Hemispheric Specialization in Human Prefrontal Cortex for Resolving Certain and Uncertain Inferences
Bibliographic record
Abstract
Uncertainty is a fact of life that must be accommodated in real-world decision making. Although it has been suggested that the right prefrontal cortex (PFC) has a special role to play in decision making under uncertainty, there is very little hard data to support this hypothesis. To better understand the roles of left and right PFCs in reasoning and decision making in situations with complete and incomplete information, we administered simple inference problems to 18 patients with lateralized focal lesions to PFC (9 right hemisphere, 9 left hemisphere) and 22 age- and education-matched normal controls. The stimuli were systematically manipulated for completeness of information regarding the status of the conclusion. Our results demonstrated a 2-way interaction such that patients with left PFC lesions were selectively impaired in trials with complete information, whereas patients with right PFC lesions were selectively impaired in trials with incomplete information. These results provide compelling evidence for hemispheric specialization for reasoning in PFC and suggest that the right PFC has a critical role to play in reasoning about incompletely specified situations. We postulate this role involves the maintenance of ambiguous mental representations that temper premature overinterpretation by the left hemisphere.
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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) | 0.000 | 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".