Understanding Human Object Vision: A Picture Is Worth a Thousand Representations.

Objects are the core meaningful elements in our visual environment. Classic theories of object vision focus upon object recognition and are elegant and simple. Some of their proposals still stand, yet the simplicity is gone. Recent evolutions in behavioral paradigms, neuroscientific methods, and computational modeling have allowed vision scientists to uncover the complexity of the multidimensional representational space that underlies object vision. We review these findings and propose that the key to understanding this complexity is to relate object vision to the full repertoire of behavioral goals that underlie human behavior, running far beyond object recognition. There might be no such thing as core object recognition, and if it exists, then its importance is more limited than traditionally thought.

Untangling the Animacy Organization of Occipitotemporal Cortex.

Some of the most impressive functional specializations in the human brain are found in the occipitotemporal cortex (OTC), where several areas exhibit selectivity for a small number of visual categories, such as faces and bodies, and spatially cluster based on stimulus animacy. Previous studies suggest this animacy organization reflects the representation of an intuitive taxonomic hierarchy, distinct from the presence of face- and body-selective areas in OTC. Using human functional magnetic resonance imaging, we investigated the independent contribution of these two factors-the face-body division and taxonomic hierarchy-in accounting for the animacy organization of OTC and whether they might also be reflected in the architecture of several deep neural networks that have not been explicitly trained to differentiate taxonomic relations. We found that graded visual selectivity, based on animal resemblance to human faces and bodies, masquerades as an apparent animacy continuum, which suggests that taxonomy is not a separate factor underlying the organization of the ventral visual pathway.SIGNIFICANCE STATEMENT Portions of the visual cortex are specialized to determine whether types of objects are animate in the sense of being capable of self-movement. Two factors have been proposed as accounting for this animacy organization: representations of faces and bodies and an intuitive taxonomic continuum of humans and animals. We performed an experiment to assess the independent contribution of both of these factors. We found that graded visual representations, based on animal resemblance to human faces and bodies, masquerade as an apparent animacy continuum, suggesting that taxonomy is not a separate factor underlying the organization of areas in the visual cortex.

The unreliable influence of multivariate noise normalization on the reliability of neural dissimilarity.

Representational similarity analysis (RSA) is a key element in the multivariate pattern analysis toolkit. The central construct of the method is the representational dissimilarity matrix (RDM), which can be generated for datasets from different modalities (neuroimaging, behavior, and computational models) and directly correlated in order to evaluate their second-order similarity. Given the inherent noisiness of neuroimaging signals it is important to evaluate the reliability of neuroimaging RDMs in order to determine whether these comparisons are meaningful. Recently, multivariate noise normalization (NNM) has been proposed as a widely applicable method for boosting signal estimates for RSA, regardless of choice of dissimilarity metrics, based on evidence that the analysis improves the within-subject reliability of RDMs (Guggenmos et al. 2018; Walther et al. 2016). We revisited this issue with three fMRI datasets and evaluated the impact of NNM on within- and between-subject reliability and RSA effect sizes using multiple dissimilarity metrics. We also assessed its impact across regions of interest from the same dataset, its interaction with spatial smoothing, and compared it to GLMdenoise, which has also been proposed as a method that improves signal estimates for RSA (Charest et al. 2018). We found that across these tests the impact of NNM was highly variable, as also seems to be the case for other analysis choices. Overall, we suggest being conservative before adding steps and complexities to the (pre)processing pipeline for RSA.

The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks.

Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here, we explore one such perceptual phenomenon, perceiving animacy, and use the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (what an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were better explained by object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, the appearance of an object interfered with proper object identification, such as failing to signal that a cow mug is a mug. The preference in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to represent objects when visual appearance is dissociated from animacy, probably due to a preferred processing of visual features typical of animate objects.SIGNIFICANCE STATEMENT How does the brain represent objects that we perceive around us? Recent advances in artificial intelligence have suggested that object categorization and its neural correlates have now been approximated by neural networks. Here, we show that neural networks can predict animacy according to human behavior but do not explain visual cortex representations. In ventral occipitotemporal cortex, neural activity patterns were strongly biased toward object appearance, to the extent that objects with visual features resembling animals were represented closely to real animals and separated from other objects from the same category. This organization that privileges animals and their features over objects might be the result of learning history and evolutionary constraints.

An investigation of the effect of temporal contiguity training on size-tolerant representations in object-selective cortex.

The human visual system has a remarkable ability to reliably identify objects across variations in appearance, such as variations in viewpoint, lighting and size. Here we used fMRI in humans to test whether temporal contiguity training with natural and altered image dynamics can respectively build and break neural size tolerance for objects. Participants (N â€‹= â€‹23) were presented with sequences of images of "growing" and "shrinking" objects. In half of the trials, the object also changed identity when the size change happened. According to the temporal contiguity hypothesis, and studies with a similar paradigm in monkeys, this training process should alter size tolerance. After the training phase, BOLD responses to each of the object images were measured in the scanner. Neural patterns in LOC and V1 contained information on size, similarity and identity. In LOC, the representation of object identity was partially invariant to changes in size. However, temporal contiguity training did not affect size tolerance in LOC. Size tolerance in human object-selective cortex is more robust to variations in input statistics than expected based on prior work in monkeys supporting the temporal contiguity hypothesis.

Development of visual category selectivity in ventral visual cortex does not require visual experience.

To what extent does functional brain organization rely on sensory input? Here, we show that for the penultimate visual-processing region, ventral-temporal cortex (VTC), visual experience is not the origin of its fundamental organizational property, category selectivity. In the fMRI study reported here, we presented 14 congenitally blind participants with face-, body-, scene-, and object-related natural sounds and presented 20 healthy controls with both auditory and visual stimuli from these categories. Using macroanatomical alignment, response mapping, and surface-based multivoxel pattern analysis, we demonstrated that VTC in blind individuals shows robust discriminatory responses elicited by the four categories and that these patterns of activity in blind subjects could successfully predict the visual categories in sighted controls. These findings were confirmed in a subset of blind participants born without eyes and thus deprived from all light perception since conception. The sounds also could be decoded in primary visual and primary auditory cortex, but these regions did not sustain generalization across modalities. Surprisingly, although not as strong as visual responses, selectivity for auditory stimulation in visual cortex was stronger in blind individuals than in controls. The opposite was observed in primary auditory cortex. Overall, we demonstrated a striking similarity in the cortical response layout of VTC in blind individuals and sighted controls, demonstrating that the overall category-selective map in extrastriate cortex develops independently from visual experience.

Face Repetition Probability Does Not Affect Repetition Suppression in Macaque Inferotemporal Cortex.

Repetition suppression, which refers to reduced neural activity for repeated stimuli, is typically explained by bottom-up or local adaptation mechanisms. However, recent theories have emphasized the role of top-down processes, suggesting that this response reduction reflects the fulfillment of perceptual expectations. To support this, an influential human fMRI study showed that the magnitude of suppression is modulated by the probability of a repetition. No such repetition probability effect was found in macaque inferior temporal (IT) cortex for spiking activity despite the presence of repetition suppression. Contrary to the human fMRI studies that showed an effect of repetition probability, the macaque single-unit study used a large variety of unfamiliar stimuli and the monkeys were not required to attend the stimuli. Here, as in the human fMRI studies, we used faces as stimuli and made the monkeys attend to the stimulus content. We simultaneously recorded spiking activity and local field potentials (LFPs) in the middle lateral face patch (ML) of one monkey (male) and a face-responsive region of another (female). Although we observed significant repetition suppression of spiking activity and high gamma-band LFPs in both animals, there were no effects of repetition probability even when repetitions were task relevant and repetition probability affected behavioral decisions. In conclusion, despite the use of face stimuli and a stimulus-related task, no neural signature of repetition probability was present for faces in a face responsive patch of macaque IT. This further challenges a general perceptual expectation account of repetition suppression.SIGNIFICANCE STATEMENT Repetition suppression is a reduced brain activity for repeated stimuli commonly observed across species. In the predictive coding framework, such suppression is thought to reflect fulfilled perceptual expectations. Although this hypothesis is supported by several human fMRI studies reporting an effect of repetition probability on repetition suppression, this could not be replicated in single-cell recordings in monkey inferior temporal (IT) cortex. Subsequent studies narrowed down the conditions for the effect to requiring attention and being limited to particular stimulus categories such as faces. Here, we show that, even under these conditions, repetition suppression in monkey IT neurons is still unaffected by repetition probability, even in a task with a behavioral effect, challenging the perceptual expectation account of repetition suppression.

The representation of symmetry in multi-voxel response patterns and functional connectivity throughout the ventral visual stream.

Several computational models explain how symmetry might be detected and represented in the human brain. However, while there is an abundance of psychophysical studies on symmetry detection and several neural studies showing where and when symmetry is detected in the brain, important questions remain about how this detection happens and how symmetric patterns are represented. We studied the representation of (vertical) symmetry in regions of the ventral visual stream, using multi-voxel pattern analyses (MVPA) and functional connectivity analyses. Our results suggest that neural representations gradually change throughout the ventral visual stream, from very similar part-based representations for symmetrical and asymmetrical stimuli in V1 and V2, over increasingly different representations for symmetrical and asymmetrical stimuli which are nevertheless still part-based in both V3 and V4, to a more holistic representation for symmetrical compared to asymmetrical stimuli in high-level LOC. This change in representations is accompanied by increased communication between left and right retinotopic areas, evidenced by higher interhemispheric functional connectivity during symmetry perception in areas V2 and V4.

Multi-method brain imaging reveals impaired representations of number as well as altered connectivity in adults with dyscalculia.

Two hypotheses have been proposed about the etiology of neurodevelopmental learning disorders, such as dyslexia and dyscalculia: representation impairments and disrupted access to representations. We implemented a multi-method brain imaging approach to directly investigate these representation and access hypotheses in dyscalculia, a highly prevalent but understudied neurodevelopmental disorder in learning to calculate. We combined several magnetic resonance imaging methods and analyses, including univariate and multivariate analyses, functional and structural connectivity. Our sample comprised 24 adults with dyscalculia and 24 carefully matched controls. Results showed a clear deficit in the non-symbolic magnitude representations in parietal, temporal and frontal regions, as well as hyper-connectivity in visual brain regions in adults with dyscalculia. Dyscalculia in adults was thereby related to both impaired number representations and altered connectivity in the brain. We conclude that dyscalculia is related to impaired number representations as well as altered access to these representations.

Deep Neural Networks as a Computational Model for Human Shape Sensitivity.

Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional 'deep' neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development.

Neural Representations of Natural and Scrambled Movies Progressively Change from Rat Striate to Temporal Cortex.

In recent years, the rodent has come forward as a candidate model for investigating higher level visual abilities such as object vision. This view has been backed up substantially by evidence from behavioral studies that show rats can be trained to express visual object recognition and categorization capabilities. However, almost no studies have investigated the functional properties of rodent extrastriate visual cortex using stimuli that target object vision, leaving a gap compared with the primate literature. Therefore, we recorded single-neuron responses along a proposed ventral pathway in rat visual cortex to investigate hallmarks of primate neural object representations such as preference for intact versus scrambled stimuli and category-selectivity. We presented natural movies containing a rat or no rat as well as their phase-scrambled versions. Population analyses showed increased dissociation in representations of natural versus scrambled stimuli along the targeted stream, but without a clear preference for natural stimuli. Along the measured cortical hierarchy the neural response seemed to be driven increasingly by features that are not V1-like and destroyed by phase-scrambling. However, there was no evidence for category selectivity for the rat versus nonrat distinction. Together, these findings provide insights about differences and commonalities between rodent and primate visual cortex.

Visual and Haptic Shape Processing in the Human Brain: Unisensory Processing, Multisensory Convergence, and Top-Down Influences.

Humans are highly adept at multisensory processing of object shape in both vision and touch. Previous studies have mostly focused on where visually perceived object-shape information can be decoded, with haptic shape processing receiving less attention. Here, we investigate visuo-haptic shape processing in the human brain using multivoxel correlation analyses. Importantly, we use tangible, parametrically defined novel objects as stimuli. Two groups of participants first performed either a visual or haptic similarity-judgment task. The resulting perceptual object-shape spaces were highly similar and matched the physical parameter space. In a subsequent fMRI experiment, objects were first compared within the learned modality and then in the other modality in a one-back task. When correlating neural similarity spaces with perceptual spaces, visually perceived shape was decoded well in the occipital lobe along with the ventral pathway, whereas haptically perceived shape information was mainly found in the parietal lobe, including frontal cortex. Interestingly, ventrolateral occipito-temporal cortex decoded shape in both modalities, highlighting this as an area capable of detailed visuo-haptic shape processing. Finally, we found haptic shape representations in early visual cortex (in the absence of visual input), when participants switched from visual to haptic exploration, suggesting top-down involvement of visual imagery on haptic shape processing.

How learning might strengthen existing visual object representations in human object-selective cortex.

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties.

Homologous involvement of striatum and prefrontal cortex in rodent and human water maze learning.

The multiple memory systems hypothesis posits that dorsal striatum and hippocampus are central nodes in independent memory systems, supporting response-based and place-based learning, respectively. Although our understanding of the function of hippocampus within this framework is relatively well established, the contribution of dorsal striatum is less clear. This in part seems to be due to the heterogeneous nature of dorsal striatum, which receives extensive topographically organized projections from higher cortical areas. Here we quantified neural activity in the intact brain while mice and humans acquired analogous versions of the Morris water maze. We found that dorsomedial striatum and medial prefrontal cortex support the initial acquisition of what is typically considered a hippocampus-dependent spatial learning task. We suggest that the circuit involving dorsomedial striatum and medial prefrontal cortex identified here plays a more task-independent role in early learning than currently thought. Furthermore, our results demonstrate that dorsomedial and dorsolateral striatum serve fundamentally different roles during place learning. The remarkably high degree of anatomical overlap in brain function between mouse and human observed in our study emphasizes the extent of convergence achievable with a well-matched multilevel approach.

Visual categorization of natural movies by rats.

Visual categorization of complex, natural stimuli has been studied for some time in human and nonhuman primates. Recent interest in the rodent as a model for visual perception, including higher-level functional specialization, leads to the question of how rodents would perform on a categorization task using natural stimuli. To answer this question, rats were trained in a two-alternative forced choice task to discriminate movies containing rats from movies containing other objects and from scrambled movies (ordinate-level categorization). Subsequently, transfer to novel, previously unseen stimuli was tested, followed by a series of control probes. The results show that the animals are capable of acquiring a decision rule by abstracting common features from natural movies to generalize categorization to new stimuli. Control probes demonstrate that they did not use single low-level features, such as motion energy or (local) luminance. Significant generalization was even present with stationary snapshots from untrained movies. The variability within and between training and test stimuli, the complexity of natural movies, and the control experiments and analyses all suggest that a more high-level rule based on more complex stimulus features than local luminance-based cues was used to classify the novel stimuli. In conclusion, natural stimuli can be used to probe ordinate-level categorization in rats.

Visual number beats abstract numerical magnitude: format-dependent representation of Arabic digits and dot patterns in human parietal cortex.

In numerical cognition, there is a well-known but contested hypothesis that proposes an abstract representation of numerical magnitude in human intraparietal sulcus (IPS). On the other hand, researchers of object cognition have suggested another hypothesis for brain activity in IPS during the processing of number, namely that this activity simply correlates with the number of visual objects or units that are perceived. We contrasted these two accounts by analyzing multivoxel activity patterns elicited by dot patterns and Arabic digits of different magnitudes while participants were explicitly processing the represented numerical magnitude. The activity pattern elicited by the digit "8" was more similar to the activity pattern elicited by one dot (with which the digit shares the number of visual units but not the magnitude) compared to the activity pattern elicited by eight dots, with which the digit shares the represented abstract numerical magnitude. A multivoxel pattern classifier trained to differentiate one dot from eight dots classified all Arabic digits in the one-dot pattern category, irrespective of the numerical magnitude symbolized by the digit. These results were consistently obtained for different digits in IPS, its subregions, and many other brain regions. As predicted from object cognition theories, the number of presented visual units forms the link between the parietal activation elicited by symbolic and nonsymbolic numbers. The current study is difficult to reconcile with the hypothesis that parietal activation elicited by numbers would reflect a format-independent representation of number.

Influence of lexical status and orthographic similarity on the multi-voxel response of the visual word form area.

Previous studies demonstrated that a region in the left fusiform gyrus, often referred to as the 'visual word form area' (VWFA), is responsive to written words, but the precise functional role of VWFA remains unclear. In the present study, we investigated the influence of orthographic similarity, and lexical factors on the multivoxel response patterns to written stimuli. Using high-resolution fMRI at 7 T, we compared the organization of visual word representations in VWFA to the organization in early visual cortex and a language region in the superior temporal gyrus. Sets of four letter words and pseudowords were presented, in which orthographic similarity was parametrically manipulated. We found that during a lexical decision task VWFA is responsive to the lexical status of a stimulus, but both real words and pseudowords were further processed in terms of orthographic similarity. In contrast, early visual cortex was only responsive to the visual aspects of the stimuli and in the left superior temporal gyrus there was an interaction between lexical status and orthography such that only real words were processed in terms of orthographic similarity. These findings indicate that VWFA represents the word/non-word status of letter strings as well as their orthographic similarity.

Cue-invariant shape recognition in rats as tested with second-order contours.

Nonhuman primates are the main animal model to investigate high-level properties of human cortical vision. For one property, transformation-invariant object recognition, recent studies have revealed interesting and unknown capabilities in rats. Here we report on the ability of rats to rely upon second-order cues that are important to structure the incoming visual images into figure and background. Rats performed a visual shape discrimination task in which the shapes were not only defined by first-order luminance information but also by a variety of second-order cues such as a change in texture properties. Once the rats were acquainted with a first set of second-order stimuli, they showed a surprising degree of generalization towards new second-order stimuli. The limits of these capabilities were tested in various ways, and the ability to extract the shapes broke down only in extreme cases where no local cues were available to solve the task. These results demonstrate how rats are able to make choices based on fairly complex strategies when necessary.

Representations of facial identity information in the ventral visual stream investigated with multivoxel pattern analyses.

The neural basis of face recognition has been investigated extensively. Using fMRI, several regions have been identified in the human ventral visual stream that seem to be involved in processing and identifying faces, but the nature of the face representations in these regions is not well known. In particular, multivoxel pattern analyses have revealed distributed maps within these regions, but did not reveal the organizing principles of these maps. Here we isolated different types of perceptual and conceptual face properties to determine which properties are mapped in which regions. A set of faces was created with systematic manipulations of featural and configural visual characteristics. In a second part of the study, personal and spatial context information was added to all faces except one. The perceptual properties of faces were represented in face regions and in other regions of interest such as early visual and object-selective cortex. Only representations in early visual cortex were correlated with pixel-based similarities between the stimuli. The representation of nonperceptual properties was less distributed. In particular, the spatial location associated with a face was only represented in the parahippocampal place area. These findings demonstrate a relatively distributed representation of perceptual and conceptual face properties that involves both face-selective/sensitive and non-face-selective cortical regions.

Functional specialization in rat occipital and temporal visual cortex.

Recent studies have revealed a surprising degree of functional specialization in rodent visual cortex. Anatomically, suggestions have been made about the existence of hierarchical pathways with similarities to the ventral and dorsal pathways in primates. Here we aimed to characterize some important functional properties in part of the supposed "ventral" pathway in rats. We investigated the functional properties along a progression of five visual areas in awake rats, from primary visual cortex (V1) over lateromedial (LM), latero-intermediate (LI), and laterolateral (LL) areas up to the newly found lateral occipito-temporal cortex (TO). Response latency increased >20 ms from areas V1/LM/LI to areas LL and TO. Orientation and direction selectivity for the used grating patterns increased gradually from V1 to TO. Overall responsiveness and selectivity to shape stimuli decreased from V1 to TO and was increasingly dependent upon shape motion. Neural similarity for shapes could be accounted for by a simple computational model in V1, but not in the other areas. Across areas, we find a gradual change in which stimulus pairs are most discriminable. Finally, tolerance to position changes increased toward TO. These findings provide unique information about possible commonalities and differences between rodents and primates in hierarchical cortical processing.