Placebo, suggestion and FND – interactions and clues to pathophysiology
Bibliographic record
Abstract
Devin Terhune. I joined the Department of Psychology at KCL in 2022 as a Reader in Experimental Psychology. I completed my BA in Philosophy and Psychology at Concordia University (Canada), my MSc in Psychology (with distinction) at the University of Liverpool, and my PhD in Psychology at Lund University (Sweden). After completing my PhD, I was a Postdoctoral Research Fellow in the Department of Experimental Psychology at the University of Oxford. More recently, before joining KCL, I lectured in statistics and coding in the Department of Psychology at Goldsmiths, University of London. Mark Edwards is a Professor of Neurology and Interface Disorders at the Institute of Psychiatry, Psychology and Neuroscience at King’s College London and works clinically at the Maudsley Hospital and Kings College Hospital. He has a specialist clinical and research interest in Movement Disorders and Functional Neurological Disorder (FND). He did his PhD with Professor John Rothwell and Professor Kailash Bhatia at the UCL Institute of Neurology, studying the pathophysiology of genetic dystonia. Following completion of neurology training he became a Senior Lecturer and Honorary Consultant Neurologist at UCL and the National Hospital for Neurology. Here he developed an NIHR funded research program and specialist diagnostic and treatment service for patients with FND. He is President of the Association of British Neurology Movement Disorders Group, International Executive Committee member of the International Parkinson’s and Movement Disorders Society, Board Member of the Functional Neurological Disorder Society, Associate Editor of the European Journal of Neurology, and medical advisor for FNDHope, the UK Dystonia Society and the British Association of Performing Arts Medicine. Abstract Both abnormal beliefs (predictions) and attention are proposed as key pathophysiological processes in Functional Neurological Disorder. These processes are also central to our current understanding of the effects of suggestion (for example in the context of hypnosis) and placebo and nocebo phenomena and thereby suggest mechanistic overlap across these domains. In this talk we will discuss the mechanisms of suggestion, placebo/nocebo and FND. We will then consider ways in which these phenomena might overlap, including whether dissociation and predictive processing may be useful unifying themes here, the relevance of placebo and suggestion as methods for modelling FND and their potential therapeutic role.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".