# Efficiency of evidence accumulation (EEA) as a higher-order, computationally defined RDoc construct > **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $195,000 ## Abstract PROJECT SUMMARY/ABSTRACT Cognitive constructs relevant to self-regulation, including cognitive control, attention, and working memory, are a prominent focus of the Research Domain Criteria (RDoC) initiative to characterize dimensions of individual variation that convey risk for mental disorders. However, many of these constructs are limited by their vague definitions, ambiguous links to neurobiology, and evidence that putative measures of such constructs have weak psychometric properties, including poor reliability and an incoherent factor structure. Further, consistent findings that people with multiple psychiatric disorders tend to display non-specific cognitive deficits that span this array of constructs suggest that cognitive aberrations associated with psychopathology may be better- explained by a higher-order factor than by discrete functions. We propose to evaluate whether efficiency of evidence accumulation (EEA)—a cognitive construct that has been well-characterized in computational modeling and neurophysiological research but has yet to be integrated with RDoC—can overcome many of these limitations by operating as a higher-order factor within the RDoC matrix. EEA is a core mechanism of evidence accumulation models (EAMs)—a predominant mathematical framework for explaining cognitive performance— that has a precise computational definition across both psychological and neurophysiological levels of analysis, clear biological plausibility, and strong psychometric properties. Prior work has established EEA as a reliable factor that accounts for individual differences in performance across a wide variety of cognitive tasks—from simple decisions to complex cognitive control and working memory paradigms—and is impaired in multiple disorders linked to self-regulatory difficulties. We posit that EEA represents a higher-order factor that accounts for a substantial proportion of the variation across cognitive domains in the RDoC matrix and that weak EEA conveys risk for multiple psychopathologies, potentially by impairing decision making across contexts. EEA has yet to be integrated with RDoC and, although trait (between-subjects) variation in EEA is linked to psychopathology, the correlates of state (within-subjects) variation in EEA across real-world contexts are unknown. We propose to evaluate EEA’s role as a candidate higher-order factor in the RDoC framework and set the stage for a larger program of computationally rigorous research on EEA as a bridge between neurobiological mechanisms and real-world behavior by completing the following aims: 1) define the structure and boundaries of trait EEA as a higher-order cognitive domain in the RDoC matrix, 2) develop and pilot tools for daily assessment of state EEA and its relations with real-world fluctuations in contextual factors and behavior. This project has the potential to refine RDoC in a way that that better represents cognitive risk factors for psychopathology (i.e., task-general and transdi... ## Key facts - **NIH application ID:** 10824311 - **Project number:** 5R21MH130939-02 - **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR - **Principal Investigator:** Alexander Weigard - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $195,000 - **Award type:** 5 - **Project period:** 2023-04-15 → 2026-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10824311 ## Citation > US National Institutes of Health, RePORTER application 10824311, Efficiency of evidence accumulation (EEA) as a higher-order, computationally defined RDoc construct (5R21MH130939-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10824311. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*