Implicit prediction as a consequence of statistical learning
Bibliographic record
Abstract
The sensory input that we encounter while navigating through each day is highly structured, containing patterns that repeat over time. Statistical learning is the process of becoming attuned to these patterns and can facilitate online processing. These online facilitation effects are often ascribed to prediction, in which information about an upcoming event is represented before it occurs. However, previously observed facilitation effects could also be due to retrospective processing. Here, using a speech-based segmentation paradigm, we tested whether statistical learning leads to the prediction of upcoming syllables. Specifically, we probed for a behavioural hallmark of genuine prediction, in which a given prediction benefits online processing when confirmed, but incurs costs if disconfirmed. In line with the idea that prediction is a key outcome of statistical learning, we found a trade-off in which a greater benefit for processing predictable syllables was associated with a greater cost in processing syllables that occurred in a "mismatch" context, outside of their expected positions. This trade-off in making predictions was evident at both the participant and the item (i.e., individual syllable) level. Further, we found that prediction did not emerge indiscriminately to all syllables in the input stream, but was deployed selectively according to the trial-by-trial demands of the task. Explicit knowledge of a given word was not required for prediction to occur, suggesting that prediction operates largely implicitly. Overall, these results provide novel behavioural evidence that prediction arises as a natural consequence of statistical learning.
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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 | 0.009 |
| 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".