Limits on the contribution of priming to attentional control settings: Evidence from long-term memory control sets.
Bibliographic record
Abstract
An active area in attention capture research is to understand the role of priming in establishing attentional control settings (ACSs). When participants repeatedly select target stimuli across trials of an attention task these items are primed in a bottom-up manner, which may cause attention to be preferentially captured by distracting stimuli that resemble the targets. The present work uses our recent discovery of long-term memory (LTM) ACSs to shed light on the contributions of priming. Studies evaluating priming typically have participants establish ACSs for single features or feature domains, rendering manipulations of priming dependent on changes in the ACSs across trials. This methodology confounds the contributions of priming with the potential costs of switching ACSs. The use of an LTM ACS is valuable because the ACS can consist of multiple targets, allowing participants to maintain a consistent ACS while we manipulate the amount of priming of targets within it. Across two experiments participants memorized a set of 16 (Experiment 1) or 18 (Experiment 2) images of complex, naturalistic visual objects that were then designated as targets in a rapid serial visual presentation (RSVP) task. We have previously shown that only studied items produce an attentional blink (i.e., capture attention) when they appear as distractors, suggesting participants adopt studied-item specific ACSs. In the present experiments we varied the amount of priming each target item received: frequent, infrequent, or no priming. Items that were never primed did not capture attention when presented as distractors. However, as long as items were primed, the frequency of priming (i.e., each target appeared on average every 12th trial versus 36th trial) did not affect the magnitude of capture. Together, these data reveal that although priming may support the establishment of ACSs, there are important limits to its role in the maintenance of ACSs. Meeting abstract presented at VSS 2016
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".