Bibliographic record
Abstract
The purpose of this study was to determine whether Anderson’s ACT-R fan model could account for the fan effect under more complex conditions. Specifically, overlapping datasets were used so that related facts learned in one experiment could potentially affect the fan in other experiments. The study of the overlapping datasets made it possible to study an inference task by combining facts learned in separate experiments. Four experiments with human subjects were carried out and human performance was compared to predictions from Anderson’s ACT-R fan model. The results showed ACT-R fan model could be used as a basic building block for explaining complex fan tasks (some high-level cognitive tasks); ACT-R fan model with no adjustments to the parameters provided a reasonable account for human performance across all of the experiments. The results suggest that recently learned related facts have an effect on the fan (Experiment 2). But, it was found that the related facts learned ten months earlier showed no interference due to fan but there was a main learning effect which affected reaction times (Experiment 4). In terms of relational inferences from overlapping datasets, the results indicated a dual retrieval process with additional search process is more consistent with Anderson’s fan model than with Radvansky’s mental models approach or a parallel retrieval approach. Both Radvansky’s mental models approach and a parallel retrieval approach predicted a single retrieval process (Experiment 3).
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".