Does the brain's ventral visual pathway compute object shape?

Manipulating and measuring variation in deep neural network (DNN) representations of objects

We explore how DNNs can be used to develop a computational understanding of individual differences in high-level visual cognition given their ability to generate rich meaningful object representations informed by their architecture, experience, and training protocols. As a first step to quantifying individual differences in DNN representations, we systematically explored the robustness of a variety of representational similarity measures: Representational Similarity Analysis (RSA), Centered Kernel Alignment (CKA), and Projection-Weighted Canonical Correlation Analysis (PWCCA), with an eye to how these measures are used in cognitive science, cognitive neuroscience, and vision science. To manipulate object representations, we next created a large set of models varying in random initial weights and random training image order, training image frequencies, training category frequencies, and model size and architecture and measured the representational variation caused by each manipulation. We examined both small (All-CNN-C) and commonly-used large (VGG and ResNet) DNN architectures. To provide a comparison for the magnitude of representational differences, we established a baseline based on the representational variation caused by image-augmentation techniques used to train those DNNs. We found that variation in model randomization and model size never exceeded baseline. By contrast, differences in training image frequency and training category frequencies caused representational variation that exceeded baseline, with training category frequency manipulations exceeding baseline earlier in the networks. These findings provide insights into the magnitude of representational variations that can be expected with a range of manipulations and provide a springboard for further exploration of systematic model variations aimed at modeling individual differences in high-level visual cognition.

Neural substrate for the engagement of the ventral visual stream in motor control in the macaque monkey

The present study aimed to describe the cortical connectivity of a sector located in the ventral bank of the superior temporal sulcus in the macaque (intermediate area TEa and TEm [TEa/m]), which appears to represent the major source of output of the ventral visual stream outside the temporal lobe. The retrograde tracer wheat germ agglutinin was injected in the intermediate TEa/m in four macaque monkeys. The results showed that 58-78% of labeled cells were located within ventral visual stream areas other than the TE complex. Outside the ventral visual stream, there were connections with the memory-related medial temporal area 36 and the parahippocampal cortex, orbitofrontal areas involved in encoding subjective values of stimuli for action selection, and eye- or hand-movement related parietal (LIP, AIP, and SII), prefrontal (12r, 45A, and 45B) areas, and a hand-related dysgranular insula field. Altogether these data provide a solid substrate for the engagement of the ventral visual stream in large scale cortical networks for skeletomotor or oculomotor control. Accordingly, the role of the ventral visual stream could go beyond pure perceptual processes and could be also finalized to the neural mechanisms underlying the control of voluntary motor behavior.

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

JOURNAL/nrgr/04.03/01300535-202408000-00038/figure1/v/2023-12-16T180322Z/r/image-tiff Memory deficit, which is often associated with aging and many psychiatric, neurological, and neurodegenerative diseases, has been a challenging issue for treatment. Up till now, all potential drug candidates have failed to produce satisfactory effects. Therefore, in the search for a solution, we found that a treatment with the gene corresponding to the RGS14414 protein in visual area V2, a brain area connected with brain circuits of the ventral stream and the medial temporal lobe, which is crucial for object recognition memory (ORM), can induce enhancement of ORM. In this study, we demonstrated that the same treatment with RGS14414 in visual area V2, which is relatively unaffected in neurodegenerative diseases such as Alzheimer's disease, produced long-lasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer's disease. Furthermore, we found that the prevention of memory deficits was mediated through the upregulation of neuronal arborization and spine density, as well as an increase in brain-derived neurotrophic factor (BDNF). A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer's disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits. These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer's disease. Therefore, our findings of RGS14414 gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

The features analysis of hemoglobin expression on visual information transmission pathway in early stage of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized primarily by cognitive impairment. The motivation of this paper is to explore the impact of the visual information transmission pathway (V-H pathway) on AD, and the following feature were observed: Hemoglobin expression on the V-H pathway becomes dysregulated as AD occurs so as to the pathway becomes dysfunctional. According to the feature, the following conclusion was proposed: As AD occurs, abnormal tau proteins penetrate bloodstream and arrive at the brain regions of the pathway. Then the tau proteins or other toxic substances attack hemoglobin molecules. Under the attack, hemoglobin expression becomes more dysregulated. The dysfunction of V-H pathway has an impact on early symptoms of AD, such as spatial recognition disorder and face recognition disorder.

Natural scenes reveal diverse representations of 2D and 3D body pose in the human brain

Human pose, defined as the spatial relationships between body parts, carries instrumental information supporting the understanding of motion and action of a person. A substantial body of previous work has identified cortical areas responsive to images of bodies and different body parts. However, the neural basis underlying the visual perception of body part relationships has received less attention. To broaden our understanding of body perception, we analyzed high-resolution fMRI responses to a wide range of poses from over 4,000 complex natural scenes. Using ground-truth annotations and an application of three-dimensional (3D) pose reconstruction algorithms, we compared similarity patterns of cortical activity with similarity patterns built from human pose models with different levels of depth availability and viewpoint dependency. Targeting the challenge of explaining variance in complex natural image responses with interpretable models, we achieved statistically significant correlations between pose models and cortical activity patterns (though performance levels are substantially lower than the noise ceiling). We found that the 3D view-independent pose model, compared with two-dimensional models, better captures the activation from distinct cortical areas, including the right posterior superior temporal sulcus (pSTS). These areas, together with other pose-selective regions in the LOTC, form a broader, distributed cortical network with greater view-tolerance in more anterior patches. We interpret these findings in light of the computational complexity of natural body images, the wide range of visual tasks supported by pose structures, and possible shared principles for view-invariant processing between articulated objects and ordinary, rigid objects.

The large-scale organization of shape processing in the ventral and dorsal pathways is dissociable from attention

The two visual pathways model posits that visual information is processed through two distinct cortical systems: The ventral pathway promotes visual recognition, while the dorsal pathway supports visuomotor control. Recent evidence suggests the dorsal pathway is also involved in shape processing and may contribute to object perception, but it remains unclear whether this sensitivity is independent of attentional mechanisms that were localized to overlapping cortical regions. To address this question, we conducted two fMRI experiments that utilized different parametric scrambling manipulations in which human participants viewed novel objects in different levels of scrambling and were instructed to attend to either the object or to another aspect of the image (e.g. color of the background). Univariate and multivariate analyses revealed that the large-scale organization of shape selectivity along the dorsal and ventral pathways was preserved regardless of the focus of attention. Attention did modulate shape sensitivity, but these effects were similar across the two pathways. These findings support the idea that shape processing is at least partially dissociable from attentional processes and relies on a distributed set of cortical regions across the visual pathways.

Predicting attentional allocation in real-world environments: The need to investigate crossmodal semantic guidance

Real-world environments are multisensory, meaningful, and highly complex. To parse these environments in a highly efficient manner, a subset of this information must be selected both within and across modalities. However, the bulk of attention research has been conducted within sensory modalities, with a particular focus on vision. Visual attention research has made great strides, with over a century of research methodically identifying the underlying mechanisms that allow us to select critical visual information. Spatial attention, attention to features, and object-based attention have all been studied extensively. More recently, research has established semantics (meaning) as a key component to allocating attention in real-world scenes, with the meaning of an item or environment affecting visual attentional selection. However, a full understanding of how semantic information modulates real-world attention requires studying more than vision in isolation. The world provides semantic information across all senses, but with this extra information comes greater complexity. Here, we summarize visual attention (including semantic-based visual attention), crossmodal attention, and argue for the importance of studying crossmodal semantic guidance of attention. This article is categorized under: Psychology > Attention Psychology > Perception and Psychophysics.

Revealing the mechanisms of semantic satiation with deep learning models

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.

Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception

Introduction

The research in consumer neuroscience has identified computational methods, particularly artificial intelligence (AI) and machine learning, as a significant frontier for advancement. Previously, we utilized functional magnetic resonance imaging (fMRI) and artificial neural networks (ANNs) to model brain processes related to brand preferences in a paradigm exempted from motor actions. In the current study, we revisit this data, introducing recent advancements in explainable artificial intelligence (xAI) to gain insights into this domain. By integrating fMRI data analysis, machine learning, and xAI, our study aims to search for functional brain networks that support brand perception and, ultimately, search for brain networks that disentangle between preferred and indifferent brands, focusing on the early processing stages.

Methods

We applied independent component analysis (ICA) to overcome the expected fMRI data's high dimensionality, which raises hurdles in AI applications. We extracted pertinent features from the returned ICs. An ANN is then trained on this data, followed by pruning and retraining processes. We then apply explanation techniques, based on path-weights and Shapley values, to make the network more transparent, explainable, and interpretable, and to obtain insights into the underlying brain processes.

Results

The fully connected ANN model obtained an accuracy of 54.6%, which dropped to 50.4% after pruning. However, the retraining process allowed it to surpass the fully connected network, achieving an accuracy of 55.9%. The path-weights and Shapley-based analysis concludes that, regarding brand perception, the expected initial participation of the primary visual system is followed. Other brain areas participate in early processing and discriminate between preferred and indifferent brands, such as the cuneal and the lateral occipital cortices.

Discussion

The most important finding is that a split between processing brands|preferred from brands|indifferent may occur during early processing stages, still in the visual system. However, we found no evidence of a "decision pipeline" that would yield if a brand is preferred or indifferent. The results suggest the existence of a "tagging"-like process in parallel flows in the extrastriate. Network training dynamics aggregate specific processes within the hidden nodes by analyzing the model's hidden layer. This yielded that some nodes contribute to both global brand appraisal and specific brand category classification, shedding light on the neural substrates of decision-making in response to brand stimuli.

Visual mental imagery: Evidence for a heterarchical neural architecture

Theories of Visual Mental Imagery (VMI) emphasize the processes of retrieval, modification, and recombination of sensory information from long-term memory. Yet, only few studies have focused on the behavioral mechanisms and neural correlates supporting VMI of stimuli from different semantic domains. Therefore, we currently have a limited understanding of how the brain generates and maintains mental representations of colors, faces, shapes - to name a few. Such an undetermined scenario renders unclear the organizational structure of neural circuits supporting VMI, including the role of the early visual cortex. We aimed to fill this gap by reviewing the scientific literature of five semantic domains: visuospatial, face, colors, shapes, and letters imagery. Linking theory to evidence from over 60 different experimental designs, this review highlights three main points. First, there is no consistent activity in the early visual cortex across all VMI domains, contrary to the prediction of the dominant model. Second, there is consistent activity of the frontoparietal networks and the left hemisphere's fusiform gyrus during voluntary VMI irrespective of the semantic domain investigated. We propose that these structures are part of a domain-general VMI sub-network. Third, domain-specific information engages specific regions of the ventral and dorsal cortical visual pathways. These regions partly overlap with those found in visual perception studies (e.g., fusiform face area for faces imagery; lingual gyrus for color imagery). Altogether, the reviewed evidence suggests the existence of domain-general and domain-specific mechanisms of VMI selectively engaged by stimulus-specific properties (e.g., colors or faces). These mechanisms would be supported by an organizational structure mixing vertical and horizontal connections (heterarchy) between sub-networks for specific stimulus domains. Such a heterarchical organization of VMI makes different predictions from current models of VMI as reversed perception. Our conclusions set the stage for future research, which should aim to characterize the spatiotemporal dynamics and interactions among key regions of this architecture giving rise to visual mental images.

A focus on the multiple interfaces between action and perception and their neural correlates

Successful behaviour relies on the appropriate interplay between action and perception. The well-established dorsal and ventral stream theories depicted two distinct functional pathways for the processes of action and perception, respectively. In physiological conditions, the two pathways closely cooperate in order to produce successful adaptive behaviour. As the coupling between perception and action exists, this requires an interface that is responsible for a common reading of the two functions. Several studies have proposed different types of perception and action interfaces, suggesting their role in the creation of the shared interaction channel. In the present review, we describe three possible perception and action interfaces: i) the motor code, including common coding approaches, ii) attention, and iii) object affordance; we highlight their potential neural correlates. From this overview, a recurrent neural substrate that underlies all these interface functions appears to be crucial: the parieto-frontal circuit. This network is involved in the mirror mechanism which underlies the perception and action interfaces identified as common coding and motor code theories. The same network is also involved in the spotlight of attention and in the encoding of potential action towards objects; these are manifested in the perception and action interfaces for common attention and object affordance, respectively. Within this framework, most studies were dedicated to the description of the role of the inferior parietal lobule; growing evidence, however, suggests that the superior parietal lobule also plays a crucial role in the interplay between action and perception. The present review proposes a novel model that is inclusive of the superior parietal regions and their relative contribution to the different action and perception interfaces.

Perceptual learning in humans: An active, top-down-guided process

Deep neural network (DNN) models of human-like vision are typically built by feeding blank slate DNN visual images as training data. However, the literature on human perception and perceptual learning suggests that developing DNNs that truly model human vision requires a shift in approach in which perception is not treated as a largely bottom-up process, but as an active, top-down-guided process.

A review of the impairments, preserved visual functions, and neuropathology in 21 patients with visual form agnosia - A unique defect with line drawings

We present a comprehensive review of the rare syndrome visual form agnosia (VFA). We begin by documenting its history, including the origins of the term, and the first case study labelled as VFA. The defining characteristics of the syndrome, as others have previously defined it, are then described. The impairments, preserved aspects of visual perception, and areas of brain damage in 21 patients who meet these defining characteristics are described in detail, including which tests were used to verify the presence or absence of key symptoms. From this, we note important similarities along with notable areas of divergence between patients. Damage to the occipital lobe (20/21), an inability to recognise line drawings (19/21), preserved colour vision (14/21), and visual field defects (16/21) were areas of consistency across most cases. We found it useful to distinguish between shape and form as distinct constructs when examining perceptual abilities in VFA patients. Our observations suggest that these patients often exhibit difficulties in processing simplified versions of form. Deficits in processing orientation and size were uncommon. Motion perception and visual imagery were not widely tested for despite being typically cited as defining features of the syndrome - although in the sample described, motion perception was never found to be a deficit. Moreover, problems with vision (e.g., poor visual acuity and the presence of hemianopias/scotomas in the visual fields) are more common than we would have thought and may also contribute to perceptual impairments in patients with VFA. We conclude that VFA is a perceptual disorder where the visual system has a reduced ability to synthesise lines together for the purposes of making sense of what images represent holistically.

Long-term Intensive Soccer Training Induced Dynamic Reconfiguration of Brain Network

Long-term motor skill learning has been shown to impact the functional plasticity of the brain. Athletes, as a unique population, exhibit remarkable adaptive changes in the static properties of their brain networks. However, studying the differences between expert and novice athletes using a dynamic brain network framework can provide a fresh perspective on how motor skill learning affects the functional organization of the brain. In this study, we investigated the dynamic properties of brain networks in expert and novice soccer players at the whole-brain, network, and region-based levels. Our findings revealed that expert soccer players displayed reduced integration and increased segregation at the whole-brain level. As for network level, experts exhibited increased segregation and reduced flexibility in the visual network, enhanced integration between the visual and ventral attention networks, and decreased integration in the subcortical-sensorimotor and subcortical-cerebellar networks. Additionally, specific brain regions within the visual network exhibited greater recruitment in expert soccer players compared to novices at the nodal level. Furthermore, classification analyses demonstrated the critical role played by the visual network in the classification process. In conclusion, our study provides new insights into the dynamic properties of brain networks in expert and novice soccer players, and suggests that reduced integration and increased segregation in the visual network may be neuroimaging marker that distinguish expert soccer players from novices. Our findings may have implications for the training and development of athletes and advance our understanding of how motor skill learning affects brain functional organization.

A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations

Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet it remains poorly understood how CNNs actually make their decisions, what the nature of their internal representations is, and how their recognition strategies differ from humans. Specifically, there is a major debate about the question of whether CNNs primarily rely on surface regularities of objects, or whether they are capable of exploiting the spatial arrangement of features, similar to humans. Here, we develop a novel feature-scrambling approach to explicitly test whether CNNs use the spatial arrangement of features (i.e. object parts) to classify objects. We combine this approach with a systematic manipulation of effective receptive field sizes of CNNs as well as minimal recognizable configurations (MIRCs) analysis. In contrast to much previous literature, we provide evidence that CNNs are in fact capable of using relatively long-range spatial relationships for object classification. Moreover, the extent to which CNNs use spatial relationships depends heavily on the dataset, e.g. texture vs. sketch. In fact, CNNs even use different strategies for different classes within heterogeneous datasets (ImageNet), suggesting CNNs have a continuous spectrum of classification strategies. Finally, we show that CNNs learn the spatial arrangement of features only up to an intermediate level of granularity, which suggests that intermediate rather than global shape features provide the optimal trade-off between sensitivity and specificity in object classification. These results provide novel insights into the nature of CNN representations and the extent to which they rely on the spatial arrangement of features for object classification.

Multisensory perception constrains the formation of object categories: a review of evidence from sensory-driven and predictive processes on categorical decisions

Although object categorization is a fundamental cognitive ability, it is also a complex process going beyond the perception and organization of sensory stimulation. Here we review existing evidence about how the human brain acquires and organizes multisensory inputs into object representations that may lead to conceptual knowledge in memory. We first focus on evidence for two processes on object perception, multisensory integration of redundant information (e.g. seeing and feeling a shape) and crossmodal, statistical learning of complementary information (e.g. the 'moo' sound of a cow and its visual shape). For both processes, the importance attributed to each sensory input in constructing a multisensory representation of an object depends on the working range of the specific sensory modality, the relative reliability or distinctiveness of the encoded information and top-down predictions. Moreover, apart from sensory-driven influences on perception, the acquisition of featural information across modalities can affect semantic memory and, in turn, influence category decisions. In sum, we argue that both multisensory processes independently constrain the formation of object categories across the lifespan, possibly through early and late integration mechanisms, respectively, to allow us to efficiently achieve the everyday, but remarkable, ability of recognizing objects. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

When the whole is only the parts: non-holistic object parts predominate face-cell responses to illusory faces

Humans are inclined to perceive faces in everyday objects with a face-like configuration. This illusion, known as face pareidolia, is often attributed to a specialized network of 'face cells' in primates. We found that face cells in macaque inferotemporal cortex responded selectively to pareidolia images, but this selectivity did not require a holistic, face-like configuration, nor did it encode human faceness ratings. Instead, it was driven mostly by isolated object parts that are perceived as eyes only within a face-like context. These object parts lack usual characteristics of primate eyes, pointing to the role of lower-level features. Our results suggest that face-cell responses are dominated by local, generic features, unlike primate visual perception, which requires holistic information. These findings caution against interpreting neural activity through the lens of human perception. Doing so could impose human perceptual biases, like seeing faces where none exist, onto our understanding of neural activity.

Neuronal Representations Supporting Three-Dimensional Vision in Nonhuman Primates

The visual system must reconstruct the dynamic, three-dimensional (3D) world from ambiguous two-dimensional (2D) retinal images. In this review, we synthesize current literature on how the visual system of nonhuman primates performs this transformation through multiple channels within the classically defined dorsal (where) and ventral (what) pathways. Each of these channels is specialized for processing different 3D features (e.g., the shape, orientation, or motion of objects, or the larger scene structure). Despite the common goal of 3D reconstruction, neurocomputational differences between the channels impose distinct information-limiting constraints on perception. Convergent evidence further points to the little-studied area V3A as a potential branchpoint from which multiple 3D-fugal processing channels diverge. We speculate that the expansion of V3A in humans may have supported the emergence of advanced 3D spatial reasoning skills. Lastly, we discuss future directions for exploring 3D information transmission across brain areas and experimental approaches that can further advance the understanding of 3D vision.

Backward masking reveals coarse-to-fine dynamics in human V1

Natural images exhibit luminance variations aligned across a broad spectrum of spatial frequencies (SFs). It has been proposed that, at early stages of processing, the coarse signals carried by the low SF (LSF) of the visual input are sent rapidly from primary visual cortex (V1) to ventral, dorsal and frontal regions to form a coarse representation of the input, which is later sent back to V1 to guide the processing of fine-grained high SFs (i.e., HSF). We used functional resonance imaging (fMRI) to investigate the role of human V1 in the coarse-to-fine integration of visual input. We disrupted the processing of the coarse and fine content of full-spectrum human face stimuli via backward masking of selective SF ranges (LSFs: <1.75cpd and HSFs: >1.75cpd) at specific times (50, 83, 100 or 150ms). In line with coarse-to-fine proposals, we found that (1) the selective masking of stimulus LSF disrupted V1 activity in the earliest time window, and progressively decreased in influence, while (2) an opposite trend was observed for the masking of stimulus' HSF. This pattern of activity was found in V1, as well as in ventral (i.e. the Fusiform Face area, FFA), dorsal and orbitofrontal regions. We additionally presented subjects with contrast negated stimuli. While contrast negation significantly reduced response amplitudes in the FFA, as well as coupling between FFA and V1, coarse-to-fine dynamics were not affected by this manipulation. The fact that V1 response dynamics to strictly identical stimulus sets differed depending on the masked scale adds to growing evidence that V1 role goes beyond the early and quasi-passive transmission of visual information to the rest of the brain. It instead indicates that V1 may yield a 'spatially registered common forum' or 'blackboard' that integrates top-down inferences with incoming visual signals through its recurrent interaction with high-level regions located in the inferotemporal, dorsal and frontal regions.

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal, and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together, our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings.SIGNIFICANCE STATEMENT When we see a line drawing, we effortlessly recognize it as an object in the world despite its simple and abstract style. Here we asked to what extent this correspondence in perception is reflected in the brain. To answer this question, we measured how neural processing of objects depicted as photographs and line drawings with varying levels of detail (from natural images to abstract line drawings) evolves over space and time. We find broad commonalities in the spatiotemporal dynamics and the neural representations underlying the perception of photographs and even abstract drawings. These results indicate a shared basic mechanism supporting recognition of drawings and natural images.

Temporal asymmetries and interactions between dorsal and ventral visual pathways during object recognition

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.