Temporal specificity of perceptual learning in an auditory discrimination task

Abstract

Although temporal processing is used in a wide range of sensory and motor tasks, there is little evidence as to whether a single centralized clock or a distributed system underlies timing in the range of tens to hundreds of milliseconds. We investigated this question by studying whether learning on an auditory interval discrimination task generalizes across stimulus types, intervals, and frequencies. The degree to which improvements in timing carry over to different stimulus features constrains the neural mechanisms underlying timing. Human subjects trained on a 100- or 200-msec interval discrimination task showed an improvement in temporal resolution. This learning generalized to a perceptually distinct duration stimulus, as well as to the trained interval presented with tones at untrained spectral frequencies. The improvement in performance did not generalize to untrained intervals. To determine if spectral generalization was dependent on the importance of frequency information in the task, subjects were simultaneously trained on two different intervals identified by frequency. As a whole, our results indicate that the brain uses circuits that are dedicated to specific time spans, and that each circuit processes stimuli across nontemporal stimulus features. The patterns of generalization additionally indicate that temporal learning does not rely on changes in early, subcortical processing, because the nontemporal features are encoded by different channels at early stages.

Figure 1

Training on the 100-msec/1-kHz task results in perceptual learning. (A) Average learning curve for 13 subjects. Subjects were trained for approximately an hour a day (12 blocks of 60 trials) over 10 d. (B) Individual learning curves for each subject. Subjects that showed significant learning are in gray, nonlearners in black.

Figure 2

Generalization data from learners trained on the 100-msec/1-kHz task. Two days of tests before (pretest) and after (posttest) training were obtained. Solid bars indicate the trained condition. There was a significant decrease in the threshold for the 100-msec/1-kHz (P < 0.001), 100-msec/3.75-kHz (P < 0.001), and 100-msec-dur (P < 0.001) tasks, but not in the 200-msec/1-kHz task (P = 0.98).

Figure 3

Generalization data from learners trained on the 200-msec/1-kHz task. Solid bars indicate the trained condition. A significant decrease from pre- to posttest threshold was seen in the 200-msec/1-kHz (P < 0.01), 200-msec/3.75-kHz (P < 0.05), and 200-msec-dur conditions as measured by paired t-test. There was no significant decrease in the 100-msec/1-kHz task (P = 0.25).

Figure 4

Parallel learning on two different conditions. (A) Average learning curve for the 50-msec/1-kHz interval for subjects. The curve shows significant learning as measured by orthogonal trend analysis (F_1,190 = 49.63, P < 0.001). (B) Learning curve for the 200-msec/4-kHz interval. The average learning is significant (F_1,190 = 14.02, P < 0.001). Subjects were trained on the 50-msec and 200-msec intervals simultaneously for 10 d.

Figure 5

Generalization data for the learners in the parallel learning experiments. Trained conditions are indicated by solid bars, and show a significant decrease in threshold as measured by paired t-tests (P < 0.001 for both). A significant improvement in performance is seen in the 50-msec/4-kHz (P < 0.01) and 200-msec/1-kHz (P < 0.001) tasks.