Attentional flexibility during approach and avoidance motivational states: The role of context in shifts of attentional breadth.
Bibliographic record
Abstract
In the present studies, we aimed to understand how approach and avoidance states affect attentional flexibility by examining attentional shifts on a trial-by-trial basis. We also examined how a novel construct in this area, task context, might interact with motivation to influence attentional flexibility. Participants completed a modified composite letter task in which the ratio of global to local targets was varied by block, making different levels of attentional focus beneficial to performance on different blocks. Study 1 demonstrated that, in the absence of a motivation manipulation, switch costs were lowest on blocks with an even ratio of global and local trials and were higher on blocks with an uneven ratio. Other participants completed the task while viewing pictures (Studies 2 and 3) and assuming arm positions (Studies 2 and 4) to induce approach, avoidance, and neutral motivational states. Avoidance motivation reduced switch costs in evenly proportioned contexts, whereas approach motivation reduced switch costs in mostly global contexts. Additionally, approach motivation imparted a similar switch cost magnitude across different contexts, whereas avoidance and neutral states led to variable switch costs depending on the context. Subsequent analyses revealed that these effects were driven largely by faster switching to local targets on mostly global blocks in the approach condition. These findings suggest that avoidance facilitates attentional shifts when switches are frequent, whereas approach facilitates responding to rare or unexpected local stimuli. The main implication of these results is that motivation has different effects on attentional shifts depending on the context.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".