Temporal Perceptual Learning

in Timing & Time Perception
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Our interaction with the environment and each other is inherently time-varying in nature. It is thus not surprising that the nervous systems of animals have evolved sophisticated mechanisms to not only tell time, but to learn to discriminate and produce temporal patterns. Indeed some of the most sophisticated human behaviors, such as speech and music, would not exist if the human brain was unable to learn to discriminate and produce temporal patterns. Compared to the study of other forms of learning, such as visual perceptual learning, the study of the learning of interval and temporal pattern discrimination in the subsecond range is relatively recent. A growing number of studies over the past 15 years, however, have established that perceptual and motor timing undergo robust learning. One of the principles to have emerged from these studies is that temporal learning is generally specific to the trained interval, an observation that has important implications to the neural mechanisms underlying our ability to tell time.

Temporal Perceptual Learning

in Timing & Time Perception



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    Temporal discrimination tasks. Two-interval alternative forced choice tasks (2AFC). (A) Schematic representation of a single trial where a standard (T) and a comparison duration (T±ΔT) are sequentially presented. By pressing one of two response keys subjects have to decide which one of the two intervals lasted longer. After the response a feedback about performance accuracy is provided. In an alternate version of this 2AFC task, the comparison stimulus is equal to T+ΔT, and the order of the standard and comparison are randomized. (B) Trial representation of a 2AFC task where only a single comparison duration (T±ΔT) is presented in every trial. This figure is published in colour in the online version.

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    Interval specific learning and interval generalization. (A) Pretest and posttest interval discrimination thresholds for three different conditions: 100 ms standard interval bounded by a 1 kHz tone; 100 ms standard interval bounded by a 3.75 kHz tone; and a 200 ms interval bounded by 1 kHz tones. Between the pre- and posttests, subjects were trained for 10 days on the 100 ms/1 kHz condition (solid bars). In addition to the learning in the trained condition (solid bars), subjects demonstrated robust generalization to the same-interval-different-frequency condition (100 ms/3.75 kHz), but not to the novel interval. (B) Data from a separate experiment in which subjects were trained on the 200 ms/1 kHz condition (solid bars). Again, in addition to learning the trained condition (solid bars), there was robust generalization to the same-interval-different-frequency condition, but not to a novel interval. Modified from Karmarkar and Buonomano (2003). This figure is published in colour in the online version.

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    Improvements in precision and accuracy during learning of a complex motor timing task. Subjects learned to produce an aperiodic spatiotemporal pattern consisting of a sequence of six timed responses (six component intervals) using four fingers (upper inset in Panel A). (A) Single subject results from the first (upper graph) and the third (lower graph) day of training. Light dashed lines represent the response distributions, and the solid lines represent Gaussian fits of the data. (B) Average (12 subjects) precision (top) and accuracy (bottom) of each element of the pattern across three days of training. Both the precision (F2,22=73, p<106) and accuracy (F2,22=7.2, p<0.005) exhibited a main effect of training across the three days. Modified from Experiment 2 from Laje et al. (2011). This figure is published in colour in the online version.

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    Timing-related plasticity in the human brain. (A, B) Brain areas showing training-related plasticity as measured by fMRI. In both panels the contrast tested is: (trained duration–untrained duration) PRE > (trained duration–untrained duration) POST. Statistical threshold was set to pFWE<0.05 corrected for multiple comparisons. Activations are overlaid on the single subject T1-MNI template. For all cluster of voxels we plot the parameter estimates for the ΔT1 condition (i.e., the actual discrimination threshold) in pre (light shade) and post-training (dark shade) fMRI sessions. A.U. is arbitrary unit. (A) Upper row shows left and right mid-occipital regions activated during the visual task (dark bars). The lower row shows the left posterior insula activated in both the visual (dark) and the auditory (light) task. (B) Left inferior parietal cluster activated only in the auditory task (light). (C) Training-related plasticity as measured by grey and white-matter structural indexes. The panel shows right cerebellar clusters where grey matter volume (GM) and fractional anisotropy (WM) were greater in post compared to pre-training session. Statistical threshold was set to pFWE<0.05 corrected for multiple comparisons. For both clusters we also show the correlations between the structural indexes (i.e., T1 post–T1 pre/T1 pre and FA post–FA pre/FA pre) and the behavioral performance. Clusters are overlaid on the single subject T1 MNI template. Modified from Figures 2 and 3 from Bueti et al. (2012). This figure is published in colour in the online version.

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