How strong is the rhythm of perception? A registered replication of Hickok <i>et al</i> . (2015)
Bibliographic record
Abstract
Our ability to predict upcoming events is a fundamental component of human cognition. One way in which we do so is by exploiting temporal regularities in sensory signals: the ticking of a clock, falling of footsteps and the motion of waves each provide a structure that may facilitate anticipation. But how strong is the effect of rhythmic anticipation on perception? And to what degree do people vary in their ability to capitalize on these regularities? In 2015, Hickok et al . introduced a behavioural paradigm to assess how a rhythmic auditory stimulus affects perception of subsequent targets (Hickok G, Farahbod H, Saberi K. 2015 The rhythm of perception: entrainment to acoustic rhythms induces subsequent perceptual oscillation. Psychol. Sci. 26 , 1006–1013. (doi: 10.1177/0956797615576533 )). They tested five listeners and found that perception (target detection accuracy) fluctuated rhythmically just like the sound rhythm. Here, we replicate the original finding, assess how likely the finding is to be observed for any individual, and quantify effect size in a large sample of adult listeners ( n = 149). We introduce a model-based analysis approach that allows separate estimates of amplitude and phase information in target detection responses, and quantifies effect size for individual listeners. Together our results strongly support the presence of oscillatory influences on target detection accuracy, as well as substantial variability in the magnitude of this effect across listeners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".