Predictive coding as a unifying principle for explaining a broad range of brightness phenomena

Abstract

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a predictive coding mechanism, which reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other (response equalization). Response equalization is implemented with a dynamic filtering process, which (dynamically) adapts to each input image. Dynamic filtering is applied to the responses of complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast. Author summaryWe hardly notice that what we see is often different from the physical world "outside" 1 of the brain. This means that the visual experience that the brain actively constructs 2 may be different from the actual physical properties of objects in the world. In this 3 work, we propose a hypothesis about how the visual system of the brain may construct 4 a representation for achromatic images. Since this process is not unambiguous, 5 sometimes we notice "errors" in our perception, which cause visual illusions. The 6 challenge for theorists, therefore, is to propose computational principles that recreate a 7 large number of visual illusions and to explain why they occur. Notably, our proposed 8 mechanism explains a broader set of visual illusions than any previously published 9 proposal. We achieved this by trying to suppress predictable information. For example, 10 if an image contained repetitive structures, then these structures are predictable and 11 would be suppressed. In this way, non-predictable structures stand out. Predictive 12 coding mechanisms act as early as in the retina (which enhances luminance changes but 13 suppresses uniform regions of luminance), and our computational model holds that this 14 principle also acts at the next stage in the visual system, where representations of 15 perceived luminance (brightness) are created. 16April 18, 2020 1/30 42 suggested a pattern-specific inhibition mechanism acting in the visual cortex, which 43 inhibits regularly arranged patterns of a visual stimulus. 44Anchoring theory is a rule-based mechanism for segregating the contributions of 45 illumination and reflectance in brightness, and thus to map luminance to perceived gray 46 levels [4]. Accordingly, a visual image is first divided into one or more perceptual 47 frameworks (a framework is a set of surfaces that are grouped together). Within each 48 framework, the highest luminance value is anchored at perceived white, and smaller 49 luminance values are mapped to...

Machine learning classification of retinal imaging of neuropsychiatric and healthy cases in a subset of the UK Biobank cohort

“…This account of predictive processing of perceptual stimuli is bidirectional; incoming stimuli activate preconceptions about the environment, and prediction errors help to further categorize and organize mental models to cooperate with reality (Yon et al, 2020). This bidirectional information flow accounts for visual illusions, wherein estimations of space or lighting in images that don't align with preformed beliefs standout out as some form of perceptual trickery (Lerer et al, 2020). In other words, a visual illusion stands out because it is askew of prior beliefs about lighting, spacing, or line edges that humans have come to understand about images.…”