Finding tau rhythms in EEG: An independent component analysis approach.

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.

Not fully remembered, but not forgotten: interfering sounds worsen but do not eliminate the representation of pitch in working memory.

Recent research has begun measuring auditory working memory with a continuous adjustment task in which listeners adjust attributes of a sound to match a stimulus presented earlier. This approach captures auditory memory's continuous nature better than standard change detection paradigms that collect binary ("same or different") memory measurements. In two experiments, we assessed the impact of different interference stimuli (multitone complexes vs. white noise vs. silence) on the precision and accuracy of participants' reproductions of pitch from memory. Participants were presented with a target multitone complex stimulus followed by eight successive interference signals. Across trials, these signals alternated between additional multitone complexes, randomly generated white noise samples, or (in Experiment 2) silence. This was followed by a response period where participants adjusted the pitch of a response stimulus using a MIDI touchpad to match the target. Experiment 1 found a significant effect of interference type on performance, with tone interference signals producing the greatest impairments to participants' accuracy and precision compared to white noise. Interestingly, it also found a compression in the participants' responses, with overestimations of low-frequency targets and underestimations for high-frequency targets. Experiment 2 replicated results from Experiment 1, with an additional silence condition showing the best performance, suggesting that non-tonal signals also generate interference. In general, results support a shared resource model of working memory with a limited capacity that can be flexibly allocated to hold items in memory with varying levels of fidelity. Interference does not appear to knock items out of a fixed precision slot, but rather robs a portion of capacity from stored items.

Learning to detect auditory signals in noise: Active top-down selection and stable change in signal representations.

Training can improve detection of auditory signals in noise. This learning could potentially occur through active top-down selection mechanisms or stable changes in signal representations. Here, participants were trained and tested (pretest vs. posttest design) on abilities to detect pure tone signals in noise. Auditory evoked potentials (AEPs) to tones were gathered under dichotic listening conditions where participants attended to nontonal stimuli in the opposite ear. Improvements in detection sensitivity were observable regardless of tested tone frequency. This was true in generalization between 861 Hz and 1058-Hz tones (Experiment 1a), and when testing a frequency range > 1 octave (Experiment 2). Such learning was not apparent without training (Experiment 1b). In contrast to behavior, AEP amplitude increases from pre- to posttest were partially specific to trained tone frequencies, even when selective attention was diverted to the opposite ear of tone presentation. Placed in the context of previous work, results suggest that changes in active top-down selection mechanisms and stable signal representations both play a role in auditory detection learning. The mismatch between AEP and behavioral effects suggests a need to consider how these different learning processes can impact detection performance in the variety of listening scenarios a listener may face. (PsycInfo Database Record (c) 2023 APA, all rights reserved).