Intensity discrimination as a function of frequency and sensation level

Jesteadt, Walt; Wier, Craig C.; Green, David M.

doi:10.1121/1.381278

Cited by 355 publications

(177 citation statements)

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“…All these results together exhibit the same general trend and appear quite consistent with one another. The absolute size of the M.s, between 0.3 and 0.7 dB, agree quite well with intensity DLs measured with l000-Hz tones and welltrained subjects (Jesteadt, Wier, & Green, 1977;Rabinowitz, Lim, Braida, & Durlach, 1976).…”

Section: Resultssupporting

confidence: 70%

“…when T increases from 10 to 20 msec, then decreases much less rapidly or remains rather constant for Tvalues between 20 and 40 msec. This is equivalent to the socalled near miss to Weber's law reported in the literature for intensity discrimination of pure tones (Jesteadt et al, 1977). There are several possible reasons for the differences between our results, shown in Figure 8, and those of Henning (1970) and Florentine (1986).…”

Section: Resultscontrasting

confidence: 53%

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Auditory discrimination of tone-pulse onsets

Smurzyński¹,

Houtsma²

1989

Perception & Psychophysics

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Two experiments are reported in which difference limens (DLs) were measured for onset times of a 1000-Hz tone pulse. An adaptive two-alternative forced-choice procedure and (mostly) welltrained subjects were used. In the first experiment, DLs were measured for the rise time of linear onset ramps at rise-time values between 10 and 60 msec. The DLs follow Weber's law up to a rise time of about 50 msec, and do not support the notion that rise times are perceived in a categorical manner. In the second experiment, DLs were obtained for linear, exponential, and raisedcosine onset envelopes at rise-time values between 10 and 40 msec. When energy differences in the critical band around 1000 Hz are computed for just-discriminable onsets, values between 0.7 dB (10-msec rise time) and 0.3 dB (40-sec rise time) are found. These equivalent intensity DLs show the same "near miss to Weber's law" behavior as do intensity DLs for pure tones.Recognition of a musical instrument by its sound is based on quasi-stationary attributes such as tone spectrum, as well as on transient attributes such as tone attack and decay. It has been shown (Berger, 1964;Clark, Luce, Abrams, Schlossberg, & Rome, 1964; Saldanha & Corso, 1964) that subjects' abilities to identify musical instruments correctly by their sound decreases significantly when sounds are presented without attack.Further insight into the perceptual role of a tone attack was obtained through studies of differential sensitivity for a tone's onset time. A central question in these studies was whether a tone's rise (onset, attack) time forms a perceptual continuum in which, for instance, Weber's law would hold, or whether the physical onset-time continuum maps perceptually into a small number of subjective categories, resulting in so-called categorical perception. Especially during the last few years, there has been a renewed interest in categorical perception for nonspeech signals. Rosen and Howell (1981) determined that categorical discrimination functions could not be obtained for music-like stimuli spaced with equal intervals of the rise time, as had been reported by Cutting and Rosner (1974). Their subject's responses appeared rather to follow Weber's law; that is, difference limens (DLs) for rise time increased proportionally with increasing rise-time durations. These findings were confirmed by Kewley-Port and Pisoni (1984).Smurzyriski (1985) that subjects trained in auditory perception, when presented with 300-Hz sawtooth-wave bursts with six different rise times between 10 and 60 msec, generated typical confusion matrices with responses clustered around the main diagonal, rather than two sharply defined response categories ("pluck" and "bow"), as Cutting and Rosner (1974) had found. Further analysis of these identification data revealed that average received (or mutual) information (Garner & Hake, 1951) in that experiment amounted to 1.3 bits. This is significantly more than the prediction of a categorical perception model that divides the 10-to 6O-msec rise-time range in...

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Section: Resultssupporting

confidence: 70%

Section: Resultscontrasting

confidence: 53%

Auditory discrimination of tone-pulse onsets

Smurzyński¹,

Houtsma²

1989

Perception & Psychophysics

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“…Thus, pairs of stimuli, equally spaced in decibels should be equally discriminable (Weber's law). Because Weber's law does not hold for pure tones (e.g., Jesteadt, Wier, & Green, 1977), Braida and Durlach (1972) argue that a logarithmic mapping should be considered as a first-order approximation to a(I ). When they reanalyzed their data after correcting for the near-miss to Weber's law, they concluded that a better approximation to a(I ) is (3) where I 0 is the threshold intensity.…”

Section: Discussionmentioning

confidence: 99%

Top-down gain control in the auditory system: Evidence from identification and discrimination experiments

Parker

Murphy

Schneider

2002

Perception & Psychophysics

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The influence of intensity range in auditory identification and intensity discrimination experiments is well documented and is usually attributed to nonsensory factors. Recent studies, however, have suggested that the stimulus range effect might be sensory in origin. To test this notion, in one set of experiments, we had listeners identify the individual tones in a set. One baseline condition consisted of identifying four 1-kHz, low-intensity tones; the other consisted of identifying four 1-kHz, high-intensity tones. In the experimental conditions, these baseline tone sets were augmented by adding a fifth tone at either 1 or 5 kHz. Added 5-kHz tones had little effect on identification accuracy for the four baseline tones. When an added 1-kHz tone differed substantially in intensity from the four baseline tones, it adversely affected performance, with the addition of a high-intensity tone to a set of low-intensity tones having a more deleterious effect than the addition of a low-intensity tone to a set of high-intensity tones. These and further results, obtained in an exploration of this asymmetrical range effect in a third identification experiment and in two intensity-discriminationexperiments, were consistent with the notion of a nonlinear amplifier under top-down control whose functions include protection against sensory overload from loud sounds. The identification data were well described by a signal-detection model using equal-variance Laplace distributions instead of the usual Gaussian distributions.

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“…In conclusion, signal-in-noise (segmentation) and fine (feature discrimination) paradigms have been used widely to study sensory processing in both human and nonhuman primates (e.g., Hawkins and Stevens, 1950;Jesteadt et al, 1977;Orban et al, 1984;Newsome and Paré, 1988). Achieving a clear understanding of the benefits of training on such tasks has implications that extend to sensory rehabilitation (Green and Bavelier, 2003, 2006a, 2006bLi et al, 2009).…”

Section: Generalization Across Image Featuresmentioning

confidence: 99%

Mechanisms for Extracting a Signal from Noise as Revealed through the Specificity and Generality of Task Training

Chang

Kourtzi

Welchman

2013

Journal of Neuroscience

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Visual judgments critically depend on (1) the detection of meaningful items from cluttered backgrounds and (2) the discrimination of an item from highly similar alternatives. Learning and experience are known to facilitate these processes, but the specificity with which these processes operate is poorly understood. Here we use psychophysical measures of human participants to test learning in two types of commonly used tasks that target segmentation (signal-in-noise, or "coarse" tasks) versus the discrimination of highly similar items (feature difference, or "fine" tasks). First, we consider the processing of binocular disparity signals, examining performance on signalin-noise and feature difference tasks after a period of training on one of these tasks. Second, we consider the generality of learning between different visual features, testing performance on both task types for displays defined by disparity, motion, or orientation. We show that training on a feature difference task also improves performance on signal-in-noise tasks, but only for the same visual feature. By contrast, training on a signal-in-noise task has limited benefits for fine judgments of the same feature but supports learning that generalizes to signal-in-noise tasks for other features. These findings indicate that commonly used signal-in-noise tasks require at least three distinct components: feature representations, signal-specific selection, and a generalized process that enhances segmentation. As such, there is clear potential to harness areas of commonality (both within and between cues) to improve impaired perceptual functions.

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Intensity discrimination as a function of frequency and sensation level

Cited by 355 publications

References 0 publications

Auditory discrimination of tone-pulse onsets

Auditory discrimination of tone-pulse onsets

Top-down gain control in the auditory system: Evidence from identification and discrimination experiments

Mechanisms for Extracting a Signal from Noise as Revealed through the Specificity and Generality of Task Training

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