Neuropsychology’s social landscape: Common ground with social neuroscience.
Bibliographic record
Abstract
Looking back 25 years into neuropsychology's past coincides almost perfectly with the birth of social neuroscience as a discipline. Social neuroscience aims to identify the biological bases of social behavior through multilevel analyses of neural, cognitive, and social processes. Neuropsychology, on the other hand, aspires to understand brain-behavior relationships more generally. Given that much of human behavior comprises social interactions, the goals, theories, methods, and findings derived from social neuroscience are likely to have bearing on the issues and interests of neuropsychologists. This review summarizes some of the main developments that have emerged from social neuroscience and their relevance to neuropsychology. Applications of social neuroscience principles are presented in the context of brain insult, assessment, and intervention. Recommendations are made for improving neuropsychological approaches to the evaluation of social cognition and competence. In closing, a discussion of the challenges and possible future directions for the 2 disciplines is offered. (PsycINFO Database Record
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".