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Do we perceive a group of dancers moving in synchrony differently from a group of drones flying in-sync? The brain has dedicated networks for perception of coherent motion and interacting human bodies. However, it is unclear to what extent the underlying neural mechanisms overlap. Here we delineate these mechanisms by independently manipulating the degree of motion synchrony and the humanoid quality of multiple point-light displays (PLDs). Four PLDs moving within a group were changing contrast in cycles of fixed frequencies, which permits the identification of the neural processes that are tagged by these frequencies. In the frequency spectrum of the steady-state EEG we found two emergent frequency components, which signified distinct levels of interactions between PLDs. The first component was associated with motion synchrony, the second with the human quality of the moving items. These findings indicate that visual processing of synchronously moving dancers involves two distinct neural mechanisms: one for the perception of a group of items moving in synchrony and one for the perception of a group of moving items with human quality. We propose that these mechanisms underlie high-level perception of social interactions.

Shape perception is intrinsically holistic: combinations of features give rise to configurations with emergent properties that are different from the sum of the parts. The current study investigated neural markers of holistic shape representations learned by means of categorization training. We used the EEG frequency tagging technique, where two parts of a shape stimulus were 'tagged' by modifying their contrast at different temporal frequencies. Signals from both parts are integrated and, as a result, emergent frequency components (so-called, intermodulation responses, IMs), caused by nonlinear interaction of two frequency signals, are observed in the EEG spectrum. First, participants were trained in 4 sessions to discriminate highly similar, unfamiliar shapes into two categories, defined based on the combination of features. After training, EEG was recorded while frequency-tagged shapes from either the trained or the untrained shape family were presented. For all IMs combined, no learning effects were detected, but post hoc analyses of higher-order IMs revealed stronger occipital and occipito-temporal IMs for both trained and untrained exemplars of the trained shape family as compared to the untrained shape family. In line with recent findings, we suggest that the higher-order IMs may reflect high-level visual computations, like holistic shape categorization, resulting from a cascade of non-linear operations. Higher order frequency responses are relatively low in power, hence results should be interpreted cautiously and future research is needed to confirm these effects. In general, these findings are, to our knowledge, the first to show IMs as a neural correlate of perceptual learning.

Symmetry is a highly salient feature of the natural world which requires integration of visual features over space. The aim of the current work is to isolate dynamic neural correlates of symmetry-specific integration processes. We measured steady-state visual evoked potentials (SSVEP) as participants viewed symmetric patterns comprised of distinct spatial regions presented at two different frequencies (f1 and f2). We measured intermodulation components, shown to reflect non-linear processing at the neural level, indicating integration of spatially separated parts of the pattern. We generated a wallpaper pattern containing two reflection symmetry axes by tiling the plane with a two-fold reflection symmetric unit-pattern and split each unit-pattern diagonally into separate parts which could be presented at different frequencies. We compared SSVEPs measured for wallpapers and control patterns for which both images were equal in terms of translation and rotation symmetry but reflection symmetry could only emerge for the wallpaper pattern through integration of the image-pairs. We found that low-frequency intermodulation components differed between the wallpaper and control stimuli, indicating the presence of integration mechanisms specific to reflection symmetry. These results showed that spatial integration specific to symmetry perception can be isolated through a combination of stimulus design and the frequency tagging approach.

The perception of an illusory surface, a subjectively perceived surface that is not given in the image, is one of the most intriguing phenomena in vision. It strongly influences the perception of some fundamental properties, namely, depth, lightness and contours. Recently, we suggested (1) that the context-sensitive mechanism of depth computation plays a key role in creating the illusion, (2) that the illusory lightness perception can be explained by an influence of depth perception on the lightness computation, and (3) that the perception of variations of the Kanizsa figure can be well-reproduced by implementing these principles in a model (Kogo, Strecha, et al., 2010). However, depth perception, lightness perception, contour perception, and their interactions can be influenced by various factors. It is essential to measure the differences between the variation figures in these aspects separately to further understand the mechanisms. As a first step, we report here the results of a new experimental paradigm to compare the depth perception of the Kanizsa figure and its variations. One of the illusory figures was presented side-by-side with a non-illusory variation whose stereo disparities were varied. Participants had to decide in which of these two figures the central region appeared closer. The results indicate that the depth perception of the illusory surface was indeed different in the variation figures. Furthermore, there was a non-linear interaction between the occlusion cues and stereo disparity cues. Implications of the results for the neuro-computational mechanisms are discussed.