Expectations Versus Reality: The Effect of Semantic Knowledge on Statistical Learning
Bibliographic record
Abstract
Throughout our daily lives, we are constantly perceiving a flow of visual information. Previous research has shown that we extract frequencies and similarities (i.e., statistical learning) to help us understand and organise incoming information. Statistical learning can be used to increase efficiency in visual search tasks (Jiang & Sisk, 2019), but it is unclear how that learning is affected by prior knowledge about the world. In the present study, we investigated the effect of semantic knowledge on statistical learning. The experiment consisted of a learning and memory phase. In the learning phase, 129 participants were presented with four objects on a blank background (one per quadrant) and were asked to search for a target object, cued by its picture. Targets appeared in either high or low probability locations (80% or 20% of trials, respectively). Of theoretical interest, targets were placed in either semantically consistent (e.g., basketball net in upper quadrants) or inconsistent locations (e.g., chandelier in lower quadrants). In the memory phase, participants indicated where each target most often appeared (i.e., the high probability location). We found that participants had slower response times for low compared to high probability locations, which supports previous research. Critically, we found a significant interaction, where participants responded faster overall to targets in semantically consistent than inconsistent locations. These results indicated that where these objects typically appear in the world influenced learning, even when displayed without context. In the memory phase, we found that although accuracy was high for both, it was significantly higher for consistent (87%) than inconsistent (84%) high probability locations. Overall, these findings suggest that learning does not occur in a vacuum. Despite the short learning window, semantic knowledge had a significant influence on learning and performance.
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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 | 0.009 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".