Individual differences in computational psychiatry: A review of current challenges

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 148, P. 105137 - 105137

Published: March 20, 2023

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is development computational assays: integrating models with cognitive tasks infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements modelling cross-sectional patient studies, much less attention has been paid basic psychometric properties (reliability construct validity) measures provided by assays. In this review, we assess extent issue examining emerging empirical evidence. We find that suffer from poor properties, which poses a risk invalidating previous findings undermining ongoing research efforts using assays study (and even group) provide recommendations how address these problems and, crucially, embed them within broader perspective on key developments are needed translating clinical practice.

Language: Английский

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From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology

Review of Philosophy and Psychology, Journal Year: 2022, Volume and Issue: 13(4), P. 829 - 857

Published: March 18, 2022

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as

Language: Английский

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The empirical status of predictive coding and active inference

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 157, P. 105473 - 105473

Published: Nov. 28, 2023

Language: Английский

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Reward Maximization Through Discrete Active Inference

Neural Computation, Journal Year: 2023, Volume and Issue: 35(5), P. 807 - 852

Published: March 21, 2023

Active inference is a probabilistic framework for modeling the behavior of biological and artificial agents, which derives from principle minimizing free energy. In recent years, this has been applied successfully to variety situations where goal was maximize reward, often offering comparable sometimes superior performance alternative approaches. article, we clarify connection between reward maximization active by demonstrating how when agents execute actions that are optimal maximizing reward. Precisely, show conditions under produces solution Bellman equation, formulation underlies several approaches model-based reinforcement learning control. On partially observed Markov decision processes, standard scheme can produce planning horizons 1 but not beyond. contrast, recently developed recursive (sophisticated inference) on any finite temporal horizon. We append analysis with discussion broader relationship learning.

Language: Английский

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An Introduction to Predictive Processing Models of Perception and Decision‐Making

Topics in Cognitive Science, Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 29, 2023

Abstract The predictive processing framework includes a broad set of ideas, which might be articulated and developed in variety ways, concerning how the brain may leverage models when implementing perception, cognition, decision‐making, motor control. This article provides an up‐to‐date introduction to two most influential theories within this framework: coding active inference. first half paper (Sections 2–5) reviews evolution coding, from early ideas about efficient visual system more general model encompassing theory is characterized terms claims it makes at Marr's computational, algorithmic, implementation levels description, conceptual mathematical connections between Bayesian inference, variational free energy (a quantity jointly evaluating accuracy complexity) are explored. second 6–8) turns recent Like inference assume that perceptual learning processes minimize as means approximating biologically plausible manner. However, these focus primarily on planning decision‐making were not address. Under agent evaluates potential plans (action sequences) based their expected combines anticipated reward information gain). assumed represent world partially observable Markov decision process with discrete time states. Current research applications described, including range simulation work, well studies fitting empirical data. concludes by considering future directions will important for further development both models.

Language: Английский

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Individuals with Methamphetamine Use Disorder Show Reduced Directed Exploration and Learning Rates Independent of an Aversive Interoceptive State Induction

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 21, 2024

Abstract Methamphetamine Use Disorder (MUD) is associated with substantially reduced quality of life. Yet, decisions to use persist, due in part avoidance anticipated withdrawal states. However, the specific cognitive mechanisms underlying this decision process, and possible modulatory effects aversive states, remain unclear. Here, 56 individuals MUD 58 healthy comparisons (HCs) performed a task, both without an interoceptive state induction. Computational modeling measured tendency test beliefs about uncertain outcomes (directed exploration) ability update response (learning rates). Compared HCs, those exhibited less directed exploration slower learning rates, but these differences were not affected by Follow-up analyses further suggested that was best explained greater uncertainty on trait reflectiveness might account for task behavior. These results suggest novel, state-independent computational whereby may have difficulties testing tolerability abstinence adjusting behavior consequences continued use.

Language: Английский

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Transdiagnostic failure to adapt interoceptive precision estimates across affective, substance use, and eating disorders: A replication and extension of previous results

Biological Psychology, Journal Year: 2024, Volume and Issue: 191, P. 108825 - 108825

Published: May 31, 2024

Language: Английский

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Theory-driven computational models of drug addiction in humans: Fruitful or futile?

Addiction Neuroscience, Journal Year: 2023, Volume and Issue: 5, P. 100066 - 100066

Published: Jan. 18, 2023

Maladaptive behavior in drug addiction is widely regarded as a result of neurocognitive dysfunctions. Recently, there has been growing trend to adopt computational methods study these dysfunctions drug-addicted patients, not least because it provides quantitative framework infer the psychological mechanisms that may have gone awry addiction. We therefore sought evaluate extent which theory-driven models fulfilled this purpose research. discuss several learning and decision-making theories proposed explain symptoms characterize impaired control intense urge use drugs addiction, outline algorithms frequently used model processes. Specifically, behavioral over explained by aberrant reinforcement an imbalance between model-based model-free control, whereas strong desire for might be neurocomputational incentive sensitization economic theory. argue while appear useful tools generate novel mechanistic insights into their should informed theory, experimental data, clinical observations.

Language: Английский

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A computational neuroscience perspective on subjective wellbeing within the active inference framework

International Journal of Wellbeing, Journal Year: 2022, Volume and Issue: 12(4), P. 102 - 131

Published: Nov. 1, 2022

Understanding and promoting subjective wellbeing (SWB) has been the topic of increasing research, due in part to its potential contributions health productivity. To date, conceptualization SWB grounded within social psychology largely focused on self-report measures. In this paper, we explore potentially complementary tools theoretical perspectives offered by computational neuroscience, with a focus active inference (AI) framework. This framework is motivated fact that brain does not have direct access world; select actions, it must instead infer most likely external causes sensory input receives from both body world. Because always consistent multiple interpretations, brain’s internal model use background knowledge, form prior expectations, make “best guess” about situation how will change taking one action or another. best guess arises minimizing an error signal representing deviation between predicted observed sensations given chosen action—quantified mathematically variable called free energy (FE). Crucially, recent proposals illustrated emotional experience may emerge AI as natural consequence keeping track success selecting actions minimize FE. draw concepts mathematics highlight different strategies can be used FE—some more successfully than others. affords characterization diverse individuals adopt unique for achieving high SWB. It also highlights novel ways which could effectively improved. These considerations lead us propose understanding We several parameters these models explain individual cultural differences SWB, they might inspire interventions. conclude proposing line future empirical research based modelling complement current approaches study improvement.

Language: Английский

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Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

Drug and Alcohol Dependence, Journal Year: 2023, Volume and Issue: 252, P. 110945 - 110945

Published: Aug. 25, 2023

Language: Английский

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