A Digital Test Chart for Visual Assessment of Color Appearance Scales

A digital color appearance test chart, akin to a ColorChecker® Chart for human perception, was developed and evaluated both perceptually and computationally. The chart allows an observer to adjust the appearance of a limited number of color patches to allow a quick evaluation of perceived brightness, colorfulness, lightness, saturation, and hue on a display. The resulting data can then be used to compared observed results with the predictions of various color appearance models. Analyses in this paper highlight some known shortcomings of CIELAB, CIECAM02, and CAM16. Differences between CIECAM02 and CAM16 are also highlighted. This paper does not provide new psychophysical data for model testing, it simply describes a technique to generate such data and a computational comparison of models.

Prior expectations in visual speed perception predict encoding characteristics of neurons in area MT

Bayesian inference provides an elegant theoretical framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate due to its flexibility and the fact that suitable constraints on the structure of the sensory uncertainty have been missing. Here, we demonstrate that a Bayesian observer model constrained by efficient coding not only well fits extensive psychophysical data of human visual speed perception but also provides an accurate quantitative account of the tuning characteristics of neurons known for representing visual speed. Specifically, we found that the population coding accuracy for visual speed in area MT ("neural prior") is precisely predicted by the power-law, slow-speed prior extracted from fitting the Bayesian model to the psychophysical data ("behavioral prior"), to the point that they are indistinguishable in a model cross-validation comparison. Our results demonstrate a quantitative validation of the Bayesian observer model constrained by efficient coding at both the behavioral and neural levels.

Data Analysis and Power Simulations with General Linear Mixed Modelling for Psychophysical Data – A Practical, R-Based Guide

Sample size planning is not straight-forward for the complex designs that are usually employed in psychophysical (two-alternative forced-choice) experiments: characteristics such as binary response variables and nested data structures where responses may be correlated differently within participants and experimental sessions than across participants and experimental sessions make it harder to estimate the necessary number of participants and trials with traditional means. In this practical R-based guide, we first show in detail how we can simulate verisimilar psychophysical data. We then use these simulations to compare two different methods by which two-alternative forced-choice data can be analyzed: (1) the “two-step” approach, where first psychometric functions are fitted and then statistical tests are performed over the parameters of these fitted psychometric functions; (2) an approach based on Generalized Linear Mixed Modeling (GLMM) that does not require the intermediary step of fitting psychometric functions. We argue that the GLMM approach enhances statistical validity and show that it can increase statistical power. Finally, we provide a sample implementation of a simulation-based power analysis that can be used as-is for many simple designs, but is also easily adaptable for more complex designs. Overall, we show that a GLMM-based approach can be beneficial for data analysis and sample size planning for typical (two-alternative forced-choice) psychophysical designs.

Modeling Circadian Phototransduction: Retinal Neurophysiology and Neuroanatomy

The retina is a complex, but well-organized neural structure that converts optical radiation into neural signals that convey photic information to a wide variety of brain structures. The present paper is concerned with the neural circuits underlying phototransduction for the central pacemaker of the human circadian system. The proposed neural framework adheres to orthodox retinal neuroanatomy and neurophysiology. Several postulated mechanisms are also offered to account for the high threshold and for the subadditive response to polychromatic light exhibited by the human circadian phototransduction circuit. A companion paper, modeling circadian phototransduction: Quantitative predictions of psychophysical data, provides a computational model for predicting psychophysical data associated with nocturnal melatonin suppression while staying within the constraints of the neurophysiology and neuroanatomy offered here.

Changes within neural population codes can be inferred from psychophysical threshold studies

The use of population encoding models has come to dominate the study of human visual neuroscience, serving as a primary tool for making inferences about neural code changes based on indirect measurements. A popular approach in computational neuroimaging is to use such models to obtain estimates of neural population responses via inverted encoding modeling. Recent research suggests that this approach may be prone to identifiability problems, with multiple mechanisms of encoding change producing similar changes in the estimated population responses. Psychophysical data might be able to provide additional constraints to infer the encoding change mechanism underlying some behavior of interest. However, computational work aimed at determining to what extent different mechanisms can be differentiated using psychophysics is lacking. Here, we used simulation to explore exactly which of a number of changes in neural population codes could be differentiated from observed changes in psychophysical thresholds. Eight mechanisms of encoding change were under study, chosen because they have been proposed in the previous literature as mechanisms for improved task performance (e.g., due to attention or learning): specific and nonspecific gain, specific and nonspecific tuning, specific suppression, specific suppression plus gain, and inward and outward tuning shifts. We simulated psychophysical thresholds as a function of both external noise (TvN curves) or stimulus value (TvS curves) for a number of variations of each one of the models. With the exception of specific gain and specific tuning, all studied mechanisms produced qualitatively different patterns of change in the TvN and TvS curves, suggesting that psychophysical studies can be used as a complement to inverted encoding modeling, and provide strong constraints on inferences based on the latter. We use our results to provide recommendations for interested researchers and to re-interpret previous psychophysical data in terms of mechanisms of encoding change.

Comparison of visual (MUNSELL) and instrumental measures (CIELAB) of coloured textile standard samples

Purpose This paper aims to compare visual (Munsell) and instrumental (CIELAB) attributes of SCOTDIC colour standards. Design/methodology/approach SCOTDIC cotton and polyester standards of defined hue, value and chroma were subjected to spectrophotometric assessment for finding the corresponding instrumental parameters. The visual and instrumental parameters were compared. Findings The correlation between SCOTDIC value and CIELAB lightness is quite high. Correlation coefficient between SCOTDIC hue and CIELAB hue angle and the correlation between SCOTDIC chroma and CIELAB chroma were only moderate because the CIELAB chroma varied widely at higher chroma. When the standards of SCOTDIC hues having erratic hue angles at two extremes are excluded, the Correlation coefficients between SCOTDIC hue and CIELAB hue angle become high. Research limitations/implications The psychophysical data (visual) are difficult to match with physical data (instrumental). Originality/value The object of the present research is to study and compare visual (Munsell) and instrumental (CIELAB) colorimetric parameters. Munsell scale is physically exemplified by SCOTDIC fabric samples available in two sets, namely, cotton and polyester sets.