The Flexibility of Episodic Long-Term Memory-Guided Attention and the Impact of Reinstating Context
Bibliographic record
Abstract
While it may seem that salient visual events, like the flashing lights on an ambulance, can automatically capture our attention, capture is actually under our control. Depending on our current internal goals, we adopt attentional control settings (ACSs) that specify what stimuli in the environment capture our attention. It has been shown that ACSs can be defined based on long-term episodic memory representations. For example, when searching for the items on your grocery list, an ACS can be specified based on your long-term memory of the list, such that your attention will be drawn to those items, and only those items. Importantly, episodic memories incorporate contextual information that can enhance recall when reinstated (e.g., you will remember your grocery list better if it was memorized at the grocery store rather than at home). Here we asked whether reinstating context can enhance the establishment of long-term memory ACSs. Participants memorized two sets of 15 images of objects in a particular context (i.e., a coloured box in a particular spatial location), that they then searched for, inducing an episodic-based ACS for those objects. During the search task, this encoding context was either reinstated, or not. We found that individuals are able to flexibly switch between ACSs and sources of information. However, we did not find sufficient evidence for the effect of context on the establishment of ACSs or their flexibility. This study extends our understanding of the factors that influence memory-guided attention, and the impact of contextual reinstatement on the formation of ACSs.
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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 | 0.001 |
| 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".