Biology subjects

Rohe, T.

Publications and source records attributed to Rohe, T..

6 recordsLinked to original sources

bioRxiv2025

Neural Entrainment in the Theta Band Predicts Groove Perception in Popular Music

When humans listen to popular music, they often feel the groove, which is a pleasurable urge to move along with music. Previous studies suggest that groove arises from the brains entrainment to rhythmical patterns of music in the delta-theta and beta bands, which enables the brain to predict temporal structures. However, this notion has only been tested using simplified or artificial acoustic stimuli and melodies, rather than naturalistic popular music. In this study, we combined electroencephalography (EEG) measurements with two measures of neural entrainment to test whether neural entrainment in the delta, theta, and beta bands predicts individuals groove ratings of musical and clapping stimuli, which served as a control. We presented nine pop music songs and twelve MIDI-based clapping rhythms, which participants rated for their groove perception on a 5-point scale. Our results show that music songs received very variable groove ratings while ratings of clapping stimuli showed less variability. Univariate analyses demonstrated that both music and clapping stimuli consistently entrained neural oscillations in the delta-theta bands, but not beta bands, as measured by intertrial phase coherence (ITPC) and stimulus-brain coherence (SBC). ITPC and SBC in the theta band weakly, but significantly correlated with groove ratings in fronto-central clusters. Decoding of groove ratings from multivariate spatiotemporal-spectral EEG patterns of entrainment with curve-fitting and machine-learning models predicted groove perception with high accuracy, up to 0.8 Pearson correlation, where combined patterns of inter-trial phase coherence appeared more informative than patterns of stimulus-brain coherence. Our results support the theory that the perception of groove is rooted in the brains ability to entrain to ongoing dynamic temporal structures of music in the theta band. They further show that multivariate decoding models can be leveraged to predict subjective groove online from neural entrainment with high accuracy and temporal resolution. SummaryO_LIWhile participants (n = 30) rated their subjective groove perception of 9 popular songs and 12 MIDI clapping rhythms, we measured how their brains entrained to these musical stimuli using stimulus-brain coherence (SBC) and inter-trial phase coherence (ITPC) measures. C_LIO_LIBoth stimulus types strongly entrained delta-theta (1-7 Hz) oscillations. In univariate analyses, also both stimulus types at fronto-central, beat-related frequencies significantly predicted groove ratings. C_LIO_LIMultivariate decoding using EEG entrainment patterns predicted individual groove ratings far better (up to Pearson r = 0.8), with combined ITPC patterns providing the most information. C_LI

neuroscience↗

bioRxiv2025

Causal role of the individual alpha phase in multisensory perception

The phase of alpha oscillations is proposed to reflect perceptual cycles. Yet, human EEG/MEG studies have yielded inconsistent results, perhaps because they used correlative approaches that did not consider causal phase-perception relationships. In a multi-day computational EEG study, we developed the neurobehavioral link function (NBLF) as a new approach to model individual sinusoidal relationships between the alpha phase and multisensory perception and to test for causality. Rhythmic 10 Hz visual stimulation entrained the alpha phase at 0{degrees} or 180{degrees} relative to audiovisual target onset. Entrainment effects on multisensory integration only emerged when individual NBLFs, which were related to alpha phase coupling between early audio-visual cortices, were included in the analysis. A follow-up psychophysical experiment confirmed individual phase-dependent effects at 5 and 10 Hz, but not 15 Hz. Our results suggest that the individual alpha phase causally modulates perceptual cycles in multisensory integration and advocate for personalized, phase-targeting strategies in neurotechnologies.

neuroscience↗

bioRxiv2025

Causal inference shapes crossmodal postdictive perception within the temporal window of multisensory integration

1.In our environment, stimuli from different sensory modalities are processed within a temporal window of multisensory integration that spans several hundred milliseconds. During this window, stimulus processing is influenced not only by preceding and current information, but also by input that follows the stimulus. The computational mechanisms underlying crossmodal backward processing, which we refer to as crossmodal postdiction, are not well understood. We examined crossmodal postdiction in the audiovisual (AV) rabbit illusion, in which postdiction occurs when flash-beep pairs are presented shortly before and shortly after a single flash or a single beep. We collected behavioral data from 32 participants and fitted four competing models: a Bayesian causal inference (BCI), a forced-fusion, a forced-segregation, and a non-postdictive BCI model. The BCI model fit the data well and outperformed all other models. Building on findings demonstrating causal inference during non-postdictive multisensory integration, our study shows that the BCI framework is also effective in explaining crossmodal postdiction. Observers accumulate causal evidence that influences their perception of preceding stimuli following a causal decision. Our study shows that crossmodal postdiction in the AV rabbit illusion is formed within a temporal window of multisensory integration that encompasses past, present, and future input, which can be effectively explained by the BCI framework.

neuroscience↗

bioRxiv2023

Computations and neural dynamics of audiovisual causal and perceptual inference in schizophrenia

Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs during perceptual inference. In this study, we combined psychophysics, Bayesian modelling and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in medicated individuals with schizophrenia when exposed to audiovisual sequences with varying numbers of flashes and beeps from either common or independent sources. Our findings reveal that individuals with schizophrenia, like their healthy controls, balance sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both groups showed comparable weighting of prior information regarding the signals causal structure, with the schizophrenia group slightly overweighting prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of perceptual estimates that segregate and flexibly combine audiovisual inputs. In conclusion, our results demonstrate that the computational and neural mechanisms of multisensory perceptual and causal inference remain remarkably intact in medicated individuals with schizophrenia during flash-beep scenarios.

neuroscience↗

bioRxiv2019

Using the past to estimate sensory uncertainty

To form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.\n\nIn a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discounting\n\nOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

neuroscience↗

bioRxiv2018

The neural dynamics of hierarchical Bayesian inference in multisensory perception

Transforming the barrage of sensory signals into a coherent multisensory percept relies on solving the binding problem - deciding whether signals come from a common cause and should be integrated, or instead be segregated. Human observers typically arbitrate between integration and segregation consistent with Bayesian Causal Inference, but the neural mechanisms remain poorly understood. We presented observers with audiovisual sequences that varied in the number of flashes and beeps. Combining Bayesian modelling and EEG representational similarity analyses, we show that the brain initially represents the number of flashes and beeps and their numeric disparity mainly independently. Later, it computes them by averaging the forced-fusion and segregation estimates weighted by the probabilities of common and independent cause models (i.e. model averaging). Crucially, prestimulus oscillatory alpha power and phase correlate with observers prior beliefs about the worlds causal structure that guide their arbitration between sensory integration and segregation.

neuroscience↗