Learning-Induced Plasticity in Auditory Spatial Representations Revealed by Electrical Neuroimaging

Abstract

Auditory spatial representations are likely encoded at a population level within human auditory cortices. We investigated learninginduced plasticity of spatial discrimination in healthy subjects using auditory-evoked potentials (AEPs) and electrical neuroimaging analyses. Stimuli were 100 ms white-noise bursts lateralized with varying interaural time differences. In three experiments, plasticity was induced with 40 min of discrimination training. During training, accuracy significantly improved from near-chance levels to ϳ75%. Before and after training, AEPs were recorded to stimuli presented passively with a more medial sound lateralization outnumbering a more lateral one (7:1). In experiment 1, the same lateralizations were used for training and AEP sessions. Significant AEP modulations to the different lateralizations were evident only after training, indicative of a learning-induced mismatch negativity (MMN). More precisely, this MMN at 195-250 ms after stimulus onset followed from differences in the AEP topography to each stimulus position, indicative of changes in the underlying brain network. In experiment 2, mirror-symmetric locations were used for training and AEP sessions; no training-related AEP modulations or MMN were observed. In experiment 3, the discrimination of trained plus equidistant untrained separations was tested psychophysically before and 0, 6, 24, and 48 h after training. Learning-induced plasticity lasted Ͻ6 h, did not generalize to untrained lateralizations, and was not the simple result of strengthening the representation of the trained lateralizations. Thus, learning-induced plasticity of auditory spatial discrimination relies on spatial comparisons, rather than a spatial anchor or a general comparator. Furthermore, cortical auditory representations of space are dynamic and subject to rapid reorganization.

Topographic ERP Analyses: A Step-by-Step Tutorial Review

“…Consequently, a non-parametric statistical test has to be conducted, wherein the dependent measure is the DISS between two maps at a given point in time, t. We and others have colloquially referred to this analysis as topographic ANOVA or TANOVA (e.g. [6,7,42,44,54,63,71], see also [29]), though we would immediately remind the reader that no analysis of variance is being conducted. Instead, TANOVA entails a non-parametric randomization test [34].…”

“…In addition, these dependent measures can in turn be correlated with behavioral measures (e.g. [1,43,63]), behavioral/mental states (e.g. [26,28]), and/or parametric variations in stimulus conditions (e.g.…”

Where sound position influences sound object representations: A 7-T fMRI study

“…The separate processing of spatial and non-spatial information confers advantages, such as a very rapid discrimination of sound categories (e.g. living vs. non-living; Murray et al, 2006, or the rapidly induced, positionspecific plasticity of spatial representations, Spierer et al, 2007).…”

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“…Ample task learning reports [15,17,19,21] using other training paradigms such as ITD/ILD training are well documented in the literature. The confusion matrix (stimulus response) showed that there was a decline of perceptual confusions in the post-training phase.…”

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