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• W3192162758 abstract "The concept of canonical ratings is introduced in which each S describes all the visual sensations produced by signal and noise trials in the expected spatial and temporal location of the stimulus. After many practice sessions, the S assigns one and only one numerical rating to each visual sensation. These canonical ratings are determined by the S, not the E, and are a, bbreviations for verbal descriptions of subjectively distinct visual sensations. The data consisted of canonical ratings at absolute visual detection for dim visual stimuli (signal) and blank (noise) trials containing no light at all. The physical stimulus is discrete since it is made up of absorptions of quanta of light that result in isomerizations of rhodopsin molecules or thermal decompositions of rhodopsin which are discrete noise events that mimic the action of quantal absorptions. Under these conditions, it is known from the laws of physics that these quantum-like events labsorptions plus thermal decompositions) follow a Poisson distribution. Previously, it had been shown that the canonical ratings follow the same Poisson distributions that the quantum-like events do. It was also shown that the data for one S were consistent with the hypothesis that the rating on any trial was equal to the number of quantum-like events that had occurred and for two other Ss, either one less or two less than this number. A signal detection theory analysis of these canonical ratings is performed, resulting in ROC curves and estimates of d’. In addition, it is shown that the Poisson canonical rating distributions can be approximated by cutoff Gaussian distributions. Hence it is possible to use a probit analysis, which is computationally simple, to calculate the maximum likelihood solutions for all means, standard deviations, d’, and b, as well as the standard errors of all these estimates. The rating is shown to be a linear function of the internal decision variable. The internal criteria are all greater than the mean of the noise distribution and they are all separated by steps of equal size. The probit analysis may be used whenever all the individual rating distributions are Gaussian in order to obtain the maximum likelihood estimates and standard errors of all parameters for each Gaussian distribution. Thus, this analysis may be applied to rating experiments, other than the one described here." @default.
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• W3192162758 date "1974-05-01" @default.
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• W3192162758 title "Canonical ratings" @default.
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• W3192162758 doi "https://doi.org/10.3758/bf03198575" @default.
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