Examining the influence of working memory on updating mental models
Bibliographic record
Abstract
The ability to accurately build and update mental representations of our environment depends on our ability to integrate information over a variety of time scales and detect changes in the regularity of events. As such, the cognitive mechanisms that support model building and updating are likely to interact with those involved in working memory (WM). To examine this, we performed three experiments that manipulated WM demands concurrently with the need to attend to regularities in other stimulus properties (i.e., location and shape). That is, participants completed a prediction task while simultaneously performing an n-back WM task with either no load or a moderate load. The distribution of target locations (Experiment 1) or shapes (Experiments 2 and 3) included some level of probabilistic regularity, which, unbeknown to participants, changed abruptly within each block. Moderate WM load hampered the ability to benefit from target regularities and to adapt to changes in those regularities (i.e., the prediction task). This was most pronounced when both prediction and WM requirements shared the same target feature. Our results show that representational updating depends on free WM resources in a domain-specific fashion.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".