One bad apple spoils the whole bushel: The neural basis of outlier processing
Bibliographic record
Abstract
How are outliers in an otherwise homogeneous object ensemble represented by our visual system? Are outliers ignored because they are the minority? Or do outliers alter our perception of an otherwise homogeneous ensemble? We have previously demonstrated ensemble representation in human anterior-medial ventral visual cortex (overlapping the scene-selective parahippocampal place area; PPA). In this study we investigated how outliers impact object-ensemble representation in this human brain region as well as visual representation throughout posterior brain regions. We presented a homogeneous ensemble followed by an ensemble containing either identical elements or a majority of identical elements with a few outliers. Human participants ignored the outliers and made a same/different judgment between the two ensembles. In PPA, fMRI adaptation was observed when the outliers in the second ensemble matched the items in the first, even though the majority of the elements in the second ensemble were distinct from those in the first; conversely, release from fMRI adaptation was observed when the outliers in the second ensemble were distinct from the items in the first, even though the majority of the elements in the second ensemble were identical to those in the first. A similarly robust outlier effect was also found in other brain regions, including a shape-processing region in lateral occipital cortex (LO) and task-processing fronto-parietal regions. These brain regions likely work in concert to flag the presence of outliers during visual perception and then weigh the outliers appropriately in subsequent behavioral decisions. To our knowledge, this is the first time the neural mechanisms involved in outlier processing have been systematically documented in the human brain. Such an outlier effect could well provide the neural basis mediating our perceptual experience in situations like "one bad apple spoils the whole bushel".
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".