The Dorsal Visual Pathway Represents Object-Centered Spatial Relations for Object Recognition

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.

Beyond visual integration: sensitivity of the temporal-parietal junction for objects, places, and faces

One important role of the TPJ is the contribution to perception of the global gist in hierarchically organized stimuli where individual elements create a global visual percept. However, the link between clinical findings in simultanagnosia and neuroimaging in healthy subjects is missing for real-world global stimuli, like visual scenes. It is well-known that hierarchical, global stimuli activate TPJ regions and that simultanagnosia patients show deficits during the recognition of hierarchical stimuli and real-world visual scenes. However, the role of the TPJ in real-world scene processing is entirely unexplored. In the present study, we first localized TPJ regions significantly responding to the global gist of hierarchical stimuli and then investigated the responses to visual scenes, as well as single objects and faces as control stimuli. All three stimulus classes evoked significantly positive univariate responses in the previously localized TPJ regions. In a multivariate analysis, we were able to demonstrate that voxel patterns of the TPJ were classified significantly above chance level for all three stimulus classes. These results demonstrate a significant involvement of the TPJ in processing of complex visual stimuli that is not restricted to visual scenes and that the TPJ is sensitive to different classes of visual stimuli with a specific signature of neuronal activations.

Understanding the role of pathways in a deep neural network

Deep neural networks have demonstrated superior performance in artificial intelligence applications, but the opaqueness of their inner working mechanism is one major drawback in their application. The prevailing unit-based interpretation is a statistical observation of stimulus-response data, which fails to show a detailed internal process of inherent mechanisms of neural networks. In this work, we analyze a convolutional neural network (CNN) trained in the classification task and present an algorithm to extract the diffusion pathways of individual pixels to identify the locations of pixels in an input image associated with object classes. The pathways allow us to test the causal components which are important for classification and the pathway-based representations are clearly distinguishable between categories. We find that the few largest pathways of an individual pixel from an image tend to cross the feature maps in each layer that is important for classification. And the large pathways of images of the same category are more consistent in their trends than those of different categories. We also apply the pathways to understanding adversarial attacks, object completion, and movement perception. Further, the total number of pathways on feature maps in all layers can clearly discriminate the original, deformed, and target samples.

Visuospatial memory in apraxia: Exploring quantitative drawing metrics to assess the representation of local and global information

Neuropsychological evidence suggests that visuospatial memory is subserved by two separable processing systems, with dorsal underpinnings for global form and ventral underpinnings for the integration of part elements. Previous drawing studies have explored the effects of Gestalt organisation upon memory for hierarchical stimuli, and we here present an exploratory study of an apraxic dorsal stream patient's (MH) performance. We presented MH with a stimulus set (previously reported by Riddoch et al., Cognitive Neuropsychology, 20(7), 641-671, 2003) and devised a novel quantitative scoring system to obtain a finer grain of insight into performance. Stimuli possessed either good or poor Gestalt qualities and were reproduced in a copy condition and two visual memory conditions (with unlimited viewing before the model was removed, or with 3 s viewing). MH's copying performance was impaired in comparison to younger adult and age-matched older adult controls, with a variety of errors at the local level but relatively few at the global level. However, his performance in the visual memory conditions revealed impairments at the global level. For all participants, drawing errors were modulated by the Gestalt qualities of the stimuli, with accuracy at the global and local levels being lesser for poor global stimuli in all conditions. These data extend previous observations of this patient, and support theories that posit interaction between dorsal and ventral streams in the representation of hierarchical stimuli. We discuss the implications of these findings for our understanding of visuospatial memory in neurological patients, and also evaluate the application of quantitative metrics to the interpretation of drawings.

Differential functional reorganization of ventral and dorsal visual pathways following childhood hemispherectomy

Hemispherectomy is a surgical procedure in which an entire hemisphere of a patient's brain is resected or functionally disconnected to manage seizures in individuals with drug-resistant epilepsy. Despite the extensive loss of both ventral and dorsal visual pathways in one hemisphere, pediatric patients who have undergone hemispherectomy show a remarkably high degree of perceptual function across many domains. In the current study, we sought to understand the extent to which functions of the ventral and dorsal visual pathways reorganize to the contralateral hemisphere following childhood hemispherectomy. To this end, we collected fMRI data from an equal number of left and right hemispherectomy patients who completed tasks that typically elicit lateralized responses from the ventral or the dorsal pathway, namely, word (left ventral), face (right ventral), tool (left dorsal), and global form (right dorsal) perception. Overall, there was greater evidence of functional reorganization in the ventral pathway than in the dorsal pathway. Importantly, because ventral and dorsal reorganization was tested within the very same patients, these results cannot be explained by idiosyncratic factors such as disease etiology, age at the time of surgery, or age at testing. These findings suggest that because the dorsal pathway may mature earlier, it may have a shorter developmental window of plasticity than the ventral pathway and, hence, be less malleable after perturbation.

Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making

Objective.When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.Approach.We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.Main results.We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.Significance.Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

Differential functional reorganization of ventral and dorsal visual pathways following childhood hemispherectomy

Hemispherectomy is a surgical procedure in which an entire hemisphere of a patient's brain is resected or functionally disconnected to manage seizures in individuals with drug-resistant epilepsy. Despite the extensive loss of input from both ventral and dorsal visual pathways of one hemisphere, pediatric patients who have undergone hemispherectomy show a remarkably high degree of perceptual function across many domains. In the current study, we sought to understand the extent to which functions of the ventral and dorsal visual pathways reorganize to the contralateral hemisphere following childhood hemispherectomy. To this end, we collected fMRI data from an equal number of left and right hemispherectomy patients who completed tasks that typically elicit lateralized responses from the ventral or the dorsal pathway, namely, word (left ventral), face (right ventral), tool (left dorsal), and global form (right dorsal) perception. Overall, there was greater evidence of functional reorganization in the ventral pathway than in the dorsal pathway. Importantly, because ventral and dorsal reorganization was tested in the very same patients, these results cannot be explained by idiosyncratic factors such as disease etiology, age at the time of surgery, or age at testing. These findings suggest that because the dorsal pathway may mature earlier, it may have a shorter developmental window of plasticity than the ventral pathway and, hence, be less malleable.

Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of the human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and the dorsal pathway of the HVS, a dual-pathway convolutional neural network is proposed for BIQA tasks. The proposed method consists of two pathways: the "what" pathway, which mimics the ventral pathway of the HVS to extract the content features of distorted images, and the "where" pathway, which mimics the dorsal pathway of the HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, gradient images weighted by contrast sensitivity are used as the input to the "where" pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. Experiments conducted on six databases show that the proposed method achieves state-of-the-art performance.

Spatiotemporal neural dynamics of object recognition under uncertainty in humans

While there is a wealth of knowledge about core object recognition-our ability to recognize clear, high-contrast object images-how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla (7T) fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

Disentangling Object Category Representations Driven by Dynamic and Static Visual Input

Humans can label and categorize objects in a visual scene with high accuracy and speed, a capacity well characterized with studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed by the brain in the absence of luminance-defined form information, we created a novel stimulus set of "object kinematograms" to isolate motion-defined signals from other sources of visual information. Object kinematograms were generated by extracting motion information from videos of 6 object categories and applying the motion to limited-lifetime random dot patterns. Using functional magnetic resonance imaging (fMRI) (n = 15, 40% women), we investigated whether category information from the object kinematograms could be decoded within the occipitotemporal and parietal cortex and evaluated whether the information overlapped with category responses to static images from the original videos. We decoded object category for both stimulus formats in all higher-order regions of interest (ROIs). More posterior occipitotemporal and ventral regions showed higher accuracy in the static condition, while more anterior occipitotemporal and dorsal regions showed higher accuracy in the dynamic condition. Further, decoding across the two stimulus formats was possible in all regions. These results demonstrate that motion cues can elicit widespread and robust category responses on par with those elicited by static luminance cues, even in ventral regions of visual cortex that have traditionally been associated with primarily image-defined form processing.SIGNIFICANCE STATEMENT Much research on visual object recognition has focused on recognizing objects in static images. However, motion is a rich source of information that humans might also use to categorize objects. Here, we present the first study to compare neural representations of several animate and inanimate objects when category information is presented in two formats: static cues or isolated dynamic motion cues. Our study shows that, while higher-order brain regions differentially process object categories depending on format, they also contain robust, abstract category representations that generalize across format. These results expand our previous understanding of motion-derived animate and inanimate object category processing and provide useful tools for future research on object category processing driven by multiple sources of visual information.

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.

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

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.