The effects of cue placement on the relative dominance of boundaries and landmark arrays in goal localization
Why this work is in the frame
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Bibliographic record
Abstract
Two types of visual features are identified as reference points used by individuals to encode locations: surface-based boundaries and discrete-object-based landmarks. Previous research show that learning locations relative to a boundary can overshadow learning relative to a landmark, but not vice versa, suggesting that environmental boundaries play a privileged role in representing individual locations. However, other research has revealed that a less accurate cognitive map is derived from boundary-related learning than from landmark-related learning, suggesting that a boundary is less privileged in representing inter-location spatial relations. The current study aims to reconcile these inconsistent findings. Experiment 1, using both a cue-competition paradigm and a cognitive mapping task, replicated the finding that participants preferred a circular boundary to a four-landmark array for encoding four locations (1A), but that the cognitive maps of the locations derived from the landmark array were more accurate (1B). Using the cue-competition paradigm, Experiments 2-4 manipulated the placement and distinctiveness of the two cues. The results showed that manipulating the placement of the landmark array effectively modulated the relative reliance upon the boundary/landmark-array in encoding individual location. Whereas increasing the distinctiveness of the landmark-array alone is not sufficient to eliminate the boundary advantage in localization. We propose that the boundary privilege occurs in selecting reference points for encoding locations due to its relative peripheral placement in the environment, whereas the landmark advantage occurs in inferring inter-location spatial relations due to the common reference point provided by the single landmark.
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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 it