Biology subjects

Pearce, M.

Publications and source records attributed to Pearce, M..

10 recordsLinked to original sources

bioRxiv2026

DOME Copilot: Making transparency and reproducibility for artificial intelligence methods simple

Artificial intelligence (AI) methods are transforming life science research and witnessing unprecedented adoption across literature. While AI is driving impactful discoveries, this rapid growth in application has inundated researchers with poorly described models and datasets which lack transparency, impeding reusability and eroding trust. The DOME Recommendations aimed to address this issue and established structured reporting guidelines to standardize AI methodology descriptions. However, the need to manually create these comprehensive disclosures imposed a major bottleneck, requiring substantial effort from authors to comply. To bridge this gap, we present DOME Copilot, an open-source, easily deployable system that automates the generation of transparent AI methodology reports supplementing publications. Evaluated against a benchmark of human-created annotations, DOME Copilot was determined to match or exceed manual annotation quality across the majority of reporting fields while reducing the creation time from hours to minutes. Ultimately, the system provides a novel and scalable solution to restore confidence in complex life science AI method publications.

bioinformatics↗

bioRxiv2025

Neural tracking of melodic prediction is pre-attentive

Musics ability to modulate arousal and manipulate emotions relies upon formation and violation of predictions. Music is often used to modulate arousal and mood while individuals focus on other tasks, suggesting that sophisticated musical prediction may be implicit and obligatory, operating regardless of attention. Here we investigated the role of attention in musical prediction by presenting participants simultaneously with a musical passage in one ear and an audiobook in the other. Participants were asked to attend to either speech or music to perform a recognition memory task. Musical prediction was investigated using a computational model of music which was trained on a corpus of Western music (long-term learning) and/or incrementally on each stimulus (short-term learning). While neural tracking of the acoustics of music, including encoding of note onset and pitch interval size, was enhanced by attention, musical prediction tracked the neural signal robustly regardless of attention. However, although neural tracking of long-term musical prediction based on previously-learned statistical patterns was unaffected by attention, tracking of short-term musical prediction learned from the structure of each stimulus was present only when the music was attended. Thus, attention is crucial for short-term musical learning and prediction but is not required for musical prediction based on long-term stylistic exposure.

neuroscience↗

bioRxiv2025

The Effect of Previously Encountered Sensory Information on Neural Representations of Predictability: Evidence from Human EEG

Accumulating evidence suggests that the brain continuously monitors the predictability of rapidly evolving sound sequences, even when they are not behaviorally relevant. An increasing body of empirical evidence links sustained tonic M/EEG activity to evidence accumulation and tracking the predictability, or inferred precision, of the auditory stimulus. However, it remains unclear whether, and how, this process depends on auditory contextual memory. In the present EEG study, we examined neural responses to sound sequences across two experiments, and compared them to predictions from ideal observer models with varying memory spans. Stimuli were sequences of 50 ms long tone-pips. In Experiment 1 (N=26; both sexes), a regularly repeating sequence of 10 tones (REG) transitioned directly to a different regular sequence (REGxREGy). In Experiment 2 (N=28; both sexes), the same regular sequence was repeated after an intervening random segment (REGxINTREGx). Results from Experiment 2 revealed that the inferred predictability of the resumed REGx pattern was influenced by the preceding INT tones, even several seconds after they ended, indicating that the brain retains contextual memory over time. In contrast, neural responses in Experiment 1 were best explained by models with minimal memory. This dissociation implies that the brain can dynamically adjust its strategy based on inferred environmental structure--resetting context when interruptions signal change, and preserving context when patterns are likely to resume.

neuroscience↗

bioRxiv2025

Discovery, Interruption, and Updating of Auditory Regularities in Memory: Evidence from Low-Frequency Brain Dynamics in Human MEG

During passive listening, the brain maintains a hierarchy of predictive models to monitor the statistics of its surroundings. The automatic discovery of regular patterns has been associated with a gradual increase in sustained tonic M/EEG activity, sourced in auditory, hippocampal, and frontal areas -- reflecting evidence accumulation and establishment of a regularity model. Conversely, when a regular pattern is interrupted, the sustained activity drops -- indicating disengagement from the model. However, how such models are established in and retrieved from memory as well as the conditions under which they are activated and interrupted remain underexplored. In this MEG experiment (N=26; both sexes), we examined how neural responses related to model establishment and interruption are influenced by (1) the rate of stimulus presentation (tone presentation rate 20 Hz vs. 40 Hz), and (2) the novelty of the experienced acoustic structure (novel vs resumed REG pattern). The results show that (1) the dynamics of model interruption and establishment are independent of stimulus presentation rate, and that (2) model establishment occurred much faster when an experienced vs novel pattern was presented after pattern interruption, suggesting re-activation of the stored original model facilitated by the hippocampus. (3) Finally, sustained response rises in response to pattern establishment and interruption were localized in auditory, hippocampal, and frontal sources, supporting top-down model information flow. These results unveil the temporal dynamics and neural network underlying the brains construction and selection of predictive models to monitor changes in sensory statistics. Significance statementSustained neural activity in response to statistical shifts in auditory sequences reflects how the brain integrates sensory information to build and update predictive models. The present magnetoencephalography (MEG) study aims to uncover the factors that affect this updating. Using tone-pip sequences as a controlled model system, we found that the brain tracks sequence information regardless of tempo and automatically retains a memory of previously encountered patterns, facilitating detection when they re-emerge. Source localization revealed activation in auditory, hippocampal, and frontal areas, highlighting a distributed network involved in top-down model updating and predictive processing. These findings provide new insights into the neural mechanisms of statistical learning and memory, revealing how the brain adapts perception in dynamic environments.

neuroscience↗

bioRxiv2024

Expectation elicits music-evoked chills

Music-evoked chills are pleasurable shivers, goosebumps or tingling sensations experienced during musical listening. Research on the characteristics of music perception that elicit chills has focused on low-level acoustic features in small samples of music. We collected a dataset of 1,019 pieces of music timestamped with onset times of 1,806 chills from 402 participants and used computational methods to predict chills onsets from simulations of low-level acoustic and high-level expectation-based elicitors. A machine learning classifier was trained to discriminate passages containing chills from those not containing chills. The results show that chills onsets are predicted better than chance and corroborate existing evidence for acoustic elicitors of chills with a much larger dataset. They also provide empirical evidence that music-evoked chills are elicited by surprise and uncertainty resulting from a high-level psychological process of expectation, which proved a more effective predictor than acoustic elicitors.

neuroscience↗

bioRxiv2021

Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection

When listening to music, pitch deviations are more salient and elicit stronger prediction error responses when the melodic context is predictable and when the listener is a musician. Yet, the neuronal dynamics and changes in synaptic efficacy underlying such effects remain unclear. Here, we employed dynamic causal modeling (DCM) to investigate whether the magnetic mismatch negativity response (MMNm)--and its modulation by context predictability and musical expertise--are associated with enhanced neural gain of auditory areas, as a plausible mechanism for encoding precision-weighted prediction errors. Using Bayesian model comparison, we asked whether models with intrinsic connections within primary auditory cortex (A1) and superior temporal gyrus (STG)--typically related to gain control--or extrinsic connections between A1 and STG--typically related to propagation of prediction and error signals--better explained magnetoencephalography (MEG) responses. We found that, compared to regular sounds, out-of-tune pitch deviations were associated with lower intrinsic (inhibitory) connectivity in A1 and STG, and lower backward (inhibitory) connectivity from STG to A1, consistent with disinhibition and enhanced neural gain in these auditory areas. More predictable melodies were associated with disinhibition in right A1, while musicianship was associated with disinhibition in left A1 and reduced connectivity from STG to left A1. These results indicate that musicianship and melodic predictability, as well as pitch deviations themselves, enhance neural gain in auditory cortex during deviance detection. Our findings are consistent with predictive processing theories suggesting that precise and informative error signals are selected by the brain for subsequent hierarchical processing. Significance statementIn complex auditory contexts, being able to identify informative signals is of paramount importance. Such is the case of music listening, where surprising sounds play a fundamental role in its perceptual, aesthetical, and emotional experience. Crucially, surprising sounds in the pitch dimension are more easily detected and generate stronger cortical responses when melodies are predictable and when the listener is a musician. Using Dynamic Causal Modelling, here we show that such effects arise from a local increase in neural gain within auditory areas, rather than from passing of prediction and error signals between brain regions. Consistent with predictive processing theories, this suggests that the enhanced precision of auditory predictive models--through melodic predictability and musical training--up-regulates the processing of informative error signals in the brain.

neuroscience↗

bioRxiv2020

Spatiotemporal brain dynamics during recognition of the music of Johann Sebastian Bach

Music is a non-verbal human language, built on logical structures and articulated in balanced hierarchies between sounds, offering excellent opportunities to explore how the brain creates meaning for complex spatiotemporal auditory patterns. Using the high temporal resolution of magnetoencephalography in 70 participants, we investigated their unfolding brain dynamics during the recognition of previously memorized J.S. Bachs musical patterns from prelude in C minor BWV 847 compared to novel patterns matched in terms of entropy and information content. Remarkably, the recognition of the memorized music ignited a widespread brain network comprising primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus and hippocampus. Furthermore, measures of both brain activity and functional connectivity presented an overall increase over time, following the evolution and unfolding of the memorized musical patterns. Specifically, while the auditory cortex responded mainly to the first tones of the patterns, the activity and synchronization of higher-order brain areas such as cingulate, frontal operculum, hippocampus and orbitofrontal cortex largely increased over time, arguably representing the key whole-brain mechanisms for conscious recognition of auditory patterns as predicted by the global neuronal workspace hypothesis. In conclusion, our study described the fine-grained whole-brain activity and functional connectivity dynamics responsible for processing and recognition of previously memorized music. Further, the study highlights how the use of musical patterns in combination with a wide array of analytical tools and neuroscientific measures spanning from decoding to fast neural phase synchronization can shed new light on meaningful, complex cognitive processes.

neuroscience↗

bioRxiv2019

Domain-relevant auditory expertise modulates the additivity of neural mismatch responses in humans

It is unknown whether domain-relevant expertise is associated with more independent or more dependent predictive processing of acoustic features. Here, mismatch negativity (MMNm) was recorded with magnetoencephalography (MEG) from 25 musicians and 25 non-musicians, exposed to complex musical multi-feature and simple oddball control paradigms. Deviants differed in frequency (F), intensity (I), perceived location (L), or any combination of these (FI, IL, LF, FIL). Neural processing overlap was assessed through MMNm additivity by comparing double- and triple-deviant MMNms ("empirical") to summed constituent single-deviant MMNms ("modelled"). Significantly greater subadditivity was present in musicians compared to non-musicians, specifically for frequency-related deviants in complex contexts. Despite using identical sounds, expertise effects were absent from the simple paradigm. This novel finding supports the dependent processing hypothesis whereby experts recruit overlapping neural resources facilitating more integrative representations of domain-relevant stimuli. Such specialized predictive processing may enable experts such as musicians to capitalise on complex acoustic cues.

neuroscience↗

bioRxiv2019

Harmonisation of Physical Activity Variables by Indirect Validation: A Doubly Labelled Water Study

Harmonisation of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation data are often unavailable. This study examines an alternative method of harmonisation by indirect validation. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-measured physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct validation models (linear regression of DLW-measured PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10-63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonised models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-measured PAEE were identical after harmonisation using continuous linear but not categorical models. Wrist acceleration harmonised to DLW-measured PAEE via combined accelerometry and heart rate sensing had lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: -1.6; 3.4) kJ*day-1*kg-1. Associations between PAEE and BMI were similar for directly and indirectly harmonised values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonisation. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.

epidemiology↗

bioRxiv2018

Precision weighting of musical prediction error: converging neurophysiological and behavioral evidence

Theories of predictive processing propose that prediction error responses are modulated by the certainty of the predictive model or precision. While there is some evidence for this phenomenon in the visual and, to a lesser extent, the auditory modality, little is known about whether it operates in the complex auditory contexts of daily life. Here, we examined how prediction error responses behave in a more complex and ecologically valid auditory context than those typically studied. We created musical tone sequences with different degrees of pitch uncertainty to manipulate the precision of participants auditory expectations. Magnetoencephalography was used to measure the magnetic counterpart of the mismatch negativity (MMNm) as a neural marker of prediction error in a multi-feature paradigm. Pitch, slide, intensity and timbre deviants were included. We compared high-entropy stimuli, consisting of a set of non-repetitive melodies, with low-entropy stimuli consisting of a simple, repetitive pitch pattern. Pitch entropy was quantitatively assessed with an information-theoretic model of auditory expectation. We found a reduction in pitch and slide MMNm amplitudes in the high-entropy as compared to the low-entropy context. No significant differences were found for intensity and timbre MMNm amplitudes. Furthermore, in a separate behavioral experiment investigating the detection of pitch deviants, similar decreases were found for accuracy measures in response to more fine-grained increases in pitch entropy. Our results are consistent with a precision modulation of auditory prediction error in a musical context, and suggest that this effect is specific to features that depend on the manipulated dimension--pitch information, in this case.\n\nHighlightsO_LIThe mismatch negativity (MMNm) is reduced in musical contexts with high pitch uncertainty\nC_LIO_LIThe MMNm reduction is restricted to pitch-related features\nC_LIO_LIAccuracy during deviance detection is reduced in contexts with higher uncertainty\nC_LIO_LIThe results suggest a feature-selective precision modulation of prediction error\nC_LI\n\nMaterials, data and scripts can be found in the Open Science Framework repository: http://bit.ly/music_entropy_MMN\n\nDOI: 10.17605/OSF.IO/MY6TE

neuroscience↗