An objective signature for visual binding of face parts in the human brain

Abstract

Whether and how the parts of a visual object are grouped together to form an integrated ("holistic") representation is a central question in cognitive neuroscience. Although the face is considered to be the quintessential example of holistic representation, this issue has been the subject of much debate in face perception research. The implication of holistic processing is that the response to the whole cannot be predicted from the sum of responses to the parts. Here we apply techniques from nonlinear systems analysis to provide an objective measure of the nonlinear integration of parts into a whole, using the left and right halves of a face stimulus as the parts. High-density electroencephalogram (EEG) was recorded in 15 human participants presented with two halves of a face stimulus, flickering at different frequencies (5.88 vs. 7.14 Hz). Besides specific responses at these fundamental frequencies, reflecting part-based responses, we found intermodulation components (e.g., 7.14 - 5.88 = 1.26 Hz) over the right occipito-temporal hemisphere, reflecting nonlinear integration of the face halves. Part-based responses did not depend on the relative alignment of the two face halves, their spatial separation, or whether the face was presented upright or inverted. By contrast, intermodulations were virtually absent when the two halves were spatially misaligned and separated. Inversion of the whole face configuration also reduced specifically the intermodulation components over the right occipito-temporal cortex. These observations indicate that the intermodulation components constitute an objective, configuration-specific signature of an emergent neural representation of the whole face that is distinct from that generated by the parts themselves.

Neural markers of predictive coding under perceptual uncertainty revealed with Hierarchical Frequency Tagging

“…Several studies have demonstrated a relationship between IM components and perception (Boremanse et al, 2013; Gundlach and Müller, 2013; Zhang et al, 2011). In all of these studies, the reported increase in IM signal strength potentially reflects the integration of different input elements within a single neural representation.…”

“…(note that throughout the paper we denote stimulus frequencies with capital letters (e.g., F1 ) and response frequencies with small letters (e.g., f1 )). Intermodulation components in EEG recordings have been used to study non-linear interactions in the visual system (Clynes, 1961; Regan and Regan, 1988; Zemon and Ratliff, 1984), with some recent applications for the study of high-level visual-object recognition systems (Boremanse et al, 2013; Gundlach and Müller, 2013; Zhang et al, 2011). Instead of tagging two ‘bottom-up’ signals, however, our paradigm was designed to enable the examination of the integration between both bottom-up and top-down inputs to the lower visual areas.…”

EEG frequency tagging dissociates between neural processing of motion synchrony and human quality of multiple point-light dancers

“…The usage of the SNR spectrum instead of the amplitude spectrum is a common practice in the SSVEP and frequency tagging research141819202425. Since only small fraction of the noise is relevant with the frequency of interest26, the SNR spectrum provides much clearer brain responses (i.e., sharper spectral peaks) than the amplitude spectrum, especially for low frequencies.…”

Exploring how musical rhythm entrains brain activity with electroencephalogram frequency-tagging

“…While originally designed to investigate 'low level' sensory processes [26] and their attentional modulation (e.g. [33,34]), the frequency-tagging approach has been recently extended to characterize higher levels of perception and cognition, for instance, figure-ground segregation [35] or face perception [31,36,37]. In our own studies, we investigated whether the musical beat and meter-which refer to perceived periodicities induced by, but not necessarily present within, the sound input-would elicit neural responses which could be tagged in the EEG based on their expected frequencies, i.e.…”