No role for activated long-term memory in attentional control settings.
Bibliographic record
Abstract
Visual spatial attentional capture is contingent on an observer's goals, or attentional control settings. Recent research has demonstrated that observers can adopt attentional control settings based on numerous visual objects represented in episodic long-term memory (LTM). But why do LTM representations that comprise an attentional control set bias attentional capture, when other LTM representations do not? In the present study, we tested the activated LTM account-that LTM representations form an attentional control set if, and only if, they are represented in activated LTM-by mixing a working memory task to test for representation in activated LTM, with a spatial blink task to test for the state of participants' attentional control settings. In Experiments 1 and 2, inducing participants to represent complex visual objects in activated LTM did not result in those objects forming an attentional control set. In Experiment 3, we found a dissociation between activated LTM and attentional control settings; objects that were represented in activated LTM produced greater intrusion effects (indicating representation in activated LTM) than objects that were part of an attentional control set, yet smaller capture effects. These results do not support the activated LTM account. We conclude that representation in activated LTM is not the factor that determines which LTM representations comprise an attentional control set, and discuss the implications of these findings for research on attentional templates and hybrid visual and memory search. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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 | 0.000 | 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".