Motor effort predicts memory use in active visual search
Bibliographic record
Abstract
The role of memory for target locations in visual search has been the subject of considerable research, with important implications for naturalistic search – wherein target and distractor configurations are typically quite stable over time. In previous research, the authors have provided evidence that the use of memory during search is at least partly dependent on the difficulty of search (Solman & Smilek, 2012). In particular, it is proposed that as purely random (or ‘brute force’) search becomes more costly, searchers are more likely to use memory. This results in more robust effects of memory on search times and efficiencies when comparing search through repeated versus non-repeated displays. In the present work, we extend these findings to the embodied realm. Contrasting eye-driven and head-driven search, we examine search performance in repeated and non-repeated displays, evaluating RTs, slopes, and early orienting performance. In the eye-driven condition, participants searched via a gaze-contingent window with head position fixed. In the head-driven condition, we used motion-tracking equipment to produce a head-contingent window. By using a large screen and holding position constant, all stimulus dimensions including window size were matched across conditions with respect to visual angle. Comparing these conditions tests the hypothesis that there will be enhanced memory effects in head-driven search, extending the difficulty-dependent memory use hypothesis to the realm of physical / energetic cost. The results are particularly important in light of the necessary recruitment of multiple motor systems during naturalistic search, and suggest that caution is warranted when interpreting higher-order cognitive influences on search using only eye-movements. 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 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 | 0.001 |
| 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".