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Dissecting time-varying decision dynamics in the basal ganglia: how the weighing of distinct sensory information contributes to task difficulty and perceptual conflict

Authors
Dr. Nadja Ging-Jehli
Brown University ~ Carney Institute for Brain Science; Department of Cognitive, Linguistic & Psychological Sciences
Dr. James Cavanagh
University of New Mexico ~ Psychology
Dr. Minkyu Ahn
Handong Global University ~ School of Computer Science and Electrical Engineering
Dr. David Segar
Harvard Medical School ~ Department of Neurosurgery, Brigham and Women’s Hospital
Dr. Wael Asaad
Brown University ~ Warren Alpert Medical School, Department of Neuroscience, The Carney Institute
Dr. Michael Frank
Brown University ~ Carney Institute for Brain Science; Department of Cognitive, Linguistic & Psychological Sciences
Abstract

Perceptual decision-making evolves from interactions between cortical neurons which decode sensory information, and the basal ganglia which integrate across sources of information to prepare for action. The subthalamic nucleus (STN) dynamically controls the decision threshold, determining the necessary amount of corticostriatal evidence for response initiation. Past research focused on evidence accumulation models with fixed decision bounds, while neural data and biophysical simulations suggest that STN activity is highly dynamic. These dynamics may be mechanistically reflected by theta- and beta-band oscillations. In this study, we aimed to use these e-phys biosignatures to determine if dynamic activity in the STN is mostly modulated by active conflict, task difficulty, or both. To do so, we recorded electrophysiological activity in the STN and globus pallidus (GP) of 17 patients with Parkinson’s disease (n=14) or dystonia (n=3) during a direction discrimination task. Stimuli involved moving random-dot patterns that independently varied in motion strength (coherence), and motion direction (angular trajectory). These conditions separately manipulated task difficulty (easy, hard) and active conflict (low, high). Leveraging recent advances in likelihood-free inference, we tested the aforementioned aims by using a wide variety of sequential sampling models (SSMs). First, we found that models with Weibull-informed collapsing boundaries outperformed classical Diffusion Decision models, Ornstein-Uhlenbeck models, and models with linear collapsing boundaries. Second, motion strength coherence (difficulty) affected evidence accumulation (drift rate), while angular trajectory (active conflict) affected response caution (decision threshold). Finally, preliminary modeling results based on neural regressors suggest that within-trial dynamics of STN and GP theta and beta activity influence decision dynamics. This study advances our understanding of how neural dynamics in the basal ganglia influence decision dynamics as a function of task difficulty and perceptual conflict. It has the potential to resolve some discrepancies in previous studies assuming a fixed decision boundary that is impacted by a single measure of neural activity.

Tags

Keywords

Model-based cognitive neuroscience
sequential sampling modeling
perceptual conflict
intracranial recording in basal ganglia
computational psychiatry
parkinson’s disease
dystonia
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Cite this as:

Ging-Jehli, N. R., Cavanagh, J. F., Ahn, M., Segar, D. J., Asaad, W. F., & Frank, M. J. (2023, July). Dissecting time-varying decision dynamics in the basal ganglia: how the weighing of distinct sensory information contributes to task difficulty and perceptual conflict. Abstract published at MathPsych/ICCM/EMPG 2023. Via mathpsych.org/presentation/1013.