Assessing the Role of Long-Term Memory and Visual Working-Memory Attentional Templates in Guiding Attentional Capture and Decision Making
Bibliographic record
Abstract
When searching the environment for a visual target, observers adopt an attentional template—an internal representation of the target they are searching for. One open area of study in attention research is to understand where these templates are stored and how they guide attentional capture. Across five experiments, participants used long-term memory (LTM) to learn a set of objects with specific colours and were then asked to find the objects amongst new or old distractors. We varied the role of visual working memory (VWM) in this search task by either telling participants which target to search for at the start of each trial (VWM-based template), or by asking them to search for all objects on every trial (LTM-based template). In the first three experiments, we showed that with and without invoking VWM, participants found the targets faster when presented in their memorized colour, and slower when a distracting object matched that colour. It is possible, however, that these effects emerged during post-perceptual processes like decision-making. To test this idea, we used a probe-dot detection paradigm to measure attentional effects separately from those on decision making. This involved briefly presenting a probe at the target’s location on some trials, and having participants indicate if they saw the probe or not. For VWM-based attentional templates, probe RTs were significantly affected by the previously learned colour association, suggesting that LTM indirectly tunes the attentional template in VWM. On the other hand, when search is guided directly by LTM, the effects on search time are likely related to a post-perceptual process rather than attention. Altogether, this work clarifies the interactive roles of VWM and LTM in controlling attentional capture during visual search.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".