Interference between summary representations of average and range in ensemble perception
Bibliographic record
Abstract
Ensemble perception refers to the visual system’s ability to compress redundant information from multiple objects (e.g., multiple sizes, orientations) into a single summary representation (e.g., average size, orientation). These summary representations are often formed more accurately than any single-item representation, which tend to be biased towards the average of the set. Interestingly, we have recently demonstrated that single-item perception is also biased towards the range of the set (Srikanthan et al., VSS 2018). Here we investigate this sensitivity to ensemble range further, by asking whether implicit processing of set range can interfere with representations of average orientation. Participants were shown 8 triangles of varying orientations and were instructed to remember the location and orientation of each triangle. In a 2AFC task, a target and distractor were presented and participants either reported the average orientation (global condition), or the orientation of a single triangle (local condition). In order to investigate whether set range can interfere with average orientation, the distractor in the global condition could either be an item within the range but not in the set, or an item outside the range. Similarly, in the local condition the distractor could either be an item within the range but not in the set, or the average orientation. Reports of single-item orientation in the local condition were significantly below chance for both distractor types, again demonstrating that representations of ensemble average and range bias single-object perception. Critically, reports of average orientation in the global condition were significantly less accurate when the distractor was an item within the range (but not in the set), compared with an item outside the range. Together, these results demonstrate implicit sensitivity to the range of ensemble items, and further our understanding of ensemble processing by revealing the presence of interference in the formation of different summary representations.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".