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Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting

Cited 2 time in Web of Science Cited 3 time in Scopus
Authors

Kwon, Mina; Lee, Sang Ho; Ahn, Woo-Young

Issue Date
2023-08
Publisher
Elsevier Inc.
Citation
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Vol.8 No.8, pp.1-804
Abstract
A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.
ISSN
2451-9022
URI
https://hdl.handle.net/10371/202803
DOI
https://doi.org/10.1016/j.bpsc.2022.12.003
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  • College of Social Sciences
  • Department of Psychology
Research Area Addiction, computational neuroscience, decision neuroscience, 계산 신경과학, 의사결정 신경과학, 중독

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