Bayesian sampling in visual perception

It is well-established that some aspects of perception and action can be understood as probabilistic inferences over underlying probability distributions. In some situations, it would be advantageous for the nervous system to sample interpretations from a probability distribution rather than commit to a particular interpretation. In this study, we asked whether visual percepts correspond to samples from the probability distribution over image interpretations, a form of sampling that we refer to as Bayesian sampling. To test this idea, we manipulated pairs of sensory cues in a bistable display consisting of two superimposed moving drifting gratings, and we asked subjects to report their perceived changes in depth ordering. We report that the fractions of dominance of each percept follow the multiplicative rule predicted by Bayesian sampling. Furthermore, we show that attractor neural networks can sample probability distributions if input currents add linearly and encode probability distributions with probabilistic population codes.     

Processing multiple visual objects is limited by overlap in neural channels

High-level visual categories (e.g., faces, bodies, scenes, and objects) have separable neural representations across the visual cortex. Here, we show that this division of neural resources affects the ability to simultaneously process multiple items. In a behavioral task, we found that performance was superior when items were drawn from different categories (e.g., two faces/two scenes) compared to when items were drawn from one category (e.g., four faces). The magnitude of this mixed-category benefit depended on which stimulus categories were paired together (e.g., faces and scenes showed a greater behavioral benefit than objects and scenes). Using functional neuroimaging (i.e., functional MRI), we showed that the size of the mixed-category benefit was predicted by the amount of separation between neural response patterns, particularly within occipitotemporal cortex. These results suggest that the ability to process multiple items at once is limited by the extent to which those items are represented by separate neural populations.An influential idea in neuroscience is that there is an intrinsic relationship between cognitive capacity and neural organization. For example, seminal cognitive models claim there are distinct resources devoted to perceiving and remembering auditory and visual information (1, 2). This cognitive distinction is reflected in the separate cortical regions devoted to processing sensory information from each modality (3). Similarly, within the domain of vision, when items are placed near each other, they interfere more than when they are spaced farther apart (4, 5). These behavioral effects have been linked to receptive fields and the retinotopic organization of early visual areas, in which items that are farther apart activate more separable neural populations (6–8). Thus, there are multiple cognitive domains in which it has been proposed that capacity limitations in behavior are intrinsically driven by competition for representation at the neural level (4, 7–10).However, in the realm of high-level vision, evidence linking neural organization to behavioral capacities is sparse, although neural findings suggest there may be opportunities for such a link. For example, results from functional MRI (fMRI) and single-unit recording have found distinct clusters of neurons that selectively respond to categories such as faces, bodies, scenes, and objects (11, 12). These categories also elicit distinctive activation patterns across the ventral stream as measured with fMRI (13, 14). Together, these results raise the interesting possibility that there are partially separate cognitive resources available for processing different object categories.In contrast, many prominent theories of visual cognition do not consider the possibility that different categories are processed by different representational mechanisms. For example, most models of attention and working memory assume or imply that these processes are limited by content-independent mechanisms such as the number of items that can be represented (15–18), the amount of information that can be processed (19–21), or the degree of spatial interference between items (4, 22–24). Similarly, classical accounts of object recognition are intended to apply equally to all object categories (25, 26). These approaches implicitly assume that visual cognition is limited by mechanisms that are not dependent on any major distinctions between objects.Here, we examined (i) how high-level visual categories (faces, bodies, scenes, and objects) compete for representational resources in a change-detection task, and (ii) whether this competition is related to the separation of neural patterns across the cortex. To estimate the degree of competition between different categories, participants performed a task that required encoding multiple items at once from the same category (e.g., four faces) or different categories (e.g., two faces and two scenes). Any benefit in behavioral performance for mixed-category conditions relative to same-category conditions would suggest that different object categories draw on partially separable representational resources. To relate these behavioral measures to neural organization, we used fMRI to measure the neural responses of these categories individually and quantified the extent to which these categories activate different cortical regions.Overall, we found evidence for separate representational resources for different object categories: performance with mixed-category displays was systematically better than performance with same-category displays. Critically, we also observed that the size of this mixed-category benefit was correlated with the degree to which items elicited distinct neural patterns, particularly within occipitotemporal cortex. These results support the view that a key limitation to simultaneously processing multiple high-level items is the extent to which those items are represented by nonoverlapping neural channels within occipitotemporal cortex.