Search

Recently, we drew on evidence from neuroimaging, computational modelling, and neuropsychology to suggest that global shape information may be computed in the dorsal visual pathway and transmitted to the ventral pathway to support object recognition. In their commentary, Goodale and Milner argued that this framework was inconsistent with the response profile of a single patient, RV, who had bilateral dorsal lesions. They found that RV could discriminate simple geometric figures from one another but had difficulty grasping these very same stimuli – a finding consistent with their characterization of the dorsal pathway as a system for processing visuomotor information. Goodale and Milner further argued that because the stimuli they used were matched along many visual dimensions, RV could only have discriminated them on the basis of global shape. Given that RV had lesions to the dorsal pathway, Goodale and Milner took their results to mean that the dorsal pathway is not involved in global shape perception. Here we evaluated this assumption by asking whether deep neural network models (DNNs), which have difficulty representing global shape, could also discriminate between these stimuli.

Despite their anatomical and functional distinctions, there is growing evidence that the dorsal and ventral visual pathways interact to support object recognition. However, the exact nature of these interactions remains poorly understood. Is the presence of identity-relevant object information in the dorsal pathway simply a byproduct of ventral input? Or, might the dorsal pathway be a source of input to the ventral pathway for object recognition? In the current study, we used high-density EEG—a technique with high temporal precision and spatial resolution sufficient to distinguish parietal and temporal lobes—to characterise the dynamics of dorsal and ventral pathways during object viewing. Using multivariate analyses, we found that category decoding in the dorsal pathway preceded that in the ventral pathway. Importantly, the dorsal pathway predicted the multivariate responses of the ventral pathway in a time-dependent manner, rather than the other way around. Together, these findings suggest that the dorsal pathway is a critical source of input to the ventral pathway for object recognition.

A rich behavioral literature has shown that human object recognition is supported by a representation of shape that is tolerant to variations in an object's appearance. Such 'global' shape representations are achieved by describing objects via the spatial arrangement of their local features, or structure, rather than by the appearance of the features themselves. However, accumulating evidence suggests that the ventral visual pathway - the primary substrate underlying object recognition - may not represent global shape. Instead, ventral representations may be better described as a basis set of local image features. We suggest that this evidence forces a reevaluation of the role of the ventral pathway in object perception and posits a broader network for shape perception that encompasses contributions from the dorsal pathway.

Object recognition is a fundamental cognitive ability that helps humans organize the visual world into meaningful perceptual units. To understand the nature of object recognition in humans fully, it is important to understand its developmental origins in infancy and the processes by which it reaches maturity. At birth, infants demonstrate a wide range of visual competencies including the ability to discriminate and categorize simple shapes. By 6-months, infants readily form holistic and three-dimensional shape representations which allow them to recognize objects in a viewpoint-invariant manner and categorize objects after exposure to only a few exemplars. Remarkably, infants acquire these skills with experience with just a few objects and, on average, only three faces. Several infant-specific biological and experiential constraints support the rapid development of object recognition. For instance, infants are born with innate perceptual biases for features like top-heaviness and symmetry that provide a scaffold for learning objects. Moreover, rather than being a hindrance, low visual acuity in infancy supports the development of holistic shape representations, allowing even newborns to perceptually group elements into a complete shape. Finally, infants visual experience with objects is seemingly ‘self-curated’ to support object learning, such that they display a bias for viewpoints that maximally display objects’ shape structure. Indeed, incorporating infant-like constraints into artificial neural network models improves the model’s performance on object recognition tasks. The neural mechanisms that support these abilities in infancy, however, may be different than adults. Although early visual cortex is relatively mature in infants, category-selective regions in higher-level areas of the ventral visual pathway do not mature fully until at least adolescence. Instead, neuroimaging and computational modelling research suggests that object recognition in infancy is supported by a more distributed neural code that represents lower-level features. Altogether, these studies suggest that the development of object recognition is bootstrapped by biological and experiential factors specific to infants’ developmental niche. By understanding the origins and development of object recognition, we gain a deeper understanding of the mechanisms that support object recognition in adulthood.

To accurately represent an object, it must be individuated from the surrounding objects and then classified with the appropriate category or identity. To this end, adults flexibly weight different visual cues when perceiving objects. However, less is known about whether, and how, the weighting of visual object information changes over development. The current study examined how children use different types of information— spatial (e.g., left/right location) and featural (e.g., color)—in different object tasks. In Experiment 1, we tested whether infants and preschoolers extract both the spatial and featural properties of objects, and, importantly, how these cues are weighted when pitted against each other. We found that infants relied primarily on spatial cues and neglected featural cues. By contrast, preschoolers showed the opposite pattern of weighting, placing greater weight on featural information. In Experiment 2, we tested the hypothesis that the developmental shift from spatial to featural weighting reflects a shift from a priority on object individuation (how many objects) in infancy to object classification (what are the objects) at preschool age. Here, we found that preschoolers weighted spatial information more than features when the task required individuating objects without identifying them, consistent with a specific role for spatial information in object individuation. We discuss the relevance of spatial-featural weighting in relation to developmental changes in children’s object representations.

Although there is mounting evidence that input from the dorsal visual pathway is crucial for object processes in the ventral pathway, the specific functional contributions of dorsal cortex to these processes remain poorly understood. Here, we hypothesized that dorsal cortex computes the spatial relations among an object’s parts – a processes crucial for forming global shape percepts – and transmits this information to the ventral pathway to support object categorization. Using fMRI with human participants (females and males), we discovered regions in the intraparietal sulcus (IPS) that were selectively involved in computing object-centered part relations. These regions exhibited task-dependent functional and effective connectivity with ventral cortex, and were distinct from other dorsal regions, such as those representing allocentric relations, 3D shape, and tools. In a subsequent experiment, we found that the multivariate response of posterior IPS, defined on the basis of part-relations, could be used to decode object category at levels comparable to ventral object regions. Moreover, mediation and multivariate effective connectivity analyses further suggested that IPS may account for representations of part relations in the ventral pathway. Together, our results highlight specific contributions of the dorsal visual pathway to object recognition. We suggest that dorsal cortex is a crucial source of input to the ventral pathway and may support the ability to categorize objects on the basis of global shape.Significance StatementHumans categorize novel objects rapidly and effortlessly. Such categorization is achieved by representing an object’s global shape structure, that is, the relations among object parts. Yet, despite their importance, it is unclear how part relations are represented neurally. Here, we hypothesized that object-centered part relations may be computed by the dorsal visual pathway, which is typically implicated in visuospatial processing. Using fMRI, we identified regions selective for the part relations in dorsal cortex. We found that these regions can support object categorization, and even mediate representations of part relations in the ventral pathway, the region typically thought to support object categorization. Together, these findings shed light on the broader network of brain regions that support object categorization.

What is the relation between navigation and object analysis? Although researchers agree that such systems are, to some extent, dissociable, open questions remain about the potential interactions between them. Using an individual differences approach with young children (4-year-olds; N = 90), we examined the novel proposal that navigation and object analysis interact indirectly by way of magnitude perception. A system for magnitude perception may be ideally suited to support such interactions because it is present from early in development and is capable of discriminating geometric properties on the basis of spatial extent. Across two experiments, we tested for relations between children’s ability to reorient within an environment, analyze the geometric properties of object forms, and discriminate area. We found that area precision was correlated with both reorientation ability and geometric form analysis, though reorientation and form analysis did not correlate with one another. Importantly, we also found that the precision of children’s area discrimination supported an indirect link between their reorientation ability in a physical environment and visual analysis of object forms. Together, these results suggest a network for representing Euclidean geometry that includes both direct and indirect relations among navigation, object analysis, and magnitude perception. The potential role of magnitude perception within such a network is discussed.

Shape perception is crucial for object recognition. However, it remains unknown exactly how shape information is represented and used by the visual system. Here, we tested the hypothesis that the visual system represents object shape via a skeletal structure. Using functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA), we found that a model of skeletal similarity explained significant unique variance in the response profiles of V3 and LO. Moreover, the skeletal model remained predictive in these regions even when controlling for other models of visual similarity that approximate low-to high-level visual features (i.e., Gabor-jet, GIST, HMAX, and AlexNet), and across different surface forms, a manipulation that altered object contours while preserving the underlying skeleton. Together, these findings shed light on shape processing in human vision, as well as the computational properties of V3 and LO. We discuss how these regions may support two putative roles of shape skeletons: namely, perceptual organization and object recognition.

Shape perception is crucial for object recognition. However, it remains unknown exactly how shape information is represented, and, consequently, used by the visual system. Here, we hypothesized that the visual system represents “shape skeletons” to both (1) perceptually organize contours and component parts into a shape percept, and (2) compare shapes to recognize objects. Using functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA), we found that a model of skeletal similarity explained significant unique variance in the response profiles of V3 and LO, regions known to be involved in perceptual organization and object recognition, respectively. Moreover, the skeletal model remained predictive in these regions even when controlling for other models of visual similarity that approximate low- to high-level visual features (i.e., Gabor-jet, GIST, HMAX, and AlexNet), and across different surface forms, a manipulation that altered object contours while preserving the underlying skeleton. Together, these findings shed light on the functional roles of shape skeletons in human vision, as well as the computational properties of V3 and LO.

A representation of shape that is low dimensional and stable across minor disruptions is critical for object recognition. Computer vision research suggests that such a representation can be supported by the medial axis-a computational model for extracting a shape's internal skeleton. However, few studies have shown evidence of medial axis processing in humans, and even fewer have examined how the medial axis is extracted in the presence of disruptive contours. Here, we tested whether human skeletal representations of shape reflect the medial axis transform (MAT), a computation sensitive to all available contours, or a pruned medial axis, which ignores contours that may be considered ''noise.'' Across three experiments, participants (N = 2062) were shown complete, perturbed, or illusory two-dimensional shapes on a tablet computer and were asked to tap the shapes anywhere once. When directly compared with another viable model of shape perception (based on principal axes), participants' collective responses were better fit by the medial axis, and a direct test of boundary avoidance suggested that this result was not likely because of a task-specific cognitive strategy (Experiment 1). Moreover, participants' responses reflected a pruned computation in shapes with small or large internal or external perturbations (Experiment 2) and under conditions of illusory contours (Experiment 3). These findings extend previous work by suggesting that humans extract a relatively stable medial axis of shapes. A relatively stable skeletal representation, reflected by a pruned model, may be well equipped to support real-world shape perception and object recognition.

With seemingly little effort, humans can both identify an object across large changes in orientation and extend category membership to novel exemplars. Although researchers argue that object shape is crucial in these cases, there are open questions as to how shape is represented for object recognition. Here we tested whether the human visual system incorporates a three-dimensional skeletal descriptor of shape to determine an object’s identity. Skeletal models not only provide a compact description of an object’s global shape structure, but also provide a quantitative metric by which to compare the visual similarity between shapes. Our results showed that a model of skeletal similarity explained the greatest amount of variance in participants’ object dissimilarity judgments when compared with other computational models of visual similarity (Experiment 1). Moreover, parametric changes to an object’s skeleton led to proportional changes in perceived similarity, even when controlling for another model of structure (Experiment 2). Importantly, participants preferentially categorized objects by their skeletons across changes to local shape contours and non-accidental properties (Experiment 3). Our findings highlight the importance of skeletal structure in vision, not only as a shape descriptor, but also as a diagnostic cue of object identity.

Leibovich et al. claim that number representations are non-existent early in life and that the associations between number and continuous magnitudes reside in stimulus confounds. We challenge both claims – positing, instead, that number is represented independently of continuous magnitudes already in infancy, but is nonetheless more deeply connected to other magnitudes through adulthood than acknowledged by the “sense of magnitude” theory.

Despite general agreement that number and other magnitudes share analog format, there is disagreement about the extent to which representations of numerical and non-numerical magnitude recruit common cognitive and neural resources. Cross-dimensional interactions between number and other magnitudes on Stroop-like tasks have been taken as evidence for integration across magnitudes, but such effects are subject to alternative interpretations that allow for differentiated representations. Here we use a subliminal priming paradigm to test for interactions between different magnitudes (number and area) when one magnitude is not consciously detectable. Across two experiments, we first provide evidence for the feasibility of this paradigm by demonstrating that transfer occurs within the dimension of number; that is, symbolic numerals (Arabic digits) that were subliminally primed affected judgments of non-symbolic numerosities in target displays. Crucially, we also found transfer across magnitudes-from subliminally primed numerals to target displays of cumulative surface area whether participants made an ordinal judgment (i.e., "which array is larger in area?") or judged whether two arrays were the same or different in area. These findings suggest that representations of number and area are not fully differentiated. Moreover, they provide unique support for a general magnitude system that includes direct connections, or overlap, between the neural codes for numerical and non-numerical magnitudes.