Representation in activated long-term memory is not sufficient to induce an attentional control setting
Bibliographic record
Abstract
Recent work in our lab has shown that participants can adopt an attentional control set (ACS) for 30 visual objects, indicating that the contents of ACSs can be stored in long-term memory (LTM). It has been suggested that ACS representations may be stored with greater than normal baseline activation—a state referred to as activated LTM (ALTM)—however, it has yet to be directly tested whether representing an object in ALTM is sufficient to induce an ACS for that object. In the present study, we induce participants to represent objects in ALTM using a working memory change detection task similar to Oberauer's (2001, JEP:LMC) modified Sternberg task, and test whether objects represented in ALTM form an ACS using a spatial blink task. Participants were presented with two sets of objects (set sizes 1 or 3 for each set) for retention in working memory, and were cued that one set was irrelevant. Following the cue, mixed across trials we either probed participants' memory to assess the state of representation of irrelevant items, or used a spatial blink task to assess whether irrelevant items capture attention. On working memory trials, we found the number of relevant objects affected response times (RTs), but the number of irrelevant objects did not; this suggests that participants successfully transferred irrelevant objects out of working memory. Irrelevant objects produced an intrusion effect (slower rejection of irrelevant probes than novel probes), indicating that they were represented in ALTM. Critically, on spatial blink trials, irrelevant items presented as distractors did not impair performance, indicating that irrelevant objects were not part of participants' ACS. These results support the conclusion that representing objects in ALTM is not sufficient to induce an ACS for those objects. More broadly, the present findings enhance our understanding of how long-term memory and visual attention interact. Meeting abstract presented at VSS 2018
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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 | 0.004 |
| 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 | 0.001 | 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".