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Ongoing neural oscillations reflect cycles of excitation and inhibition in local neural populations, with individual neurons being more or less likely to fire depending upon the oscillatory phase. As a result, the oscillations could determine whether or not a sound is perceived and/or whether its neural representation enters into later processing stages. While empirical support for this idea has come from sound detection studies, large gaps in knowledge still exist regarding memory for sound events. In the current study, it was investigated how sensory entrainment impacts the fidelity of working memory representations for pitch. In two separate experiments, an 8 Hz amplitude modulated (AM) entraining stimulus was presented prior to a multitone complex having an f0 between 270 and 715 Hz. This "target" sound could be presented at phases from 0 to 2π radians in relation to the previous AM. After a retention interval of 4 s (Experiment 1; n = 26) or 2 s (Experiment 2; n = 28), listeners were tasked to reproduce the target sound's pitch by moving their finger along the horizontal axis of a response pad. It was hypothesized that if entrainment modulates auditory working memory fidelity, reproductions of a target's pitch would be more accurate and precise when targets were presented in phase with the entrainment. Cosine fits of the average data for both experiments showed a significant entrainment "echo" in the accuracy of pitch matches. There was no apparent echo in the matching precision. Fitting of the individual data accuracy showed that the optimal phase was consistent across individuals, aligning near the next AM peak had the AM continued. The results show that sensory entrainment modulates auditory working memory in addition to stimulus detection, consistent with the proposal that ongoing neural oscillatory activity modulates higher-order auditory processes. [ABSTRACT FROM AUTHOR]

Accuracy in auditory distance perception can improve with practice and varies for sounds differing in familiarity. Here, listeners were trained to judge the distances of English, Bengali, and backwards speech sources pre-recorded at near (2-m) and far (30-m) distances. Listeners' accuracy was tested before and after training. Improvements from pre-test to post-test were greater for forward speech, demonstrating a learning advantage for forward speech sounds. Independent component (IC) processes identified in electroencephalographic (EEG) data collected during pre- and post-testing revealed three clusters of ICs across subjects with stimulus-locked spectral perturbations related to learning and accuracy. One cluster exhibited a transient stimulus-locked increase in 4-8 Hz power (theta event-related synchronization; ERS) that was smaller after training and largest for backwards speech. For a left temporal cluster, 8-12 Hz decreases in power (alpha event-related desynchronization; ERD) were greatest for English speech and less prominent after training. In contrast, a cluster of IC processes centered at or near anterior portions of the medial frontal cortex showed learning-related enhancement of sustained increases in 10-16 Hz power (upper-alpha/low-beta ERS). The degree of this enhancement was positively correlated with the degree of behavioral improvements. Results suggest that neural dynamics in non-auditory cortical areas support distance judgments. Further, frontal cortical networks associated with attentional and/or working memory processes appear to play a role in perceptual learning for source distance.

Several acoustic cues contribute to auditory distance estimation. Nonacoustic cues, including familiarity, may also play a role. We tested participants' ability to distinguish the distances of acoustically similar sounds that differed in familiarity. Participants were better able to judge the distances of familiar sounds. Electroencephalographic (EEG) recordings collected while participants performed this auditory distance judgment task revealed that several cortical regions responded in different ways depending on sound familiarity. Surprisingly, these differences were observed in auditory cortical regions as well as other cortical regions distributed throughout both hemispheres. These data suggest that learning about subtle, distance-dependent variations in complex speech sounds involves processing in a broad cortical network that contributes both to speech recognition and to how spatial information is extracted from speech.

Tau rhythms are largely defined by sound responsive alpha band (~8–13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high‐pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low‐pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi‐model AMICA [mAMICA]). Tau‐related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8–13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high‐pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high‐pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high‐pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms. [ABSTRACT FROM AUTHOR]

Training can improve perceptual sensitivities. We examined whether the temporal dynamics and the incidental versus intentional nature of training are important. Within the context of a birdsong rate discrimination task, we examined whether the sequencing of pretesting exposure to the stimuli mattered. Easy-to-hard (progressive) sequencing of stimuli during preexposure led to a more accurate performance with the critical difficult contrast and greater generalization to new contrasts in the task, compared with equally variable training in either a random or an antiprogressive order. This greater accuracy was also evident when participants experienced the progressively sequenced stimuli in a different incidental learning task that did not involve direct auditory training. The results clearly show the importance of temporal dynamics (sequencing) in learning and show that the progressive training advantages cannot be fully explained by direct associations between stimulus features and the corresponding responses. The current findings are consistent with a hierarchical account of perceptual learning, among other possibilities, but not with explanations that focus on stimulus variability. (Contains 1 figure, 2 tables and 8 footnotes.)