10 papers · ranked by Valyu relevance
Axel Hutt, Scott Rich, Taufik A Valiante, Jérémie Lefebvre
Heterogeneity is the norm in biology. The brain is no different: neuronal cell-types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs and ion channel distributions. While this biophysical diversity enriches neural systems’ dynamical repertoire, it remains challenging to…
Payam Piray, Nathaniel D. Daw
Sound principles of statistical inference dictate that uncertainty shapes learning. In this work, we revisit the question of learning in volatile environments, in which both the first and second-order statistics of environments dynamically evolve over time. We propose a new model, the volatile Kalman filter (VKF)…
Jie Xu, Nicholas T. Van Dam, Yuejia Luo, André Aleman + 2 more
Humans adapt their learning strategies to changing environments by estimating the volatility of the reinforcement conditions. Here, we examine how volatility affects learning and the underlying functional brain organizations using a probabilistic reward reversal learning task. We found that the order of conditions was…
Gargi Majumdar, Fahd Yazin, Arpan Banerjee, Dipanjan Roy
Understanding the mechanisms behind the variability of neural signals holds the key to the characterization of developmental and lifespan aging trajectories. Here we propose that tracking temporally structured neural fluctuations or volatility in brain areas during naturalistic tasks provides a more salient…
D. Tuzsus, I. Pappas, J. Peters
Natural environments often exhibit various degrees of volatility, ranging from slowly changing to rapidly changing contingencies. How learners adapt to changing environments is a central issue in both reinforcement learning theory and psychology. For example, learners may adapt to changes in volatility by increasing…
Zhihao Wang, Tian Nan, Katharina S. Goerlich, Yiman Li + 3 more
Humans are able to adapt to the fast-changing world by estimating statistical regularities of the environment. Although fear can profoundly impact adaptive behaviors, the neural mechanisms underlying this phenomenon remain elusive. Here, we conducted a behavioral experiment (n = 21) and a functional magnetic resonance…
Taiji Yamada, Kazuyuki Samejima
Action selection involves two systems: a model-free reinforcement learning strategy, which relies on experience with action–outcome pairs, and a model-based reinforcement learning strategy, which enables more flexible behavior via inference using a model of the invariant environmental structure. Although environmental…
Wanjun Lin, Laurence T. Hunt, Erdem Pulcu, Michael Browning
The ability to seek reward and avoid punishment is a fundamental survival instinct. In natural environments, however, the statistics of rewards and punishments can change independently of one another. In addition, individuals experiencing anxiety and depression may be selectively biased to process rewards and…
Jamie M. Waterman, Gareth J. Moore, Loren K. Amdahl-Culleton, Sara Hoefer + 1 more
Biological responses to environmental stimuli are inherently dynamic. Recent technological advances enable detailed time-resolved measurements of such responses. However, a unifying model for the quantitative characterisation of dynamic response curves is lacking, thus limiting biological insights and comparisons. We…
Brónagh McCoy, Rebecca P. Lawson
Anxiety is known to alter learning in uncertain environments. Standard experimental paradigms and computational models addressing these differences have mainly assessed the impact of volatility, and anxious individuals have been shown to have a reduced learning rate when moving from a stable to volatile environment.…