The effects of statistical learning and congruency on the development of multi-modal objects
Bibliographic record
Abstract
The effects of statistical learning and congruency on multi-modal binding were examined. As pattern acquisition is stronger for within-object than for between-object associations, extending bias from within-object to within-modality was tested, and the statistical learning effect on between-modality learning assessed. Dyson and Ishfaq‟s (2008) paradigm was adapted, with frequency of within- and between-modality associations manipulated (Experiment 1), and frequency and congruency manipulated (Experiment 2). Each experiment comprised baseline (no predictive value), intra-modal (intramodal predictive value), and inter-modal (intermodal predictive value) conditions. Experiment 1 showed faster performance for within-object judgments, and fewer errors on within-object judgments, excluding the inter-modal condition. Experiment 2 replicated this, with cross-experimental analyses showing weak congruency effects. Data showed probability manipulations led mostly to interference on same-modality trials rather than facilitation on different-modality trials, suggesting while frequency of differentmodality associations did not facilitate superior performance, perhaps expectancies of frequent different-modality associations weakened sensitivity to the within-modality bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".