Registered Report: Replication and Extension of Nozaradan, Peretz, Missal and Mouraux (2011)
Bibliographic record
Abstract
Abstract Cognitive neuroscience has long sought to disentangle stimulus-driven processing from conscious perceptual processing. Some prior evidence for neural processing of perceived musical beat (periodic pulse) may be confounded by stimulus-driven activity. Notably, Nozaradan et al. (2011) controlled for stimulus factors and used frequency tagging to show increased brain activity at imagery-related frequencies when listeners imagined a beat pattern during an isochronous stimulus. However, it remains unclear whether this effect is replicable and whether it reliably reflects conscious beat perception. This registered report presents 13 independent replications using the same vetted protocol. Listeners performed the same experimental paradigm as in Nozaradan et al. (2011), with an added behavioral task on each trial to assess conscious perception of the imagined beat. Pre-registered meta-analyses revealed smaller raw effect sizes of imagery condition (Binary: 0.03 µV, Ternary: 0.03 µV) than the original study (Binary: 0.12 µV, Ternary: 0.20 µV), with confidence intervals all overlapping with 0. Differences in full-sample estimated effect sizes (this study: n = 152, η p 2 = .03–.04; 2011 study: n = 8, η p 2 = .62– .76) suggest larger sample sizes are necessary to detect these effects reliably, if they exist. Additionally, only neural activity at the stimulus frequency predicted imagery task accuracy, contradicting our hypothesis that beat-related frequencies would predict performance. Our findings suggest an overall failure to replicate all main effects from the original study. We discuss potential reasons for discrepancies with the original study as well as implications for the utility of frequency tagging for studying beat perception.
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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 | 0.048 | 0.304 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".