Individual Differences in Working Memory Capacity and Visual Search While Reading
Bibliographic record
Abstract
Individual differences in working memory capacity are related to variations in a wide range of cognitive tasks. Surprisingly, effects of individual differences in working memory capacity are somewhat limited in visual search tasks. Here we tested the hypothesis that such an effect would be robust when search was one component of a dual task. Participants were presented strings of letters using rapid serial visual presentation and were required to detect all instances of a particular target letter. In Experiment 1, participants performed the letter search task in three contexts, while: a) reading a prose passage, b) processing a stream of random words, or c) processing a random stream of non-words. In the absence of the dual task of reading prose, and in line with much of the literature on individual differences in working memory capacity and visual search, search performance was unaffected by working memory capacity. As hypothesized, however, higher working memory capacity participants detected more target letters than lower capacity participants in the “true” dual task (searching while reading prose). The hypothesized results from the prose passage were replicated in Experiment 2. These results show that visual search efficiency is dramatically affected by working memory capacity when searching is combined with another cognitive task but not when it is performed in isolation. Our findings are consistent with recent suggestions that visual search efficiency will be affected by working memory capacity so long as searching is embedded in a context that entails managing resource allocation between concurrent tasks.
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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) | 0.000 | 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 | 0.000 |
| 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".