Sonja A. Kotz

Auditory perception emerges from the continuous integration of incoming acoustic signals with statistical predictions, a process complemented by simpler local adaptation. Yet, how all these concurrent mechanisms unfold in time remains underexplored. Using magnetoencephalography (MEG) in participants listening to stochastic tone sequences with periodically shifting inferential statistics, we modelled the temporal, spatial, and spectral dynamics of stimulus-specific repetition suppression, surprisal, model updating, and prior precision. By concurrently tracking these computational parameters within this dynamic auditory environment, we revealed a distinct chronological hierarchy governing predictive and adaptive processing. Prior precision states - reflecting expectations - emerged earliest, followed by rapid and concurrent repetition suppression and surprisal - reflecting local acoustic attenuation and global mismatch detection. These initial dynamics culminated in subsequent, distributed model updates within fronto-parietal networks. Spectrally, these components mapped onto diverging oscillatory fingerprints, ranging from theta-alpha modulation during adaptation to beta suppression during surprisal and broad spectral dampening during precision weighting. Together, these findings demonstrate that the brain navigates uncertain auditory environments through a cascade of neural processes, where fast, local sensory gating and mismatch signals precede the hierarchical revision of internal models.

Auditory perception relies on prior sensations to form expectations and adapt to repeated inputs. Yet, how these processes interact with frequency-specific preference across cortical depth remains unclear. Using ultra-high-field functional magnetic resonance imaging (at 7T), participants listened to stochastic tone sequences varying in their repetition structure and predictability. We modelled voxel-wise responses for stimulus preference, repetition suppression, and expectations, partitioning their unique and shared variance across three depth bins (infragranular, granular, supragranular). Expectation-related signals explained most unique variance in deep layers, aligning with internal model representations. Variance jointly explained by stimulus preference and expectation was instead strongest in superficial layers, consistent with the content-specific alignment of top-down predictions (prediction error signalling). Finally, repetition suppression accounted for variance uniformly across depth. Together, the data suggest that deep layers contain content-specific predictive models, superficial layers register prediction-input alignment, and repetition suppression provides a depth-invariant, local gain control that complements predictive processing.

Interactions among the basic tastes shape taste perception, yet the relative contributions of individual tastes within mixtures remain unclear. This study examined how varying concentrations of sweetness, sourness, and saltiness influence perceived intensity, pleasantness, and each taste’s relative contribution to mixture perception. To assess these relationships, participants rated solutions using a Visual Analog Scale (VAS) and a novel method, the Taste Triangle.Participants (N = 25) completed two experimental sessions. In the first session, sensitivity to sweetness, sourness, and saltiness was assessed. In the second session, participants received individualized taste solutions containing one, two, or all three tastants. For overall intensity (VAS ratings), increasing individual taste concentrations generally increased perceived intensity. However, when two tastes were present at high concentrations, these increases became less pronounced, indicating perceived intensity compression in complex mixtures. For relative taste contribution (Taste Triangle ratings), increasing the concentration of a taste shifted mixture perception toward that taste and reduced the contribution of the other tastes. Furthermore, the influence of sweetness and sourness on mixture perception depended on the saltiness concentration, suggesting that saltiness modulated the contribution of the other tastes. Notably, adding a low level of sweetness altered the perceived composition of mixtures, whereas adding a low level of sourness or saltiness had little effect.Overall, these findings demonstrate that overall intensity and relative taste contribution capture distinct aspects of taste perception in complex mixtures. Together, VAS ratings and the Taste Triangle revealed how concentration influences perceived intensity and how tastant interactions shape the relative taste contributions, even at low concentrations.

Rhythm is a key building block of human music, speech and numerous other human activities. Understanding the computational substrates of rhythm perception requires models that bridge algorithmic function with biological implementation. We propose a physiologically grounded spiking neural network (SNN) framework to investigate the emergent representation and interpretation of auditory rhythms. Utilizing a recurrent SNN architecture trained on an auditory entrainment task, we characterize the network’s latent dynamics through the analysis of firing rates and membrane potential fluctuations. Our results demonstrate that simulated neural populations exhibit phase-locking to the stimulus beat, with endogenous oscillations driven by rhythmic input. We further show that anticipatory dynamics—characterized by pre-stimulus depolarization—emerge naturally from the network’s synaptic plasticity and temporal integration properties, rather than from explicitly defined oscillators. By treating network layers as functional analogs of cortical populations, this framework allows for the application of spectral and information-theoretic analyses typical of empirical electrophysiology. More in general, this approach establishes SNNs as robust exploratory tools for uncovering how predictive coding and rhythmic entrainment arise from the inherent constraints of biological neural computation.

The Music Ensemble dataset is a large-scale, cross-national database that provides detailed information about the musical, cognitive, personality, and demographic profiles of young adult musicians and nonmusicians. Data were collected from 1438 participants (aged 18–30) across thirty-five research sites in Europe, North America, South America, and Australia. Participants completed an in-person, in-lab battery of objective tests, including measures of verbal, visuospatial and musical short-term memory, executive functions (updating component), nonverbal reasoning, verbal comprehension, and music perception skills. The battery also included standardized and custom self-report questionnaires assessing music sophistication, music reward, personality traits, socioeconomic status, and demographic characteristics. Music Ensemble was preregistered, and the research protocol followed a standardized procedure across sites. The dataset also includes a large subsample of musicians and nonmusicians that are pair-matched for age, gender, and education (678 pairs). It enables well-powered investigations into the relationship between musical expertise and individual differences in cognition, personality, and demographic variables. It is also suitable for training in statistical and psychometric methods.

The human brain continuously forms predictions about the unfolding sensory environment, relying on contextual information to anticipate upcoming events while remaining sensitive to unexpected changes. This study examined how pupil‐linked phasic arousal, a putative proxy for the locus coeruleus–norepinephrine system, reflects the interplay between tonal surprisal (unexpectedness) and precision (reliability of the inferred context) in dynamic auditory contexts. Twenty‐eight participants passively listened to stochastic tone sequences transitioning between periods of low‐entropy (informative context) and high‐entropy (less informative context). We quantified tone‐by‐tone surprisal and precision using Bayesian modeling. Despite their slow time evolution, pupil dilation responses revealed sensitivity to both surprisal and precision, showing that arousal tracks momentary deviations and the stability of contextual predictions. Analyses of context boundaries showed that transitions between distinct low‐entropy environments (LE‐dLE) evoked significant pupil dilation, whereas shifts between low‐ and high‐entropy environments (LE‐HE and HE‐LE) did not. These findings indicate that pupil‐linked arousal primarily responds to salient contextual shifts involving stable environments rather than to changes in entropy per se. The results emphasize the role of the locus coeruleus–norepinephrine system in adaptive model updating during passive listening and demonstrate the brain's continuous and implicit monitoring of uncertainty to navigate dynamic auditory environments.

Ageing is associated with declines in mobility and balance that threaten independence and increase fall risk. Although these changes are often attributed to deterioration of muscular and sensory systems, growing evidence suggests that age-related alterations in cognitive control also contribute to postural instability and gait impairments. Here, we examined whether shifts in cognitive control strategy underlie impaired gait initiation in older adults by modelling how individuals update beliefs about upcoming actions. Older and younger adults performed a choice-stepping go/no-go task in which step likelihood was manipulated across trials. We measured anticipatory postural adjustments (APAs), the preparatory weight shifts preceding stepping and modelled age-related differences in APA onset using a hierarchical Bayesian framework. Older adults initiated slower APAs, particularly after repeated go trials. Computational modelling revealed reduced learning rates and heightened sensitivity to uncertainty in older adults, identifying altered belief updating as a cognitive mechanism shaping age-related changes in adaptive stepping.

Parkinson’s disease (PD) is associated with disruptions in the temporal organization of motor and non-motor behavior, likely arising from dysfunction within basal ganglia–thalamocortical circuits. Despite extensive evidence for impaired timing in PD, the relationship between temporal processing variability, disease progression, and established clinical severity markers remains poorly defined. We conducted a systematic meta-analysis to quantify temporal processing efficiency in PD using the coefficient of variation (CV), a standardized measure of performance variability across timing tasks. CV values were extracted from empirical studies encompassing a broad range of temporal processing paradigms. Associations between CV, participant age, and disease severity were examined using Unified Parkinson’s Disease Rating Scale (UPDRS) scores and Hoehn and Yahr (H&Y) stages. Results revealed a robust positive correlation between CV and UPDRS score, independent of age, indicating that increased temporal variability reflects disease-specific dysfunction rather than normal aging. In contrast, CV correlated negatively with H&Y stage and age, consistent with medication-related influences on motor symptom severity. Secondary analyses showed no reliable differences in CV across task type, temporal range, or medication status, supporting the notion of a generalized temporal processing impairment in PD. Together, these findings suggest that temporal processing variability represents a sensitive marker of disease progression and may inform diagnostic assessment and intervention strategies.

Auditory perception relies on prior sensations both to form expectations about upcoming sounds and to adapt to repeated inputs. Yet, how these processes interact with bottom-up frequency-specific stimulus drive, and how they are organised across cortical depth, remains unclear. Using ultra-high-field functional magnetic resonance imaging (7T fMRI), participants listened to stochastic tone sequences whose repetition structure and predictability varied over time. For each voxel independently, we used computational modelling to estimate responses for stimulus drive, repetition suppression, and expectations. We then partitioned the unique and shared variance of these components to estimate their relative importance in modulating BOLD activity across cortical layers in auditory cortices. Expectation-related signals explained most unique variance in deep (infagranular) layers, in line with accounts that place internal model representations and belief updates in deeper cortical populations. Variance jointly explained by stimulus drive and expectation was instead strongest in superficial layers, consistent with these compartments expressing computations that combine bottom-up input with top-down predictions, such as prediction error signalling. Finally, repetition suppression accounted for variance uniformly across depth, suggesting an adaptation-like mechanism that modulates responsiveness without a clear laminar bias. Together, the data suggest that deep layers contain content-specific predictive models, superficial layers register prediction–input alignment, and repetition suppression provides a depth-invariant, local gain control that complements predictive processing.