Alexander Weigard
University of Michigan, MI·Psychology·Early-career
Self-control, working memory and sound decision making all mature between childhood and the end of adolescence, and problems with them are linked to the onset of mental health conditions. Yet no single ability maps neatly onto a single psychiatric diagnosis.
- Fields
Psychology
Neuroscience
Public Health & Epidemiology
- Topics
developmental psychopathology
computational psychiatry
efficiency of evidence accumulation
- Grants
- 2 known grants, through Mar 2031
What they work on
- Map the typical maturational trajectory of efficiency of evidence accumulation (EEA), a model-derived measure of how well the brain gathers information for decisions, and its relations to other abilities, across four large longitudinal neuroimaging datasets.
- Quantify genetic and environmental contributions, including family conflict and socioeconomic resources, to how EEA develops from childhood through adolescence.
- Examine the co-development of EEA with transdiagnostic psychopathology, environmental adversity and neuroimaging metrics of large-scale brain network maturation.
- Quantify how much sequential sampling model parameters, which decompose response times and choices, add to multivariate machine learning prediction of substance use disorder outcomes in the Michigan Longitudinal Study and ABCD.
- Identify the multivariate brain network signature of the sequential sampling parameter v (drift rate) and test whether it predicts substance use initiation in ABCD adolescents and distinguishes young adults with SUD in a 60-person pilot sample.
More about the research
These projects ask whether a more general brain process underlies that link, and use mathematical models of how people make decisions to measure it precisely in large groups of young people. One project tests a general decision-making efficiency factor as a shared risk mechanism across disorders; the other tests whether model-based measures can predict which individual youths go on to develop substance use problems.
Why it matters
Psychiatric and substance use disorders often take root during adolescence, and clinicians still lack dependable ways to tell which young people are most at risk. Precise, model-based measures of decision efficiency could sharpen individual risk prediction for substance use disorders and for psychopathology more broadly. Identifying which environments slow this development, such as family conflict or limited resources, could point to concrete targets for prevention programs.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Evaluating efficiency of evidence accumulation as a neurodevelopmental mechanism of transdiagnostic risk for psychopathology: a computational model-based approach
NIH · Jun 2026 – Mar 2031
Leveraging computational models of neurocognition to improve predictions about individual youths' risk for substance use disorders
NIH · May 2021 – Apr 2027
Closest labs
Chandra Sripada
University of Michigan, MI·Neuroscience
Test whether reduced efficiency of evidence accumulation in bipolar disorder comes from weaker flexible rewiring of brain networks, comparing 130 patients and 130 healthy adults with behavioral tasks and task-based neuroimaging.
Erin Thompson
Florida International University, FL·Psychology·Early-career
Examine how exclusionary school discipline, such as suspension, relates to adolescent mental health and substance use in the ABCD Study, accounting for neighborhood, school, behavioral and socioeconomic factors.
Carolina Makowski
University of California-San Diego, CA·Neuroscience·Early-career
Identify behavioral and neuroimaging correlates of eating disorders in youth from the ABCD Study (ages 11–14) and in a clinical sample of adolescent girls in treatment (ages 13–18), using measures of brain shape and whole-brain microstructure.
The school
University of Michigan: 124 labs on PI Pages, $2.1B a year on research, 963 research doctorates a year.
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