Visual experience is not necessary for the development of face-selectivity in the lateral fusiform gyrus.

The fusiform face area responds selectively to faces and is causally involved in face perception. How does face-selectivity in the fusiform arise in development, and why does it develop so systematically in the same location across individuals? Preferential cortical responses to faces develop early in infancy, yet evidence is conflicting on the central question of whether visual experience with faces is necessary. Here, we revisit this question by scanning congenitally blind individuals with fMRI while they haptically explored 3D-printed faces and other stimuli. We found robust face-selective responses in the lateral fusiform gyrus of individual blind participants during haptic exploration of stimuli, indicating that neither visual experience with faces nor fovea-biased inputs is necessary for face-selectivity to arise in the lateral fusiform gyrus. Our results instead suggest a role for long-range connectivity in specifying the location of face-selectivity in the human brain.

Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.

Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively.

Seeing a straight line on a curved surface: decoupling of patterns from surfaces by single IT neurons.

We have no difficulty seeing a straight line drawn on a paper even when the paper is bent, but this inference is in fact nontrivial. Doing so requires either matching local features or representing the pattern after factoring out the surface shape. Here we show that single neurons in the monkey inferior temporal (IT) cortex show invariant responses to patterns across rigid and nonrigid changes of surfaces. We recorded neuronal responses to stimuli in which the pattern and the surrounding surface were varied independently. In a subset of neurons, we found pattern-surface interactions that produced similar responses to stimuli across congruent pattern and surface transformations. These interactions produced systematic shifts in curvature tuning of patterns when overlaid on convex and flat surfaces. Our results show that surfaces are factored out of patterns by single neurons, thereby enabling complex perceptual inferences. NEW & NOTEWORTHY: We have no difficulty seeing a straight line on a curved piece of paper, but in fact, doing so requires decoupling the shape of the surface from the pattern itself. Here we report a novel form of invariance in the visual cortex: single neurons in monkey inferior temporal cortex respond similarly to congruent transformations of patterns and surfaces, in effect decoupling patterns from the surface on which they are overlaid.

Effect of silhouetting and inversion on view invariance in the monkey inferotemporal cortex.

We effortlessly recognize objects across changes in viewpoint, but we know relatively little about the features that underlie viewpoint invariance in the brain. Here, we set out to characterize how viewpoint invariance in monkey inferior temporal (IT) neurons is influenced by two image manipulations-silhouetting and inversion. Reducing an object into its silhouette removes internal detail, so this would reveal how much viewpoint invariance depends on the external contours. Inverting an object retains but rearranges features, so this would reveal how much viewpoint invariance depends on the arrangement and orientation of features. Our main findings are 1) view invariance is weakened by silhouetting but not by inversion; 2) view invariance was stronger in neurons that generalized across silhouetting and inversion; 3) neuronal responses to natural objects matched early with that of silhouettes and only later to that of inverted objects, indicative of coarse-to-fine processing; and 4) the impact of silhouetting and inversion depended on object structure. Taken together, our results elucidate the underlying features and dynamics of view-invariant object representations in the brain.NEW & NOTEWORTHY We easily recognize objects across changes in viewpoint, but the underlying features are unknown. Here, we show that view invariance in the monkey inferotemporal cortex is driven mainly by external object contours and is not specialized for object orientation. We also find that the responses to natural objects match with that of their silhouettes early in the response, and with inverted versions later in the response-indicative of a coarse-to-fine processing sequence in the brain.

Dynamics of 3D view invariance in monkey inferotemporal cortex.

Rotations in depth are challenging for object vision because features can appear, disappear, be stretched or compressed. Yet we easily recognize objects across views. Are the underlying representations view invariant or dependent? This question has been intensely debated in human vision, but the neuronal representations remain poorly understood. Here, we show that for naturalistic objects, neurons in the monkey inferotemporal (IT) cortex undergo a dynamic transition in time, whereby they are initially sensitive to viewpoint and later encode view-invariant object identity. This transition depended on two aspects of object structure: it was strongest when objects foreshortened strongly across views and were similar to each other. View invariance in IT neurons was present even when objects were reduced to silhouettes, suggesting that it can arise through similarity between external contours of objects across views. Our results elucidate the viewpoint debate by showing that view invariance arises dynamically in IT neurons out of a representation that is initially view dependent.

Modeling short visual events through the BOLD moments video fMRI dataset and metadata.

Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1000 short (3 s) naturalistic video clips of visual events across ten human subjects. We use the videos' extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex. Furthermore, we reveal a match in hierarchical processing between cortical regions of interest and video-computable deep neural networks, and we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. With its rich metadata, BMD offers new perspectives and accelerates research on the human brain basis of visual event perception.

A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition.

Prior work has identified cortical regions selectively responsive to specific categories of visual stimuli. However, this hypothesis-driven work cannot reveal how prominent these category selectivities are in the overall functional organization of the visual cortex, or what others might exist that scientists have not thought to look for. Furthermore, standard voxel-wise tests cannot detect distinct neural selectivities that coexist within voxels. To overcome these limitations, we used data-driven voxel decomposition methods to identify the main components underlying fMRI responses to thousands of complex photographic images. Our hypothesis-neutral analysis rediscovered components selective for faces, places, bodies, and words, validating our method and showing that these selectivities are dominant features of the ventral visual pathway. The analysis also revealed an unexpected component with a distinct anatomical distribution that responded highly selectively to images of food. Alternative accounts based on low- to mid-level visual features, such as color, shape, or texture, failed to account for the food selectivity of this component. High-throughput testing and control experiments with matched stimuli on a highly accurate computational model of this component confirm its selectivity for food. We registered our methods and hypotheses before replicating them on held-out participants and in a novel dataset. These findings demonstrate the power of data-driven methods and show that the dominant neural responses of the ventral visual pathway include not only selectivities for faces, scenes, bodies, and words but also the visually heterogeneous category of food, thus constraining accounts of when and why functional specialization arises in the cortex.

Decoding perceptual awareness across the brain with a no-report fMRI masking paradigm.

Does perceptual awareness arise within the sensory regions of the brain or within higher-level regions (e.g., the frontal lobe)? To answer this question, researchers traditionally compare neural activity when observers report being aware versus being unaware of a stimulus. However, it is unclear whether the resulting activations are associated with the conscious perception of the stimulus or the post-perceptual processes associated with reporting that stimulus. To address this limitation, we used both report and no-report conditions in a visual masking paradigm while participants were scanned using functional MRI (fMRI). We found that the overall univariate response to visible stimuli in the frontal lobe was robust in the report condition but disappeared in the no-report condition. However, using multivariate patterns, we could still decode in both conditions whether a stimulus reached conscious awareness across the brain, including in the frontal lobe. These results help reconcile key discrepancies in the recent literature and provide a path forward for identifying the neural mechanisms associated with perceptual awareness.

Computational models of category-selective brain regions enable high-throughput tests of selectivity.

Cortical regions apparently selective to faces, places, and bodies have provided important evidence for domain-specific theories of human cognition, development, and evolution. But claims of category selectivity are not quantitatively precise and remain vulnerable to empirical refutation. Here we develop artificial neural network-based encoding models that accurately predict the response to novel images in the fusiform face area, parahippocampal place area, and extrastriate body area, outperforming descriptive models and experts. We use these models to subject claims of category selectivity to strong tests, by screening for and synthesizing images predicted to produce high responses. We find that these high-response-predicted images are all unambiguous members of the hypothesized preferred category for each region. These results provide accurate, image-computable encoding models of each category-selective region, strengthen evidence for domain specificity in the brain, and point the way for future research characterizing the functional organization of the brain with unprecedented computational precision.

Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence.

A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains of human intelligence as executable, neurally mechanistic models. Years of research have led to models that capture experimental results in individual behavioral tasks and individual brain regions. We here advocate for taking the next step: integrating experimental results from many laboratories into suites of benchmarks that, when considered together, push mechanistic models toward explaining entire domains of intelligence, such as vision, language, and motor control. Given recent successes of neurally mechanistic models and the surging availability of neural, anatomical, and behavioral data, we believe that now is the time to create integrative benchmarking platforms that incentivize ambitious, unified models. This perspective discusses the advantages and the challenges of this approach and proposes specific steps to achieve this goal in the domain of visual intelligence with the case study of an integrative benchmarking platform called Brain-Score.

A Balanced Comparison of Object Invariances in Monkey IT Neurons.

Our ability to recognize objects across variations in size, position, or rotation is based on invariant object representations in higher visual cortex. However, we know little about how these invariances are related. Are some invariances harder than others? Do some invariances arise faster than others? These comparisons can be made only upon equating image changes across transformations. Here, we targeted invariant neural representations in the monkey inferotemporal (IT) cortex using object images with balanced changes in size, position, and rotation. Across the recorded population, IT neurons generalized across size and position both stronger and faster than to rotations in the image plane as well as in depth. We obtained a similar ordering of invariances in deep neural networks but not in low-level visual representations. Thus, invariant neural representations dynamically evolve in a temporal order reflective of their underlying computational complexity.