Understanding how decision making changes across the lifespan is a central challenge for neuroscience, yet research on cognitive aging has remained largely disconnected from the theoretical and computational advances that now shape modern systems neuroscience. Over the past two decades, theoretical frameworks have transformed how we study cognition in young, healthy brains, providing principled tools to model latent decision states, neural dynamics, population codes, and interareal communication. In contrast, aging research has often relied on single metric behavioral readouts, cross sectional comparisons, and descriptive neural analyses, limiting our ability to explain fundamental differences in individual aging trajectories. This gap represents a missed opportunity because aging offers a powerful platform for testing theories of neural computation, stability, and flexibility under changing biological constraints. Here, we argue that closer integration between aging research and contemporary theoretical neuroscience can move the field beyond descriptive accounts toward more mechanistic explanations of decision making across the lifespan. To this end, we outline how recent advances in behavioral quantification, latent state modeling, dynamical systems, encoding models, representational geometry, and recurrent neural networks offer a rich theoretical toolkit for neuroscientists studying decision making across the lifespan.
Visually comparing brain networks, or connectomes, is an essential task in the field of neuroscience. Especially relevant to the field of clinical neuroscience, group studies that examine differences between populations or changes over time within a population enable neuroscientists to reason about effective diagnoses and treatments for a range of neuropsychiatric disorders. In this paper, we specifically explore how visual analytics tools can be used to facilitate various clinical neuroscience tasks, in which observation and analysis of meaningful patterns in the connectome can support patien…
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. Here we propose a unifying framework for modeling neural activity during decision-making tasks. The framework includes the canonical drift-diffusion model and enables extensions such as multi-dimensional accumulators, variable and collapsing boundaries, and discrete jumps. Our framework is based on co…
School of Mathematics, South China University of Technology（华南理工大学数学学院）；Centre for Artificial Intelligence, University of Technology Sydney（悉尼科技大学人工智能中心）
Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks. A key problem is that they fail to record all the relevant neurons in the animal brain. To alleviate this problem, we develop an RNN-based Actor-Critic framework, which is trained through reinforcement learning (RL) to solve two tasks analogous to the monkeys' decision-making tasks. The trained model is capable of reproducing some features of neural activities recorded from animal brain, or some behavior properties exhibited in animal…
How choices are made within noisy environments is a central question in the neuroscience of decision making. Previous work has characterized temporal accumulation of evidence for decision-making in static environments. However, real-world decision-making involves environments with statistics that change over time. This requires discounting old evidence that may no longer inform the current state of the world. Here we designed a rat behavioral task with a dynamic environment, to probe whether rodents can optimally discount evidence by adapting the timescale over which they accumulate it. Extend…
Recent advances in neuroscience data acquisition allow for the simultaneous recording of large populations of neurons across multiple brain areas while subjects perform complex cognitive tasks. Interpreting these data requires us to index how task-relevant information is shared across brain regions, but this is often confounded by the mixing of different task parameters at the single neuron level. Here, inspired by a method developed for a single brain area, we introduce a new technique for demixing variables across multiple brain areas, called demixed shared component analysis (dSCA). dSCA de…
Institute of Technologies and Management of Digital Transformation（数字化转型技术与管理研究所）；Institute of Information Management in Mechanical Engineering（机械工程信息管理研究所）
The need for more transparency of the decision-making processes in artificial neural networks steadily increases driven by their applications in safety critical and ethically challenging domains such as autonomous driving or medical diagnostics. We address today's lack of transparency of neural networks and shed light on the roles of single neurons and groups of neurons within the network fulfilling a learned task. Inspired by research in the field of neuroscience, we characterize the learned representations by activation patterns and network ablations, revealing functional neuron populations …
Confidence-weighted integration of human and machine judgments for superior decision-making
Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany（马克斯·普朗克行为神经生物学研究所——caesar，波恩，德国）；Department of Experimental Psychology, University College London, London, United Kingdom（实验心理学系，伦敦大学学院，伦敦，英国）
National Institute of Information and Communications Technology（信息通信研究机构）；Keio University（庆应义塾大学）；Hiroshima University（广岛大学）；Tokyo University of Science（东京理科大学）；Nagoya University（名古屋大学）；University of Ottawa（渥太华大学）；University of Yamanashi（山梨大学）
Decision making based on behavioral and neural observations of living systems has been extensively studied in brain science, psychology, and other disciplines. Decision-making mechanisms have also been experimentally implemented in physical processes, such as single photons and chaotic lasers. The findings of these experiments suggest that there is a certain common basis in describing decision making, regardless of its physical realizations. In this study, we propose a local reservoir model to account for choice-based learning (CBL). CBL describes decision consistency as a phenomenon where mak…
Emory University（埃默里大学）；University of California, Los Angeles（加州大学洛杉矶分校）；The University of British Columbia（不列颠哥伦比亚大学）；Lehigh University（理海大学）
Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged as a potential method for modeling complex network data. Deep models, on the other hand, have low interpretability, which prevents their us…
Universidad Autónoma de Madrid（马德里自治大学）；Sano - Centre for Computational Personalised Medicine（Sano计算个性化医学中心）
Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories, and how computations emerge in the trained networks. Surprisin…
Psychiatric neuroscience is increasingly aware of the need to define psychopathology in terms of abnormal neural computation. The central tool in this endeavour is the fitting of computational models to behavioural data. The most prominent example of this procedure is fitting reinforcement learning (RL) models to decision-making data collected from mentally ill and healthy subject populations. These models are generative models of the decision-making data themselves, and the parameters we seek to infer can be psychologically and neurobiologically meaningful. Currently, the gold standard approa…
