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Mental health assessment: Inference, explanation, and coherence
Author(s) -
Thagard Paul,
Larocque Laurette
Publication year - 2018
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.12885
Subject(s) - coherence (philosophical gambling strategy) , inference , mental health , interpretation (philosophy) , bayesian inference , psychology , interview , bayesian probability , epistemology , psychiatry , computer science , artificial intelligence , sociology , statistics , mathematics , philosophy , anthropology , programming language
Mental health professionals such as psychiatrists and psychotherapists assess their patients by identifying disorders that explain their symptoms. This assessment requires an inference to the best explanation that compares different disorders with respect to how well they explain the available evidence. Such comparisons are captured by the theory of explanatory coherence that states 7 principles for evaluating competing hypotheses in the light of evidence. The computational model ECHO shows how explanatory coherence can be efficiently computed. We show the applicability of explanatory coherence to mental health assessment by modelling a case of psychiatric interviewing and a case of psychotherapeutic evaluation. We argue that this approach is more plausible than Bayesian inference and hermeneutic interpretation.