Visual working memory and long-term memory attentional control settings: Can we maintain both simultaneously?
Bibliographic record
Abstract
Attentional control settings (ACSs) guide attention in our complex visual environments by determining both which stimuli capture our spatial attention, and which stimuli do not. For example, when searching for a blue shirt, other blue objects will capture attention, but red objects will not. Recent research indicates that humans can maintain a long-term memory (LTM) ACS for 4-30 complex visual objects. Additional recent research indicates that humans can maintain a visual working memory (VWM) ACS for one colour. The purpose of the current experiment was to determine whether it is possible to maintain both an LTM ACS and a VWM ACS simultaneously, such that both kinds of representations are capable of biasing visual spatial attention. Participants memorized and searched for 10 complex visual objects (i.e., the LTM ACS), and on each trial a random colour was presented that participants also memorized and searched for (i.e., the VWM ACS). While searching for the colour and objects, participants completed a modified Posner cueing task designed to measure spatial attentional capture. The results indicate that participants were able to adopt both a VWM ACS and an LTM ACS at the same time, as only cues that matched what participants were currently searching for were able to capture spatial attention. This experiment contributes two important findings: 1) it is possible to maintain both a VWM ACS and an LTM ACS simultaneous, such that both VWM and LTM representations can bias visual spatial attention, and 2) VWM and LTM ACSs operate independently using different resources; if they used the same attentional resources or the same memory resources, it would likely not be possible for both representations to bias visual spatial attentional capture simultaneously.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| 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".