The deterioration of hearing with age: Frequency selectivity, the critical ratio, the audiogram, and speech threshold

Abstract

The frequency selectivity of the auditory system was measured by masking a sinusoidal signal (0.5, 2.0, or 4.0 kHz) or a filtered-speech signal with a wideband noise having a notch, or stopband, centered on the signal. As the notch was widened performance improved for both types of signal but the rate of improvement decreased as the age of the 16 listeners increased from 23 to 75 years, indicating a loss in frequency selectivity with age. Auditory filter shapes derived from the tone-in-noise data show (a) that the passband of the filter broadens progressively with age, and (b) that the dynamic range of the filter ages like the audiogram. That is, the range changes little with age before 55, but beyond this point there is an accelerating rate of loss. The speech experiment shows comparable but smaller effects. The filter-width measurements show that the critical ratio is a poor estimator of frequency selectivity because it confounds the tuning of the system with the efficiency of the signal-detection and speech-processing mechanisms that follow the filter. An alternative, one-point measure of frequency selectivity, which is both sensitive and reliable, is developed via the filter-shape model of masking.

Computational Modeling of Individual Differences in Behavioral Estimates of Cochlear Nonlinearities

“…Correlation analyses were performed to test whether k can be interpreted as truly representing differences in detection efficiency among listeners. These analyses were based on the assumptions that (1) efficiency is a central process (e.g., Patterson et al 1982; Werner and Bargones 1991) and (2) certain behavioral measurements should vary with k if such measurements are minimally influenced by peripheral nonlinearities. To test these assumptions, two behavioral measurements were evaluated: (1) temporal integration and (2) the "input SMR" for the off-frequency masker at 0-ms delay (i.e., a form of critical ratio; Hawkins and Stevens (1950)).…”

“…Detection efficiency may explain superior performance in masking tasks for musicians than non-musicians (Fine and Moore 1993;Bidelman et al 2014), adults than children (Hill et al 2004), and in individuals without than with dyslexia or language impairments (Hartley and Moore 2002). Studies suggest that detection efficiency in normal-hearing listeners is primarily influenced by factors central to cochlear processing including memory, attention, and experience (Patterson et al 1982;Werner and Bargones 1991). Detection efficiency is a standard component of techniques used to estimate frequency selectivity from masking thresholds (Patterson and Nimmo-Smith 1980;Patterson et al 1982) and is often modeled as the degree to which probe power must exceed masker power (in dB) through an auditory filter at detection threshold (i.e., the signal-to-masker ratio or SMR).…”

“…Studies suggest that detection efficiency in normal-hearing listeners is primarily influenced by factors central to cochlear processing including memory, attention, and experience (Patterson et al 1982;Werner and Bargones 1991). Detection efficiency is a standard component of techniques used to estimate frequency selectivity from masking thresholds (Patterson and Nimmo-Smith 1980;Patterson et al 1982) and is often modeled as the degree to which probe power must exceed masker power (in dB) through an auditory filter at detection threshold (i.e., the signal-to-masker ratio or SMR). Detection efficien-cy can vary appreciably among normal-hearing listeners (Patterson 1976;Leek and Summers 1993) and accounts for individual differences in estimates of frequency resolution, suggesting that it may be a source of individual differences in TMCs.…”

Relating binaural pitch perception to the individual listener’s auditory profile

“…Only six of the NH subjects participated in this experiment. The best-fitting rounded-exponential filter was estimated using the roex(p u , p l , r) filter model (Patterson et al, 1982;Glasberg and Moore, 1990). The average rms fitting error over all ears was 0.67 6 0.24 dB, indicating reasonable fits provided by the model.…”

Non-isomorphism in efficient coding of complex sound properties

“…SS was manipulated by adding instrument endpoints in different proportions, ranging from 0.2 to 0.8 for each instrument and always summing to 1.0. Differences between mixtures were derived by calculating Euclidean distances between ERB-scaled spectra (Glasberg and Moore, 1990) that had been processed by a bank of auditory filters (Patterson et al, 1982). AD and SS series were then exhaustively adjusted across hundreds of participants until every pair of sounds separated by three stimulus steps (out of 18 steps total) was equally discriminable to every other pair within and across stimulus series ( 65% correct for changes along one dimension, 70% for changes along both dimensions; see Stilp et al, 2010 for details).…”