Perceptual learning and generalization resulting from training on an auditory amplitude-modulation detection task

Abstract

Fluctuations in sound amplitude provide important cues to the identity of many sounds including speech. Of interest here was whether the ability to detect these fluctuations can be improved with practice, and if so whether this learning generalizes to untrained cases. To address these issues, normal-hearing adults (n = 9) were trained to detect sinusoidal amplitude modulation (SAM; 80-Hz rate, 3-4 kHz bandpass carrier) 720 trials/day for 6-7 days and were tested before and after training on related SAM-detection and SAM-rate-discrimination conditions. Controls (n = 9) only participated in the pre- and post-tests. The trained listeners improved more than the controls on the trained condition between the pre- and post-tests, but different subgroups of trained listeners required different amounts of practice to reach asymptotic performance, ranging from 1 (n = 6) to 4-6 (n = 3) sessions. This training-induced learning did not generalize to detection with two untrained carrier spectra (5 kHz low-pass and 0.5-1.5 kHz bandpass) or to rate discrimination with the trained rate and carrier spectrum, but there was some indication that it generalized to detection with two untrained rates (30 and 150 Hz). Thus, practice improved the ability to detect amplitude modulation, but the generalization of this learning to untrained cases was somewhat limited.

Figures

Figure 1

Mean pre-test (open symbols) and post-test (filled symbols) thresholds of the trained listeners (n = 9; squares) and controls (n = 9; triangles) for each of the six conditions. Threshold refers to the modulation depth needed to distinguish an amplitude-modulated from an unmodulated noise (SAM-detection) or to the difference in modulation rate needed to distinguish a faster from a slower rate (rate discrimination) on 79% of trials. Error bars reflect ± one standard error of the mean.

Figure 2

For each of the six conditions (panels), pre-test (x axis) and post-test (y axis) thresholds are shown for the trained listeners (filled squares) and controls (open triangles). The linear regression of the post-test thresholds on the pre-test thresholds was determined for each data set. Separate lines were estimated for trained listeners (long dashes) and controls (short dashes). The solid diagonal line in each panel indicates a regression-line slope of 1; points below this solid line reflect improvement between the pre- and post-tests.

Figure 3

Thresholds on the trained condition from the pre- and post-tests (filled squares) and during the training phase (open squares) are shown for all nine trained listeners (panels). Error bars indicate ± one standard error of the mean within a given listener. Three listeners improved across the six training sessions (left column; asterisks next to the listener label). The remaining six listeners also improved between the pre- and post-tests, but not across the six training sessions (middle and right columns).

Figure 4

Mean thresholds on the trained condition from the pre- and post-tests (filled symbols) as well as from the first and second halves of each training session (open symbols). Data are shown separately for the trained listeners who improved significantly across the six training sessions (across-session learners, n = 3; squares), for the trained listeners who improved between the pre- and post-tests, but not across the six training sessions (within-session learners, n = 6; circles), and for controls (triangles, n = 9; triangles). Error bars indicate ± one standard error of the mean across listeners. Thresholds that differed significantly between the first and second halves of a training session are enclosed by a dashed rectangle.