Component processes in task switching: cue switch costs are dependent on a mixed block of trials
Bibliographic record
Abstract
People are slower when shifting than repeating tasks (switch cost). A considerable portion of the switch cost is due to the possibility of a shift in a mixed block where switches are possible (mixing cost), and to processing a cue that signals a task change (cue switch cost). We use an online sample (n = 12,533) and double cuing paradigm to examine the independent and interactive effects of cue switch costs and mixing costs. All effects were significant, with medium effects for cue changes (ηp2 = 0.06) and task changes (ηp2 = 0.10), a large effect for block context (ηp2 = 0.37), and a small block by cue interaction (ηp2 = 0.04) indicating that the role of the cue depends on the possibility of a switch. These findings offer empirical completeness by measuring the cue change in both blocks of a switching paradigm. The integrative approach quantifies how separable empirical components contribute to the overall switch cost.
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 | 0.004 | 0.046 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".