An exponential filter model predicts lightness illusions

Simple Assumptions to Improve Markov Illuminance and Reflectance

Murray recently introduced a novel computational lightness model, Markov illuminance and reflectance (MIR). MIR is a promising new approach that simulates human lightness processing using a conditional random field (CRF) where natural-scene statistics of reflectance and illumination are implemented. Although MIR can account for various lightness illusions and phenomena, it has limitations, such as the inability to predict reverse-contrast phenomena. In this study, we improved MIR performance by modifying its inference process, the prior on X-junctions, and that on general illumination changes. Our modified model improved predictions for Checkerboard assimilation, the simplified Checkershadow and its control figure, the influence of luminance noise, and White's effect and its several variants. In particular, White's effect is a partial reverse contrast that is challenging for computational models, so this improvement is a significant advance for the MIR framework. This study showed the high extensibility and potential of MIR, which shows the promise for further sophistication.

Shining a Light on Race: Contrast and Assimilation Effects in the Perception of Skin Tone and Racial Typicality

Researchers have long debated the extent to which an individual’s skin tone influences their perceived race. Brooks and Gwinn (2010) demonstrated that the race of surrounding faces can affect the perceived skin tone of a central target face without changing perceived racial typicality, suggesting that skin lightness makes a small contribution to judgments of race compared to morphological cues (the configuration and shape of the facial features). However, the lack of a consistent light source may have undermined the reliability of skin tone cues, encouraging observers to rely disproportionately on morphological cues instead. The current study addresses this concern by using 3D models of male faces with typically Black African or White European appearances that are illuminated by the same light source. Observers perceived target faces surrounded by White faces to have darker skin than those surrounded by Black faces, particularly for faces of intermediate lightness. However, when asked to judge racial typicality, a small assimilation effect was evident, with target faces perceived as more stereotypically White when surrounded by White than when surrounded by Black faces at intermediate levels of typicality. This evidence of assimilation effects for perceived racial typicality despite concurrent contrast effects on perceived skin lightness supports the previous conclusion that perceived skin lightness has little influence on judgments of racial typicality for racially ambiguous faces, even when lighting is consistent.

Kitaoka's Tomato: Two Simple Explanations Based on Information in the Stimulus

Kitaoka’s Tomato is a color illusion in which a semitransparent blue-green field is placed on top of a red object (a tomato). The tomato appears red even though the pixels would appear green if viewed in isolation. We show that this phenomenon can be explained by a high-pass filter and by histogram equalization. The results suggest that this illusion does not require complex inferences about color constancy; rather, the tomato’s red is available in the physical stimulus at the appropriate spatial scale and dynamic range.

Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping

Why does our visual system fail to reconstruct reality, when we look at certain patterns? Where do Geometrical illusions start to emerge in the visual pathway? How far should we take computational models of vision with the same visual ability to detect illusions as we do? This study addresses these questions, by focusing on a specific underlying neural mechanism involved in our visual experiences that affects our final perception. Among many types of visual illusion, 'Geometrical' and, in particular, 'Tilt Illusions' are rather important, being characterized by misperception of geometric patterns involving lines and tiles in combination with contrasting orientation, size or position. Over the last decade, many new neurophysiological experiments have led to new insights as to how, when and where retinal processing takes place, and the encoding nature of the retinal representation that is sent to the cortex for further processing. Based on these neurobiological discoveries, we provide computer simulation evidence from modelling retinal ganglion cells responses to some complex Tilt Illusions, suggesting that the emergence of tilt in these illusions is partially related to the interaction of multiscale visual processing performed in the retina. The output of our low-level filtering model is presented for several types of Tilt Illusion, predicting that the final tilt percept arises from multiple-scale processing of the Differences of Gaussians and the perceptual interaction of foreground and background elements. The model is a variation of classical receptive field implementation for simple cells in early stages of vision with the scales tuned to the object/texture sizes in the pattern. Our results suggest that this model has a high potential in revealing the underlying mechanism connecting low-level filtering approaches to mid- and high-level explanations such as 'Anchoring theory' and 'Perceptual grouping'.