Task influences pattern discriminability for faces and bodies in ventral occipitotemporal cortex

The Effect of Task on Object Processing revealed by EEG decoding

Recent studies showed that task demand affects object representations in higher-level visual areas and beyond, but not so much in earlier areas. There are, however, limitations in those studies including the relatively weak manipulation of task due to the use of familiar real-life objects, the low temporal resolution in fMRI, and the emphasis on the amount and not the source of information carried by brain activations. In the current study, observers categorized images of artificial objects in one of two orthogonal dimensions, shape and texture, while their brain activity was recorded with electroencephalogram (EEG). Results showed that object processing along the texture dimension was affected by task demand starting from a relatively late time (320-370ms time window) after image onset. The findings are consistent with the view that task exerts an effect on the later phases of object processing.

Network Representations of Facial and Bodily Expressions: Evidence From Multivariate Connectivity Pattern Classification

Emotions can be perceived from both facial and bodily expressions. Our previous study has found the successful decoding of facial expressions based on the functional connectivity (FC) patterns. However, the role of the FC patterns in the recognition of bodily expressions remained unclear, and no neuroimaging studies have adequately addressed the question of whether emotions perceiving from facial and bodily expressions are processed rely upon common or different neural networks. To address this, the present study collected functional magnetic resonance imaging (fMRI) data from a block design experiment with facial and bodily expression videos as stimuli (three emotions: anger, fear, and joy), and conducted multivariate pattern classification analysis based on the estimated FC patterns. We found that in addition to the facial expressions, bodily expressions could also be successfully decoded based on the large-scale FC patterns. The emotion classification accuracies for the facial expressions were higher than that for the bodily expressions. Further contributive FC analysis showed that emotion-discriminative networks were widely distributed in both hemispheres, containing regions that ranged from primary visual areas to higher-level cognitive areas. Moreover, for a particular emotion, discriminative FCs for facial and bodily expressions were distinct. Together, our findings highlight the key role of the FC patterns in the emotion processing, indicating how large-scale FC patterns reconfigure in processing of facial and bodily expressions, and suggest the distributed neural representation for the emotion recognition. Furthermore, our results also suggest that the human brain employs separate network representations for facial and bodily expressions of the same emotions. This study provides new evidence for the network representations for emotion perception and may further our understanding of the potential mechanisms underlying body language emotion recognition.

Decisional space modulates visual categorization - Evidence from saccadic reaction times

Manual and saccadic reaction times (SRTs) have been used to determine the minimum time required for different types of visual categorizations. Such studies have demonstrated extremely rapid detection of faces within natural scenes, whereas increasingly complex decisions (i.e. levels of processing) require longer processing times. We reasoned that visual categorization speed is not only dependent on the processing level, but is further affected by decisional space constraints. In the context of two different tasks, observers performed choice saccades towards female (gender categorization) or personally familiar (familiarity categorization) faces. Additionally, familiarity categorizations were completed with stimulus sets that differed in the number of individuals presented (3 vs. 7 identities) to investigate the effect of decisional space constraints. We observe an inverse relationship between visual categorization proficiency and decisional space. Observers were most accurate for categorization of gender, which could be achieved in as little as 140 ms. Categorization of highly predictable targets was more error-prone and required an additional ∼100 ms processing time. Our findings add to an increasing body of evidence demonstraing that pre-activation of identity-information can modulate early visual processing in a top-down manner. They also emphasize the importance of considering procedural aspects, as well as terminology when aiming to characterize cognitive processes.

The influence of behavioral relevance on the processing of global scene properties: An ERP study

Recent work studying the temporal dynamics of visual scene processing (Harel et al., 2016) has found that global scene properties (GSPs) modulate the amplitude of early Event-Related Potentials (ERPs). It is still not clear, however, to what extent the processing of these GSPs is influenced by their behavioral relevance, determined by the goals of the observer. To address this question, we investigated how behavioral relevance, operationalized by the task context impacts the electrophysiological responses to GSPs. In a set of two experiments we recorded ERPs while participants viewed images of real-world scenes, varying along two GSPs, naturalness (manmade/natural) and spatial expanse (open/closed). In Experiment 1, very little attention to scene content was required as participants viewed the scenes while performing an orthogonal fixation-cross task. In Experiment 2 participants saw the same scenes but now had to actively categorize them, based either on their naturalness or spatial expense. We found that task context had very little impact on the early ERP responses to the naturalness and spatial expanse of the scenes: P1, N1, and P2 could distinguish between open and closed scenes and between manmade and natural scenes across both experiments. Further, the specific effects of naturalness and spatial expanse on the ERP components were largely unaffected by their relevance for the task. A task effect was found at the N1 and P2 level, but this effect was manifest across all scene dimensions, indicating a general effect rather than an interaction between task context and GSPs. Together, these findings suggest that the extraction of global scene information reflected in the early ERP components is rapid and very little influenced by top-down observer-based goals.

Linear Representation of Emotions in Whole Persons by Combining Facial and Bodily Expressions in the Extrastriate Body Area

Our human brain can rapidly and effortlessly perceive a person’s emotional state by integrating the isolated emotional faces and bodies into a whole. Behavioral studies have suggested that the human brain encodes whole persons in a holistic rather than part-based manner. Neuroimaging studies have also shown that body-selective areas prefer whole persons to the sum of their parts. The body-selective areas played a crucial role in representing the relationships between emotions expressed by different parts. However, it remains unclear in which regions the perception of whole persons is represented by a combination of faces and bodies, and to what extent the combination can be influenced by the whole person’s emotions. In the present study, functional magnetic resonance imaging data were collected when participants performed an emotion distinction task. Multi-voxel pattern analysis was conducted to examine how the whole person-evoked responses were associated with the face- and body-evoked responses in several specific brain areas. We found that in the extrastriate body area (EBA), the whole person patterns were most closely correlated with weighted sums of face and body patterns, using different weights for happy expressions but equal weights for angry and fearful ones. These results were unique for the EBA. Our findings tentatively support the idea that the whole person patterns are represented in a part-based manner in the EBA, and modulated by emotions. These data will further our understanding of the neural mechanism underlying perceiving emotional persons.