The Schurr Lab

Computational models of reinforcement learning (RL) have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, potentially oversimplifying the nuanced relationship between human behavior and rewards. To address these challenges and explore models of RL, we utilized a method of model discovery using equation discovery algorithms. This method, currently used mainly in physics and biology, attempts to capture data by proposing a differential equation from an array of suggested linear and nonlinear functions. Using this method, we were able to identify a model of RL which we termed the Quadratic Q-Weighted model. The model suggests that reward prediction errors obey nonlinear dynamics and exhibit negativity biases, resulting in an underweighting of reward when expectations are low, and an overweighting of the absence of reward when expectations are high. We tested the generalizability of our model by comparing it to classical models used in nine published studies. Our model surpassed traditional models in predictive accuracy across eight out of these nine published datasets, demonstrating not only its generalizability but also its potential to offer insights into the complexities of human learning. This work showcases the integration of a behavioral task with advanced computational methodologies as a potent strategy for uncovering the intricate patterns of human cognition, marking a significant step forward in the development of computational models that are both interpretable and broadly applicable.

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individuals computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.

Uncovering the architecture of white matter axons is fundamental to the study of brain networks. We developed a method for quantifying axonal orientations at a resolution of ~15 micrometers. This method is based on the common Nissl staining technique for postmortem histological slices. Nissl staining reveals the spatial organization of glial cells along axons. Using structure tensor analysis, we leveraged this patterned organization to uncover local axonal orientation. We used Nissl-based structure tensor analysis to extract fine details of axonal architecture and demonstrated its applicability in multiple datasets of humans and nonhuman primates. Nissl-based structure tensor analysis can be used to compare fine-grained features of axonal architecture across species and is widely applicable to existing datasets.

Diffusion MRI tractography is essential for reconstructing white-matter projections in the living human brain. Yet tractography results miss some projections and falsely identify others. A challenging example is the optic radiation (OR) that connects the thalamus and the primary visual cortex. Here, we tested whether OR tractography can be optimized using quantitative T1 mapping. Based on histology, we proposed that myelin-sensitive T1 values along the OR should remain consistently low compared with adjacent white matter. We found that complementary information from the T1 map allows for increasing the specificity of the reconstructed OR tract by eliminating falsely identified projections. This T1-filtering outperforms other, diffusion-based tractography filters. These results provide evidence that the smooth microstructural signature along the tract can be used as constructive input for tractography. Finally, we demonstrate that this approach can be applied in a case of multiple sclerosis, and generalized to the HCP-available MRI measurements. We conclude that multimodal MRI microstructural information can be used to eliminate spurious tractography results in the case of the OR.