When affect overlaps with concept: emotion recognition in semantic variant of primary progressive aphasia

Abstract

The most recent theories of emotions have postulated that their expression and recognition depend on acquired conceptual knowledge. In other words, the conceptual knowledge derived from prior experiences guide our ability to make sense of such emotions. However, clear evidence is still lacking to contradict more traditional theories, considering emotions as innate, distinct and universal physiological states. In addition, whether valence processing (i.e. recognition of the pleasant/unpleasant character of emotions) also relies on semantic knowledge is yet to be determined. To investigate the contribution of semantic knowledge to facial emotion recognition and valence processing, we conducted a behavioural and neuroimaging study in 20 controls and 16 patients with the semantic variant of primary progressive aphasia, a neurodegenerative disease that is prototypical of semantic memory impairment, and in which an emotion recognition deficit has already been described. We assessed participants' knowledge of emotion concepts and recognition of 10 basic (e.g. anger) or self-conscious (e.g. embarrassment) facial emotional expressions presented both statically (images) and dynamically (videos). All participants also underwent a brain MRI. Group comparisons revealed deficits in both emotion concept knowledge and emotion recognition in patients, independently of type of emotion and presentation. These measures were significantly correlated with each other in patients and with semantic fluency in patients and controls. Neuroimaging analyses showed that both emotion recognition and emotion conceptual knowledge were correlated with reduced grey matter density in similar areas within frontal ventral, temporal, insular and striatal regions, together with white fibre degeneration in tracts connecting frontal regions with each other as well as with temporal regions. We then performed a qualitative analysis of responses made during the facial emotion recognition task, by delineating valence errors (when one emotion was mistaken for another of a different valence), from other errors made during the emotion recognition test. We found that patients made more valence errors. The number of valence errors correlated with emotion conceptual knowledge as well as with reduced grey matter volume in brain regions already retrieved to correlate with this score. Specificity analyses allowed us to conclude that this cognitive relationship and anatomical overlap were not mediated by a general effect of disease severity. Our findings suggest that semantic knowledge guides the recognition of emotions and is also involved in valence processing. Our study supports a constructionist view of emotion recognition and valence processing, and could help to refine current theories on the interweaving of semantic knowledge and emotion processing.

Keywords: emotion recognition; semantic dementia; semantic memory; semantic variant primary progressive aphasia.

Figure 1

Facial emotion recognition. Individual and group performance of controls (orange triangles and boxes) and patients with svPPA (blue dots and boxes) at the Amsterdam Dynamic Facial Expression Set for positive (happiness, pride), non-positive/negative (surprise, neutral), and negative (sadness, anger, fear, contempt, embarrassment, disgust) emotions in static (top) and dynamic (bottom) presentations. In the box and whisker plots, the rectangle represents the interquartile segment; group mean is indicated by a cross and median by a broken line within the box. Red asterisks represent the extent of the difference: *η² > 0.150; **η² > 0.250; ***η² > 0.400.

Figure 2

Correlations plot. Correlations plot for svPPA (top, blue dots) and Control (bottom, orange triangles) groups between FER or valence errors committed during FER and semantic processing, including EEC and category fluency. Asterisks indicate significant correlations at **P < 0.0001 and *P < 0.01.

Figure 3

VBM (A) and DTI (B) contrasts between patients with svPPA and controls (P < 0.05 FWE-corrected) on sagittal and coronal views.

Figure 4

Grey matter correlates of EEC and FER (A), and white matter correlates of FER (B). Both at P < 0.05 FWE-corrected. POST = posterior.

Figure 5

Grey matter correlates of EEC (blue) and valence errors (green) on the FER. Both at P < 0.05 FWE-corrected represented on a 3D view (A) or on axial slices (B).