Sequence preparation is not always associated with a reaction time cost
Bibliographic record
Abstract
Abstract The extent to which a sequence of movements is prepared before initiating the first movement is a longstanding question in motor neuroscience. The observation that reaction time (RT) increases for longer sequences has been used as evidence of sequence preparation – reflecting the additional demands of preparing multiple movements before initiating a sequential action. However, many processes contribute to RT, making it unclear whether the observed RT increases specifically reflect sequence preparation. For example, with longer sequences, participants face greater ambiguity in selecting their first movement in the sequence. Here, we test how much of the observed RT increases can be explained by the first-target ambiguity when reaching toward spatial targets. In our paradigm, we independently manipulate: (i) the number of future targets displayed, (ii) the number of targets to be acquired, and (iii) the spatial arrangement of the targets. This approach allows us to vary the demands of sequence preparation and first-target ambiguity, thereby enabling a direct assessment of their respective influence on RT. We report that RT increases with additional sequence elements but that this effect is fully explained by the ambiguity in selecting the first reach target. That is, sequence preparation causes no RT increase. In fact, when first-target ambiguity is eliminated, RT is constant across the number of displayed targets even though kinematic analysis reveals that participants have prepared a sequence. Together, these results indicate that preparing multiple reaches to spatial targets does not impose additional temporal costs relative to preparing a single reach.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".