Welcome to the Rodebaugh lab website! Here you can learn about what Dr. Rodebaugh & colleagues are working on and what research is being conducted.

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Graduate students in psychology need hands-on support to conduct research using quantitative techniques that exceed their curricular training. Where supervisors are not willing or able to provide this...

Current Employees: If you are currently employed at any of the Universities of Wisconsin, log in to Workday to apply through the internal application process.Job Category:FacultyEmployment Type:Regula...

NIH Funding Opportunities and Notices in the NIH Guide for Grants and Contracts: REQUEST FOR INFORMATION (RFI): INPUT ON POTENTIAL NIH FUNDING APPROACHES TO SUPPORT THE DEVELOPMENT OF FUTURE CLINICAL ...

New study by Czekalla et al. shows how people with depression form and update self-related beliefs in response to positive and negative feedback.

Current Research Topics Leadership Emergence and Development Tackett, J. L., Reardon, K. W., Fast, N. J., Johnson, L., Kang, S. K., Lang, J....

This review integrates computational approaches to provide a unified view on how data analysis methods, biophysical mechanistic models and machine learning approaches can help to uncover the origins a...

Author summary When we learn about cause and effect in everyday life—such as whether a medicine helps recovery from illness—we experience outcomes that vary in degree rather than simply happening or n...

Learning from potential disinformation introduces specific cognitive biases, causing individuals to systematically deviate from an idealized Bayesian updating strategy.

PhD Position on “Mental disorders as harmful stable states” The departments Clinical Psychology and Methodology & Statistics, at the Institute of Psychology, Faculty of Social Sciences, Leiden Univers...

Self-related information automatically modulates early attentional selection into awareness through mechanisms distinct from physical salience, revealing an obligatory, individualized self-prioritization...

Predictive models have emerged as a normative lens for understanding neural function. In this Perspective, de Cothi, Shipley and Barry plausibly unify the diverse roles of hippocampal acetylcholine in...

Large language models offer new opportunities for behavioural science, but their rapid evolution poses challenges for research rigour. We introduce a consensus-based reporting checklist to improve tra...

Nature Human Behaviour, Published online: 22 June 2026; doi:10.1038/s41562-026-02438-zStagnaro et al. compare nine opt-in participant samples on measures of response validity, representativeness and professionalism. They find substantial variation across samples and offer guidance for choosing samples based on the research question.

Nature Human Behaviour, Published online: 22 June 2026; doi:10.1038/s41562-026-02503-7In this Review, Preston, Smith and Voytek examine aperiodic neural activity in the brain and how measuring aperiodic activity can shed light on brain function and disease.

Meta-theoretical perspectives on the research problems and activities of (cognitive) scientists often emphasize empirical problems and problem-solving as the main aspects that account for scientific p...

Genome-wide analyses in multiancestry and European cohorts show that in complex traits, rare alleles have disproportionately large effects at the tails of the phenotypic spectrum compared with common ...

Abstract. Understanding how neural activity relates to behavior remains a central challenge in cognitive neuroscience. Joint modeling offers a principled method by simultaneously fitting behavioral an...

A web page that tells you what your browser gave away the moment you arrived. No login, no form, no permission. Most pages do this. None of them tell you.

hBayesDM 2.0 — modernize stack & toolchain Top-to-bottom refactor of both the R and Python packages onto current Stan tooling, plus the supporting work to keep behavior, docs, and CI in sync. H...

Successful strategic behavior must be grounded in beliefs about the opponent and her intentions. While many potential models have been proposed to explain choices in such situations, the neural mechanisms that govern learning and choice in complex strategic contexts remain poorly understood. Here, we use a computational model that combines dynamic learning and choice mechanisms to explain both choices and response times of human participants engaged in a competitive strategic task. Using electroencephalography (EEG), we identify temporally structured stages of neural processing that support an evolving value-based decision process, corresponding to first- and second-order belief updates and evidence accumulation related to the comparison of action values. Gamma-band phase coupling between central and parietal EEG signals varied with individual winning rate, suggesting that strategic behavior involves coordinated information transfer across spatially remote areas. Together, our data characterize the temporally evolving neural dynamics of belief and valuation processes that underlie strategic choice and provide neural validation for assumptions embedded in computational models of this behavior. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, 725355

Recent work by Siems et al. shows that the brain rhythmically samples competing alternatives through covert spatial attention. This challenges continuous models of decision-making and suggests that evaluation is temporally structured by oscillatory dynamics, with attention determining when alternatives are accessed rather than reflecting changes in their representations.

Nature Human Behaviour - Impaired social functioning is a core feature of autism spectrum disorders. This systematic review and meta-analysis synthesizes the evidence for this across studies...