Functional Anatomy of Perceptual and Attentional Systems in the Primate Brain

National Institute Of Mental Health

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Abstract

A typical scene contains many different objects that compete for neural representation due to the limited processing capacity of the visual system. At the neural level, competition among multiple objects is evidenced by the mutual suppression of their visually evoked responses. The competition among multiple objects can be biased by both bottom-up sensory-driven mechanisms (exogenous attention), such as stimulus salience, and top-down, goal-directed influences, such as selective, endogenous attention. Although the competition among multiple objects for representation is ultimately resolved within visual cortex, the source of top-down biasing signals likely derives from a distributed network of areas in frontal and parietal cortex. During the past year, we completed two studies and made progress on two others. In the first completed study (Yue et al., 2020), we investigated cortical areas involved in the processing of the curvature of objects. We found a network of curvature preferring cortical patchessome of which overlapped with typical face-selective areas. We also found that curvilinear features of visual stimuli are associated with topographic maps in visual cortex. Our results support the hypothesis that curvilinearity preference interacts with topographic biases as primary features underlying the organization of temporal cortex in the adult human brain. In the second completed study (Yetter et al., 2021), we investigated the extent to which the ability to discriminate animate and inanimate objects is facilitated by image-based features that distinguish the two object categories. In a behavioral study in monkeys, we showed that macaques can classify novel images into animate and inanimate categories with high accuracy after nominal training. Next, we used synthetic images that distorted the global shape of the images while maintaining their intermediate features. We showed that the monkeys could classify these images above chance based on the amount of curvilinear information in the image, i.e., images with more curvature were classified as animate and images with more rectilinear features as inanimate. Our results demonstrate that macaques can use an intermediate image feature such as curvilinearity, to facilitate their categorization based on animacy. In an ongoing study, we explored how humans attend to and extract information from others actions. Using behavioral experiments and extensive analysis of videos of human grasping actions, we showed that humans are able to predict the target of a grasp when viewing hand movement. Participants made systematic errors in their judgments. In addition, when asked to judge the similarity of grasp movements, their judgments systematically deviated from objective grasp movements. These results suggest that the understanding of others grasp movements relies on mechanisms (and possibly neural underpinnings) that are distinct from those used for performing grasp movements. The analysis for this study is ongoing. We also made progress on an experiment to investigate the processing of visual objects for grasp. To judge the similarity between objects, we attend to a collection of features (geometric, semantic, etc.). When making an action to grasp an object, we also attend to a subset of geometric features. It is not clear if the same mechanisms that underlie general object knowledge also support processing object shapes for grasp. We recorded grasp movements towards 58 3D-printed objects. We used these movements to extract features relevant for grasp. In a separate experiment, we asked other participants to judge the similarity between these objects and then used these data to extract features relevant for general object similarity ratings. Surprisingly, the features relevant for grasp were largely distinct from those used for general similarity judgments, suggesting that the mechanisms for extracting these features may diverge in the brain. These experimental measures are now being used in an fMRI study to identify visual brain regions that process object features for grasp. We designed a setup to allow participants to view and grasp 3D-printed objects inside an MRI scanner. Using this setup and correlating the behavioral measures with the fMRI responses to the objects, we will explore the role of occipitotemporal and parietal cortices in processing the shape of objects to plan a grasping movement.