Masking by Inaudible Sounds and the Linearity of Temporal Summation

Abstract

Many natural sounds, including speech and animal vocalizations, involve rapid sequences that vary in spectrum and amplitude. Each sound within a sequence has the potential to affect the audibility of subsequent sounds in a process known as forward masking. Little is known about the neural mechanisms underlying forward masking, particularly in more realistic situations in which multiple sounds follow each other in rapid succession. A parsimonious hypothesis is that the effects of consecutive sounds combine linearly, so that the total masking effect is a simple sum of the contributions from the individual maskers. The experiment reported here tests a counterintuitive prediction of this linear-summation hypothesis, namely that a sound that itself is inaudible should, under certain circumstances, affect the audibility of subsequent sounds. The results show that, when two forward maskers are combined, the second of the two maskers can continue to produce substantial masking, even when it is completely masked by the first masker. Thus, inaudible sounds can affect the perception of subsequent sounds. A model incorporating instantaneous compression (reflecting the nonlinear response of the basilar membrane in the cochlea), followed by linear summation of the effects of the maskers, provides a good account of the data. Despite the presence of multiple sources of nonlinearity in the auditory system, masking effects by sequential sounds combine in a manner that is well captured by a time-invariant linear system.

Precursor effects on behavioral estimates of frequency selectivity and gain in forward masking

“…Although the pattern of results discussed above is consistent with a decrease in gain in the on-frequency precursor condition, there are other potential interpretations, the most prominent of which involves the additivity of masking ͑Ox-enham and Moore, 1994;Plack and O'Hanlon, 2003;Plack et al, 2006. Under this interpretation, the precursor and masker are viewed as two maskers whose intensities add after being processed by the auditory periphery.…”

“…To further test the gain-reduction model, predictions were obtained for the mean data in Plack et al ͑2006͒. This study involved measuring signal threshold in the presence of one or two temporally non-overlapping on-frequency forward maskers ͑M1 and M2͒.…”

Individual Differences in Behavioral Estimates of Cochlear Nonlinearities

“…Damage to outer hair cells results in elevated thresholds, narrower dynamic ranges, less compressive response growth, and poorer tuning (e.g., Ruggero et al 1997). Several behavioral methods have been proposed to estimate cochlear nonlinearities in humans, including growth-of-masking functions (Oxenham and Plack 1997), additivity of masking (Plack et al 2006), and temporal masking curves (TMC; Nelson et al 2001). These methods provide a way to infer the characteristics of the basilar membrane response in humans that cannot be directly measured (see Oxenham and Bacon 2003 for review).…”

Pitfalls in behavioral estimates of basilar-membrane compression in humans

“…GOMs are typically measured using a single short masker-signal delay ͑Oxenham and Plack, 1997͒, and thus the potential errors in compression estimates are likely to be smaller than those from the TMCs measured over a wide range of delays. However, the method based on GOM may be inadequate for estimating compression in frequency regions below about 4 kHz, due to the possibility that the response to the off-frequency masker at the CF s place is compressive at lower frequencies ͑e.g., Lopez-Poveda et al, 2003͒. Additivity of forward masking may provide a desirable alternative method because it avoids comparisons between the effects of on-and off-frequency maskers ͑Plack and O 'Hanlon, 2003;Plack et al, 2006͒. The mean compression estimates of 0.17 in the study by Plack and O'Hanlon ͑2003͒ and 0.21 in the study by Plack et al ͑2006͒ fall in between the average compression exponents ␣ dat and ␣ bគon obtained from the third-order polynomial fits to the BM input-output functions derived from the TMCs shown in Fig.…”