Right Hemisphere Brain Damage Impairs Strategy Updating
Bibliographic record
Abstract
Our behavior is predicated on mental models of the environment that must be updated to accommodate incoming information. We had 13 right-brain-damaged (RBD) patients and 10 left-brain-damaged (LBD) patients play the children's game "rock, paper, scissors" against a computer opponent that covertly altered its strategy. Healthy age-matched controls and LBD patients quickly detected extreme departures from uniform play ("paper" chosen on 80% of trials), but the RBD patient group did not. Seven RBD patients presented with neglect and although this was associated with greater impairment in strategy updating, there were exceptions: 2 of 7 neglect patients performed above the median of the patient group and 1 of the 6 nonneglect participants was severely impaired. Although speculative, lesion analyses contrasting high and low performing patients showed that severe impairments were associated with insula and putamen lesions. Interestingly, relative to the controls, the LBD group tended to "maximize" choices in the strongly biased condition (i.e., optimal strategy chosen on 100% of the trials), whereas controls "matched" the computer's strategy (i.e., optimal strategy chosen on 80% of the trials). We conclude that RBD leads to impaired updating of mental models to exploit environmental changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".