ReviewBiological psychiatry. Cognitive neuroscience and neuroimaging2023
From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research.
Review in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed.
- Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high risk for depression.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Replicate Me if You Can: Assessing Measurement Reliability of Individual Differences in Reading Across Measurement Occasions and Methods.Cognitive science · 2026Article
- Associations Between Steep Delay Discounting and Punishment Learning.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Development and Piloting of Co.Ge.: A Web-Based Digital Platform for Generative and Clinical Cognitive Assessment.Journal of personalized medicine · 2025Article
- Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice.Behavior research methods · 2025Article
- Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Improving the Reliability of the Pavlovian Go/No-Go Task for Computational Psychiatry Research.Computational psychiatry (Cambridge, Mass.) · 2025Article
- Clarifying the reliability paradox: poor measurement reliability attenuates group differences.Frontiers in psychology · 2025Article
- Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability : Reliability of computational model parameters.Psychonomic bulletin & review · 2024Review
- Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control.bioRxiv : the preprint server for biology · 2024Article
- The value of error-correcting responses for cognitive assessment in games.Scientific reports · 2024Article
- Test-retest reliability of the play-or-pass version of the Iowa Gambling Task.Cognitive, affective & behavioral neuroscience · 2024Article
- Evidence for a competitive relationship between executive functions and statistical learning.NPJ science of learning · 2024Article
- Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.PLoS computational biology · 2024Article
- Predicting cognitive function 3 months after surgery in patients with a glioma.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.