Right brain damage failures of perceptual updating in ambiguous figures.
Bibliographic record
Abstract
Every day we face the world with beliefs about the rules that govern our environment and what will happen if we take particular actions. When incoming information does not match our predictions, we either need to abandon or update these beliefs. There is accumulating evidence that the right hemisphere is responsible for processing the statistical properties of an uncertain environment, which is important for building accurate representations of our environment, and adapting those representations when necessary. However, mental models may be more than 'look-up tables’ of conditional probabilities. Hence, updating failures should also be found when probability learning is less critical. To evaluate this hypothesis, we used a series of pictures that were based on well-known ambiguous figures (e.g., rabbit/duck). Participants saw pictures of unambiguous objects (e.g., rabbit) that incrementally changed over successive presentations to eventually show different unambiguous figures (e.g., duck). The point of transition from reporting the first object to reporting the second, provided an index of updating. RBD patients (n = 16) took significantly longer to switch their reports from the first unambiguous picture (e.g., "it is a rabbit") to the second (e.g., "now it is a duck") than did healthy controls (n = 18) [F(1,31) = 22.55, p <.001, h[sup]2[/sup] = .42]. This failure of updating occurred over a short time scale (15 pictures) and was not dependent on statistical learning. Other tests confirmed that results were neither due to a higher tendency to perseverate nor due to general cognitive impairment of the RBD patients. These findings are in accordance with a more generic role for the right hemisphere in model building and updating beyond what comes from the simple amalgamation of probabilities. Meeting abstract presented at VSS 2013
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".