Bayesian Inference for Psychology and Psychiatry
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 20 January 2025 | Viewed by 767
Special Issue Editors
2. Early Detection and Intervention Team (EDIT), Department of Anxiety Disorders, Parnassia Academy, Parnassia Group, PsyQ, 2512 VA The Hague,The Netherlands
Interests: active inference; psychiatry; (evolutionary) psychology; psychiatric nosology; Bayesian inference; diagnostic modeling; prognostic modeling; psychometrics; systems theory; Bayesian ethics
2. Orygen, Parkville, VIC 3052, Australia
Interests: theoretical psychology; human evolution and development; mood and affective disorders; youth mental health; active inference; evolutionary systems theory; complex adaptive systems
Special Issues, Collections and Topics in MDPI journals
2. Institute for Advanced Consciousness Studies, Santa Monica, CA 90403, USA
Interests: neuroscience; psychology; philosophy; dynamical systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Bayesian inference as a statistical tool is on the rise and bound to change psychology and psychiatry for many decades to come. Its systematic incorporation of prior knowledge in estimating the posterior probability of some outcome is arguably the most optimal way of dealing with information in clinical practice, where bits and pieces of new information come in each day and shed a new light on our diagnostic models. The mild mischief of model inversion allows a tentative glance at a hidden world that probably caused the diagnostic data. Whether using variational inference to iteratively update diagnostic and prognostic models based on continuously gathered data, Bayesian classification to classify patients, or knowledge graphs to predict clinical outcomes from unstructured and disparate data, Bayesian inference has the cards to guarantee optimal use of the rich datasets gathered in clinical settings.
Across the globe, exact or approximate Bayesian methods are used in areas as diverse as statistical physics, machine learning, systems biology, neuroscience, and clinical medicine, where Bayes’ rule governs the equations of motion that are required to model the evolution of living as well as non-living systems across timescales. At the heart of such dynamics is the Free Energy Principle, which defines how optimal predictive models of some system depend on a balancing act between model accuracy and complexity, or, equivalently, model energy and entropy. This ties immediately to active inference and the Bayesian brain hypothesis, where subjects iteratively act to change their worlds to sample the right kind of sensory data and optimize their predictive models of the world, which in turn inspire action. In this view, all subjective experience is ‘just a model’ that serves as the best explanation that subjects can give for their sensory events, and problems of (active) inference define mental health problems.
In this Special Issue, we examine the application of Bayesian methods to the field of clinical psychology and psychiatry. Of special interest are the use of Bayesian methods in the diagnosis and treatment of mental health problems in clinical practice, Bayesian inference in network analyses of experience sampling data (ESM), as well as theoretical papers and neuroimaging studies that focus on active inference to understand the nature and dynamics of mental health and its problems. We welcome contributions from diverse fields of science to promote interdisciplinary dialogue and a fruitful exchange of ideas.
Dr. Rutger Goekoop
Dr. Paul Badcock
Dr. Adam Safron
Guest Editors
Manuscript Submission Information
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Keywords
- Bayesian inference
- mental health
- clinical psychology
- psychiatry
- diagnostic modeling
- prognostic modeling
- active inference
- free energy principle
- computational psychiatry
- network science
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Bayesian Self-Evidencing in Human Neural Development
Authors: Don M. Tucker; Phan Luu
Affiliation: Brain Electrophysiology Laboratory Company; University of Oregon
Abstract: Biological development is a cumulative, Bayesian process in which the initial priors are the instructions of the genome and the posterior distribution is the organism’s self-organization as a result of the conditions in the womb, the early plastic interactions with the environment, and the ongoing adaptive effort to cope with conditions of growth and reproduction. For humans, adaptation involves not only the implicit prediction of the environmental information embodied in the genome, but the powerful acceleration of the individual mind’s complexity through incorporation of the information of the culture. The implicit self-evidencing (reflection of the emerging identity in the prediction of the world) of simpler animals becomes reentrant in human personality development with the emergence of self-awareness. Ongoing consciousness inherently negotiates the stability-plasticity dilemma as the implicit self constrains the experience of the world (the Bayesian prior of a stable self), and yet that ongoing experience introduces a continuing flux of information demanding plasticity and the developmental evolution of the self.
