Attention to different statistical structures changes over the course of learning
Bibliographic record
Abstract
Previous studies have shown that attention allocation can be determined by the statistical structure of our visual environments: infants attend to moderately predictable input over highly predictable or highly unpredictable input (Kidd et. al., 2012), and adults attend to regular over irregular stimuli (Zhao et. al., 2013). While these results show learners are sensitive to how predictable different input is, no study to date has directly examined how attention to differently structured input shifts as a function of experience. Moreover, learners may be able to extract more or less information from a particular part of their world at any given moment, but it remains unknown whether we flexibly shift attention based on how much information could be gained from a particular stimulus. Here, we had adults (n=75) complete a visual statistical learning experiment in which streams of information were presented simultaneously in four locations, in four levels of predictability: (1) completely random, (2) low predictability, (3) medium predictability, and (4) completely predictable. Intermittent search trials measured where participants attended over the course of the experiment by indexing reaction times in each location. We modeled trial-by-trial entropy in each location to measure how much information could be gained from that location at any given point during learning. Our results show that as the experiment progressed, participants shifted from attending to medium predictability location to attending to lower levels of regularity locations (low predictability and random stream). Additionally, trial-by-trial entropy and time interacted strongly to predict where participants attended, such that over the course of learning higher entropy locations were attended more. This provides the first demonstration that as adults learn the environmental regularities, they gradually shift their attention to less predictable sources of information, and that learners are sensitive to how much information they can gain from a particular source.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".