Orbito-frontal lesions cause impairment during late but not early emotional prosodic processing
Bibliographic record
Abstract
The orbitofrontal cortex (OFC) is functionally linked to a variety of cognitive and emotional functions. In particular, lesions of the human OFC lead to large-scale changes in social and emotional behavior. For example, patients with OFC lesions are reported to suffer from deficits in affective decision-making, including impaired emotional face and voice expression recognition (e.g., Hornak et al., 1996, 2003). However, previous studies have failed to acknowledge that emotional processing is a multistage process. Thus, different stages of emotional processing (e.g., early vs. late) in the same patient group could be affected in a qualitatively different manner. The present study investigated this possibility and tested implicit emotional speech processing in an ERP experiment followed by an explicit behavioral emotional recognition task. OFC patients listened to vocal emotional expressions of anger, fear, disgust, and happiness compared to a neutral baseline spoken either with or without lexical content. In line with previous evidence (Paulmann & Kotz, 2008b), both patients and healthy controls differentiate emotional and neutral prosody within 200 ms (P200). However, the recognition of emotional vocal expressions is impaired in OFC patients as compared to healthy controls. The current data serve as first evidence that emotional prosody processing is impaired only at a late, and not at an early processing stage in OFC patients.
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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".