The Influence of Task-Irrelevant Spatial Regularities on Statistical Learning
Bibliographic record
Abstract
Research into statistical and sequence learning has demonstrated that we are sensitive to the statistical properties of events and can learn to approximate the probability of their occurrences. Research has also demonstrated that the spatial properties of an event can influence our perception of its non-spatial features, even if the spatial features are not relevant to the task itself. The goal of the current research was to test whether task-irrelevant spatial features could influence our ability to learn the regularities associated with non-spatial events. Using a computerized version of the children’s game ‘rock’-‘paper’-‘scissors’ (RPS), undergraduates were instructed in two separate experiments to win as often as possible against a computer that played varying RPS strategies. For each strategy, the computer’s plays were either presented 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 screen locations). Results showed that, although irrelevant to the task itself, spatial regularities had a moderate influence when learning to play against easy strategies (Experiment 1 and 2a), and a more pronounced influence when learning to play against harder strategies (Experiment 2b). When exposed to harder strategies, we also found that, in addition to improving learning, participants were also able to detect switches in the computer’s strategies more readily when spatial regularities were evident. Our results suggest that task-irrelevant spatial features can improve statistical learning, especially when the regularities of task relevant non-spatial features are difficult to learn. Meeting abstract presented at VSS 2013
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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".