Transitive inference reasoning is impaired by focal lesions in parietal cortex rather than rostrolateral prefrontal cortex
Bibliographic record
Abstract
Transitive inference reasoning involves the examination and comparison of a given number of relational pairs in order to understand overall group hierarchy (e.g., A>B, B>C, C>D; therefore is A>D?). A number of imaging studies have demonstrated the role of the parietal cortex for resolving transitive inferences. Some studies also identify the rostrolateral prefrontal cortex as being critical for "relational integration" processes supporting transitive reasoning. To clarify this issue, we carried out a transitive inference study involving neurological patients with focal lesions to the rostrolateral prefrontal (n=5) or parietal cortices (n=7), as well as normal controls (n=6). The patients and controls were statistically matched on age, education, pre-injury IQ, general memory, working memory, and performance/full IQ, though the rostrolateral patients did score significantly higher than the normal controls on verbal IQ. Results indicate that patients with focal lesions to the parietal cortex were impaired in the task relative to both the patients with focal lesions to rostrolateral prefrontal cortex and the control group, and there was no difference in task performance between the rostrolateral prefrontal and the control groups. This result continued to hold after controlling for verbal IQ as a covariate. These findings point to a critical role for the parietal cortex, rather than the rostrolateral prefrontal, in transitive inference. Since the groups performed similarly on a working memory task, working memory cannot fully account for the result, suggesting a specific role of parietal cortex in transitive inference.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".