Learning what from where: Effects of Spatial Regularity on Nonspatial Sequence Learning and Updating
Bibliographic record
Abstract
The current study examined the influence of redundant stimulus features on our ability to build and update representations of our environment. We hypothesized that our ability to process redundant spatial features would speed our ability to adapt to changing nonspatial regularities. Using a computerized version of the children's game "rock-paper-scissors", undergraduates were instructed to win as often as possible against a computer opponent. The computer's plays were repeating sequences of five choices that were presented either with spatial regularity (i.e., "rock" would always appear on the left, "paper" in the middle, and "scissors" on the right) or without spatial regularity (i.e., the items were equally likely to appear in any of the three locations). Once participants learned a sequence, the computer switched to a different sequence without participants being informed that a switch had occurred. Redundant spatial regularity improved a participant's ability both to learn sequences of plays and to update their plays to reflect new computer sequences. Our results suggest that our perceptual system is sensitive to redundant spatial stimulus features and that this information can improve learning and updating.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".