Digit magnitude does not influence the spatial parameters of goal-directed reaching movements
Bibliographic record
Abstract
Movement times are advantaged when numerical magnitude is used to prompt the initiation of a goal-directed reaching response. In particular, movement times in left and right visual space are reported to be faster when respectively paired with smaller (i.e., 1, 2) and larger (i.e., 8, 9) digits (Fisher 2003: Vis Cogn). In other words, the well-documented spatial numerical association of response codes (the so-called SNARC effect) can be extended to the movement domain. The present study sought to determine whether the SNARC effect differentially influences not only the temporal properties of a reaching response, but also the spatial properties of the unfolding trajectory. To accomplish this objective, participants completed left and right space reaches following movement cuing via numerical stimuli (i.e., 1, 2, 8, or 9). Importantly, placeholders were used to denote the amplitude of the reaching response and were either continuously visible to participants (Experiment 1) or occluded prior to movement onset (Experiment 2). Results for Experiments 1 and 2 elicited a SNARC effect for reaction time; that is, smaller and larger digits produced faster response latencies when used to cue left and right space reaches, respectively. In terms of movement time, Experiment 1 yielded a reversed SNARC effect: reaches were completed faster to larger and smaller digits in left and right space, respectively. For Experiment 2, movement times were not influenced by digit magnitude and the direction of the reaching response. Further, spatial analysis of movement trajectories (Experiments 1 and 2) did not yield reliable interactions between digit magnitude and reaching direction. In general, our results support the assertion that numerical magnitude influences the planning of a response, but does not reliably influence the temporal or spatial parameters of the unfolding reaching trajectory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".