A three channel model of temporal frequency perception

Mandler, Marc B.; Makous, Walter

doi:10.1016/0042-6989(84)90021-x

Cited by 117 publications

(58 citation statements)

References 15 publications

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“…This expression, when combined with prior distributions sketched below, enables us to fit the parameters by maximizing the posterior likelihood (2). C++ programs was written to do this, using version 3.1.1 of the discrete Fourier transform library FFTW [23] to compute convolutions (A4) and version 2.4 of Sandia's OPT++ library [24] to do the optimization.…”

Section: Likelihood Of the Data Given Parametersmentioning

confidence: 99%

“…It is now generally thought that three overlapping temporal frequency-selective filters subserve perception of achromatic stimuli [1][2][3][4][5]. These three temporal filters (or channels) can be revealed using a detection task combined with a task of identification of a stimulus, namely detection of pattern, movement and pure flicker [1,6] and corresponds to the three perceptual phenomena [7].…”

Section: Introductionmentioning

confidence: 99%

“…These three temporal filters (or channels) can be revealed using a detection task combined with a task of identification of a stimulus, namely detection of pattern, movement and pure flicker [1,6] and corresponds to the three perceptual phenomena [7]. Mandler and Makous [2] also reported three achromatic temporal frequency filters: one low-pass, often referred to as sustained (actually weak band-pass, peak around 0.6-1 Hz), one intermediate band-pass referred to as transient-1 (peak around 3-8 Hz), and a second band-pass referred to as transient-2 (peak 9-16 Hz). The temporal frequencies at which the peaks occur depend on mean luminance, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Bar-like S-cone stimuli reveal the importance of an intermediate temporal filter

2010

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1The relative involvement of different temporal frequency-selective filters underlying detection of chromatic stimuli were studied. Diverse spectral stimuli were used, namely flashed blue and yellow light spots, wide bars and narrow bars. The stimuli were temporally modulated in luminance having constant wavelength. Although the bar-like stimuli apparently reduced the sensitivity at short and long wavelengths, the cone-opponent mechanism still remained responsible for the actual stimulus detection at different temporal frequencies. The bar-like stimuli increased sensitivity for temporal frequencies around 3-6 Hz, revealing involvement of an intermediate temporal frequencyselective filter to detection, the so-called transient-1 filter. A probability summation model for the method of adjustment was developed that assumes that detection depends on the properties of the temporal filters underlying the temporal frequency-sensitivity curve. The model supports the notion that at least two temporal frequency-selective filters are necessary to account for the shape of the sensitivity curves obtained for blue bar-like stimuli.

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Section: Likelihood Of the Data Given Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bar-like S-cone stimuli reveal the importance of an intermediate temporal filter

2010

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“…For a period of 2 min, the stimulus flickered in luminance or chromaticity (but not both) at each of a range of frequencies (2-60 Hz) and modulation levels (10-100%). After this initial exposure to the adapting flicker at a given frequency and modulation level, the modulation threshold (the minimum modulation required for flicker detection) was determined for either a 10-or 30-Hz test flicker; test frequency was selected to ensure that both adaptation and detection were mediated by a single temporal channel (25,26). To maintain the observers' level of flicker adaptation throughout the experiment, test presentations were interleaved with repeated 3-s presentations of the adapting flicker ( Fig.…”

Section: Stimulationmentioning

confidence: 99%

Adaptation from invisible flicker

Shady

MacLeod

Fisher

2004

Proc. Natl. Acad. Sci. U.S.A.

102

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Human ability to resolve temporal variation, or flicker, in the luminance (brightness) or chromaticity (color) of an image declines with increasing frequency and is limited, within the central visual field, to a critical flicker frequency of Ϸ50 and 25 Hz, respectively. Much remains unknown about the neural filtering that underlies this frequency-dependent attenuation of flicker sensitivity, most notably the number of filtering stages involved and their neural loci. Here we use the process of flicker adaptation, by which an observer's flicker sensitivity is attenuated after prolonged exposure to flickering lights, as a functional landmark. We show that flicker adaptation is more sensitive to high temporal frequencies than is conscious perception and that prolonged exposure to invisible flicker of either luminance or chromaticity, at frequencies above the respective critical flicker frequency, can compromise our visual sensitivity. This suggests that multiple filtering stages, distributed across retinal and cortical loci that straddle the locus for flicker adaptation, are involved in the neural filtering of high temporal frequencies by the human visual system.

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“…Thus, the single-filter assumption may not strictly hold over the whole range of contrasts employed here. Second, it has been shown that the temporal processing of a flickering pattern, as revealed by the discriminability, masking, and threshold temporal frequency functions, is characterized not by a single channel, but by multiple channels (Lehky, 1985;Mandler & Makous, 1984;Ohtani & Ejima, 1988).…”

Section: Derivation Of the Visual Latency Function On The Basis Of A mentioning

confidence: 99%

Analysis of simple reaction time to sinusoidal grating by means of a linear filter model of the detection process

Ejima

Ohtani

1989

Perception & Psychophysics

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Simple reaction time (RT) to a sinusoidal grating was analyzed in terms of a linear filter model of the detection process. First, RT contrast functions were determined over a wide range of spatial frequencies and retinal illuminances. Second, calculating the time course of the linear filter's response, theoretical visual latency contrast functions were derived for the same conditions of spatial frequency and retinal illuminance as those in the RT measurements. Comparison of the two functions showed that the contrast dependence of the RT functions was much larger than that of the visual latency functions. The discrepancy between the two functions was satisfactorily described as a power function of the slope ofthe filter's response at threshold level. On the basis of these results, we propose a model of the RT process. According to the model, the RT process is mediated by a cascade that consists of a level detector, which includes a linear filter followed by a threshold device, and a differentiator of the filter's response.Simple reaction time (RT), defined as the interval between stimulus onset and response initiation, has been a useful tool for psychophysical investigation of the human sensory decision process. Suprathreshold temporal properties of the visual system have been examined with measurement ofRT as a function of stimulus parameters such as intensity (Bartlett & MacLeod, 1954;Mansfield, 1973; Roufs, 1963Roufs, , 1974Vaughan, Costa, & Gilden, 1966), exposure duration (Bruder & Kietzman, 1973;Grossberg, 1968;Kietzman & Gillam, 1972;Mansfield, 1973; Ueno, 1976 Ueno, , 1977, size (Mansfield, 1973; Ueno, 1978), retinal location (Bartlett & Sticht, 1968;Rains, 1963), wavelength (Bartlett & Sticht, 1968;Nissen & Pokorny, 1977;Pollack, 1968; Ueno, Pokorny, & Smith, 1985), and adaptation level (Bartlett & MacLeod, 1954;Burkhardt, Gottesman, & Keenan, 1987).If one assumes that the delay in the decision and motor process in a simple reaction task is constant for different stimuli, then the variation of RT with stimulus parameters may be identified with that of the sensory processing delay. This may be expressed mathematically as RT = min[t; R(t) ;:: del + K, (1) where R(t) stands for the time course of the sensory signal, de for the detection threshold, and K for an irreducible minimum component of RT. This equation means that the sensory signal exceeds the detection threshold at the This work was supported in part by Grants in Aid for Scientific Research from the Ministry of Education, Nos. 63510056 and 62790025, and also by the Yamarnura-Tamura Foundation. The second author is supported by a fellowship from the Japan Society for the Promotion of Science for Japanese Junior Scientists.time of tmin = min [t; R(t) ;:: del (i.e., visual latency) and that the reaction occurs at tmin + K so that RT is an index of visual latency.However, there are some indications that RT does not always serve as an accurate index of the sensory processing delay. Roufs (1974) showed that, as stimulus intensity was decreased, RT ...

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A three channel model of temporal frequency perception

Cited by 117 publications

References 15 publications

Bar-like S-cone stimuli reveal the importance of an intermediate temporal filter

Bar-like S-cone stimuli reveal the importance of an intermediate temporal filter

Adaptation from invisible flicker

Analysis of simple reaction time to sinusoidal grating by means of a linear filter model of the detection process

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