# Central Processing of Visual Information > **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2020 · $423,750 ## Abstract This project consists of a set of inter-related experimental, computational, and theoretical studies, whose goal is to advance the understanding of the design principles of visual processing, and how these design principles are reduced to computations that can be carried out by neurons and neural circuitry. The many successes of “normative theories” constitute our starting point: for example, how a sensory system's limited capacity should be deployed to effectively represent and transmit task-relevant information about its inputs. However, here we recognize that along with these successes – in our lab and many others -- there are many divergences between normative predictions and what the visual system actually does. These discrepancies indicate that there are important constraints not recognized by current normative theories, such as limits to the detail with which natural-image priors are used. We focus on the extraction of figure from ground: this is a computationally-challenging process that is centrally important to visual function, and it also has a number of characteristics that we can use to advantage, building on recent advances in our lab. Distinguishing figure from ground is a fundamentally statistical process, so understanding how the visual system processes local image statistics is critical. In previous years, we developed a theoretical and experimental framework for this: we showed how luminance, contrast, orientation, and shape could be dissociated via the construction of a space of synthetic textures, and we then used this space to measure human visual sensitivity to these components individually and in combination and to analyze its relationship with natural-image statistics. Aim 1 consists of psychophysical experiments to characterize three key aspects of figure-ground processing: Aim 1A, the influence of the statistics of figure, ground, and figure-ground differences, Aim 1B, the influence of figure shape, and Aim 1C, the influence of task-specific knowledge. Aim 2 is motivated by models that formalize the hypothesis that visual computations make use of simplified Gaussian approximations to natural image statistics. To test these models, Aim 2A consists of computational studies to determine the statistics of image patches in figure and ground. Aim 2B makes further psychophysical measures that will determine a phenomenological model. Comparison of the phenomenological model and normative models built from natural-image statistics will proceed in stages: does the phenomenological model have the form predicted by normative theories (i.e., do measured threshold surfaces have the predicted shape)?I f so, what is the level of detail of natural-image priors that are needed to account, quantitatively, for perceptual thresholds? Successful completion of this research is expected to provide both specific and generalizable insights into principles of sensory processing, which in turn will provide the groundwork for advanced neural pro... ## Key facts - **NIH application ID:** 9989835 - **Project number:** 5R01EY007977-29 - **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV - **Principal Investigator:** Jonathan D Victor - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $423,750 - **Award type:** 5 - **Project period:** 1989-01-01 → 2024-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9989835 ## Citation > US National Institutes of Health, RePORTER application 9989835, Central Processing of Visual Information (5R01EY007977-29). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9989835. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*