Auditory Cues and Feedback in the Serial Reaction Time Task: Evidence for Sequence Acquisition and Sensory Transfer
Bibliographic record
Abstract
The present experiment used the Serial Reaction Time Task(SRTT) to investigate if auditory cueing or feedback in the form of spatially compatible tones benefited sequence learning similarly. Fifty-three neurotypical adults (18-35 years; 32 cis-females; 21 cis-males) were randomly assigned to three groups in which they practiced a visual SRTT: Group AC was supplemented with auditory cues; group AF received auditory feedback; group NS performed without sound. Retention and transfer tests (i.e., in the other two sensory conditions), and an explicit awareness test were conducted 48 h after practice. Changes in Total Sequence Time (TST), Total Error (TE), and acquired knowledge of the 10-item sequence order quantified sequence learning and were assessed using a two-way mixed ANOVA with repeated measures (p ≤ 0.05). A significant group-by-time interaction indicated only the AC group maintained their performance improvements when the sequence was perturbed. Overall, improvements in TST on day 1 and day 2 were consistent with all groups acquiring task-general and sequence-specific knowledge. TE outcomes suggested no speed-accuracy tradeoff. On Day 2, all groups performed best in the no-sound condition, indicating performance was maintained when sound cues or feedback were removed. All groups acquired equivalent implicit motor sequence knowledge regardless of sound condition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".