Investigating task and modality switching costs using bimodal stimuli
Bibliographic record
Abstract
Investigations of concurrent task and modality switching effects have to date been studied under conditions of uni-modal stimulus presentation. As such, it is difficult to directly compare resultant task and modality switching effects, as the stimuli afford both tasks on each trial, but only one modality. The current study investigated task and modality switching using bi-modal stimulus presentation under various cue conditions: task and modality (double cue), either task or modality (single cue) or no cue. Participants responded to either the identity or the position of an audio–visual stimulus. Switching effects were defined as staying within a modality/task (repetition) or switching into a modality/task (change) from trial n − 1 to trial n, with analysis performed on trial n data. While task and modality switching costs were sub-additive across all conditions replicating previous data, modality switching effects were dependent on the modality being attended, and task switching effects were dependent on the task being performed. Specifically, visual responding and position responding revealed significant costs associated with modality and task switching, while auditory responding and identity responding revealed significant gains associated with modality and task switching. The effects interacted further, revealing that costs and gains associated with task and modality switching varying with the specific combination of modality and task type. The current study reconciles previous data by suggesting that efficiently processed modality/task information benefits from repetition while less efficiently processed information benefits from change due to less interference of preferred processing across consecutive 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".