Psychophysical Correlates of Retinal Processing

Baraas, Rigmor C.; Zele, Andrew J.

doi:10.1007/978-3-319-44978-4_5

Cited by 6 publications

(4 citation statements)

References 158 publications

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“…Output evaluation after color development is empirically eye-based; consequently, positive sample recognition is subjective (Pappas, 1994;Waner et al, 1996) and particularly error-prone. Such errors may stem from overlapping dot colorations resulting from host pigment contamination (Chen et al, 2012;Olkkonen and Ekroll, 2016), or from the inherent color discrimination thresholds concerning saturation and hue of the human eye (Krudy and Ladunga, 2001;Reinhard, 2008; Baraas and Zele, 2016;Olkkonen and Ekroll, 2016). Two types of errors are possible in binary classification tests: false positives (FP) or "false alarms" and false negatives (FN) or "missing values," referring to the incorrect recognition of true negatives (TN) as positives and true positives (TP) as negatives, respectively.…”

Section: Introductionmentioning

confidence: 99%

Pixel-Based Machine Learning and Image Reconstitution for Dot-ELISA Pathogen Diagnosis in Biological Samples

et al. 2021

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Serological methods serve as a direct or indirect means of pathogen infection diagnosis in plant and animal species, including humans. Dot-ELISA (DE) is an inexpensive and sensitive, solid-state version of the microplate enzyme-linked immunosorbent assay, with a broad range of applications in epidemiology. Yet, its applicability is limited by uncertainties in the qualitative output of the assay due to overlapping dot colorations of positive and negative samples, stemming mainly from the inherent color discrimination thresholds of the human eye. Here, we report a novel approach for unambiguous DE output evaluation by applying machine learning-based pattern recognition of image pixels of the blot using an impartial predictive model rather than human judgment. Supervised machine learning was used to train a classifier algorithm through a built multivariate logistic regression model based on the RGB (“Red,” “Green,” “Blue”) pixel attributes of a scanned DE output of samples of known infection status to a model pathogen (Lettuce big-vein associated virus). Based on the trained and cross-validated algorithm, pixel probabilities of unknown samples could be predicted in scanned DE output images, which would then be reconstituted by pixels having probabilities above a cutoff. The cutoff may be selected at will to yield desirable false positive and false negative rates depending on the question at hand, thus allowing for proper dot classification of positive and negative samples and, hence, accurate diagnosis. Potential improvements and diagnostic applications of the proposed versatile method that translates unique pathogen antigens to the universal basic color language are discussed.

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Section: Introductionmentioning

confidence: 99%

Pixel-Based Machine Learning and Image Reconstitution for Dot-ELISA Pathogen Diagnosis in Biological Samples

et al. 2021

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“…This requires extremely careful physical light and individual observer calibrations (Uprety et al 2021). The observer corrections can minimize the individual differences in the inert optical pigments (e.g., lens, macular pigment) and photoreceptor spectral sensitivities between the individual and the CIE standard observer sensitivity functions (Baraas and Zele 2016, Mollon et al 2017, Spitschan et al 2017. In practice, these individual differences can be effectively determined using HFP (Lennie et al 1993, Uprety et al 2021.…”

Section: Methodological Considerations For the Experimental Control O...mentioning

confidence: 99%

Melanopsin Vision

Joyce

Houser

Peirson

et al. 2022

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Intrinsically photosensitive retinal ganglion cells (ipRGGs) are the most recently discovered photoreceptor class in the human retina. This Element integrates new knowledge and perspectives from visual neuroscience, psychology, sleep science and architecture to discuss how melanopsin-mediated ipRGC functions can be measured and their circuits manipulated. It reveals contemporary and emerging lighting technologies as powerful tools to set mind, brain and behaviour.

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“…By now it is established that, at different light conditions and for different regions of the VF, the following retinal cells selectively sensitive to different wavelengths contribute to human color perception: three types of cones determining daytime color vision and contributing to the twilight vision (short-wave S-cones, with peak sensitivity at ~430 nm; middle-wave M-cones with peak sensitivity at ~530 nm; and long-wave L-cones with peak sensitivity at ~560 nm); rods, the receptors much more sensitive to light than cones (with peak sensitivity between S-and M-cones at ~520 nm) enabling night vision and contributing to twilight vision; and intrinsically photosensitive retinal ganglion cells (ipRGCs) containing melanopsin with peak sensitivity between rods and L-cones (at about 490 nm). The number of publications on the ipRGSs and their contribution to visual functions is rapidly growing (Berson et al, 2002;Hattar et al, 2002;Dacey et al, 2005;Graham, 2014;Cao & Barrionuevo, 2015;Baraas & Zele, 2016;Hannibal et al, 2017;Zele et al, 2018;Schroeder et al, 2018;Allen et al, 2019;Spitschan, 2019;Yamakawa et al, 2019, etc. ).…”

Section: Introductionmentioning

confidence: 99%

A simple method for comparing peripheral and central color vision by means of two smartphones

et al. 2022

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Information on peripheral color perception is far from being sufficient since it was predominantly obtained using small stimuli, limited ranges of eccentricities, and sophisticated experimental conditions. Our purpose was to consider a possibility of facilitating technical realization of the classical method of asymmetric color matching (ACM) developed by Moreland and Cruz (1959) for assessing appearance of color stimuli in the peripheral visual field (VF). We adopted the ACM method by employing two smartphones to implement matching procedure at various eccentricities. Although smartphones were successfully employed in vision studies, we are aware that some photometric parameters of smartphone displays are not sufficiently precise to ensure accurate color matching in foveal vision; moreover, certain technical characteristics of commercially available devices are variable. In the present study we provide evidence that, despite these shortages, smartphones can be applied for general and wide investigations of the peripheral vision. In our experiments, the smartphones were mounted on a mechanical perimeter to simultaneously present colored stimuli foveally and peripherally. Trying to reduce essential discomfort and fatigue experienced by most observers in peripheral vision studies, we did not apply bite bars, pupil dilatation, and Maxwellian view. The ACM measurements were performed without prior training of observers and in a wide range of eccentricities, varying between 0 and 95°. Color appearance was measured in the HSV color space coordinates as a function of eccentricity and stimulus luminance. We demonstrate that our easy-to-conduct method provides a reliable means to estimate color appearance in the peripheral vision and to assess interindividual differences.

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Psychophysical Correlates of Retinal Processing

Cited by 6 publications

References 158 publications

Pixel-Based Machine Learning and Image Reconstitution for Dot-ELISA Pathogen Diagnosis in Biological Samples

Pixel-Based Machine Learning and Image Reconstitution for Dot-ELISA Pathogen Diagnosis in Biological Samples

Melanopsin Vision

A simple method for comparing peripheral and central color vision by means of two smartphones

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