Statistical Learning Ability as a Measure of Cognitive Function
Bibliographic record
Abstract
Herff, S. A., Nur, A., Lee, J., Lee, T., & Agres, K. (2019). Statistical Learning Ability as a Measure of Cognitive Function. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society, Montreal, QB: Cognitive Science Society. Statistical Learning (SL), the ability to extract probabilistic information from the environment, is a subject of much debate. It appears intuitive that such a profound mechanism of learning should carry predictive power towards general cognitive ability. Yet, previous attempts have struggled to link SL ability to measures of general cognitive function, suffering from low correlations and mediocre test-retest reliability. Here, we deploy a new continuous auditory SL task that achieves high test-retest reliability (~ r = .8) and shows that SL ability does significantly correlate with general cognitive function (up to r =. 56). Results are discussed in light of i) the theoretical implications of the high test-retest reliability of our novel SL task, ii) SL ability as a marker of general cognitive function, and iii) future methodological considerations. -This manuscript has been peer-reviewed and accepted for publication in poster-from at the 41st Annual Meeting of the Cognitive Science Society in Montreal, Canada at the Palais des Congrès de Montréal on Wednesday July 24th – Saturday July 27th, 2019 -
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".