Attentional modulation of beta-power aligns with the timing of behaviorally relevant rhythmic sounds

Abstract

It is largely unknown how attention adapts to the timing of acoustic stimuli. To address this, we investigated how hemispheric lateralization of alpha (7–13 Hz) and beta (14–24 Hz) oscillations, reflecting voluntary allocation of auditory spatial attention, is influenced by tempo and predictability of sounds. We recorded electroencephalography while healthy adults listened to rhythmic sound streams with different tempos that were presented dichotically to separate ears, thus permitting manipulation of spatial–temporal attention. Participants responded to stimulus-onset-asynchrony (SOA) deviants (−90 ms) for given tones in the attended rhythm. Rhythm predictability was controlled via the probability of SOA deviants per block. First, the results revealed hemispheric lateralization of beta-power according to attention direction, reflected as ipsilateral enhancement and contralateral suppression, which was amplified in high- relative to low-predictability conditions. Second, fluctuations in the time-resolved beta-lateralization aligned more strongly with the attended than the unattended tempo. Finally, a trend-level association was found between the degree of beta-lateralization and improved ability to distinguish between SOA-deviants in the attended versus unattended ear. Differently from previous studies, we presented continuous rhythms in which task-relevant and irrelevant stimuli had different tempo, thereby demonstrating that temporal alignment of beta-lateralization with attended sounds reflects top-down attention to sound timing.

Predicting the Beat Bin: Beta Oscillations Predict the Envelope Sharpness in a Rhythmic Sequence

“…Overall, the results are in line with studies indicating that temporal sensory predictions are implemented via beta oscillations (Foldal et al, 2023; Fujioka et al, 2015; Iversen et al, 2009; Morillon and Baillet, 2017; Rimmele et al, 2018; Saleh et al, 2010). Relatedly, Arnal et al (2015) reported that pre-target beta power was enhanced for correctly detected targets in a timing judgment task, along with a significantly enhanced cross-frequency coupling between pre-target delta phase and beta power.…”

“…At the neural level, the results replicated previous findings that modulations in beta-band power synchronize with a regular beat (Fujioka et al, 2015;Merchant et al, 2015) and encode temporal predictions (Foldal et al, 2023;Morillon and Baillet, 2017). Importantly, this study revealed that beta power is modulated in a top-down manner by the cue, enabling the neural system to flexibly adapt to different acoustic envelope shapes in accordance with task-specific demands, thereby enhancing sensory selection and performance.…”

“…In accordance with studies investigating temporal prediction and beat perception (Arnal et al, 2015;Foldal et al, 2023;Fujioka et al, 2015Fujioka et al, , 2012Kulashekhar et al, 2016;Merchant et al, 2015;Morillon and Baillet, 2017;Rimmele et al, 2018), we hypothesized that cue validity would affect performance in the timing-judgment task (Ede et al, 2012;Golob et al, 2002;Nobre and van Ede, 2023;Posner et al, 1980), especially for sharp target sounds, given that these require high perceptual timing precision. Furthermore, we anticipated enhanced pre-target beta power when participants expected a sharp sound (relative to a smooth sound), supporting predictions of high perceptual timing precision.…”

“…This is in line with a study by Grabot et al (56), which found increased alphabeta phase-amplitude coupling for increased precision in a temporal production task. Since phase coherence (57) and beta oscillations (58) enable communication between different brain regions, these mechanisms might be involved in the prediction of beat timing and the transfer of beat timing information between brain areas.…”

Rhythmic temporal prediction enhances neural representations of movement intention for brain–computer interface

“…Recent research investigating motor-auditory connectivity during the grouping of musical beats into a march or waltz has highlighted the involvement of the motor system in active meter imagination, extending motor hypotheses from beat perception to hierarchical timing [31]. In summary, both the movement intention and rhythmic temporal prediction rely on overlapping neural structures/circuits, and exhibit specific power changes in the beta band [32].…”