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Nara, S.

Publications and source records attributed to Nara, S..

2 recordsLinked to original sources

Temporal Dynamics of Neural Processing of Facial Expressions and Emotions

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.

neuroscience↗

Temporal uncertainty affects the visual processing of predicted stimulus properties

Predictive processing has been proposed as a fundamental cognitive mechanism to account for how the brain interacts with the external environment via its sensory systems. In vision, contextual information triggers predictions about the content ("what") of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability ("when") may influence the impact of internal predictions on visual processing. In this magnetoencephalography (MEG) study, we investigated how processing feature specific information ("what") is affected by temporal predictability ("when"). In line with previous findings, we observed suppression of evoked neural responses in the visual cortex for predictable stimuli. Interestingly, we found that temporal uncertainty increased expectation suppression. This suggests that in temporally uncertain scenarios the neurocognitive system invests less resources in integrating bottom-up information. Multivariate pattern analysis showed that predictable visual features could be decoded from neural responses. Temporal uncertainty did not affect decoding accuracy for early visual responses, with the feature specificity of early visual neural activity preserved across conditions. However, decoding accuracy was less sustained over time for temporally jittered than for isochronous predictable visual stimuli. These findings converge to suggest that the cognitive system processes visual features of temporally predictable stimuli in higher detail, while processing temporally uncertain stimuli may rely more heavily on abstract internal expectations.

neuroscience↗