The empirical status of predictive coding and active inference

R Hodson, M Mehta, R Smith - Neuroscience & Biobehavioral Reviews, 2024 - Elsevier
Research on predictive processing models has focused largely on two specific algorithmic
theories: Predictive Coding for perception and Active Inference for decision-making. While …

Computational models of behavioral addictions: State of the art and future directions

A Kato, K Shimomura, D Ognibene, MA Parvaz… - Addictive …, 2023 - Elsevier
Non-pharmacological behavioral addictions, such as pathological gambling, videogaming,
social networking, or internet use, are becoming major public health concerns. It is not yet …

Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making

M Albarracin, I Hipólito, SE Tremblay, JG Fox… - … Workshop on Active …, 2023 - Springer
This paper investigates the prospect of developing human-interpretable, explainable
artificial intelligence (AI) systems based on active inference and the free energy principle …

The complex nature of human operant gambling behaviour involving slot games: Structural characteristics, verbal rules and motivation

P Delfabbro, D King, J Parke - Addictive Behaviors, 2023 - Elsevier
Gambling behaviour is likely to be strongly influenced by operant learning principles. Most
forms of gambling, and most notably slot machine play, follow a random ratio (RR) schedule …

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

S Taylor, CA Lavalley, N Hakimi, JL Stewart… - Drug and Alcohol …, 2023 - Elsevier
Abstract Background Substance use disorders (SUDs) represent a major public health risk.
Yet, our understanding of the mechanisms that maintain these disorders remains …

Listening to the data: computational approaches to addiction and learning

CS Wilkinson, MÁ Luján, C Hales… - Journal of …, 2023 - Soc Neuroscience
Computational approaches hold great promise for identifying novel treatment targets and
creating translational therapeutics for substance use disorders. From circuitries underlying …

[HTML][HTML] Slower learning rates from negative outcomes in substance use disorder over a 1-year period and their potential predictive utility

R Smith, S Taylor, JL Stewart, SM Guinjoan… - Computational …, 2022 - ncbi.nlm.nih.gov
Computational modelling is a promising approach to parse dysfunctional cognitive
processes in substance use disorders (SUDs), but it is unclear how much these processes …

Dysfunctional feedback processing in male methamphetamine abusers: Evidence from neurophysiological and computational approaches

S Ghaderi, JA Rad, M Hemami, R Khosrowabadi - Neuropsychologia, 2024 - Elsevier
Methamphetamine use disorder (MUD) as a major public health risk is associated with
dysfunctional neural feedback processing. Although dysfunctional feedback processing in …

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

TV Lim, KD Ersche - Addiction Neuroscience, 2023 - Elsevier
Maladaptive behavior in drug addiction is widely regarded as a result of neurocognitive
dysfunctions. Recently, there has been a growing trend to adopt computational methods to …

[HTML][HTML] People with a tobacco use disorder exhibit misaligned Bayesian belief updating by falsely attributing non-drug cues as worse predictors of positive outcomes …

S Kalhan, P Schwartenbeck, R Hester… - Drug and Alcohol …, 2024 - Elsevier
Adaptive behaviours depend on dynamically updating internal representations of the world
based on the ever-changing environmental contingencies. People with a substance use …