Reconsidering the Automaticity of Visual Statistical Learning
Bibliographic record
Abstract
Statistical learning refers to the process of extracting regularities from the world without feedback. What are the necessary conditions for statistical learning to arise? It has been argued that visual statistical learning (VSL) is “automatic”, such that subjects will passively and even unconsciously extract statistical regularities from streams of visual input as long as they attend to the stimuli. In contrast, our data indicate that simply attending to stimuli is not, on its own, sufficient for learning. In Experiments 1 & 2, we provided incidental exposure to regularities in a stream of images and observed little to zero VSL across a range of conditions. In Experiment 3, we found that explicitly instructing participants to seek regularities dramatically improved their performance on direct measures of learning, but not on an indirect response time measure. Finally, in Experiments 4 & 5, we demonstrated that a methodological confound in prior work using the indirect response time measure could account for some previous evidence of automatic and implicit VSL.Overall, we found very little evidence of learning using direct measures of VSL, and no evidence of learning using an indirect response time measure. Participants who recognized visual sequence regularities in a forced-choice task could also often recreate the sequences when explicitly probed, indicating their knowledge was not entirely implicit. We suggest that some form of active engagement with stimuli may be needed to extract sequential regularities, and that VSL does not occur automatically.
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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 | 0.004 | 0.031 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".