The Neural Representation of Personally Familiar and Unfamiliar Faces in the Distributed System for Face Perception

Personally familiar faces are processed more robustly and efficiently than unfamiliar faces. The human face processing system comprises a core system that analyzes the visual appearance of faces and an extended system for the retrieval of person-knowledge and other nonvisual information. We applied multivariate pattern analysis to fMRI data to investigate aspects of familiarity that are shared by all familiar identities and information that distinguishes specific face identities from each other. Both identity-independent familiarity information and face identity could be decoded in an overlapping set of areas in the core and extended systems. Representational similarity analysis revealed a clear distinction between the two systems and a subdivision of the core system into ventral, dorsal and anterior components. This study provides evidence that activity in the extended system carries information about both individual identities and personal familiarity, while clarifying and extending the organization of the core system for face perception

Prioritized Detection of Personally Familiar Faces

We investigated whether personally familiar faces are preferentially processed in conditions of reduced attentional resources and in the absence of conscious awareness. In the first experiment, we used Rapid Serial Visual Presentation (RSVP) to test the susceptibility of familiar faces and faces of strangers to the attentional blink. In the second experiment, we used continuous flash interocular suppression to render stimuli invisible and measured face detection time for personally familiar faces as compared to faces of strangers. In both experiments we found an advantage for detection of personally familiar faces as compared to faces of strangers. Our data suggest that the identity of faces is processed with reduced attentional resources and even in the absence of awareness. Our results show that this facilitated processing of familiar faces cannot be attributed to detection of low-level visual features and that a learned unique configuration of facial features can influence preconscious perceptual processing

Graph schemas as abstractions for transfer learning, inference, and planning

We propose schemas as a model for abstractions that can be used for rapid transfer learning, inference, and planning. Common structured representations of concepts and behaviors -- schemas -- have been proposed as a powerful way to encode abstractions. Latent graph learning is emerging as a new computational model of the hippocampus to explain map learning and transitive inference. We build on this work to show that learned latent graphs in these models have a slot structure -- schemas -- that allow for quick knowledge transfer across environments. In a new environment, an agent can rapidly learn new bindings between the sensory stream to multiple latent schemas and select the best fitting one to guide behavior. To evaluate these graph schemas, we use two previously published challenging tasks: the memory & planning game and one-shot StreetLearn, that are designed to test rapid task solving in novel environments. Graph schemas can be learned in far fewer episodes than previous baselines, and can model and plan in a few steps in novel variations of these tasks. We further demonstrate learning, matching, and reusing graph schemas in navigation tasks in more challenging environments with aliased observations and size variations, and show how different schemas can be composed to model larger 2D and 3D environments.Comment: 12 pages, 5 figures in main paper, 12 pages and 8 figures in appendi

High-resolution 7-Tesla fMRI data on the perception of musical genres – an extension to the studyforrest dataset

Here we present an extension to the studyforrest dataset – a versatile resource for studying the behavior of the human brain in situations of real-life complexity (http://studyforrest.org). This release adds more high-resolution, ultra high-field (7 Tesla) functional magnetic resonance imaging (fMRI) data from the same individuals. The twenty participants were repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm. The data release includes raw fMRI data, as well as precomputed structural alignments for within-subject and group analysis. In addition to fMRI, simultaneously recorded cardiac and respiratory traces, as well the complete implementation of the stimulation paradigm, including stimuli, are provided. An initial quality control analysis reveals distinguishable patterns of response to individual genres throughout a large expanse of areas known to be involved in auditory and speech processing. The present data can be used to, for example, generate encoding models for music perception that can be validated against the previously released fMRI data from stimulation with the “Forrest Gump” audio-movie and its rich musical content. In order to facilitate replicative and derived works, only free and open-source software was utilized

Reading Faces: From Features to Recognition

Chang and Tsao recently reported that the monkey face patch system encodes facial identity in a space of facial features as opposed to exemplars. Here, we discuss how such coding might contribute to face recognition, emphasizing the critical role of learning and interactions with other brain areas for optimizing the recognition of familiar faces

A computational model of shared fine-scale structure in the human connectome

<div><p>Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.</p></div

Spatial granularity of shared connectivity profiles.

<p>The intersubject point spread function (PSF) of connectivity profile correlations are computed as the correlation between the connectivity profile for a cortical locus in one subject and the profiles of the same locus and its spatial neighbors in other subjects at increasing distances from that locus. For the HCP rsfMRI data, within-subject PSFs are computed as the correlation between the connectivity profile for a cortical locus from one rsfMRI session and the profiles of the same locus and its spatial neighbors from a different rsfMRI session. Slope is estimated in each functional ROI as the linear fit of intersubject or within subject correlations as a function of distance. (A) Slope of PSFs for movie viewing connectivity profiles in 24 functional ROIs. (B) Average movie viewing connectivity PSF across all ROIs is plotted as ISC as a function of cortical distance. (C) Slope of PSFs for resting state connectivity profiles in 26 functional ROIs. (D) Average resting state connectivity PSF across all ROIs is plotted as ISC or WSC as a function of cortical distance.</p

Effect of CHA on ISC of representational geometries and bsMVPC of movie data.

<p>(A) ISC of representational geometry in each voxel mapped onto the cortical surface. (B) Accuracies for bsMVPC of 15 s movie segments. Classification was performed within each movie half separately, and the accuracies are then averaged across the two halves. Parameters for hyperalignment are derived from the half that was not used for classification.</p

Mean group connectivity patterns in a left lateral-occipital/inferior temporal cortical field.

<p>Connectivity patterns were measured from movie data for functional connectivity with connectivity targets in mid lateral fusiform gyrus and mid superior temporal sulcus. Mean group connectivity patterns are shown for data in the common model connectome, derived with CHA based on responses to the other half of the movie, and for anatomically aligned data. Mean ISCs for patterns after CHA are higher than after anatomical alignment for both the fusiform target (0.835 versus 0.175) and the STS target (0.826 versus 0.306). The occipitotemporal, mid fusiform, and mid STS loci are taken from the face-responsive fields identified by Visconti di Oleggio Castello, Halchenko, et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006120#pcbi.1006120.ref028" target="_blank">28</a>]. The locations of the fusiform and STS targets are indicated with green and blue dots, respectively. The inflated cortical surface is tipped to provide a clear view of the cortical field. Connectivities are correlations of time-series responses to the movie.</p