Context sensitivity in the detection of changes in facial emotion

Functional and spatial rewiring principles jointly regulate context-sensitive computation

Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network's processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.

Contextualising facial expressions: The effect of temporal context and individual differences on classification

The influence of context on facial expression classification is most often investigated using simple cues in static faces portraying basic expressions with a fixed emotional intensity. We examined (1) whether a perceptually rich, dynamic audiovisual context, presented in the form of movie clips (to achieve closer resemblance to real life), affected the subsequent classification of dynamic basic (happy) and non-basic (sarcastic) facial expressions and (2) whether people's susceptibility to contextual cues was related to their ability to classify facial expressions viewed in isolation. Participants classified facial expressions-gradually progressing from neutral to happy/sarcastic in increasing intensity-that followed movie clips. Classification was relatively more accurate and faster when the preceding context predicted the upcoming expression, compared with when the context did not. Speeded classifications suggested that predictive contexts reduced the emotional intensity required to be accurately classified. More importantly, we show for the first time that participants' accuracy in classifying expressions without an informative context correlated with the magnitude of the contextual effects experienced by them-poor classifiers of isolated expressions were more susceptible to a predictive context. Our findings support the emerging view that contextual cues and individual differences must be considered when explaining mechanisms underlying facial expression classification.

The role of healthy emotionality in the relationship between fear of COVID-19 and mental health problems: a cross-sectional study

Understanding pandemic-related psychopathology development is limited due to numerous individual and contextual factors. It is widely accepted that individual differences to endure or cope with distress predict psychopathology development. The present study investigated the influence of individual differences in neuroticism and healthy emotionality concerning the association between fear of COVID-19 and mental health problems. It was hypothesized that healthy emotionality would moderate the mediated link between fear of COVID-19 and mental health problems. A sample of 752 participants (351 males and 401 females) completed an online survey including the Emotional Style Questionnaire, Fear of COVID-19 Scale, the Neuroticism subscale of the Big Five Inventory, and General Health Questionnaire. The results showed that the fear of COVID-19 positively predicted mental health problems (β = .43, SE = .05, p < .001, Cohen's f 2 = .24). Neuroticism also showed a significant mediation effect on the relationship between fear of COVID-19 and mental health problems. Fear of COVID-19 indirectly predicted psychopathology through neuroticism (β = - .16, SE = .04, p < .001, t = 4.53, 95% CI [0.11, 0.23]). Moreover, healthy emotionality had a moderating effect on the relationship between fear of COVID-19 and mental health problems, β = - .21, SE = .03, p < .001, t = 5.91, 95% CI [- 0.26, - 0.14]. The study's findings are expected to contribute to a better understanding of the roles of both individual differences in personality traits and healthy emotionality in psychopathology development during the current pandemic.

Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework

The mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown. Therefore, this study proposes a system-level explanation for understanding the facial emotion recognition process and its alteration in autism spectrum disorder (ASD) from the perspective of predictive processing theory. Predictive processing for facial emotion recognition was implemented as a hierarchical recurrent neural network (RNN). The RNNs were trained to predict the dynamic changes of facial expression movies for six basic emotions without explicit emotion labels as a developmental learning process, and were evaluated by the performance of recognizing unseen facial expressions for the test phase. In addition, the causal relationship between the network characteristics assumed in ASD and ASD-like cognition was investigated. After the developmental learning process, emotional clusters emerged in the natural course of self-organization in higher-level neurons, even though emotional labels were not explicitly instructed. In addition, the network successfully recognized unseen test facial sequences by adjusting higher-level activity through the process of minimizing precision-weighted prediction error. In contrast, the network simulating altered intrinsic neural excitability demonstrated reduced generalization capability and impaired emotional clustering in higher-level neurons. Consistent with previous findings from human behavioral studies, an excessive precision estimation of noisy details underlies this ASD-like cognition. These results support the idea that impaired facial emotion recognition in ASD can be explained by altered predictive processing, and provide possible insight for investigating the neurophysiological basis of affective contact.

Alexithymia and the Evaluation of Emotionally Valenced Scenes

Alexithymia is a personality trait characterized by difficulties identifying and describing feelings (DIF and DDF) and an externally oriented thinking (EOT) style. The primary aim of the present study was to investigate links between alexithymia and the evaluation of emotional scenes. We also investigated whether viewers' evaluations of emotional scenes were better predicted by specific alexithymic traits or by individual differences in sensory processing sensitivity (SPS). Participants (N = 106) completed measures of alexithymia and SPS along with a task requiring speeded judgments of the pleasantness of 120 moderately arousing scenes. We did not replicate laterality effects previously described with the scene perception task. Compared to those with weak alexithymic traits, individuals with moderate-to-strong alexithymic traits were less likely to classify positively valenced scenes as pleasant and were less likely to classify scenes with (vs. without) implied motion (IM) in a way that was consistent with normative scene valence ratings. In addition, regression analyses confirmed that reporting strong EOT and a tendency to be easily overwhelmed by busy sensory environments negatively predicted classification accuracy for positive scenes, and that both DDF and EOT negatively predicted classification accuracy for scenes depicting IM. These findings highlight the importance of accounting for stimulus characteristics and individual differences in specific traits associated with alexithymia and SPS when investigating the processing of emotional stimuli. Learning more about the links between these individual difference variables may have significant clinical implications, given that alexithymia is an important, transdiagnostic risk factor for a wide range of psychopathologies.

Perceptual effects of fast and automatic visual ensemble statistics from faces in individuals with typical development and autism spectrum conditions

We investigated whether covert ensembles of high- (emotion), and low-level (brightness) visual information, extracted from peripheral faces (presentation/encoding:200 ms), unintentionally influences perception of a central target face stimulus in individuals typically developing (TD) and with autism spectrum condition (ASC). Graded alterations in the summary intensities of the emotion and brightness of the peripheral stimuli modulated the perceptions of the target face in both TD and ASC. In contrast, when we measured goal-directed (overt) ensemble face- emotion and brightness perception, we found that in half of ASC the overt ensemble emotion perception was impaired than TD. Additionally, we repeated both experiments with a backward visual mask to restrict not just encoding but also background processing in the visual system to 200 ms. This revealed that the effect of peripheral ensembles on centre perception was present only with brightness at least in TD but of overt ensembles was evident with both emotion and brightness in TD and ASC alike. These results suggest that while ensemble statistics of low-level information derived automatically and rapidly (200 ms) from contextualized faces are used for target face perception, the same takes longer with high-level information. However, overt facial ensembles are rapidly processed in both TD and ASC.