Examining the role of retrieval processes in set-alternation costs
Bibliographic record
Abstract
The goal of the experiments was to evaluate an explanation of set-alternation costs based on episodic memory principles. The assumption is that performance of any task is a consequence of memory retrieval processes that involve representations of specific prior experiences (Kolers, 1976; Leboe, Whittlesea, & Milliken, 2005; Neill & Mathis, 1998; Tenpenny, 1995; Whittlesea, 1997; Whittlesea & Jacoby, 1990). When the Event 1 and 3 targets mismatch the retrieval of the Event 1 memory episode is not entirely appropriate for performing the Event 3 task. The interference due to a partial match between Events 1 and 3 might be the source of set-alternation costs. Results of Experiment 1 revealed larger costs in the high probability set-alternation condition. The high probability set-alternation condition encouraged retrieval of Event 1. However, because the targets of Event 1 and 3 mismatched the retrieval of Event 1 interfered with the processing of Event 3’s task-set. In other words, the interference due to a match in task-sets but a mismatch in targets generated costs. If set-alternations costs originate from a partial match between Events 1 and 3, increasing the amount of overlapping information between these events should reduce costs. The findings of Experiments 2 and 3 showed reduced set-alternation costs when there was a target identity match between Events 1 and 3. Lastly, Experiment 4 showed that set-alternation costs are larger when the retrieval of the Event 1 memory episode is obstructed. That is, costs were larger when there was a combination of obstructed Event 1 retrieval and a partial match between Events 1 and 3.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".