Investigating visual working memory capacity using a highly reliable change localization task
Bibliographic record
Abstract
In change detection tasks, observers are asked to store a handful of items in working memory, and then subsequently indicate whether one of those items has changed in a test display. This task provides highly reliable estimates of working memory capacity, and exhibits robust correlations with outcome variables of interest, such as fluid intelligence. Here, we present a variant of this task, called change localization. This task closely resembles change detection, except that an item changes on every trial, and the observer’s task is to indicate which item changed. Using both color and shape stimuli, we show that performance on this task is strongly correlated with change detection performance, suggesting that change localization measures the same aspects of working memory ability. Moreover, change localization scores achieve high reliability with less than half of the trials required with change detection. Likewise, far fewer trials were required to detect known empirical effects, such as the impact of larger set sizes and the correlation between working memory capacity and the harmful effects of overload. Thus, change localization provides a highly reliable and efficient method for obtaining precise estimates of working memory performance. Finally, we will discuss data from an ongoing study in which we examine the consequences of stimulus heterogeneity on storage in visual working memory. While some theorists have proposed that working memory storage should be easier with heterogeneous displays, homogeneous displays may reduce competitions within cellular assemblies coding for a specific feature. Here we aim to test the hypothesis using the change localization paradigm. Specifically, we are interested in whether participants exhibit different capacity estimates between homogeneous arrays that contain only one type of feature (color or shape) or heterogeneous arrays that include both color and shape memoranda.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".