A guess today may be a strategic error tomorrow: Predicting intra-individual differences in visual working memory
Bibliographic record
Abstract
Inter-individual differences in visual working memory (VWM) are well documented, however, there has been less work exploring intra-individual differences or how VWM processes may fluctuate over time in healthy young adults. It has also been documented that VWM is impacted by changes in female sex hormones in menopausal woman, however, these hormones also fluctuate across the monthly cycle in young woman, granted to a lesser extent. Given the high prevalence of female participants in the majority of VWM studies, understanding whether there are in fact intra-individual changes in VWM performance over the monthly cycle may not only provide a novel contribution to the discussion of fixed vs. flexible capacities in VWM, but may also help explain unreplicable results or inconsistencies in the literature. Here, we compared performance on a VWM colour-wheel task in women at two time-points (at menstruation and ovulation). Employing the 3-component swap model of VWM (Bays, Catalao, & Husain, 2009), we estimated measures of probability of target, non-target (swaps between target and non-target items), and guess responses, addressing the question of whether VWM performance varies over time within individuals and whether this is due to changes in the number of target responses or the types of errors made. Interestingly the probability of target response did not differ between the two time-points, however, the types of errors did. When VWM is at capacity (load 4 and 6) women made more swap errors at ovulation and more guesses at menstruation. These results suggest that the amount of information held in VWM may increase at ovulation but this results in more interference, with no concomitant increase in target responses. These results reveal the presence of intra-individual differences in VWM performance (types of errors made) that follow cyclical patterns in the monthly hormone cycle in young woman. Meeting abstract presented at VSS 2015
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".