Orientation tuning in human colour vision at detection threshold

Gheiratmand, Mina; Mullen, Kathy T.

doi:10.1038/srep04285

Cited by 11 publications

(27 citation statements)

References 63 publications

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“…Subsequent psychophysical studies, however, suggested that bandpass spatial filtering with broadly similar bandwidths for color and achromatic contrast underlies the overall low-pass shape of the CSF (Bradley, Switkes, & De Valois, 1988;Humanski & Wilson, 1992;Losada & Mullen, 1994Mullen & Losada, 1999). Furthermore, psychophysical studies have demonstrated orientation tuned responses in color vision (Beaudot & Mullen, 2005;Bradley et al, 1988;Humanski & Wilson, 1993;Reisbeck & Gegenfurtner, 1998;Vimal, 1997;Webster, Switkes, & Valois, 1990;Wuerger & Morgan, 1999), although this may be lost at very low spatial frequencies (Gheiratmand, Meese, & Mullen, 2013;Gheiratmand & Mullen, 2014). Thus, the presence of both spatial frequency and orientation tuning neural responses, the prerequisites for edge detection, suggest that there may be a system for chromatic edge detection.…”

Section: Introductionmentioning

confidence: 99%

Evidence for chromatic edge detectors in human vision using classification images

McIlhagga

Mullen

2018

Journal of Vision

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Edge detection plays an important role in human vision, and although it is clear that there are luminance edge detectors, it is not known whether there are chromatic edge detectors as well. We showed observers a horizontal edge blurred by a Gaussian filter (with widths of σ = 0.1125, 0.225, or 0.45°) embedded in blurred Brown noise. Observers had to choose which of two stimuli contained the edge. Brown noise was used in preference to white noise to reveal localized edge detectors. Edges and noise were defined by either luminance or chromatic contrast (isoluminant L/M and S-cone opponent). Classification image analysis was applied to observer responses. In this analysis, the random components of the stimulus are correlated with observer responses to reveal a template that shows how observers weighted different parts of the stimulus to arrive at their decision. We found classification images for both luminance and isoluminant chromatic stimuli that had shapes very similar to derivatives of Gaussian filters. The widths of these classification images tracked the widths of the edges, but the chromatic edge classification images were wider than the luminance ones. These results are consistent with edge detection filters sensitive to luminance contrast and isoluminant chromatic contrast.

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Section: Introductionmentioning

confidence: 99%

Evidence for chromatic edge detectors in human vision using classification images

McIlhagga

Mullen

2018

Journal of Vision

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“…Johnson et al (2008) did report that the V1 neurons most biased towards L/ M gratings and away from luminance gratings did prefer lower spatial frequencies, and some were poorly tuned for orientation. This has also been reported psychophysically (Gheiratmand & Mullen, 2014). While most V1 neurons might respond differentially to color stimuli to some exten/t, it is still not clear whether all these neurons contribute to color perception, or whether this is the role of some (yet to be characterized) subset of neurons (Schmidt et al, 2014(Schmidt et al, , 2016.…”

Section: Modeling the Color Planesmentioning

confidence: 67%

V1-based modeling of discrimination between natural scenes within the luminance and isoluminant color planes

Tolhurst

2019

Journal of Vision

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#We have been developing a computational visual difference predictor model that can predict how human observers rate the perceived magnitude of suprathreshold differences between pairs of full-color naturalistic scenes . The model is based closely on V1 neurophysiology and has recently been updated to more realistically implement sequential application of nonlinear inhibitions (contrast normalization followed by surround suppression; To, Chirimuuta, & Tolhurst, 2017). The model is based originally on a reliable luminance model (Watson & Solomon, 1997) which we have extended to the red/green and blue/yellow opponent planes, assuming that the three planes (luminance, red/green, and blue/yellow) can be modeled similarly to each other with narrow-band oriented filters. This paper examines whether this may be a false assumption, by decomposing our original full-color stimulus images into monochromatic and isoluminant variants, which observers rate separately and which we model separately. The ratings for the original full-color scenes correlate better with the new ratings for the monochromatic variants than for the isoluminant ones, suggesting that luminance cues carry more weight in observers' ratings to full-color images. The ratings for the original full-color stimuli can be predicted from the new monochromatic and isoluminant rating data by combining them by Minkowski summation with power m ¼ 2.71, consistent with other studies involving feature summation. The model performed well at predicting ratings for monochromatic stimuli, but was weaker for isoluminant stimuli, indicating that mirroring the monochromatic models is not sufficient to model the color planes. We discuss several alternative strategies to improve the color modeling.

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“…In sum, relative to component gratings, color plaids have lower perceived contrast summation than achromatic plaids and this chromatic summation difference is greater at the medium-spatial frequency and smaller at the low-spatial frequency. These results may be reflective of two separate processes: greater cross-orientation suppression in color vision (Kim et al, 2013; Medina & Mullen, 2009) and increased cross-orientation summation for low compared with medium-spatial frequency color stimuli (Gheiratmand et al, 2013, 2016; Gheiratmand & Mullen, 2014). …”

Section: Resultsmentioning

confidence: 92%

“…Moreover, this effect is greater for mid-spatial frequency stimuli; for this spatial frequency, chromatic plaids have the lowest perceived contrast summation while achromatic plaids have the highest. Recent research on cross-orientation effects in color vision has identified two different and specific mechanisms: subthreshold summation, which acts to increase contrast sensitivity to cross-oriented stimuli (Gheiratmand et al, 2013, 2016; Gheiratmand & Mullen, 2014), and cross-orientation suppression, which decreases contrast sensitivity when one cross-oriented component is at a suprathreshold contrast (Kim et al, 2013; Medina & Mullen, 2009). We next discuss how the presence of these mechanisms in suprathreshold plaid contrast perception is supported by our data.…”

Section: Discussionmentioning

confidence: 99%

The Whole is Other Than the Sum: Perceived Contrast Summation Within Color and Luminance Plaids

Cherniawsky

Mullen

2016

i-Perception

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The apparent contrast of a plaid is a reflection of the neural relationship between the responses to its two orthogonal component gratings. To investigate the perceived contrast summation of the responses to component gratings in plaids, we compared the apparent contrasts of monocular plaids to a component grating presented alone across chromaticity and spatial frequency. Observers performed a contrast-matching task for red–green color and luminance stimuli at low- and medium-spatial frequencies. Using the measured points of subjective equality between plaids and gratings, we evaluate perceived contrast summation across conditions, which may vary between 1 (no summation) and 2 (full summation). We show that achromatic plaids have higher perceived contrast summation than chromatic plaids. The greatest difference occurs at the medium-spatial frequency, with summation highest for achromatic plaids (1.87) and lowest for chromatic plaids (1.49), while at low-spatial frequencies, there is a smaller summation difference between achromatic (1.72) and chromatic (1.65) plaids. These results are consistent with recent theories of distinct cross-orientation suppression and summation mechanisms in color and luminance vision. Two control experiments for binocular versus monocular viewing, and the overall size of the stimulus patches did not reveal any differences from our main results.

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Orientation tuning in human colour vision at detection threshold

Cited by 11 publications

References 63 publications

Evidence for chromatic edge detectors in human vision using classification images

Evidence for chromatic edge detectors in human vision using classification images

V1-based modeling of discrimination between natural scenes within the luminance and isoluminant color planes

The Whole is Other Than the Sum: Perceived Contrast Summation Within Color and Luminance Plaids

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