The Effect of Emotion on Processing Distractor Items in a Visual Working Memory Task
Bibliographic record
Abstract
Emotion (whether positive or negative) has been found to influence both attention and memory-related processes. Xie and Zhang (2016) found that negative affect in particular increases the resolution of information that is stored in visual working memory (VWM). However, it remains unclear whether affect acts directly upon the quality of these representations, or if it instead impacts earlier attentional processes, such as attentional selection and breadth, which have been shown to be affected by affective states. The current study investigated the relationship between affect and attentional breadth by measuring the likelihood that nearby stimuli would influence a target judgment (i.e. non-target errors). Participants were first shown an image that was positive, negative, or neutral in valence on every trial. They then completed a continuous report VWM task wherein they remembered 2 or 4 colored shapes over a short delay, then recalled one shape's color from a color wheel. A three-component mixture model analysis (Bays, Catalao, & Husain, 2009) was used to estimate the proportion of target, non-target, and guess responses made in each condition. It was found that negative and positive affect differentially affected the likelihood of making non-target errors, such that less non-target errors were made in the negative affect condition. The present findings suggest that emotion influences attentional control, which is consistent with theories suggesting that VWM performance is linked to attentional selection during encoding. Meeting abstract presented at VSS 2017
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".