Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Oct 1:11:1-7.
doi: 10.1016/j.cobeha.2016.02.001.

Challenges and promises for translating computational tools into clinical practice

Woo-Young Ahn  1 , Jerome R Busemeyer  2
Affiliations

Challenges and promises for translating computational tools into clinical practice

Woo-Young Ahn et al. Curr Opin Behav Sci. .

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.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest: The authors declare no competing financial interets.

Figures

Figure 1
Figure 1
Promising approaches to address three major changes for translating computational tools into clinical practice
See this image and copyright information in PMC

References

    1. Montague PR, Dayan P, Sejnowski TJ. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience. 1996;16:1936–1947. - PMC - PubMed
    1. Forstmann BU, Wagenmakers E-J. An Introduction to Model-Based Cognitive Neuroscience. Springer; 2015.
    1. O'Doherty JP, Hampton A, Kim H. Model-Based fMRI and Its Application to Reward Learning and Decision Making. Annals of the New York Academy of Sciences. 2007;1104:35–53. - PubMed
    1. Dayan P, Balleine BW. Reward, motivation, and reinforcement learning. Neuron. 2002;36:285–298. - PubMed
    1. Guitart-Masip M, Duzel E, Dolan R, Dayan P. Action versus valence in decision making. Trends Cogn. Sci. 2014;18:194–202. - PMC - PubMed

LinkOut - more resources