Hierarchical Predictive Information Is Channeled by Asymmetric Oscillatory Activity

A review of psychological and neuroscientific research on musical groove

When listening to music, we naturally move our bodies rhythmically to the beat, which can be pleasurable and difficult to resist. This pleasurable sensation of wanting to move the body to music has been called "groove." Following pioneering humanities research, psychological and neuroscientific studies have provided insights on associated musical features, behavioral responses, phenomenological aspects, and brain structural and functional correlates of the groove experience. Groove research has advanced the field of music science and more generally informed our understanding of bidirectional links between perception and action, and the role of the motor system in prediction. Activity in motor and reward-related brain networks during music listening is associated with the groove experience, and this neural activity is linked to temporal prediction and learning. This article reviews research on groove as a psychological phenomenon with neurophysiological correlates that link musical rhythm perception, sensorimotor prediction, and reward processing. Promising future research directions range from elucidating specific neural mechanisms to exploring clinical applications and socio-cultural implications of groove.

A deep hierarchy of predictions enables online meaning extraction in a computational model of human speech comprehension

Understanding speech requires mapping fleeting and often ambiguous soundwaves to meaning. While humans are known to exploit their capacity to contextualize to facilitate this process, how internal knowledge is deployed online remains an open question. Here, we present a model that extracts multiple levels of information from continuous speech online. The model applies linguistic and nonlinguistic knowledge to speech processing, by periodically generating top-down predictions and incorporating bottom-up incoming evidence in a nested temporal hierarchy. We show that a nonlinguistic context level provides semantic predictions informed by sensory inputs, which are crucial for disambiguating among multiple meanings of the same word. The explicit knowledge hierarchy of the model enables a more holistic account of the neurophysiological responses to speech compared to using lexical predictions generated by a neural network language model (GPT-2). We also show that hierarchical predictions reduce peripheral processing via minimizing uncertainty and prediction error. With this proof-of-concept model, we demonstrate that the deployment of hierarchical predictions is a possible strategy for the brain to dynamically utilize structured knowledge and make sense of the speech input.

Constructing the hierarchy of predictive auditory sequences in the marmoset brain

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, shorter-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, longer-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putative bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical top-down process of predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural networks and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

Oscillatory Entrainment of the Frequency-following Response in Auditory Cortical and Subcortical Structures

There is much debate about the existence and function of neural oscillatory mechanisms in the auditory system. The frequency-following response (FFR) is an index of neural periodicity encoding that can provide a vehicle to study entrainment in frequency ranges relevant to speech and music processing. Criteria for entrainment include the presence of poststimulus oscillations and phase alignment between stimulus and endogenous activity. To test the hypothesis of entrainment, in experiment 1 we collected FFR data for a repeated syllable using magnetoencephalography (MEG) and electroencephalography in 20 male and female human adults. We observed significant oscillatory activity after stimulus offset in auditory cortex and subcortical auditory nuclei, consistent with entrainment. In these structures, the FFR fundamental frequency converged from a lower value over 100 ms to the stimulus frequency, consistent with phase alignment, and diverged to a lower value after offset, consistent with relaxation to a preferred frequency. In experiment 2, we tested how transitions between stimulus frequencies affected the MEG FFR to a train of tone pairs in 30 people. We found that the FFR was affected by the frequency of the preceding tone for up to 40 ms at subcortical levels, and even longer durations at cortical levels. Our results suggest that oscillatory entrainment may be an integral part of periodic sound representation throughout the auditory neuraxis. The functional role of this mechanism is unknown, but it could serve as a fine-scale temporal predictor for frequency information, enhancing stability and reducing susceptibility to degradation that could be useful in real-life noisy environments.SIGNIFICANCE STATEMENT Neural oscillations are proposed to be a ubiquitous aspect of neural function, but their contribution to auditory encoding is not clear, particularly at higher frequencies associated with pitch encoding. In a magnetoencephalography experiment, we found converging evidence that the frequency-following response has an oscillatory component according to established criteria: poststimulus resonance, progressive entrainment of the neural frequency to the stimulus frequency, and relaxation toward the original state on stimulus offset. In a second experiment, we found that the frequency and amplitude of the frequency-following response to tones are affected by preceding stimuli. These findings support the contribution of intrinsic oscillations to the encoding of sound, and raise new questions about their functional roles, possibly including stabilization and low-level predictive coding.

Combining predictive coding and neural oscillations enables online syllable recognition in natural speech

On-line comprehension of natural speech requires segmenting the acoustic stream into discrete linguistic elements. This process is argued to rely on theta-gamma oscillation coupling, which can parse syllables and encode them in decipherable neural activity. Speech comprehension also strongly depends on contextual cues that help predicting speech structure and content. To explore the effects of theta-gamma coupling on bottom-up/top-down dynamics during on-line syllable identification, we designed a computational model (Precoss—predictive coding and oscillations for speech) that can recognise syllable sequences in continuous speech. The model uses predictions from internal spectro-temporal representations of syllables and theta oscillations to signal syllable onsets and duration. Syllable recognition is best when theta-gamma coupling is used to temporally align spectro-temporal predictions with the acoustic input. This neurocomputational modelling work demonstrates that the notions of predictive coding and neural oscillations can be brought together to account for on-line dynamic sensory processing.

The authors present a model to parse and recognise syllables on-line in natural speech sentences that combine predictive coding and neural oscillations. They use simulations from different versions of the model to establish the importance of both theta-gamma coupling and the reset of accumulated evidence in continuous speech processing.

Large-Scale Networks for Auditory Sensory Gating in the Awake Mouse

The amplitude of the brain response to a repeated auditory stimulus is diminished as compared to the response to the first tone (T1) for interstimulus intervals (ISI) lasting up to hundreds of milliseconds. This adaptation process, called auditory sensory gating (ASG), is altered in various psychiatric diseases including schizophrenia and is classically studied by focusing on early evoked cortical responses to the second tone (T2) using 500-ms ISI. However, mechanisms underlying ASG are still not well-understood. We investigated ASG in awake mice from the brainstem to cortex at variable ISIs (125–2000 ms) using high-density EEG and intracerebral recordings. While ASG decreases at longer ISIs, it is still present at durations (500–2000 ms) far beyond the time during which brain responses to T1 could still be detected. T1 induces a sequence of specific stable scalp EEG topographies that correspond to the successive activation of distinct neural networks lasting about 350 ms. These brain states remain unaltered if T2 is presented during this period, although T2 is processed by the brain, suggesting that ongoing networks of brain activity are active for longer than early evoked-potentials and are not overwritten by an upcoming new stimulus. Intracerebral recordings demonstrate that ASG is already present at the level of ventral cochlear nucleus (vCN) and inferior colliculus and is amplified across the hierarchy in bottom-up direction. This study uncovers the extended stability of sensory-evoked brain states and long duration of ASG, and sheds light on generators of ASG and possible interactions between bottom-up and top-down mechanisms.