The influence of verbal and spatial working memory load on the time course of the Simon effect.
Bibliographic record
Abstract
The Simon effect refers to the relatively poorer response times and accuracy when responding to targets that appear in a task-irrelevant spatial location that is incongruent with the location of the correct response key, compared with targets that appear in spatially congruent locations. Like Stroop and flanker effects, the Simon effect is thought to result from conflict between an irrelevant response tendency and an intended response. Because attentional control has been linked to conflict resolution, the Simon task has been proffered as a possible tool for measuring the efficacy of executive control mechanisms. These mechanisms are also involved in working memory (WM) processes, and are thought to be responsible for maintaining information in the presence of continued processing or distraction. The present study investigated the interface between WM and attention by examining the time course of the Simon effect over the response time distributions under varying WM load conditions. Participants completed verbal 0-back, spatial 0-back, verbal 2-back, and spatial 2-back tasks. Results show that the Simon effect is diminished in high WM load tasks compared with low-load tasks, and that the Simon effect interacts with the spatial task domain such that the effect persists across the distribution of response times. In contrast, the Simon effect peaks and decays in verbal tasks. The results demonstrate that the Simon effect interacts with WM load and task domain. The results suggest that the effect is more modifiable than expected, and support a complex interface between WM and attentional control.
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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 | 0.000 | 0.007 |
| 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".