Attention improves precision while short-term memory load increases guessing
Bibliographic record
Abstract
In an orientation judgement task Liu and Becker (2013) demonstrated that working memory load affected participants' guesses, but not response precision. As attention has previously been shown to affect the precision of orientation judgements (Anderson & Druker, 2013), we were interested in investigating the effect of attention and visual short-term memory together in the same task, and with varying memory demands and cue reliabilities. Experimentally, two gabors at different orientations briefly appeared. Participants rotated a centrally presented response gabor to match one of the indicated target gabors. Attention was manipulated by a brief luminance cue and memory by presenting the two possible targets simultaneously or sequentially. Increased memory load increased the proportion of trials on which participants guessed. When attentional cues were informative, the precision of responses improved. When the cue was not informative, participants made fewer guesses on valid trials compared to invalid trials. Precision was unaffected by a non-informative spatial cue. Our results provide evidence that under conditions of reduced target uncertainty, attention improves precision because resources do not need to be divided across two stimuli, whereas when two gabors are equally probable targets, attention improves the likelihood that the stimuli will be encoded into memory. For both informative and non-informative cues, the cueing effect was larger on simultaneous compared to sequential trials, providing evidence that attention also serves to reduce competition from irrelevant stimuli. Meeting abstract presented at VSS 2014
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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".