This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.

Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.

What constitutes normal cortical dynamics in healthy human subjects is a major question in systems neuroscience. Numerous in vitro and in vivo animal studies have shown that ongoing or resting cortical dynamics are characterized by cascades of activity across many spatial scales, termed neuronal avalanches. In experiment and theory, avalanche dynamics are identified by two measures: (1) a power law in the size distribution of activity cascades with an exponent of −3/2 and (2) a branching parameter of the critical value of 1, reflecting balanced propagation of activity at the border of premature termination and potential blowup. Here we analyzed resting-state brain activity recorded using noninvasive magnetoencephalography (MEG) from 124 healthy human subjects and two different MEG facilities using different sensor technologies. We identified large deflections at single MEG sensors and combined them into spatiotemporal cascades on the sensor array using multiple timescales. Cascade size distributions obeyed power laws. For the timescale at which the branching parameter was close to 1, the power law exponent was −3/2. This relationship was robust to scaling and coarse graining of the sensor array. It was absent in phase-shuffled controls with the same power spectrum or empty scanner data. Our results demonstrate that normal cortical activity in healthy human subjects at rest organizes as neuronal avalanches and is well described by a critical branching process. Theory and experiment have shown that such critical, scale-free dynamics optimize information processing. Therefore, our findings imply that the human brain attains an optimal dynamical regime for information processing.

Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N ϭ 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the ␤-band (16 -32 Hz) and ␥-band (32-63 Hz) networks, compared with both lower (␣-band; 8 -16 Hz) and higher (high ␥-band; 63-125 Hz) frequency intervals. Workspace configuration of ␤-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting Ͻ2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.

A key to understanding visual cognition is to determine when, how, and with what information the human brain distinguishes between visual categories. So far, the dynamics of information processing for categorization of visual stimuli has not been elucidated. By using an ecologically important categorization task (seven expressions of emotion), we demonstrate, in three human observers, that an early brain event (the N170 Event Related Potential, occurring 170 ms after stimulus onset) integrates visual information specific to each expression, according to a pattern. Specifically, starting 50 ms prior to the ERP peak, facial information tends to be integrated from the eyes downward in the face. This integration stops, and the ERP peaks, when the information diagnostic for judging a particular expression has been integrated (e.g., the eyes in fear, the corners of the nose in disgust, or the mouth in happiness). Consequently, the duration of information integration from the eyes down determines the latency of the N170 for each expression (e.g., with "fear" being faster than "disgust," itself faster than "happy"). For the first time in visual categorization, we relate the dynamics of an important brain event to the dynamics of a precise information-processing function.

Graph theory provides many metrics of complex network organization that can be applied to analysis of brain networks derived from neuroimaging data. Here we investigated the test-retest reliability of graph metrics of functional networks derived from magnetoencephalography (MEG) data recorded in two sessions from 16 healthy volunteers who were studied at rest and during performance of the n-back working memory task in each session. For each subject's data at each session, we used a wavelet filter to estimate the mutual information (MI) between each pair of MEG sensors in each of the classical frequency intervals from gamma to low delta in the overall range 1-60 Hz. Undirected binary graphs were generated by thresholding the MI matrix and 8 global network metrics were estimated: the clustering coefficient, path length, small-worldness, efficiency, cost-efficiency, assortativity, hierarchy, and synchronizability. Reliability of each graph metric was assessed using the intraclass correlation (ICC). Good reliability was demonstrated for most metrics applied to the n-back data (mean ICC=0.62). Reliability was greater for metrics in lower frequency networks. Higher frequency gamma- and beta-band networks were less reliable at a global level but demonstrated high reliability of nodal metrics in frontal and parietal regions. Performance of the n-back task was associated with greater reliability than measurements on resting state data. Task practice was also associated with greater reliability. Collectively these results suggest that graph metrics are sufficiently reliable to be considered for future longitudinal studies of functional brain network changes.

One of the most impressive disorders following brain damage to the ventral occipitotemporal cortex is prosopagnosia, or the inability to recognize faces. Although acquired prosopagnosia with preserved general visual and memory functions is rare, several cases have been described in the neuropsychological literature and studied at the functional and neural level over the last decades. Here we tested a brain-damaged patient (PS) presenting a deficit restricted to the category of faces to clarify the nature of the missing and preserved components of the face processing system when it is selectively damaged. Following learning to identify 10 neutral and happy faces through extensive training, we investigated patient PS's recognition of faces using Bubbles, a response classification technique that sampled facial information across the faces in different bandwidths of spatial frequencies [Gosselin, F., & Schyns, P. E., Bubbles: A technique to reveal the use of information in recognition tasks. Vision Research, 41, 2261-2271, 2001]. Although PS gradually used less information (i.e., the number of bubbles) to identify faces over testing, the total information required was much larger than for normal controls and decreased less steeply with practice. Most importantly, the facial information used to identify individual faces differed between PS and controls. Specifically, in marked contrast to controls, PS did not use the optimal eye information to identify familiar faces, but instead the lower part of the face, including the mouth and the external contours, as normal observers typically do when processing unfamiliar faces. Together, the findings reported here suggest that damage to the face processing system is characterized by an inability to use the information that is optimal to judge identity, focusing instead on suboptimal information.

Accurate classification of the emotional state of others is of vital importance to human social functioning and is a process that relies heavily upon the extraction and processing of specific visual cues from faces. Although the successful identification of a given facial expression has been shown to rely on the processing of specific visual features, it remains largely unknown how fixed the processing of these specific visual cues actually is. In a series of experiments we tested if observers make use of different visual information from expressive faces depending on the nature of the categorization task. To this end, we determined the facial features crucial for the categorization of three key facial expressions of emotion (fear, disgust, and anger) during "expressive or neutral" and "which expression" categorization tasks. For fearful categorizations we observed that the same high spatial-frequency features were consistently used irrespective of task, but that low spatial-frequency features were important only in limited comparison tasks with one or two alternative comparison categories. Moreover, information use from the low spatial frequency bands was not fixed and varied depending on the comparison categories used in the task as participants made use of the most salient visual information available to perform the task at hand. These results provide novel evidence of flexible information use in categorizing expressive faces and highlight the crucial importance of the nature of categorization task in determining the spatial-frequency features that are attended to and encoded during the categorization of facial expressions of emotion.