Biology subjects

Darrow, D. P.

Publications and source records attributed to Darrow, D. P..

5 recordsLinked to original sources

bioRxiv2025

A Foraging-Theory Based Model Captures The Full Spectrum of Human Behavioral Diversity in a Classic RL Task

Decision-making tasks involving multiple, simultaneously presented options are mainstays of cognitive neuroscience and psychology and are increasingly important to the emerging field of computational psychiatry. Modeling approaches to these tasks overwhelmingly assume that participants make choices based on explicitly comparing the values of the presented options. Contrary to this long-held assumption, we found instead that humans employ a compare-to-threshold decision process, similar to theories of foraging, when making sequential decisions about concurrently available options. We confirmed this result in a large (1000 participant) dataset with multiple converging lines of evidence comparing both model fits and model generative performance. Value-comparison models were restricted to a reduced area of the potential space of single-trial outcome-dependent behavior, demonstrating an intrinsic limitation in the ability to reproduce strategy diversity. Furthermore, we found that using even the best-fit value-comparison model led to a substantial, systematic bias and a compression of individual differences in reconstructed behavior compared to the foraging-based model, leading to weaker predictions of behavioral health measures. Our results imply that studies using value-comparison models to link behavior with neural activity or psychiatric symptoms may be less sensitive to individual differences than a simple alternative based on ethological foraging.

neuroscience↗

bioRxiv2025

Nonlinear modulation of human exploration by distinct sources of uncertainty

Decision-making in uncertain environments requires balancing exploration and exploitation, with exploration typically assumed to increase monotonically with uncertainty. Challenging this prevailing assumption, we demonstrate a more complex relationship by decomposing environmental uncertainty into volatility (systematic change in reward contingencies, learnable) and stochasticity (random noise in observations, unlearnable). Across two behavioral experiments (N=1001, N=747) using a probabilistic reward task, we find a robust U-shaped relationship between the volatility-to-stochasticity (v/s) ratio and exploratory behavior, with participants exploring more when either stochasticity or volatility dominates. Remarkably, this pattern extends to real-world financial behavior, as demonstrated through analysis of five years of S&P 500 stock market data, where portfolio diversity (a proxy for exploration) shows the same U-shaped relationship with market volatility (systematic price movements driven by fundamental factors, e.g., economic shifts) relative to trading noise (random fluctuations from trading activity unrelated to fundamentals). These findings reveal how humans adaptively modulate exploration strategies based on the qualitative composition of uncertainty, with optimal performance occurring at intermediate uncertainty ratios. This nonlinear relationship has important implications for understanding decision-making across domains where uncertainty arises from multiple sources.

neuroscience↗

bioRxiv2025

Metric Validation for Detection of Delayed and Directed Coupling

BackgroundThe brain functions as a complex network of billions of interconnected neurons, coordinating processes from basic reflexes to high-level cognition. Dysfunction in these networks contribute to neurological and psychiatric disorders, including epilepsy, depression, and Parkinsons disease. Understanding these network alterations is essential for developing effective therapies. However, reconstructing network topology from human electrophysiology data is challenging due to sparse sampling, measurement noise, and variable time delays in interregional communication. Effective connectivity (EC) metrics have been developed to infer directed neural interactions, but their accuracy and robustness under real-world data constraints remain unclear. This study empirically evaluates common EC metrics to determine which most accurately reconstruct network topology in the presence of data limitations. MethodsWe generated Erd[o]s-Renyi networks and simulated time series using a time-delayed vector autoregressive (VAR) model. We systematically varied network size, data length, measurement noise, and network coverage. Variations of four commonly used EC metrics, cross-correlation, Granger causality, mutual information, and transfer entropy, were evaluated for reconstruction accuracy using cosine distance to compare estimated and true connectivity matrices. ResultsMultivariate transfer entropy demonstrated the highest accuracy across various conditions but required significantly longer computation times. For small networks (<30 nodes), mutual information and Granger causality rapidly and accurately reconstructed networks. For larger networks, partial cross-correlation performed well with good computational efficiency. Notably, zero-lag metrics perform no better than chance in nearly all conditions. ConclusionThe choice of an EC metric should consider specific data constraints.While multivariate transfer entropy is the most reliable across conditions, its long runtime limits its practical application. For large networks, partial cross-correlation offers a faster and reasonably accurate alternative. Granger causality and mutual information are effective for small networks. Critically, time-lagged metrics are essential for accurate network reconstructions, as failing to account for time delays leads to reconstructions no more accurate than random network models.

bioinformatics↗

bioRxiv2025

Individual differences in sequential decision-making

1People differ widely in how they make decisions in uncertain environments. While many studies leverage this variability to measure differences in specific cognitive processes and parameters, the key dimension(s) of individual variability in uncertain decision-making tasks has not been identified. Here, we analyzed behavioral data from 1001 participants performing a restless three-armed bandit task, where reward probabilities fluctuated unpredictably over time. Using a novel analytical approach that controlled for the stochasticity in this tasks, we identified a dominant nonlinear axis of individual variability. We found that this primary axis of variability was strongly and selectively correlated with the probability of exploration, as inferred by latent state modeling. This suggests that the major factor shaping individual differences in bandit task performance is the tendency to explore (versus exploit), rather than personality characteristics, reinforcement learning model parameters, or low-level strategies. Certain demographic characteristics also predicted variance along this principle axis: participants at the exploratory end tended to be younger than participants at the exploitative end, and self-identified men were overrepresented at both extremes. Together, these findings offer a principled framework for understanding individual differences in task behavior while highlighting the cognitive and demographic factors that shape individual differences in decision-making under uncertainty.

neuroscience↗

bioRxiv2023

Flexible Multi-Step Hypothesis Testing of Human ECoG Data using Cluster-based Permutation Tests with GLMEs

BackgroundTime series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. MethodsWe first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. ResultsFirst, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. ConclusionsWe propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power and accuracy leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data. Highlights[bullet] Combining CBPT with GLMEs allows statistical analysis to match experimental design. [bullet]CBPT with GLMEs accounts for subject variability and hierarchical random effects. [bullet]The proposed method maintains control of type I error, type II error, and FWER. [bullet]CBPT with GLMEs can be applied to individual channels and pseudo-population data.

neuroscience↗