A theory divided: Current representations of the anchoring theory of lightness contradict the original’s core claims

Maniatis, Lydia Maria

doi:10.1016/j.visres.2014.04.010

Cited by 5 publications

(4 citation statements)

References 18 publications

(10 reference statements)

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“…In future studies, more realistic backgrounds and illumination should be taken into account (e.g. Matchette et al, 2020), as a variety of factors can fundamentally influence luminance contrast perception in most circumstances (Gilchrist, 2014;Gilchrist and Radonjic, 2009;Gilchrist et al, 1999;Kingdom, 2011;Maniatis, 2014). Unsurprisingly then, there is evidence that luminance contrast modulates the salience of objects at stages well beyond the retina (Einhäuser and König, 2003).…”

Section: Future Directionsmentioning

confidence: 99%

More than noise: Context-dependant luminance contrast discrimination in a coral reef fish (Rhinecanthus aculeatus)

Berg

Hollenkamp

Mitchell

et al. 2020

Journal of Experimental Biology

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Achromatic (luminance) vision is used by animals to perceive motion, pattern, space and texture. Luminance contrast sensitivity thresholds are often poorly characterised for individual species and are applied across a diverse range of perceptual contexts using over-simplified assumptions of an animal's visual system. Such thresholds are often estimated using the Receptor Noise Limited model (RNL) using quantum catch values and estimated noise levels of photoreceptors. However, the suitability of the RNL model to describe luminance contrast perception remains poorly tested.Here, we investigated context-dependent luminance discrimination using triggerfish (Rhinecanthus aculeatus) presented with large achromatic stimuli (spots) against uniform achromatic backgrounds of varying absolute and relative contrasts. ‘Dark’ and ‘bright’ spots were presented against relatively dark and bright backgrounds. We found significant differences in luminance discrimination thresholds across treatments. When measured using Michelson contrast, thresholds for bright spots on a bright background were significantly higher than for other scenarios, and the lowest threshold was found when dark spots were presented on dark backgrounds. Thresholds expressed in Weber contrast revealed increased contrast sensitivity for stimuli darker than their backgrounds, which is consistent with the literature. The RNL model was unable to estimate threshold scaling across scenarios as predicted by the Weber-Fechner law, highlighting limitations in the current use of the RNL model to quantify luminance contrast perception. Our study confirms that luminance contrast discrimination thresholds are context-dependent and should therefore be interpreted with caution.

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Section: Future Directionsmentioning

confidence: 99%

More than noise: Context-dependant luminance contrast discrimination in a coral reef fish (Rhinecanthus aculeatus)

Berg

Hollenkamp

Mitchell

et al. 2020

Journal of Experimental Biology

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“…For example, the 51 lowest luminance may be anchored at "black", or the mean luminance may be anchored 52 at the Eigengrau level. Although anchoring theory is successful in explaining the 53 perception of reflectance for simple displays, it is not clear how its rules apply to 54 natural images [5,6]. Furthermore, rather than specifying computational mechanisms, 55 anchoring theory (but also White's suggestion) represents only a phenomenological 56 proposal, and it is not clear how its empirically defined set of rules could be related to 57 any specific information processing strategy in the visual system (but see e.g., [7]).…”

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confidence: 99%

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

Lerer

Supèr

Keil

2020

Preprint

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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...

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“…In two recent critiques (Maniatis, 2014a(Maniatis, , 2014b I argued that a series of claims addressing the role of perceptual organization in lightness estimation and collected under the rubric of ''anchoring theory'' are too vague, shifting, and mutually contradictory to be testable. Explanations developed for certain effects often clash with those proposed for others and with various principles expressed at various times.…”

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confidence: 99%

A simple test of the “anchoring account” of simultaneous contrast

Maniatis¹

2015

Journal of Vision

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A theory divided: Current representations of the anchoring theory of lightness contradict the original’s core claims

Cited by 5 publications

References 18 publications

More than noise: Context-dependant luminance contrast discrimination in a coral reef fish (Rhinecanthus aculeatus)

More than noise: Context-dependant luminance contrast discrimination in a coral reef fish (Rhinecanthus aculeatus)

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

A simple test of the “anchoring account” of simultaneous contrast

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