Literature DB >> 27104211

Challenges and promises for translating computational tools into clinical practice.

Woo-Young Ahn1, Jerome R Busemeyer2.   

Abstract

Computational modeling and associated methods have greatly advanced our understanding of cognition and neurobiology underlying complex behaviors and psychiatric conditions. Yet, no computational methods have been successfully translated into clinical settings. This review discusses three major methodological and practical challenges (A. precise characterization of latent neurocognitive processes, B. developing optimal assays, C. developing large-scale longitudinal studies and generating predictions from multi-modal data) and potential promises and tools that have been developed in various fields including mathematical psychology, computational neuroscience, computer science, and statistics. We conclude by highlighting a strong need to communicate and collaborate across multiple disciplines.

Entities:  

Year:  2016        PMID: 27104211      PMCID: PMC4834893          DOI: 10.1016/j.cobeha.2016.02.001

Source DB:  PubMed          Journal:  Curr Opin Behav Sci        ISSN: 2352-1546


  58 in total

Review 1.  Computational modeling for addiction medicine: From cognitive models to clinical applications.

Authors:  Woo Young Ahn; Junyi Dai; Jasmin Vassileva; Jerome R Busemeyer; Julie C Stout
Journal:  Prog Brain Res       Date:  2015-11-04       Impact factor: 2.453

Review 2.  Model-based fMRI and its application to reward learning and decision making.

Authors:  John P O'Doherty; Alan Hampton; Hackjin Kim
Journal:  Ann N Y Acad Sci       Date:  2007-04-07       Impact factor: 5.691

3.  Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG.

Authors:  Roger Ratcliff; Marios G Philiastides; Paul Sajda
Journal:  Proc Natl Acad Sci U S A       Date:  2009-04-02       Impact factor: 11.205

Review 4.  A framework for studying the neurobiology of value-based decision making.

Authors:  Antonio Rangel; Colin Camerer; P Read Montague
Journal:  Nat Rev Neurosci       Date:  2008-06-11       Impact factor: 34.870

5.  Using diffusion models to understand clinical disorders.

Authors:  Corey N White; Roger Ratcliff; Michael W Vasey; Gail McKoon
Journal:  J Math Psychol       Date:  2010-02-01       Impact factor: 2.223

6.  Neural computations underlying arbitration between model-based and model-free learning.

Authors:  Sang Wan Lee; Shinsuke Shimojo; John P O'Doherty
Journal:  Neuron       Date:  2014-02-05       Impact factor: 17.173

7.  Bridging Levels of Understanding in Schizophrenia Through Computational Modeling.

Authors:  Alan Anticevic; John D Murray; Deanna M Barch
Journal:  Clin Psychol Sci       Date:  2015-05

8.  Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach.

Authors:  Daniel R Cavagnaro; Richard Gonzalez; Jay I Myung; Mark A Pitt
Journal:  Manage Sci       Date:  2013-02       Impact factor: 4.883

9.  Neuropsychosocial profiles of current and future adolescent alcohol misusers.

Authors:  Robert Whelan; Richard Watts; Catherine A Orr; Robert R Althoff; Eric Artiges; Tobias Banaschewski; Gareth J Barker; Arun L W Bokde; Christian Büchel; Fabiana M Carvalho; Patricia J Conrod; Herta Flor; Mira Fauth-Bühler; Vincent Frouin; Juergen Gallinat; Gabriela Gan; Penny Gowland; Andreas Heinz; Bernd Ittermann; Claire Lawrence; Karl Mann; Jean-Luc Martinot; Frauke Nees; Nick Ortiz; Marie-Laure Paillère-Martinot; Tomas Paus; Zdenka Pausova; Marcella Rietschel; Trevor W Robbins; Michael N Smolka; Andreas Ströhle; Gunter Schumann; Hugh Garavan
Journal:  Nature       Date:  2014-07-02       Impact factor: 49.962

10.  Rational regulation of learning dynamics by pupil-linked arousal systems.

Authors:  Matthew R Nassar; Katherine M Rumsey; Robert C Wilson; Kinjan Parikh; Benjamin Heasly; Joshua I Gold
Journal:  Nat Neurosci       Date:  2012-06-03       Impact factor: 24.884

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  12 in total

Review 1.  Impulsivities and addictions: a multidimensional integrative framework informing assessment and interventions for substance use disorders.

Authors:  Jasmin Vassileva; Patricia J Conrod
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-02-18       Impact factor: 6.237

2.  A computational model of the Cambridge gambling task with applications to substance use disorders.

Authors:  Ricardo J Romeu; Nathaniel Haines; Woo-Young Ahn; Jerome R Busemeyer; Jasmin Vassileva
Journal:  Drug Alcohol Depend       Date:  2019-11-03       Impact factor: 4.492

3.  What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience.

Authors:  Maria K Eckstein; Linda Wilbrecht; Anne G E Collins
Journal:  Curr Opin Behav Sci       Date:  2021-07-03

4.  Shuffle the Decks: Children Are Sensitive to Incidental Nonrandom Structure in a Sequential-Choice Task.

Authors:  Alexander D S Breslav; Nancy L Zucker; Julia C Schechter; Alesha Majors; Tatyana Bidopia; Bernard F Fuemmeler; Scott H Kollins; Scott A Huettel
Journal:  Psychol Sci       Date:  2022-03-10

5.  The Outcome-Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task.

Authors:  Nathaniel Haines; Jasmin Vassileva; Woo-Young Ahn
Journal:  Cogn Sci       Date:  2018-10-05

6.  Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package.

Authors:  Woo-Young Ahn; Nathaniel Haines; Lei Zhang
Journal:  Comput Psychiatr       Date:  2017-10-01

Review 7.  Computational neuroscience across the lifespan: Promises and pitfalls.

Authors:  Wouter van den Bos; Rasmus Bruckner; Matthew R Nassar; Rui Mata; Ben Eppinger
Journal:  Dev Cogn Neurosci       Date:  2017-10-13       Impact factor: 6.464

8.  Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity.

Authors:  Nathaniel Haines; Matthew W Southward; Jennifer S Cheavens; Theodore Beauchaine; Woo-Young Ahn
Journal:  PLoS One       Date:  2019-02-05       Impact factor: 3.240

9.  Risk preferences impose a hidden distortion on measures of choice impulsivity.

Authors:  Silvia Lopez-Guzman; Anna B Konova; Kenway Louie; Paul W Glimcher
Journal:  PLoS One       Date:  2018-01-26       Impact factor: 3.240

10.  Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.

Authors:  Woo-Young Ahn; Hairong Gu; Yitong Shen; Nathaniel Haines; Hunter A Hahn; Julie E Teater; Jay I Myung; Mark A Pitt
Journal:  Sci Rep       Date:  2020-07-21       Impact factor: 4.379

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