Spatial Response Discrimination May Elicit a Simon Effect on a Non-Complementary Task
Bibliographic record
Abstract
When paired participants are each assigned a complementary half of the Simon task, a joint Simon effect (JSE) has been observed. Co-representation, a cognitive representation of not only one's own task but also that of the co-actor, has been one of several proposed mechanisms in the JSE. Using the response-discrimination hypothesis as a framework, we tested whether it was sufficient to highlight alternative task keys in a two-person setting in which a non-complementary task was completed to elicit a Simon effect (SE). In our design, the participant's role was to perform the Go/No-Go Simon task and the co-actor's role was to initiate each trial for the participant. In one two-person setting participant group (SK group), the same task key was assigned to both the participant and the co-actor; another group (OK) was assigned spatially opposite task keys. In a third group (joint setting, TS group), the standard joint Simon task was also completed to verify that a JSE could be replicated. We hypothesized that an SE would be elicited in the OK group, since opposite task keys would uniquely promote spatial coding. We found a weak but marginally significant SE in the OK group but not in the SK group. These results suggest that, on a non-complementary task, response discrimination may contribute to the emergence of a SE in a two-person setting, while it does not have the same impact as a complementary task completed in a joint setting (TS group) that may afford more robust response representations that reveal the enhanced so-called JSE.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".