More than a surprise: The bivalency effect in task switching
Bibliographic record
Abstract
When switching tasks, if stimuli are presented that contain features that cue two of the tasks in the set (i.e., bivalent stimuli), performance slowing is observed on all tasks. This generalized slowing extends to tasks in the set which have no features in common with the bivalent stimulus and is referred to as the bivalency effect. In previous work, the bivalency effect was invoked by presenting occasionally occurring bivalent stimuli; therefore, the possibility that the generalized slowing is simply due to surprise (as opposed to bivalency) has not yet been discounted. This question was addressed in two task switching experiments where the occasionally occurring stimuli were either bivalent (bivalent version) or merely surprising (surprising version). The results confirmed that the generalized slowing was much greater in the bivalent version of both experiments, demonstrating that the magnitude of this effect is greater than can be accounted for by simple surprise. This set of results confirms that slowing task execution when encountering bivalent stimuli may be fundamental for efficient task switching, as adaptive tuning of response style may serve to prepare the cognitive system for possible future high conflict trials.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".