal affect decoding remained debated. Some previous studies indicated that social affective decoding from voice seemed restricted to the AC (Grandjean, Sander et al 2005, Ethofer, Van De Ville et al 2009) without any functional relevance of the IFC.…”