Cue integration in spatial search for jointly learned landmarks but not for separately learned landmarks.
Bibliographic record
Abstract
The authors investigated how humans use multiple landmarks to locate a goal. Participants searched for a hidden goal location along a line between 2 distinct landmarks on a computer screen. On baseline trials, the location of the landmarks and goal varied, but the distance between each of the landmarks and the goal was held constant, with 1 landmark always closer to the goal. In Experiment 1, some baseline trials provided both landmarks, and some provided only 1 landmark. On probe trials, both landmarks were shifted apart relative to the previously learned goal location. Participants searched between the locations specified by the 2 landmarks and their search locations were shifted more toward the nearer landmark, suggesting a weighted integration of the conflicting landmarks. Moreover, the observed variance in search responses when both cues were presented in their normal locations was reduced compared to the variance on tests with single landmarks. However, the variance reduction and the weightings of the landmarks did not always show Bayesian optimality. In Experiment 2, some participants were trained only with each of the single landmarks. On subsequent tests with the 2 cues in conflict, searching did not shift toward the nearer landmark and the variance of search responses of these single-cue trained participants was larger than their variance on single-landmark tests, and even larger than the variance predicted by using the 2 landmarks alternatively on different trials. Taken together, these results indicate that cue combination occurs only when the landmarks are presented together during the initial learning experience. (PsycINFO Database Record
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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 | 0.001 | 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".