Why doesn’t emotional valence affect subitising and counting in simple enumeration?
Bibliographic record
Abstract
Accurate visual-spatial enumeration involves either the subitising process (for 1–4 items) or the counting process (for larger numbers of items). Although these processes differ, both are thought to involve attentional selection. Many studies show that emotional valence, the negativity or positivity of a stimulus, influences attention and yet Watson and Blagrove found valence had no effect on simple enumeration (enumeration without distractors). To shed light on this surprising finding, we had participants enumerate 1 to 9 dots after viewing emotional scenes, using images from the International Affective Picture System ( IAPS) to manipulate valence and arousal. To ensure valence and arousal categorisations were valid for each participant, we individualised them based on their own ratings. Results indicated that both valence and arousal affected enumeration latencies, with enumeration fastest after positive high arousal images and slowest after negative low arousal images. Disengagement deficits were apparent from slowed enumeration after negative images, but there was no evidence that valence affected the breadth of the attentional focus (no interactions with display area). Despite hints that valence may affect subitising and counting differently (weak trends to a cross-over interaction in RT slopes), no firm conclusions can be made because differences were small (<20 ms/item).
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".