Bibliographic record
Abstract
The environment contains widespread regularities in terms of how objects co-occur in space and over time. How regularities alter the perception of individual objects is largely unexplored. In Experiment 1, we examined how learning spatial co-occurrences of individual objects alters the perception of the spatial location of these objects. Observers were exposed to arrays of colored circles. In the 'structured' condition, each array contained four color pairs which were arranged in fixed spatial configurations (e.g., red always appears to the left of blue). In the 'random' condition, the same configuration was maintained, but now one circle in the pair was shuffled, while the other circle remained in the same position (e.g., red appears to the left of blue, brown, or purple). After exposure, one circle was briefly presented on the screen and observers indicated the location of the circle. We found that the location of the circle was perceived to be closer to the location of its partner in the pair in the structured condition than in the random condition. To generalize this finding, in Experiment 2, we examined how regularities in line orientations alter the perception of these orientations. Observers were exposed to a sequence of lines. In the structured condition, the sequence consisted of three pairs of orientations, and in the random condition, the orientations were presented in a random order. We found that the orientation of the line was perceived to be more similar to the orientation of its partner in the pair in the structured condition than in the random condition. These results demonstrate that the representation of a stimulus is biased toward to that of another if the stimuli reliably co-occur. This suggests that incidental learning of object co-occurrences can shape the perception of individual objects, revealing fundamental ways in which learning can guide perception. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".