Physical measurements of audition and their bearing on the theory of hearing

Fletcher, Harvey

doi:10.1016/s0016-0032(23)91006-9

Cited by 38 publications

(14 citation statements)

References 48 publications

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“…Fletcher 1923;Valko, Priesol et al 2012;Ernst and Banks 2002;Lewis, Priesol et al 2011). Determining thresholds usually involves repeatedly collecting perceptual responses after exposing subjects to stimuli of different amplitudes and/or directions.…”

Section: Introductionmentioning

confidence: 99%

Determining thresholds using adaptive procedures and psychometric fits: evaluating efficiency using theory, simulations, and human experiments

Karmali

Chaudhuri

et al. 2015

Exp Brain Res

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When measuring thresholds, careful selection of stimulus amplitude can increase efficiency by increasing the precision of psychometric fit parameters (e.g., decreasing the fit parameter error bars). To find efficient adaptive algorithms for psychometric threshold (“sigma”) estimation, we combined analytic approaches, Monte Carlo simulations and human experiments for a one-interval, binary forced-choice, direction-recognition task. To our knowledge, this is the first time analytic results have been combined and compared with either simulation or human results. Human performance was consistent with theory and not significantly different from simulation predictions. Our analytic approach provides a bound on efficiency, which we compared against the efficiency of standard staircase algorithms, a modified staircase algorithm with asymmetric step sizes, and a maximum likelihood estimation (MLE) procedure. Simulation results suggest that optimal efficiency at determining threshold is provided by the MLE procedure targeting a fraction correct level of 0.92, an asymmetric 4-down, 1-up (4D1U) staircase targeting between 0.86 and 0.92 or a standard 6D1U staircase. Psychometric test efficiency, computed by comparing simulation and analytic results, was between 41%–58% for 50 trials for these three algorithms, reaching up to 84% for 200 trials. These approaches were 13%–21% more efficient than the commonly-used 3D1U symmetric staircase. We also applied recent advances to reduce accuracy errors using a bias-reduced fitting approach. Taken together, the results lend confidence that the assumptions underlying each approach are reasonable, and that human threshold forced-choice decision-making is modeled well by detection-theory models and mimics simulations based on detection theory models.

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Section: Introductionmentioning

confidence: 99%

Determining thresholds using adaptive procedures and psychometric fits: evaluating efficiency using theory, simulations, and human experiments

Karmali

Chaudhuri

et al. 2015

Exp Brain Res

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“…Such filter bank models, which were first introduced in conjunction with the classical theory of resonance [101], are well justified by experimental work ranging from early masking tests for telephony applications [64,65] to recent investigations in perceptual audio coding, where auditory models are incorporated to achieve transparent compression [26,91,92,166,223]. These auditory filter banks can be characterized in terms of the classical critical bandwidths , which were derived in experiments on noise masking and perception of complex sounds; these are generally considered to be the bandwidths of the auditory filters at certain center frequencies [248].…”

Section: Auditory Modelsmentioning

confidence: 99%

Adaptive Signal Models

Goodwin¹

1998

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“…Starting in 1923, Fletcher and Steinberg studied loudness coding of pure tones, noise, and speech ͑Fletcher, 1923a, 1923bFletcher and Steinberg, 1924;Steinberg, 1925͒, and proposed that loudness was related to neural spike count ͑Fletcher and Munson, 1933͒, and even provided detailed estimates of the relation between the number of spikes and the loudness in sones ͑Fletcher, 1953, p. 271͒. In 1943 De Vries first introduced a photon counting Poisson process model as a theoretical basis for the threshold of vision ͑De Vries, 1943͒.…”

Section: Poisson Noisementioning

confidence: 99%

Modeling the relation between the intensity just-noticeable difference and loudness for pure tones and wideband noise

Allen

Neely

1997

The Journal of the Acoustical Society of America

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A classical problem in auditory theory is the relation between the loudness L(I) and the intensity just-noticeable difference ͑JND͒ ⌬I(I). The intensity JND is frequently expressed in terms of the Weber fraction defined by J(I)ϵ⌬I/I because it is anticipated that this ratio should be a constant ͑i.e., Weber's law͒. Unfortunately, J(I) is not a constant for the most elementary case of the pure tone JND. Furthermore it remains unexplained why Weber's law holds for wide-band stimuli. We explore this problem and related issues. The loudness and the intensity JND are defined in terms of the first and second moments of a proposed random decision variable called the single-trial loudness L(I), namely the loudness is L(I)ϵEL(I), while the variance of the single trial loudness is L 2 ϵE(LϪL) 2 . The JND is given by the signal detection assumption ⌬LϭdЈ L , where we define the loudness JND ⌬L(I) as the change in loudness corresponding to ⌬I(I). Inspired by Hellman and Hellman's recent theory ͓J. Acoust. Soc. Am. 87, 1255-1271 ͑1990͔͒, we compare the Riesz ͓Phys. Rev. 31, 867-875 ͑1928͔͒ ⌬I(I) data to the Fletcher and Munson ͓J. Acoust. Soc. Am. 5, 82-108 ͑1933͔͒ loudness growth data. We then make the same comparison for Miller's ͓J. Acoust. Soc. Am. 19, 609-619 ͑1947͔͒ wideband noise JND and loudness match data. Based on this comparison, we show empirically that ⌬L(L)ϰL 1/p , where pϭ2 below Ϸ5 sones and is 1 above. Since ⌬L(I) is proportional to L , when pϭ2 the statistics of the single-trial loudness L are Poisson-like, namely L 2 ϰL. This is consistent with the idea that the pure tone loudness code is based a neural discharge rate ͑not the auditory nerve͒. Furthermore, when pϭ1 ͑above about 5 sones͒, the internal loudness signal-to-noise ratio is constant. It is concluded that Ekman's law (⌬L/L is constant͒ is true, rather than Weber's law, in this loudness range. One of the main contributions of this paper is its attempt to integrate Fletcher's neural excitation pattern model of loudness and signal detection theory.

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Physical measurements of audition and their bearing on the theory of hearing

Cited by 38 publications

References 48 publications

Determining thresholds using adaptive procedures and psychometric fits: evaluating efficiency using theory, simulations, and human experiments

Determining thresholds using adaptive procedures and psychometric fits: evaluating efficiency using theory, simulations, and human experiments

Adaptive Signal Models

Modeling the relation between the intensity just-noticeable difference and loudness for pure tones and wideband noise

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