A data-driven approach to stimulus selection reveals an image-based representation of objects in high-level visual areas

Distinct patterns of neural response to faces from different races in humans and deep networks

Social categories such as the race or ethnicity of an individual are typically conveyed by the visual appearance of the face. The aim of this study was to explore how these differences in facial appearance are represented in human and artificial neural networks. First, we compared the similarity of faces from different races using a neural network trained to discriminate identity. We found that the differences between races were most evident in the fully connected layers of the network. Although these layers were also able to predict behavioural judgements of face identity from human participants, performance was biased toward White faces. Next, we measured the neural response in face-selective regions of the human brain to faces from different races in Asian and White participants. We found distinct patterns of response to faces from different races in face-selective regions. We also found that the spatial pattern of response was more consistent across participants for own-race compared to other-race faces. Together, these findings show that faces from different races elicit different patterns of response in human and artificial neural networks. These differences may underlie the ability to make categorical judgements and explain the behavioural advantage for the recognition of own-race faces.

Spikiness and animacy as potential organizing principles of human ventral visual cortex

Considerable research has been devoted to understanding the fundamental organizing principles of the ventral visual pathway. A recent study revealed a series of 3-4 topographical maps arranged along the macaque inferotemporal (IT) cortex. The maps articulated a two-dimensional space based on the spikiness and animacy of visual objects, with "inanimate-spiky" and "inanimate-stubby" regions of the maps constituting two previously unidentified cortical networks. The goal of our study was to determine whether a similar functional organization might exist in human IT. To address this question, we presented the same object stimuli and images from "classic" object categories (bodies, faces, houses) to humans while recording fMRI activity at 7 Tesla. Contrasts designed to reveal the spikiness-animacy object space evoked extensive significant activation across human IT. However, unlike the macaque, we did not observe a clear sequence of complete maps, and selectivity for the spikiness-animacy space was deeply and mutually entangled with category-selectivity. Instead, we observed multiple new stimulus preferences in category-selective regions, including functional sub-structure related to object spikiness in scene-selective cortex. Taken together, these findings highlight spikiness as a promising organizing principle of human IT and provide new insights into the role of category-selective regions in visual object processing.

Neural substrates of the executive function construct, age-related changes, and task materials in adolescents and adults: ALE meta-analyses of 408 fMRI studies

To explore the neural substrates of executive function (EF), we conducted an activation likelihood estimation meta-analysis of 408 functional magnetic resonance imaging studies (9639 participants, 7587 activation foci, 518 experimental contrasts) covering three fundamental EF subcomponents: inhibition, switching, and working memory. Our results found that activation common to all three EF subcomponents converged in the multiple-demand network across adolescence and adulthood. The function of EF with the multiple-demand network involved, especially for the prefrontal cortex and the parietal regions, could not be mature until adulthood. In adolescents, only working memory could be separable from common EF, whereas in adults, the three EF subcomponents could be separable from common EF. However, findings of switching in adolescents should be treated with substantial caution and may be exploratory due to limited data available on switching tasks. For task materials, inhibition and working memory showed both domain generality and domain specificity, undergirded by the multiple-demand network, as well as different brain regions in response to verbal and nonverbal task materials, respectively. In contrast, switching showed only domain generality with no activation specialized for either verbal or nonverbal task materials. These findings, taken together, support and contribute to the unitary-diverse nature of EF such that EF should be interpreted in an integrative model that relies on the integration of the EF construct, development, and task materials.

Circadian Responses to Light-Flash Exposure: Conceptualization and New Data Guiding Future Directions

A growing number of studies document circadian phase-shifting after exposure to millisecond light flashes. When strung together by intervening periods of darkness, these stimuli evoke pacemaker responses rivaling or outmatching those created by steady luminance, suggesting that the circadian system's relationship to light can be contextualized outside the principle of simple dose-dependence. In the current review, we present a brief chronology of this work. We then develop a conceptual model around it that attempts to relate the circadian effects of flashes to a natural integrative process the pacemaker uses to intermittently sample the photic information available at dawn and dusk. Presumably, these snapshots are employed as building blocks in the construction of a coherent representation of twilight the pacemaker consults to orient the next day's physiology (in that way, flash-resetting of pacemaker rhythms might be less an example of a circadian visual illusion and more an example of the kinds of gestalt inferences that the image-forming system routinely makes when identifying objects within the visual field; i.e., closure). We conclude our review with a discussion on the role of cones in the pacemaker's twilight predictions, providing new electrophysiological data suggesting that classical photoreceptors—but not melanopsin—are necessary for millisecond, intermediate-intensity flash responses in ipRGCs (intrinsically photosensitive retinal ganglion cells). Future investigations are necessary to confirm this “Cone Sentinel Model” of circadian flash-integration and twilight-prediction, and to further define the contribution of cones vs. rods in transducing pacemaker flash signals.

Fast Periodic Visual Stimulation indexes preserved semantic memory in healthy ageing

Behavioural studies investigating the preservation of semantic memory in healthy ageing have reported mixed findings. One suggested reason for this discrepancy is that the processes underpinning lexical access to semantic knowledge may be sensitive to ageing. It is therefore necessary to assess semantic memory utilising tasks that are not explicitly linguistic. In this study, a fast periodic visual stimulation (FPVS) paradigm coupled with EEG was used to assess the ability of younger and older adults to automatically distinguish between images by their semantic category. Participants were presented with a 6 Hz stream of images drawn from one semantic category except every fifth image (occurring at a rate of 1.2 Hz) which was drawn from an alternate semantic category. For both younger and older adults, results demonstrate successful and comparable semantic categorisation. This was detectable at the individual level for 71% and 72% of older and younger adults, respectively. Given the rapid presentation rate and absence of explicit instruction to categorise images, the task is unlikely to utilise linguistic strategies and suggests the maintenance of semantic memory in healthy ageing. Moreover, this study utilised mobile EEG equipment and short presentation times that would be suitable for practical application outside a research setting.

Recent advances in understanding object recognition in the human brain: deep neural networks, temporal dynamics, and context

Object recognition is the ability to identify an object or category based on the combination of visual features observed. It is a remarkable feat of the human brain, given that the patterns of light received by the eye associated with the properties of a given object vary widely with simple changes in viewing angle, ambient lighting, and distance. Furthermore, different exemplars of a specific object category can vary widely in visual appearance, such that successful categorization requires generalization across disparate visual features. In this review, we discuss recent advances in understanding the neural representations underlying object recognition in the human brain. We highlight three current trends in the approach towards this goal within the field of cognitive neuroscience. Firstly, we consider the influence of deep neural networks both as potential models of object vision and in how their representations relate to those in the human brain. Secondly, we review the contribution that time-series neuroimaging methods have made towards understanding the temporal dynamics of object representations beyond their spatial organization within different brain regions. Finally, we argue that an increasing emphasis on the context (both visual and task) within which object recognition occurs has led to a broader conceptualization of what constitutes an object representation for the brain. We conclude by identifying some current challenges facing the experimental pursuit of understanding object recognition and outline some emerging directions that are likely to yield new insight into this complex cognitive process.

What do across-subject analyses really tell us about neural coding?

A key challenge in human neuroscience is to gain information about patterns of neural activity using indirect measures. Multivariate pattern analysis methods testing for generalization of information across subjects have been used to support inferences regarding neural coding. One critical assumption of an important class of such methods is that anatomical normalization is suited to align spatially-structured neural patterns across individual brains. We asked whether anatomical normalization is suited for this purpose. If not, what sources of information are such across-subject cross-validated analyses likely to reveal? To investigate these questions, we implemented two-layered feedforward randomly-connected networks. A key feature of these simulations was a gain-field with a spatial structure shared across networks. To investigate whether total-signal imbalances across conditions-e.g. differences in overall activity-affect the observed pattern of results, we manipulated the energy-profile of images conforming to a pre-specified correlation structure. To investigate whether the level of granularity of the data also influences results, we manipulated the density of connections between network layers. Simulations showed that anatomical normalization is unsuited to align neural representations. Pattern similarity-relationships were explained by the observed total-signal imbalances across conditions. Further, we observed that deceptively complex representational structures emerge from arbitrary analysis choices, such as whether the data are mean-subtracted during preprocessing. These simulations also led to testable predictions regarding the distribution of low-level features in images used in recent fMRI studies that relied on leave-one-subject-out pattern analyses. Image analyses broadly confirmed these predictions. Finally, hyperalignment emerged as a principled alternative to test across-subject generalization of spatially-structured information. We illustrate cases in which hyperalignment proved successful, as well as cases in which it only partially recovered the latent correlation structure in the pattern of responses. Our results highlight the need for robust, high-resolution measurements from individual subjects. We also offer a way forward for across-subject analyses. We suggest ways to inform hyperalignment results with estimates of the strength of the signal associated with each condition. Such information can usefully constrain ensuing inferences regarding latent representational structures as well as population tuning dimensions.

A data-driven approach to stimulus selection reveals an image-based representation of objects in high-level visual areas

The ventral visual pathway is directly involved in the perception and recognition of objects. However, the extent to which the neural representation of objects in this region reflects low-level or high-level properties remains unresolved. A problem in resolving this issue is that only a small proportion of the objects experienced during natural viewing can be shown during a typical experiment. This can lead to an uneven sampling of objects that biases our understanding of how they are represented. To address this issue, we developed a data-driven approach to stimulus selection that involved describing a large number objects in terms of their image properties. In the first experiment, clusters of objects were evenly selected from this multi-dimensional image space. Although the clusters did not have any consistent semantic features, each elicited a distinct pattern of neural response. In the second experiment, we asked whether high-level, category-selective patterns of response could be elicited by objects from other categories, but with similar image properties. Object clusters were selected based on the similarity of their image properties to objects from five different categories (bottle, chair, face, house, and shoe). The pattern of response to each metameric object cluster was similar to the pattern elicited by objects from the corresponding category. For example, the pattern for bottles was similar to the pattern for objects with similar image properties to bottles. In both experiments, the patterns of response were consistent across participants providing evidence for common organising principles. This study provides a more ecological approach to understanding the perceptual representations of objects and reveals the importance of image properties.