Phase resetting with temporal template explains complexity matching in finger tapping to fractal rhythms
Bibliographic record
Abstract
Auditory-motor synchronization refers to the coupling of motor responses to rhythmic auditory stimuli. This study examined finger-tapping dynamics under three conditions: self-paced tapping, tapping to metronomic stimuli, and tapping to fractal auditory stimuli. Using Detrended Fluctuation Analysis (DFA) to estimate Hurst exponents, H, and Diffusion Entropy Analysis (DEA) to estimate scaling exponents, d, in each condition, we found that self-paced tapping exhibited persistent or super-diffusive inter-tap intervals (H=0.63±0.145, d=0.64±0.097), while tapping to metronomic stimuli showed a trend toward random noise (H=0.55±0.101, d=0.58±0.126). Complexity matching, that is, systematic adjustment of intertap intervals to match persistence levels of fractal stimuli, was observed between the Hurst exponents of auditory stimuli (H=0.25 to H=1.5) and complexity measures of tapping (H=0.54 to H=0.81; d=0.51 to d=0.72). A Gaussian linear mixed model confirmed significant associations between the Hurst exponents of auditory stimuli and H of the corresponding intertap interval time series. In contrast, the associations between the Hurst exponents of auditory stimuli and d of the corresponding intertap interval time series were mixed. To understand these empirical observations, we utilized the neural hopping model to represent the intrinsic mechanism underlying self-paced tapping and incorporated the Van der Pol oscillator to account for auditory stimuli as a driving force. Metronomic stimuli were modeled as harmonic forcing, resulting in simulated tapping with H=0.50±0.175 or d=0.53±0.115. Complexity matching to fractal stimuli was achieved through phase resetting. We evaluated four coupling variants of phase-resetting, i.e., with or without continuous harmonic drive and including or excluding reset jitter. We performed precision-weighted root-mean-square error (WRMSE) model selection across six fractal conditions with a two-stage bootstrap. The Drive+Jitter variant best reproduced the empirical scaling for both H (pointwise WRMSE = 0.05; win probability = 0.79) and d (pointwise WRMSE = 0.09; win probability = 0.70). The Drive+Jitter phase resetting model simulated tapping persistence values ranging from H=0.57 to H=0.78 or d=0.44 to d=0.85, closely aligning with the experimental data. These results indicate that fractal auditory stimuli can elicit fractal motor outputs comparable to those in healthy states, suggesting potential therapeutic benefits for motor recovery and rehabilitation. The modeling approach provides a framework for understanding the mechanisms underlying auditory-motor synchronization across different tapping conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".