Intelligence as it relates to conscious and unconscious memory influences.
Bibliographic record
Abstract
We examine the relationship between a measure of intelligence and estimates of conscious and unconscious memory influences derived using Jacoby's (Jacoby, L. L. [1991]. A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, 513-541.) process-dissociation procedure. We find a positive relationship between intelligence and conscious memory, and no relationship between intelligence and unconscious influences once the impact of conscious influences are removed (Experiment 1). We also find that when participants cannot engage in conscious strategies, such as when there is insufficient time for learning, the relationships observed in Experiment 1 are eliminated (Experiments 2A and 2B). Our results support the notion that individual differences in intelligence reflect differences in conscious strategic processes (Karis, D., Fabiani, M., & Donchin, E. [1984]. "P300" and memory: Individual differences in the von Restorff effect. Cognitive Psychology, 16, 177-216.) and not differences in mental speed (Eysenck, H. J. (1984). Intelligence versus behavior. The Behavioral and Brain Sciences, 7, 290-291; Jensen, A. R. [1982]. Bias in mental testing. New York, NY: Free Press).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".