Comparing sensitivity estimates from MLDS and forced-choice methods in a slant-from-texture experiment

Aguilar, Guillermo; Wichmann, Felix A.; Maertens, Marianne

doi:10.1167/17.1.37

Cited by 15 publications

(40 citation statements)

References 34 publications

(69 reference statements)

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“…Consider a psychophysical "slant-from-texture" experiment that has been designed to find the functional relation of the perceived angle to the true angle of a tilted flat plane with a dotted texture (Rosas, Wichmann, & Wagemans, 2004;Rosas, Ernst, Wagemans, & Wichmann, 2005;Rosas, Wichmann, & Wagemans, 2007;. Figure 2 (top) shows the various stimuli used in the experiment by Rosas et al (2004) and Aguilar et al (2017). The bottom, left image of Figure 2 depicts an example of a triplet question designed for this task.…”

Section: Scaling and The Methods Of Triadsmentioning

confidence: 99%

Estimation of perceptual scales using ordinal embedding

2020

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In this article, we address the problem of measuring and analyzing sensation, the subjective magnitude of one's experience. We do this in the context of the method of triads: The sensation of the stimulus is evaluated via relative judgments of the following form: "Is stimulus S i more similar to stimulus S j or to stimulus S k ?" We propose to use ordinal embedding methods from machine learning to estimate the scaling function from the relative judgments. We review two relevant and well-known methods in psychophysics that are partially applicable in our setting: nonmetric multidimensional scaling (NMDS) and the method of maximum likelihood difference scaling (MLDS). Considering various scaling functions, we perform an extensive set of simulations to demonstrate the performance of the ordinal embedding methods. We show that in contrast to existing approaches, our ordinal embedding approach allows, first, to obtain reasonable scaling functions from comparatively few relative judgments and, second, to estimate multidimensional perceptual scales. In addition to the simulations, we analyze data from two real psychophysics experiments using ordinal embedding methods. Our results show that in the one-dimensional perceptual scale, our ordinal embedding approach works as well as MLDS, while in higher dimensions, only our ordinal embedding methods can produce a desirable scaling function. To make our methods widely accessible, we provide an R-implementation and general rules of thumb on how to use ordinal embedding in the context of psychophysics.

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Section: Scaling and The Methods Of Triadsmentioning

confidence: 99%

Estimation of perceptual scales using ordinal embedding

2020

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“…For example, dividing the weights in the matrix X in Figure 3 by two-giving 0.5, À1, and 0.5yields a scale for which r 2 D ¼ 4 that corresponds to r 2 b ¼ 1 for each lightness level. This would parameterize the scale in terms of d 0 (as shown in more detail in Aguilar et al, 2017;Devinck & Knoblauch, 2012).…”

Section: Mlds Analysismentioning

confidence: 99%

“…Although individual observers differed in their overall noise level, all scales measured for one observer had comparable estimated noise levels. However, these assumptions must be considered carefully, and ultimately their validity must be addressed experimentally (Aguilar et al, 2017).…”

Section: Mlds-based Lightness Scalesmentioning

confidence: 99%

“…This approximate invariance of perceived lightness across varying luminance is known as lightness constancy. We know from experience and from Citation: Wiebel, C. B., Aguilar, G., & Maertens, M. (2017). Maximum likelihood difference scales represent perceptual magnitudes and predict appearance matches.…”

Section: Introductionmentioning

confidence: 99%

“…It has been used to study various perceptual dimensions (e.g., Fleming, Jäkel, & Maloney, 2011;Obein, Knoblauch, & Viénot, 2004). Furthermore, it is based on a signal detection model, which potentially allows one to relate measurements of appearance with measurements of discriminability (Aguilar, Wichmann, & Maertens, 2017;Devinck & Knoblauch, 2012). Here we used MLDS to measure perceptual scales in different contexts using only within-context comparisons in order to avoid the procedural problems of asymmetric matching.…”

Section: Introductionmentioning

confidence: 99%

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Maximum likelihood difference scales represent perceptual magnitudes and predict appearance matches

2017

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One central problem in perception research is to understand how internal experiences are linked to physical variables. Most commonly, this relationship is measured using the method of adjustment, but this has two shortcomings: The perceptual scales that relate physical and perceptual variables are not measured directly, and the method often requires perceptual comparisons between viewing conditions. To overcome these problems, we measured perceptual scales of surface lightness using maximum likelihood difference scaling, asking observers only to compare the lightness of surfaces presented in the same context. Observers were lightness constant, and the perceptual scales qualitatively and quantitatively predicted perceptual matches obtained in a conventional adjustment experiment. Additionally, we show that a contrast-based model of lightness perception predicted 98% of the variance in the scaling and 88% in the matching data. We suggest that the predictive power was higher for scales because they are closer to the true variables of interest.

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Low level visual features support robust material perception in the judgement of metallicity

Harvey

Smithson

2021

Sci Rep

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The human visual system is able to rapidly and accurately infer the material properties of objects and surfaces in the world. Yet an inverse optics approach—estimating the bi-directional reflectance distribution function of a surface, given its geometry and environment, and relating this to the optical properties of materials—is both intractable and computationally unaffordable. Rather, previous studies have found that the visual system may exploit low-level spatio-chromatic statistics as heuristics for material judgment. Here, we present results from psychophysics and modeling that supports the use of image statistics heuristics in the judgement of metallicity—the quality of appearance that suggests an object is made from metal. Using computer graphics, we generated stimuli that varied along two physical dimensions: the smoothness of a metal object, and the evenness of its transparent coating. This allowed for the exploration of low-level image statistics, whilst ensuring that each stimulus was a naturalistic, physically plausible image. A conjoint-measurement task decoupled the contributions of these dimensions to the perception of metallicity. Low-level image features, as represented in the activations of oriented linear filters at different spatial scales, were found to correlate with the dimensions of the stimulus space, and decision-making models using these activations replicated observer performance in perceiving differences in metal smoothness and coating bumpiness, and judging metallicity. Importantly, the performance of these models did not deteriorate when objects were rotated within their simulated scene, with corresponding changes in image properties. We therefore conclude that low-level image features may provide reliable cues for the robust perception of metallicity.

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Comparing sensitivity estimates from MLDS and forced-choice methods in a slant-from-texture experiment

Cited by 15 publications

References 34 publications

Estimation of perceptual scales using ordinal embedding

Estimation of perceptual scales using ordinal embedding

Maximum likelihood difference scales represent perceptual magnitudes and predict appearance matches

Low level visual features support robust material perception in the judgement of metallicity

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