Is There One “Beam” of Attention for Searching in Space and Time?
Bibliographic record
Abstract
In their pure forms, searching in space entails the allocation of attention to items distributed in space and presented at the same time whereas searching in time entails the allocation of attention to items distributed in time and presented at the same location. In two quite independent projects we have explored whether the metaphorical “beams” operating the domains of space and time might be independent or the same. In one project we used a differential approach. Early research using spatial (Snyder, 1972) and temporal search tasks (McLean, Broadbent & Broadbent, 1983) reported a substantial degree of sloppiness (binding errors). We had participants perform both of these tasks to see if the frequency of these binding errors in the domains of space and time might be correlated. We replicated both early findings of binding errors in space and in time, but their frequency of occurrence in the two domains was not significantly correlated. In the other project, we used an experimental approach. Here we explored whether the principles described by Duncan & Humphreys (1989; hereafter D&H) for searching in space would apply similarly to searching in time. Not surprisingly, performance in spatial search conformed to the predictions of D&H's principles. Importantly, temporal search performance followed the same pattern, suggesting that D&H’s principles are indeed generalizable to temporal search. We will speculate on why these two approaches seem to yield different answers to the question posed in our title.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.019 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".