Biology subjects

Read, H. L.

Publications and source records attributed to Read, H. L..

3 recordsLinked to original sources

bioRxiv2026

Comparison scanning generalized eigendecomposition separates temporal dynamics of alpha oscillations during inhibition of spatialized acoustic distractors.

Lateralized alpha-band oscillations are thought to reflect distractor inhibition through suppression of cortical regions processing spatially-localized distracting stimuli. Standard lateralization indices (LI) quantify hemispheric asymmetries in spectral power over large time windows, while characterizations of the time-varying dynamics of alpha-mediated distractor inhibition are lacking. Here we evaluate comparison scanning generalized eigendecomposition (csGED), a multivariate signal processing technique, for its efficacy in addressing questions related to alpha-mediated distractor inhibition using a simulation of same-frequency sources at symmetric cortical locations adapted from Zuure and Cohen (2021). We show that while LI accurately captures topographic power asymmetries, csGED is effective at recovering source-projections for bilateral, same-frequency activity across a wide range of signal-to-noise ratios (SNRs). We further extend these models to a pilot sample (N = 11) performing a speech-in-noise task using spatialized naturalistic distractors through individualized head-related transfer functions (HRTFs). Our results demonstrate the efficacy of GED to characterize source projections during spatialized distractors, and provide preliminary evidence for shifts in oscillatory activity in both the alpha (7 - 13 Hz) and beta frequency ranges (15 - 25 Hz) during spatialized speech-in-noise tasks. Together, these results demonstrate the feasibility of csGED for investigating temporal dynamics of lateralized distractor inhibition and motivate larger confirmatory studies.

neuroscience↗

bioRxiv2022

Correlations across timing cues in natural vocalizations predict judging synthetic sound burst durations.

It is well known that animals rely on multiple sources of information in order to successfully identify sounds in natural environments, to make decisions that are optimal for their survival. For example, rats use duration and pitch cues to respond appropriately to prosocial and distress vocalizations (Saito et al., 2019). Vocalization duration cues are known to co-vary with other temporal cues (Khatami et al., 2018), yet little is known about whether animals rely upon such co-variations to successfully discriminate sounds. In the current study, we find natural alarm vocalizations in rats have onset and offset slopes that are correlated with their duration. Accordingly, vocalizations with faster onset slopes are more likely to have shorter durations. Given that vocalization slopes begin and end within milliseconds, they could provide rapid perceptual cues for predicting and discriminating vocalization duration. To examine this possibility, we train rodents to discriminate duration differences in sequences of synthetic vocalizations and examine how artificially changing the slope impacts duration judgments. We find animals are biased to misjudge a range of synthetic vocalizations as being shorter in duration when the onset and offset slopes are artificially fast. Moreover, this bias is reduced when rats are exposed to multiple synthetic vocalization bursts. The observed perceptual bias is accurately captured by a Bayesian decision-theoretic model that utilizes the empirical joint distribution of duration and onset slopes in natural vocalizations as a prior during duration judgements of synthetic vocalizations. This model also explains why the bias is reduced when more evidence is accumulated across multiple bursts, reducing the priors influence. These results support the theory that animals are sensitive to fine-grained statistical co-variations in auditory timing cues and integrate this information optimally with incoming sensory evidence to guide their decisions.

animal behavior and cognition↗

bioRxiv2020

Distinct midbrain response statistics are associated with sound recognition and discrimination during the construction of natural sound textures

The perception of sound textures, a class of natural sounds defined by statistical sound structure such as fire wind, and rain, has been proposed to arise through the integration of time-averaged summary statistics. Where and how the auditory system might encode these summary statistics to create internal representations of these stationary sounds, however, is unknown. Here, using natural textures and synthetic variants with reduced statistics, we show that summary statistics modulate the correlations between frequency organized neuron ensembles in the awake rabbit inferior colliculus. These neural ensemble correlation statistics capture high-order sound structure and allow for accurate neural decoding in a single trial recognition task with evidence accumulation times approaching 1 s. In contrast, the average activity across the neural ensemble (neural spectrum) provides a fast (tens of ms) and salient signal that contributes primarily to texture discrimination. Intriguingly, perceptual studies in human listeners reveals analogous trends: the sound spectrum is integrated quickly and serves as salient discrimination cue while high-order sound statistics are integrated slowly and contribute substantially more towards recognition. The findings suggest statistical sound cues such as the sound spectrum and correlation structure are represented by distinct response statistics in auditory midbrain ensembles, and that these neural response statistics may have dissociable roles and time scales for the recognition and discrimination of natural sounds. SIGNIFICANCE STATEMENTBeing able to recognize and discriminate natural sounds, such as from a running stream, a crowd clapping, or ruffling leaves is a critical task of the normal functioning auditory system. Humans can easily perform such tasks, yet they can be particularly difficult for the hearing impaired and they challenge our most sophisticated computer algorithms. This difficulty is attributed to the complex physical structure of such natural sounds and the fact they are not unique: they vary randomly in a statistically defined manner from one excerpt to the other. Here we provide the first evidence, to our knowledge, that the central auditory system is able to encode and utilize statistical sound cues for natural sound recognition and discrimination behaviors.

neuroscience↗