The Neural Representation of Outliers in Object-Ensemble Perception
Bibliographic record
Abstract
We are sounded by ensembles of objects every day. How are outliers in an otherwise homogeneous ensemble represented by our visual system? Are outliers ignored because they are the minority? Or do outliers alter our perception because their presence changes the nature of an otherwise homogenous ensemble? We have previously demonstrated that ensemble representation in human anterior-medial ventral visual cortex is sensitive to changes in the ratio of two types of objects comprising a heterogeneous ensemble. In the present study we investigated how outliers impact object-ensemble representation in this brain region. In an fMRI-adaptation paradigm, we presented a homogenous ensemble containing 25 identical elements followed by another homogenous ensemble containing a majority of identical elements with 0, 2, or 4 outliers. Observers were asked to ignore the outliers and judge whether the two ensembles were mostly same or different. For same judgments, the majority of the elements in the second ensemble were identical to those in the first, except for the outliers which were visually distinct. For different judgments, the majority of the elements in the second ensemble were distinct from those in the first, except for the outliers which were identical to those in the first ensemble. If outliers can be ignored, there should be adaptation in all same judgement trials and release from adaptation in all different judgement trials. Interestingly, in anterior-medial ventral visual cortex, with just 2 or 4 outliers in the same judgement trials, there was a significant release from adaptation compared to when 0 outliers were present. Moreover, with just 4 outliers in the different judgement trials, there was significant adaptation, comparable to the 0 outlier same judgment trials. Together, these results reveal that outliers significantly impact the perception and representation of otherwise homogeneous ensembles in human anterior-medial ventral visual cortex. Meeting abstract presented at VSS 2016
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".