Visual search for multiple targets remains efficient when supported by recollective long-term memory
Bibliographic record
Abstract
We are often faced with the task of searching our visual environment for multiple objects, such as when searching for any one of our friends at Club Vision. To accomplish this search we must retrieve information stored in long term memory (LTM). Surprisingly, searching through memory adds little cost to the efficiency of visual search. That is, visual search response times increase logarithmically with the number of targets stored in LTM (Wolfe, 2012, Psychol Sci): It doesn’t take much longer to search for sixteen friends than it does to search for eight. In this previous investigation, memorized targets were both perceptually identical to the visual search targets, and repeated frequently throughout the search task on target present trials. These conditions are conducive to a familiarity-based LTM process, which may have driven the efficiency of search. In the present study, we evaluated whether LTM-supported search remains efficient when based on flexible, abstract memory representations (i.e., recollection, rather than simple judgements of familiarity). Participants memorized 1, 2, 4, 8 or 16 targets, counterbalanced across blocks, and searched for these targets amidst twelve on-screen distractors. Experiment 1 used predominately target-absent trials and blocks terminated after the first successful target-present trial, ensuring that familiarity did not accrue over repeated presentations of the target. In Experiment 2, this task was extended by having participants memorize written object names and visually search for pictures of those objects, thereby eliminating perceptual familiarity. Despite these restrictions on the contribution of familiarity, we continued to see the efficient logarithmic relationship between visual search time and memory set size. These data suggest that interactions between visual search and LTM search are not completed solely based on familiarity; rather, participants can engage a recollection-based LTM process, calling upon flexible memory representations to support efficient visual search, even for previously unobserved information. Meeting abstract presented at VSS 2013
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".