Temporal Dynamics of Neural Processing of Facial Expressions and Emotions

Abstract

Emotion processing has been a focus of research in psychology and neuroscience for some decades. While the evoked neural markers in human brain activations in response to different emotions have been reported, the temporal dynamics of emotion processing has received less attention. Differences in processing speeds, that depend on emotion type, have not been determined. Furthermore, behavioral studies have found that the right side of the human face expresses emotions more accurately than the left side. Therefore, accounting for both the content of the emotion and the visual angle of presentation from the perspective of the viewer, here we have investigated variability in the discrimination of happy and sad faces when the visual angle of presentation was Positive (right side of the face) compared to Negative (left side of the face). Furthermore, the temporal dynamics involved in processing happy and sad emotions have been compared. Regardless of visual angle, happy emotions were processed faster than sad emotions. However, the evoked response to sad emotions significantly increased in amplitude compared to that for happy emotions, when faces were presented at Positive visual angles only. Source reconstruction from sensor-level ERFs show localized activities in ventral and dorsal stream, including fusiform gyrus, lingual gyrus, putamen and Pre and Post central gyrus. Multivariate pattern analysis (MVPA) confirmed these findings – demonstrating successful decoding of happy and sad emotions only occurred when the facial expression was viewed from a positive visual angle, and that happy emotions were processed faster than sad emotions.

Early language dissociation in bilingual minds: Non-invasive magnetoencephalography evidence through a machine learning approach

“…This process was repeated 10 times for each subject to yield stable decoding patterns. The statistical significance of decoding across time was established using cluster corrected sign permutation tests (one-tailed) (similar to Dima et al, 2018;Nara et al, 2022Nara et al, , 2021b applied to the 'auc' values obtained from the classifier with cluster-defining threshold (i.e., alpha: p<0.05; cluster-alpha: p<0.05; 10000 permutations).…”

Decoding Numeracy and Literacy in the Human Brain: Insights from MEG and MVPA

“…This model learning process was repeated twenty (20) times for each subject to yield a stable decoding pattern. To evaluate the statistical significance of decoding across time, cluster-corrected sign permutation tests [13], [20]- [22] (i.e., one-tailed) were applied to the 'AUC' values obtained from the classifier with a cluster-defining threshold (i.e., alpha) p < 0.05, cluster-alpha threshold p < 0.05 and 50000 permutations considering the chance level decoding 0.50. To evaluate the statistical decoding differences across experiments, the difference in 'AUC' for each condition (Experiment 2 -Experiment 1) was computed.…”

“…To evaluate the statistical decoding differences across experiments, the difference in 'AUC' for each condition (Experiment 2 -Experiment 1) was computed. The cluster-corrected sign permutation tests [13], [20]- [22] (one-tailed) were applied to the differential 'AUC' values across experiments cluster-defining threshold (i.e., alpha) p < 0.05, cluster-alpha threshold p < 0.05 and 50000 permutations considering the chance level decoding 0.00.…”

“…MVPA techniques such as time-resolved decoding [10]- [13] and temporal generalization [14], have a focus on how cognitive processes unfold over time, providing evidence on the temporal evolution of the processing of specific types of stimuli and how this is classifiable as compared to others types. The neural temporal dynamics shaped by numeracy and literacy have not been explored by taking advantage of these methods.…”

Decoding numeracy and literacy in the human brain: insights from MEG and MVPA

“…MVPA techniques, such as time-resolved decoding [12][13][14][15] and temporal generalization 16 have a focus on how cognitive processes unfold over time, providing evidence on the temporal evolution of the processing of specific types of stimuli and how this is classifiable as compared to others types. The neural classification of the processing dynamics shaped by numeracy and literacy have not been explored by taking advantage of these methods.…”