Biology subjects

Fengler, A.

Publications and source records attributed to Fengler, A..

6 recordsLinked to original sources

bioRxiv2026

HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neuro-cognitive Modeling

Computational models are central to cognitive neuroscience, but their rigorous application to experimental datasets is often constrained to a narrow set of canonical models that afford tractable analytical computations. We introduce the HSSM (Hierarchical Sequential Sampling Model) ecosystem, a Python toolbox that democratizes access to a broad, extensible array of neurocognitive process models through hierarchical Bayesian inference. Naturally leveraging simulation-based inference via likelihood surrogates, HSSM enables fast parameter estimation for models lacking closed-form likelihoods. Built atop PyMC and Bambi, HSSM provides a user-friendly formula syntax for specifying hierarchical mixed-effects regressions on model parameters, incorporating trial-by-trial neural or physiological covariates. The ecosystem allows fast model simulation and training data generation, as well as the neural network training utilities to deploy surrogate likelihood networks via HuggingFace. Contributions are designed to benefit not only the single researcher working on a problem, but organically, the entire research community. Together, the tools in the HSSM ecosystem bridge the interests of computational theorists as well as experimentalists, accelerating the cycle from model development to rigorous empirical testing.

neuroscience↗

bioRxiv2025

Slower transitions between control states lead to reductions in cognitive flexibility over the lifespan

Declines in cognitive flexibility are a hallmark of cognitive aging, but their causes remain elusive. Here, we examine a previously untested source of aging-related cognitive inflexibility, building on a dynamical systems framework for flexible cognition. We propose that cognitive inflexibility can arise in part from slower transitions through the space of distinct configurations of cognitive control needed to pursue different goals. To test this model, we had participants across the lifespan (aged 19-88) perform a cognitive task under different performance goals, each of which induced a different configuration of cognitive control. Using computational modeling, we measured dynamic changes in two control signals (attentional focus and response caution) as participants pursued distinct goals. This allowed us to test three potential sources of age-related decreases in cognitive flexibility: 1) diminished control capacity in environments that require more goal switching; 2) diminished range of control adjustments; and 3) slower transitions between control configurations. Of these, we found that age was only associated with transition speed. When given sufficient time to maintain a given goal, older adults were able to adjust control to a similar extent as younger adults. However, older adults were more likely to undershoot their target control configuration when frequently switching between goals, consistent with longer transition times between configurations. Our findings demonstrate the critical role that cognitive dynamics play in explaining the mechanisms through which cognitive inflexibility arises in older adulthood. SignificanceMeeting the shifting demands of daily life requires a person to constantly adjust the way in which they allocate their cognitive resources. As people get older, it becomes increasingly difficult to achieve the same levels of cognitive flexibility. We show that these deficits can be explained by age-related changes in the dynamics governing adjustments between cognitive states. Older adults take longer to adapt their cognitive state to a new goal, despite demonstrating preserved ability to reach desired states when given sufficient time. These findings validate a new model of cognitive flexibility and its variability across individuals, including forms of inflexibility seen in healthy aging and across a range of psychiatric and neurological disorders.

animal behavior and cognition↗

bioRxiv2025

Choice history biases evidence accumulation: a cross-species comparison from humans to mice

Mice are increasingly used to study the neural circuit-level basis of behavior, often with the ultimate goal of extrapolating these insights to humans. To generalize insights about neural functioning across species, it is crucial to ensure correspondence in behavioral and cognitive strategy. We previously showed that human observers evidence accumulation is biased by previous choices (Urai et al., 2019). To replicate these findings across species, we fit Diffusion Decision Models (Fengler et al., 2025) to behavioral data from 62 mice performing a standardized perceptual decision-making task (The International Brain Laboratory et al., 2021). We identified the same cognitive strategy of history-dependent evidence accumulation: individual differences in choice repetition were explained by a history-dependent bias in the rate of evidence accumulation rather than its starting point. We argue that history-biased evidence integration reflects a fundamental aspect of perceptual decision-making, that may transcend the specific species.

animal behavior and cognition↗

bioRxiv2022

Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM

Computational modeling has become a central aspect of research in the cognitive neurosciences. As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision making intended to capture processes jointly giving rise to reaction time distributions and choice data in n-alternative choice paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of sequential sampling models. In addition, recent work has motivated the combination of SSMs with reinforcement learning (RL) models, which had historically been considered in separate literatures. Here we provide a significant addition to the widely used HDDM Python toolbox and include a tutorial for how users can easily fit and assess a (user extensible) wide variety of SSMs, and how they can be combined with RL models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.

bioinformatics↗

bioRxiv2021

Predicted Utility Modulates Working Memory Fidelity in the Brain

The predicted utility of information stored in working memory (WM) is hypothesized to influence the strategic allocation of WM resources. Prior work has shown that when information is prioritized, it is remembered with greater precision relative to other remembered items. However, these paradigms often complicate interpretation of the effects of predicted utility on item fidelity due to a concurrent memory load. Likewise, no fMRI studies have examined whether the predicted utility of an item modulates fidelity in the neural representation of items during the memory delay without a concurrent load. In the current study, we used fMRI to investigate whether predicted utility influences fidelity of WM representations in the brain. Using a generative model multivoxel analysis approach to estimate the quality of remembered representations across predicted utility conditions, we observed that items with greater predicted utility are maintained in memory with greater fidelity, even when they are the only item being maintained. Further, we found that this pattern follows a parametric relationship where more predicted utility corresponded to greater fidelity. These precision differences could not be accounted for based on a redistribution of resources among already-remembered items. Rather, we interpret these results in terms of a gating mechanism that allows for pre-allocation of resources based on predicted value alone. This evidence supports a theoretical distinction between resource allocation that occurs as a result of load and resource pre-allocation that occurs as a result of predicted utility.

neuroscience↗

bioRxiv2020

Likelihood Approximation Networks (LANs) for Fast Inference of Simulation Models in Cognitive Neuroscience

In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.

neuroscience↗