Functional Anatomy of Perceptual and Attentional Systems in the Primate Brain

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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 have completed two studies and initiated two others. In the first completed study, we explored how one attends to another's actions. Using psychophysics techniques and extensive analysis of videos of human reaching actions using machine learning, we showed that humans are able to predict the actions of others from subtle preparatory movements in the body. Participants were shown videos of a reaching task taken from pairs of subjects playing a competitive or cooperative game. Videos were cut at various time points and participants were told to predict the goal of the reach from early movements in each trial. Results showed that humans can read the goal of an action from subtle preparatory movements present early in the movement. Similar results were obtained using a machine learning analysis. The preparatory movements showed more variability in cooperative compared to competitive contexts. However, even in the competitive context, in which participants had the incentive to conceal information from their opponent, there were ample cues that betrayed their goals. These results suggest that humans may have a biomechanical model of body movements that help them determine the future course of actions of others. These findings deepen our understanding of human action prediction ability and inform future neuroscience and neural modeling research aimed at understanding the neural underpinnings of action prediction. In the second completed study, we explored the neural substrates mediating statistical learning, the process by which humans and animals attend to and extract the statistical regularities in their environment. Behavioral and functional magnetic resonance imaging (fMRI) data were collected from human participants, while they viewed and classified stimuli as belonging to one of two categories (e.g. animate and inanimate). The stimuli were grouped into different sets of either patterned or random image sequences. Contrasting fMRI activation during random compared to patterned runs of images, revealed voxels with greater activation for random runs throughout visual cortex, extending from early visual areas to the ventral temporal lobe. These results suggest that visual statistical learning of patterns follows predictive coding models in which there is an attenuation of the responses for predicted patterns in the visual cortex. We also initiated 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. In this project, we asked: 1) What aspects of an object shape can best explain grasping movements? and 2) How much overlap is there between features used for grasp and features used for general object similarity judgments? To answer these questions, we recorded participants' 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 will be next used in an fMRI experiment to identify visual brain regions that process object features for grasp. Finally, a second newly initiated experiment examined the role of occipitotemporal and parietal cortices in processing the shape of objects to plan a grasp movement. We designed a set-up to allow participants to view and grasp 3D-printed objects inside an MRI scanner. The objects were put on a table positioned over the body, and participants viewed the object through a mirror. Objects were one of four mug-shaped items composed of a handle part (either curved or straight) and a body part (either round or rectangular. Participants were instructed to grasp the handle. We focused on two shape-selective regions of interest, one in the lateral occipital cortex (LOC), and one in the inferior intraparietal sulcus (inferior IPS). A pattern classification analysis was performed on the visual responses to discriminate either the two objects with the same body and different handles (handle classification) or the two objects with the same handle and different bodies (body classification). LOC showed similar classification accuracies for the body and handle, but inferior IPS showed significantly higher pattern classification for the handle than the body. These results suggest that regions in the human intraparietal sulcus extract visual information relevant for proper interaction with objects. The data collection for this project is still underway.