Shared neural codes for visual and semantic information about familiar faces in a common representational space

Familiarity enhances functional connectivity between visual and nonvisual regions of the brain during natural viewing

We explored the neural correlates of familiarity with people and places using a naturalistic viewing paradigm. Neural responses were measured using functional magnetic resonance imaging, while participants viewed a movie taken from Game of Thrones. We compared inter-subject correlations and functional connectivity in participants who were either familiar or unfamiliar with the TV series. Higher inter-subject correlations were found between familiar participants in regions, beyond the visual brain, that are typically associated with the processing of semantic, episodic, and affective information. However, familiarity also increased functional connectivity between face and scene regions in the visual brain and the nonvisual regions of the familiarity network. To determine whether these regions play an important role in face recognition, we measured responses in participants with developmental prosopagnosia (DP). Consistent with a deficit in face recognition, the effect of familiarity was significantly attenuated across the familiarity network in DP. The effect of familiarity on functional connectivity between face regions and the familiarity network was also attenuated in DP. These results show that the neural response to familiarity involves an extended network of brain regions and that functional connectivity between visual and nonvisual regions of the brain plays an important role in the recognition of people and places during natural viewing.

Decoding face recognition abilities in the human brain

Why are some individuals better at recognizing faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multimodal data-driven approach combining neuroimaging, computational modeling, and behavioral tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities-super-recognizers-and typical recognizers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 s of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared representations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognizers, we found stronger associations between early brain representations of super-recognizers and midlevel representations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognizers and representations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multimodal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain.

Using deep neural networks to disentangle visual and semantic information in human perception and memory

Mental representations of familiar categories are composed of visual and semantic information. Disentangling the contributions of visual and semantic information in humans is challenging because they are intermixed in mental representations. Deep neural networks that are trained either on images or on text or by pairing images and text enable us now to disentangle human mental representations into their visual, visual-semantic and semantic components. Here we used these deep neural networks to uncover the content of human mental representations of familiar faces and objects when they are viewed or recalled from memory. The results show a larger visual than semantic contribution when images are viewed and a reversed pattern when they are recalled. We further reveal a previously unknown unique contribution of an integrated visual-semantic representation in both perception and memory. We propose a new framework in which visual and semantic information contribute independently and interactively to mental representations in perception and memory.

The individualized neural tuning model: Precise and generalizable cartography of functional architecture in individual brains

Quantifying how brain functional architecture differs from person to person is a key challenge in human neuroscience. Current individualized models of brain functional organization are based on brain regions and networks, limiting their use in studying fine-grained vertex-level differences. In this work, we present the individualized neural tuning (INT) model, a fine-grained individualized model of brain functional organization. The INT model is designed to have vertex-level granularity, to capture both representational and topographic differences, and to model stimulus-general neural tuning. Through a series of analyses, we demonstrate that (a) our INT model provides a reliable individualized measure of fine-grained brain functional organization, (b) it accurately predicts individualized brain response patterns to new stimuli, and (c) for many benchmarks, it requires only 10-20 minutes of data for good performance. The high reliability, specificity, precision, and generalizability of our INT model affords new opportunities for building brain-based biomarkers based on naturalistic neuroimaging paradigms.

Modeling naturalistic face processing in humans with deep convolutional neural networks

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features.

Dissociable contributions of the medial parietal cortex to recognition memory

Human neuroimaging studies of episodic memory retrieval routinely observe the engagement of specific cortical regions beyond the medial temporal lobe. Of these, medial parietal cortex (MPC) is of particular interest given its ubiquitous, and yet distinct, functional characteristics during different types of retrieval tasks. Specifically, while recognition memory and autobiographical recall tasks are both used to probe episodic retrieval, these paradigms consistently drive distinct patterns of response within MPC. This dissociation adds to growing evidence suggesting a common principle of functional organization across memory related brain structures, specifically regarding the control or content demands of memory-based decisions. To carefully examine this putative organization, we used a high-resolution fMRI dataset collected at ultra-high field (7T) while subjects performed thousands of recognition-memory trials to identify MPC regions responsive to recognition-decisions or semantic content of stimuli within and across individuals. We observed interleaving, though distinct, functional subregions of MPC where responses were sensitive to either recognition decisions or the semantic representation of stimuli, but rarely both. In addition, this functional dissociation within MPC was further accentuated by distinct profiles of connectivity bias with the hippocampus during task and rest. Finally, we show that recent observations of person and place selectivity within MPC reflect category specific responses from within identified semantic regions that are sensitive to mnemonic demands. Together, these data better account for how distinct patterns of MPC responses can occur as a result of task demands during episodic retrieval and may reflect a common principle of organization throughout hippocampal-neocortical memory systems.

A large-scale fMRI dataset for the visual processing of naturalistic scenes

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

A large-scale fMRI dataset for human action recognition

Human action recognition is a critical capability for our survival, allowing us to interact easily with the environment and others in everyday life. Although the neural basis of action recognition has been widely studied using a few action categories from simple contexts as stimuli, how the human brain recognizes diverse human actions in real-world environments still needs to be explored. Here, we present the Human Action Dataset (HAD), a large-scale functional magnetic resonance imaging (fMRI) dataset for human action recognition. HAD contains fMRI responses to 21,600 video clips from 30 participants. The video clips encompass 180 human action categories and offer a comprehensive coverage of complex activities in daily life. We demonstrate that the data are reliable within and across participants and, notably, capture rich representation information of the observed human actions. This extensive dataset, with its vast number of action categories and exemplars, has the potential to deepen our understanding of human action recognition in natural environments.

Decoding semantic representations in mind and brain

A key goal for cognitive neuroscience is to understand the neurocognitive systems that support semantic memory. Recent multivariate analyses of neuroimaging data have contributed greatly to this effort, but the rapid development of these novel approaches has made it difficult to track the diversity of findings and to understand how and why they sometimes lead to contradictory conclusions. We address this challenge by reviewing cognitive theories of semantic representation and their neural instantiation. We then consider contemporary approaches to neural decoding and assess which types of representation each can possibly detect. The analysis suggests why the results are heterogeneous and identifies crucial links between cognitive theory, data collection, and analysis that can help to better connect neuroimaging to mechanistic theories of semantic cognition.

Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization

Neutral events preceding emotional experiences can be better remembered, likely by assigning them as significant to guide possible use in future. Yet, the neurobiological mechanisms of how emotional learning enhances memory for past mundane events remain unclear. By two behavioral studies and one functional magnetic resonance imaging study with an adapted sensory preconditioning paradigm, we show rapid neural reactivation and connectivity changes underlying emotion-charged retroactive memory enhancement. Behaviorally, emotional learning retroactively enhanced initial memory for neutral associations across the three studies. Neurally, emotional learning potentiated trial-specific reactivation of overlapping neural traces in the hippocampus and stimulus-relevant neocortex. It further induced rapid hippocampal-neocortical functional reorganization supporting such retroactive memory benefit, as characterized by enhanced hippocampal-neocortical coupling modulated by the amygdala during emotional learning, and a shift of hippocampal connectivity from stimulus-relevant neocortex to distributed transmodal prefrontal-parietal areas at post-learning rests. Together, emotional learning retroactively promotes memory integration for past neutral events through stimulating trial-specific reactivation of overlapping representations and reorganization of associated memories into an integrated network to foster its priority for future use.

Characterizing the shared signals of face familiarity: Long-term acquaintance, voluntary control, and concealed knowledge

In a recent study using cross-experiment multivariate classification of EEG patterns, we found evidence for a shared familiarity signal for faces, patterns of neural activity that successfully separate trials for familiar and unfamiliar faces across participants and modes of familiarization. Here, our aim was to expand upon this research to further characterize the spatio-temporal properties of this signal. By utilizing the information content present for incidental exposure to personally familiar and unfamiliar faces, we tested how the information content in the neural signal unfolds over time under different task demands - giving truthful or deceptive responses to photographs of genuinely familiar and unfamiliar individuals. For this goal, we re-analyzed data from two previously published experiments using within-experiment leave-one-subject-out and cross-experiment classification of face familiarity. We observed that the general face familiarity signal, consistent with its previously described spatio-temporal properties, is present for long-term personally familiar faces under passive viewing, as well as for acknowledged and concealed familiarity responses. Also, central-posterior regions contain information related to deception. We propose that signals in the 200-400 ms window are modulated by top-down task-related anticipation, while the patterns in the 400-600 ms window are influenced by conscious effort to deceive. To our knowledge, this is the first report describing the representational dynamics of concealed knowledge for faces, using time-resolved multivariate classification.