Assessment of Neuropsychological Functioning
Bibliographic record
Abstract
Abstract Neuropsychology, like most other subspecialties in psychology, has undergone extensive and rapid changes and development over the past few decades. This has been particularly the case for neuropsychology within the past several years, as the 1990s were designated as the decade of the brain. Neuropsychology has been shaped by both economic pressures and changes and technological developments. These alterations in the field have lead to the development of new clinical avenues, technological progress, clinical and theoretical breakthroughs, and fundamental changes in the practice and teaching of neuropsychology. This chapter will explore these changes and developments focusing on both clinical and experimental areas, as well as offer some insight and advice regarding how these changes are affecting the field today. The chapter starts with a brief historical perspective, and then discusses the exciting and popular new clinical areas of sports‐related concussion and forensic neuropsychology. This is followed by a discussion of recent developments and issues in neuropsychological assessment and how advances in psychometric properties have improved neuropsychological assessment techniques. The final section discusses recent advances in experimental neuropsychology, mainly its role in fMRI research and transcranial magnetic stimulation, followed by an overview and ideas about the future direction of neuropsychology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".