Increment thresholds at low intensities considered as signal/noise discriminations

Barlow, H. B.

doi:10.1113/jphysiol.1957.sp005774

Cited by 395 publications

(250 citation statements)

References 30 publications

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“…Mans¢eld 1974;Orban et al 1984) has been invoked in some explanations of the classical oblique e¡ect (Appelle 1972;Essock 1980). The size of this noise can be estimated by measuring how performance depends on the amount of external noise introduced into the stimulus (Barlow 1957). In such a study of orientation acuity with gratings and two-dimensional, band-pass¢ltered spatial noise (Heeley et al 1997), it was found that neither a reduced sampling density nor increased internal noise along a 45 oblique axis was su¤cient to account for the measured oblique e¡ect.…”

Section: Noise Along the Oblique Axesmentioning

confidence: 99%

Multiple groups of orientation–selective visual mechanisms underlying rapid orientated–line detection

Foster

Westland

1998

Proc. R. Soc. Lond. B

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Visual search for an edge or line element di¡ering in orientation from a background of other edge or line elements can be performed rapidly and e¡ortlessly. In this study, based on psychophysical measurements with ten human observers, threshold values of the angle between a target and background line elements were obtained as functions of background-element orientation, in brief masked displays. A repeated-loess analysis of the threshold functions suggested the existence of several groups of orientation-selective mechanisms contributing to rapid orientated-line detection; speci¢cally, coarse, intermediate and ¢ne mechanisms with preferred orientations spaced at angles of approximately 90 , 35 ^50 and 10 ^25 , respectively. The preferred orientations of coarse and some intermediate mechanisms coincided with the vertical or horizontal of the frontoparallel plane, but the preferred orientations of ¢ne mechanisms varied randomly from observer to observer, possibly re£ecting individual variations in neuronal sampling characteristics.

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Section: Noise Along the Oblique Axesmentioning

confidence: 99%

Multiple groups of orientation–selective visual mechanisms underlying rapid orientated–line detection

Foster

Westland

1998

Proc. R. Soc. Lond. B

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“…the Poisson noise property of light). Applications of ideal observer theory to simple categorization tasks have yielded important discoveries concerning, for example, the detection of signals in noise (De Vries 1943;Rose 1948;Peterson et al 1954;Barlow 1957 1983;Pelli 1990), the discrimination of colour (Vos & Walraven 1972), the discrimination of spatial patterns and spatial location (Geisler 1984(Geisler , 1989, the discrimination of surface slant using texture cues (Knill 1998a), and the discrimination of 3D shape (Liu et al 1995). These discoveries include optimal performance laws for various stimulus dimensions.…”

Section: If P(s|c I )P(c Imentioning

confidence: 99%

“…Our proposal draws upon two important research traditions in sensation and perception: ideal observer theory (De Vries 1943;Rose 1948;Peterson et al 1954;Barlow 1957;Green & Swets 1966) and probabilistic functionalism (Brunswik & Kamiya 1953;Brunswik 1956). After reviewing these two research traditions, we motivate and derive the basic formulae for maximum tness ideal observers and Bayesian natural selection.…”

Section: Introductionmentioning

confidence: 99%

“…each instance of a polymorphism) in each species under consideration is represented by a fundamental equation, which describes how the number of organisms carrying that allele vector at time t + 1 is related to: (i) the number of organisms carrying that allele vector at time t; (ii) the prior probability of a state of the environment at time t; (iii) the likelihood of a stimulus given the state of the environment; (iv) the likelihood of a response given the stimulus; and (v) the birth and death rates given the response and the state of the environment. The process of natural selection is represented by iterating these fundamental equations in parallel over time, while updating the allele vectors using appropriate probability distributions for mutation and sexual recombination.Our proposal draws upon two important research traditions in sensation and perception: ideal observer theory (De Vries 1943;Rose 1948;Peterson et al 1954;Barlow 1957;Green & Swets 1966) and probabilistic functionalism (Brunswik & Kamiya 1953;Brunswik 1956). After reviewing these two research traditions, we motivate and derive the basic formulae for maximum tness ideal observers and Bayesian natural selection.…”

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confidence: 99%

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Bayesian natural selection and the evolution of perceptual systems

Geisler

Diehl

2002

Phil. Trans. R. Soc. Lond. B

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In recent years, there has been much interest in characterizing statistical properties of natural stimuli in order to better understand the design of perceptual systems. A fruitful approach has been to compare the processing of natural stimuli in real perceptual systems with that of ideal observers derived within the framework of Bayesian statistical decision theory. While this form of optimization theory has provided a deeper understanding of the information contained in natural stimuli as well as of the computational principles employed in perceptual systems, it does not directly consider the process of natural selection, which is ultimately responsible for design. Here we propose a formal framework for analysing how the statistics of natural stimuli and the process of natural selection interact to determine the design of perceptual systems. The framework consists of two complementary components. The rst is a maximum tness ideal observer, a standard Bayesian ideal observer with a utility function appropriate for natural selection. The second component is a formal version of natural selection based upon Bayesian statistical decision theory. Maximum tness ideal observers and Bayesian natural selection are demonstrated in several examples. We suggest that the Bayesian approach is appropriate not only for the study of perceptual systems but also for the study of many other systems in biology. BACKGROUNDThe evolution of a species occurs as the result of interactions between its environment and its genome. Thus, achieving a rigorous account of evolution depends as much on understanding the properties of environments as it does on understanding the properties of nucleic acid and protein chemistry. Although many laws of nature are usefully described in deterministic terms, the complexity of evolution often requires a description of both environments and genetics in statistical/probabilistic terms. Research in the areas of population genetics and theoretical ecology has emphasized genetics and its relationship to natural selection. However, recent advances in measuring the statistical regularities of natural environments, especially in the area of perceptual systems, raise the possibility of developing more complete theories of evolution by combining precise statistical descriptions of the environment with precise statistical descriptions of genetics.Here we show that a constrained form of Bayesian statistical decision theory provides an appropriate framework for exploring the formal link between the statistics of the environment and the evolving genome. The framework consists of two components. One is a Bayesian ideal observer with a utility function appropriate for natural selection. The other is a Bayesian formulation of natural selection that neatly divides natural selection into several factors that are measured individually and then combined to characterize the process as a whole. In the Bayesian * Author for correspondence (geisler@psy.utexas.edu). formulation, each allele vector (i.e. each instance of a polymor...

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“…In addition, there are noise processes at the various stages of signal transmission. The correlation between sensitivity or rather stimulus threshold and stimulus intensity was extensively studied by Weber [16] and especially for the visual system by Barlow [2]. The relation to the strength of the excitation was then established by Fechner [6].…”

Section: Introductionmentioning

confidence: 99%

The Dynamics of the Light Adaptation of the Human Visual System; Subjective and Electroretinographic Analysis

Thoss¹,

S²,

Bartsch³

2017

Preprint

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Abstract:The excitation of the visual system increases with increasing retinal illumination. At the same time, the sensitivity of the system decreases (light adaptation). Higher excitation automatically results in a lower sensitivity. This study investigates whether this parallelism between the excitation and the sensitivity also applies in the dynamic case, that is, during the transition to a higher excitation level after an increase in the retinal illuminance.For this purpose, the courses of the subjective and the electroretinographic threshold during the transitional phase after a step of the adaptation illumination was determined by means of a special light-stimulation apparatus. As a measure of the course of the excitation during this time, the response ERG on the adaptation step was recorded with a special amplifier.The threshold curve always has an overswing, which shows subjectively very strong differences. It can be concluded that the glare caused by a sudden increase in illuminance is subjectively very different. The comparison between the response ERG on the adaptation step and the course of the electroretinographic increment threshold during this time shows a broad agreement between the two courses.It can thus be assumed that the sensitivity of the visual system follows the course of the excitation also in the dynamic case. In addition, the investigation shows that the glare experienced after a step in the illuminance clearly shows great subjective differences.

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Increment thresholds at low intensities considered as signal/noise discriminations

Cited by 395 publications

References 30 publications

Multiple groups of orientation–selective visual mechanisms underlying rapid orientated–line detection

Multiple groups of orientation–selective visual mechanisms underlying rapid orientated–line detection

Bayesian natural selection and the evolution of perceptual systems

The Dynamics of the Light Adaptation of the Human Visual System; Subjective and Electroretinographic Analysis

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