Derivation of Human Chromatic Discrimination Ability from an Information-Theoretical Notion of Distance in Color Space

Fonseca, María da; Samengo, Inés

doi:10.1162/neco_a_00903

Cited by 16 publications

(22 citation statements)

References 22 publications

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“…In psychophysics, the sensitivity of the system is characterized by its discrimination abilities (inverse of the volume of the regions determined by the just noticeable differences -JNDs- [10,11]). Discrimination depends on models to compute perceptual differences from the internal representation [12][13][14] or on models of noise at the internal representation [15][16][17]. In any of these cases, the way the sensory system, S, deforms the stimulus space is critical to understand how the discrimination regions in the internal representation transform back into the image space.…”

Section: Introductionmentioning

confidence: 99%

Derivatives and inverse of cascaded linear+nonlinear neural models

et al. 2018

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In vision science, cascades of Linear+Nonlinear transforms are very successful in modeling a number of perceptual experiences. However, the conventional literature is usually too focused on only describing the forward input-output transform. Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse. The fundamental reason for this analytical treatment is that it offers useful analytical insight into the psychophysics, the physiology, and the function of the visual system. For instance, we show how the trends of the sensitivity (volume of the discrimination regions) and the adaptation of the receptive fields can be identified in the expression of the Jacobian w.r.t. the stimulus. This matrix also tells us which regions of the stimulus space are encoded more efficiently in multi-information terms. The Jacobian w.r.t. the parameters shows which aspects of the model have bigger impact in the response, and hence their relative relevance. The analytic inverse implies conditions for the response and model parameters to ensure appropriate decoding. From the experimental and applied perspective, (a) the Jacobian w.r.t. the stimulus is necessary in new experimental methods based on the synthesis of visual stimuli with interesting geometrical properties, (b) the Jacobian matrices w.r.t. the parameters are convenient to learn the model from classical experiments or alternative goal optimization, and (c) the inverse is a promising model-based alternative to blind machine-learning methods for neural decoding that do not include meaningful biological information. The theory is checked by building and testing a vision model that actually follows a modular Linear+Nonlinear program. Our illustrative derivable and invertible model consists of a cascade of modules that account for brightness, contrast, energy masking, and wavelet masking. To stress the generality of this modular setting we show examples where some of the canonical Divisive Normalization modules are substituted by equivalent modules such as the Wilson-Cowan interaction model (at the V1 cortex) or a tone-mapping model (at the retina).

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Section: Introductionmentioning

confidence: 99%

Derivatives and inverse of cascaded linear+nonlinear neural models

et al. 2018

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“…Finally MacAdam [16] published a definitive dataset, based on laborious experiments, of JND-based metric tensors at many points of luminous colour space. More recently discrimination data has been modelled as deriving purely from receptor noise [17–19].…”

Section: Introductionmentioning

confidence: 99%

Categorical colour geometry

Griffin¹,

Mylonas²

2019

PLoS ONE

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Ordinary language users group colours into categories that they refer to by a name e.g. pale green . Data on the colour categories of English speakers was collected using online crowd sourcing – 1,000 subjects produced 20,000 unconstrained names for 600 colour stimuli. From this data, using the framework of Information Geometry, a Riemannian metric was computed throughout the RGB cube. This is the first colour metric to have been computed from colour categorization data. In this categorical metric the distance between two close colours is determined by the difference in the distribution of names that the subject population applied to them. This contrasts with previous colour metrics which have been driven by stimulus discriminability, or acceptability of a colour match. The categorical metric is analysed and shown to be clearly different from discriminability-based metrics. Natural units of categorical length, area and volume are derived. These allow a count to be made of the number of categorically-distinct regions of categorically-similar colours that fit within colour space. Our analysis estimates that 27 such regions fit within the RGB cube, which agrees well with a previous estimate of 30 colours that can be identified by name by untrained subjects.

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“…This tool can be used to bound the maximal accuracy with which two remembered colors can be discriminated [29]. An additional—and less advertised—functionality of the Fisher information, is that it constitutes a metric, allowing us to calculate distances between pairs of colors [30, 31]. As opposed to, for example, the Euclidean distance, lengths based on the Fisher metric represent how differently each individual observer holds the two colors, in terms of his or her ability to discriminate them in mnemonic tasks.…”

Section: Introductionmentioning

confidence: 99%

The subjective metric of remembered colors: A Fisher-information analysis of the geometry of human chromatic memory

et al. 2019

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In order to explore the metric structure of the space of remembered colors, a computer game was designed, where players with normal color vision had to store a color in memory, and later retrieve it by selecting the best match out of a continuum of alternatives. All tested subjects exhibited evidence of focal colors in their mnemonic strategy. We found no concluding evidence that the focal colors of different players tended to cluster around universal prototypes. Based on the Fisher metric, for each subject we defined a notion of distance in color space that captured the accuracy with which similar colors where discriminated or confounded when stored and retrieved from memory. The notions of distance obtained for different players were remarkably similar. Finally, for each player, we constructed a new color scale, in which colors are memorized and retrieved with uniform accuracy.

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Derivation of Human Chromatic Discrimination Ability from an Information-Theoretical Notion of Distance in Color Space

Cited by 16 publications

References 22 publications

Derivatives and inverse of cascaded linear+nonlinear neural models

Derivatives and inverse of cascaded linear+nonlinear neural models

Categorical colour geometry

The subjective metric of remembered colors: A Fisher-information analysis of the geometry of human chromatic memory

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