An Eye Tracking Study of the Time Course of Attention to Positive and Negative Images in Dysphoric and Non-dysphoric Individuals
Bibliographic record
Abstract
Researchers studying selective attention in depressed and dysphoric individuals have documented biases in the allocation of attention to emotional information (Gotlib & Joormann, 2010; Yiend, 2010). Recent studies using eye gaze tracking have shown that when images are presented for extended durations (5–30 seconds), depressed and dysphoric individuals attend to depression-related images more than never depressed individuals and attend to positive images less (Armstrong & Olatunji, 2012). The present study used eye gaze tracking and time course analyses to look for differences between dysphoric and non-dysphoric individuals in their attention to emotional images over time. Participants viewed sets of four images (a depression-related image, a threat-related image, a positive image, and a neutral image) while their eye fixations were tracked and recorded throughout a 10-second presentation. The time course analyses, which divided each 10-second presentation into 2-second intervals, revealed that group differences in attention to positive and depression-related images emerged only after 4 seconds had elapsed and then persisted for the remainder of the 10-second presentation. Dysphoric and non-dysphoric participants were further distinguished by the temporal profiles of their attention to positive and depression-related images. The implications for researchers' understanding of attention to emotion in dysphoria and depression are discussed.
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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 | 0.001 |
| 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 | 0.000 |
| 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".