Lesion Evidence That Two Distinct Regions within Prefrontal Cortex are Critical for <i>n</i>-Back Performance in Humans
Bibliographic record
Abstract
Although prefrontal cortex is clearly important in executive function, the specific processes carried out by particular regions within human prefrontal cortex remain a matter of debate. A rapidly growing corpus of functional imaging work now implicates various areas within prefrontal cortex in a wide range of "executive" tasks. Loss-of-function studies can help constrain the interpretation of such evidence by testing to what extent particular brain areas are necessary for a given cognitive process. Here we apply a component process analysis to understand prefrontal contributions to the n-back task, a widely used test of working memory, in a cohort of patients with focal prefrontal damage. We investigated letter 2-back task performance in 27 patients with focal damage to various regions within prefrontal cortex, compared to 29 demographically matched control subjects. Both "behavior-defined" approaches, using qualitative lesion analyses and voxel-based lesion-symptom mapping methods, and more conventional "lesion-defined" groupwise comparisons were undertaken to determine the relationships between specific sites of damage within prefrontal cortex and particular aspects of n-back task performance. We confirmed a critical role for left lateral prefrontal cortex in letter 2-back performance. We also identified a critical role for medial prefrontal cortex in this task: Damage to dorsal anterior cingulate cortex and adjacent dorsal fronto-medial cortex led to a pattern of impairment marked by high false alarm rates, distinct from the impairment associated with lateral prefrontal damage. These findings provide converging support for regionally specific models of human prefrontal function.
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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) | 0.000 | 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) | 0.001 | 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".