Bibliographic record
Abstract
Studies of the spatial representations common to navigation, perspective-taking, and scene recognition have typically limited their analysis to the memory for locational (e.g., the distance and direction of objects) components of arrays.Adapted from a change detection paradigm of Simons & Wang (1998), four experiments examined whether another spatial array feature, object orientation, could be monitored across motor perspective changes and how this process is affected by memory capacity limitations and behavioral relevance.Participants were, under some circumstances, capable of monitoring the orientation of objects on-line (Experiments 2 and 4).However, this was not the case in the traditional version of the paradigm in which location changes are readily detected even across perspective changes (Experiment 1).It was found that this ability is capacity limited, to the extent that performance may be optimal with smaller set sizes (i.e., 3 rather than 5 objects; Experiment 2) and is impaired by the added cognitive demands of updating over self-motion (Experiments 2 and 4).Generalized (i.e., applied to all objects) orientation changes could be more accurately detected than the more fine-grained modification of a single object, though this level of processing may be comparatively slower and not as readily applicable to novel views of the array.The implications of these findings for the representational mechanisms and strategies used to monitor orientation are discussed.Finally, orientation changes applied to objects shown elsewhere to elicit the sensorimotor representation of this property (man-made tools; e.g., Tucker & Ellis, 1998) were more readily detected than those made on objects that possessed orientations that were of similar perceptual salience (Experiment 3) but less behaviorally relevant (i.e., living and unfamiliar, artificial objects; Experiment 4).This slight processing advantage, as well as the findings of capacity and updating effects, are consistent with views of the online system as resource-limited, dynamic, and geared towards facilitating immediate physical interaction with the environment.
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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 | 0.000 | 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 | 0.006 |
| 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".