Is the n-back task a measure of unstructured working memory capacity? Towards understanding its connection to other working memory tasks
Why this work is in the frame
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Bibliographic record
Abstract
Working memory is fundamental to human cognitive functioning, and it is often measured with the n-back task. However, it is not clear whether the n-back task is a valid measure of working memory. Importantly, previous studies have found poor correlations with measures of complex span, whereas a recent study (Frost et al., 2019) showed that n-back performance was correlated with a transsaccadic memory task but dissociated from performance on the change detection task, a well-accepted measure of working memory capacity. To test whether capacity is involved in the n-back task we correlated a spatial version of the test with different versions of the change detection task. Experiment 1 introduced perceptual and cognitive disruptions to the change detection task. This impacted task performance, however, all versions of the change detection task remained highly correlated with one another whereas there was no significant correlation with the n-back task. Experiment 2 removed spatial and non-spatial context from the change detection task. This produced a correlation with n-back. Our results indicate that the n-back task is supported by faculties similar to those that support change detection, but that this commonality is hidden when contextual information is available to be exploited in a change detection task such that structured representations can form. We suggest that n-back might be a valid measure of working memory, and that the ability to exploit contextual information is an important faculty captured by some versions of the change detection task.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it