Impulsivities and addictions: a multidimensional integrative framework informing assessment and interventions for substance use disorders
J Vassileva, PJ Conrod - Philosophical Transactions of …, 2019 - royalsocietypublishing.org
Impulse control is becoming a critical survival skill for the twenty-first century. Impulsivity is
implicated in virtually all externalizing behaviours and disorders, and figures prominently in …
implicated in virtually all externalizing behaviours and disorders, and figures prominently in …
What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience
Highlights•'Reinforcement learning'(RL) refers to different concepts in machine learning,
psychology, and neuroscience.•In psychology and neuroscience, RL models have provided …
psychology, and neuroscience.•In psychology and neuroscience, RL models have provided …
[HTML][HTML] Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package
Reinforcement learning and decision-making (RLDM) provide a quantitative framework and
computational theories with which we can disentangle psychiatric conditions into the basic …
computational theories with which we can disentangle psychiatric conditions into the basic …
Theoretically informed generative models can advance the psychological and brain sciences: Lessons from the reliability paradox
Theories of individual differences are foundational to psychological and brain sciences, yet
they are traditionally developed and tested using superficial summaries of data (eg, mean …
they are traditionally developed and tested using superficial summaries of data (eg, mean …
The interpretation of computational model parameters depends on the context
Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences,
promising to explain behavior from simple conditioning to complex problem solving, to shed …
promising to explain behavior from simple conditioning to complex problem solving, to shed …
[PDF][PDF] Learning from the reliability paradox: How theoretically informed generative models can advance the social, behavioral, and brain sciences
A primary aim of social, behavioral, and brain sciences is to develop explanations that
answer questions of why or how observed psychological phenomena occur (Hempel & …
answer questions of why or how observed psychological phenomena occur (Hempel & …
Comprehensive review: Computational modelling of schizophrenia
Computational modelling has been used to address:(1) the variety of symptoms observed in
schizophrenia using abstract models of behavior (eg Bayesian models–top-down …
schizophrenia using abstract models of behavior (eg Bayesian models–top-down …
Challenges and recommendations to improve the installability and archival stability of omics computational tools
Developing new software tools for analysis of large-scale biological data is a key component
of advancing modern biomedical research. Scientific reproduction of published findings …
of advancing modern biomedical research. Scientific reproduction of published findings …
The outcome‐representation learning model: A novel reinforcement learning model of the iowa gambling task
Abstract The Iowa Gambling Task (IGT) is widely used to study decision‐making within
healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to …
healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to …
Utility of machine-learning approaches to identify behavioral markers for substance use disorders: impulsivity dimensions as predictors of current cocaine dependence
Background Identifying objective and accurate markers of cocaine dependence (CD) can
innovate its prevention and treatment. Existing evidence suggests that CD is characterized …
innovate its prevention and treatment. Existing evidence suggests that CD is characterized …