How Attentional Guidance and Response Selection Boost Contextual Learning: Evidence from Eye Movement
Bibliographic record
Abstract
Crowding refers to the phenomenon of increased difficulty in identifying a peripherally presented stimulus when it is surrounded by adjacent flankers compared to when it is presented in isolation. The aim of the present study was to investigate the influence of flanker and target emotion on crowding effects. Therefore, Landolt rings with certain opening directions were conditioned with neutral or negative pictures. These conditioned stimuli were afterwards used in a visual crowding task, in which the critical spacing (75% threshold) was assessed for emotional flankers as well as for emotional targets. Larger crowding effects were observed for negatively relative to neutrally conditioned flankers, indicating more interference with negative flankers. Additionally, for participants showing a strong evaluative conditioning effect, smaller crowding effects were found for negatively relative to neutrally conditioned targets, indicating enhanced identification of negative target stimuli. In conclusion, crowding effects are modulated by both flanker and target emotion, suggesting that high-level stimulus features survive crowding and influence recognition performance. The study further shows that evaluative conditioning can be a useful tool to study the effect of emotion on rather early perceptual processes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".