Why luminance discrimination may be better than detection

Lasley, David J.; Cohn, Theodore E.

doi:10.1016/0042-6989(81)90121-8

Cited by 61 publications

(31 citation statements)

References 13 publications

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“…This inadequacy stems from a lack of substance: the model is not based on biologically tractable components or known physical principles. In this respect, the transduction model is ''top down" and ''data driven", and so describes, rather than explains, the data (Lasley and Cohn, 1979). Thus the transduction model does not predict a fourth power transform for low level stimuli, the magnitude of the transform was instead obtained from the slopes of psychometric functions fitted from empirical data (Hanna et al, 1986).…”

Section: Modeling Nmmentioning

confidence: 99%

“…Consider two stimuli of magnitudes X and X + DX, where X is of a measurable level for discrimination tasks but equal to zero in a detection task. If, in a difference discrimination task, X is close to, but not equal to, zero and not of sufficient magnitude to diminish the detection of DX as per Weber's law, then X provides the observer with information about the location of DX in both space and time (Lasley and Cohn, 1979). Thus as X approaches the absolute threshold, DX becomes easier to detect until eventually Weber's law begins operating effectively and X is of sufficient level to eliminate all uncertainty and negate its effect.…”

Section: Introductionmentioning

confidence: 99%

“…An assumption of the uncertainty hypothesis is that X and X + DX are obscured by an external noise occupying all of the stimulus channels. Uncertainty increases the overall effect of such noise and consequently lowlevel stimuli are harder to detect (Lasley and Cohn, 1979).…”

Section: Introductionmentioning

confidence: 99%

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Negative masking and the units problem in audition

Shepherd¹,

Hautus²

2009

Hearing Research

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Section: Modeling Nmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Negative masking and the units problem in audition

Shepherd¹,

Hautus²

2009

Hearing Research

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“…According to Birdsall's theorem, when the variance of any source of noise prior to the nonlinear transducer is large enough so that other sources of noise in the experiment can be neglected, the resulting dЈ psychometric function will be linear (Lasley & Cohn, 1981). Because dЈ psychometric functions are generally nonlinear in high external noise, the theorem has been used to reject nonlinear transducer models in perception (Lasley & Cohn, 1981). However, Birdsall's theorem is based on three assumptions: (a) The transducer function is monotonic, (b) sources of noise other than the external noise can be neglected, and (c) observers try their best to maximize their performance accuracy.…”

Section: Discussionmentioning

confidence: 99%

Characterizing observers using external noise and observer models: Assessing internal representations with external noise.

Lü¹,

Dosher²

2008

Psychological Review

228

347

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External noise methods and observer models have been widely used to characterize the intrinsic perceptual limitations of human observers and changes of the perceptual limitations associated with cognitive, developmental, and disease processes by highlighting the variance of internal representations. The authors conducted a comprehensive review of the 5 most prominent observer models through the development of a common formalism. They derived new predictions of the models for a common set of behavioral tests that were compared with the data in the literature and a new experiment. The comparison between the model predictions and the empirical data resulted in very strong constraints on the observer models. The perceptual template model provided the best account of all the empirical data in the visual domain. The choice of the observer model has significant implications for the interpretation of data from other external noise paradigms, as well as studies using external noise to assay changes of perceptual limitations associated with observer states. The empirical and theoretical development suggests possible parallel developments in other sensory modalities and studies of high-level cognitive processes.Keywords: signal detection theory, internal representation, observer model, perceptual template, internal noise Human decisions are based on internal representations of information. Understanding how stimuli are represented internally is one of the classic problems in psychology. This article examines how modifying an external stimulus with external noise can provide insight into how the stimulus is processed by the human observer. We conducted a systematic and comprehensive review of the external noise paradigms and observer models widely used in characterizing the internal response properties of human observers. The observer approach builds on the broadly applicable framework of signal detection theory (SDT) by elaborating the relationships between external stimuli and the internal response distributions that form the basis for decision. The empirical tests introduce external noise-either masking noise or variation in the relevant stimulus dimension-to provide a reference for characterizing and quantifying the limiting factors in perceptual sensitivity. The current review, analysis, and empirical test focus on visual perception. Some of the model properties, especially the empirical findings, may be modality specific. Still, this framework and the findings could serve as an example for parallel development of the empirical methods and theoretical models in other sensory modalities. The model development and testing also have major implications for applications of the external noise paradigms in understanding the mechanisms underlying changes of perceptual sensitivity in different cognitive, disease, and/or developmental states. Internal Response DistributionsSDT provides a general framework for analyzing human decision making in perceptual and cognitive tasks (Green & Swets, 1966;Macmillan & Creelman, 1991). In a ...

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“…For example, in Figure 1 there are eight discrete grey levels in the display. The ideal detector needs to know the probability of occurrence of each of those grey levels in (1) the signal and (2) the noise, but not the actual intensities of those grey levels Downloaded by [Temple University Libraries] at 16:14 14 June 2016 (Kingdom et al, 1987;Lasley & Cohn, 1981). In other words it does not matter to the ideal detector whether those grey levels are arranged linearly (equal intervals between grey levels) or logarithmically (equal ratios between grey levels).…”

mentioning

confidence: 98%

Modelling Visual Detection: Luminance Response Non-linearity and Internal Noise

Kingdom

Moulden

1989

The Quarterly Journal of Experimental Psychology Section A

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Two experiments that investigate the effect of various display factors on the detectability of a thin line signal in random visual noise are described. Three statistical decision models are described, together with their ability to account for the results. The first is an "ideal detector" model, the second an "energy integrator" model, and the third a model based upon the operation of retinal ganglion cells which incorporates a gain control mechanism. The ideal detector model fails to give a good account of human performance, whereas the other two models provide a good fit to the data. The digital Laplacian with gain control model has the slight advantage over the energy integrating model in being able to account for a small superiority in the detection of dark as opposed to bright signals. Finally, both models require the inclusion of an estimate of the internal noise of the human visual system to account for the pattern of performance observed under changing conditions of display contrast.

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Why luminance discrimination may be better than detection

Cited by 61 publications

References 13 publications

Negative masking and the units problem in audition

Negative masking and the units problem in audition

Characterizing observers using external noise and observer models: Assessing internal representations with external noise.

Modelling Visual Detection: Luminance Response Non-linearity and Internal Noise

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