372 results found
    1. Neuroscience

    Neural computations underlying inverse reinforcement learning in the human brain

    Sven Collette et al.
    The human brain is capable of implementing inverse reinforcement learning, where an observer infers the hidden reward structure of a decision problem solely through observing another individual take actions.
    1. Neuroscience

    Effects of dopamine on reinforcement learning and consolidation in Parkinson’s disease

    John P Grogan et al.
    Memory over 24 hours was impaired in Parkinson's patients off, rather than on, dopaminergic medication during reinforcement learning, whereas dopamine did not affect positive and negative reinforcement, in contrast to previous studies.
    1. Neuroscience

    Offline replay supports planning in human reinforcement learning

    Ida Momennejad et al.
    fMRI evidence for off-task replay predicts subsequent replanning behavior in humans, suggesting that learning from simulated experience during replay helps update past policies in reinforcement learning.
    1. Computational and Systems Biology
    2. Neuroscience

    A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning

    Nicholas T Franklin, Michael J Frank
    Computational modeling suggests that feedback between striatal cholinergic neurons and spiny neurons dynamically adjusts learning rates to optimize behavior in a variable world.
    1. Neuroscience

    Homeostatic reinforcement learning for integrating reward collection and physiological stability

    Mehdi Keramati, Boris Gutkin
    A mathematical model built around the assumption that the desire to maintain internal homeostasis drives the behavior of animals, by affecting their learning processes, can explain many real-world behaviors, including some that might otherwise appear irrational.
    1. Neuroscience

    Reward-based training of recurrent neural networks for cognitive and value-based tasks

    H Francis Song et al.
    A two-part neural network models reward-based training and provides a unified framework in which to study diverse computations that can be compared to electrophysiological recordings from behaving animals.
    1. Neuroscience

    Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon

    Armin Lak et al.
    Confidence-dependent reinforcement learning is active and produces trial-to-trial choice updating even in well-learned perceptual decisions without explicit reward biases, across species and sensory modalities.
    1. Neuroscience

    Rules and mechanisms for efficient two-stage learning in neural circuits

    Tiberiu Teşileanu et al.
    Teaching signals from "tutor" brain areas should be adapted to the plasticity mechanisms in "student" areas to achieve efficient learning in two-stage systems such as the vocal control circuit of the songbird.
    1. Neuroscience

    Mesolimbic confidence signals guide perceptual learning in the absence of external feedback

    Matthias Guggenmos et al.
    Neural confidence signals can take the role of reward signals and explain perceptual learning without external feedback as a form of internal reinforcement learning.
    1. Neuroscience

    Distinct roles of striatal direct and indirect pathways in value-based decision making

    Shinae Kwak, Min Whan Jung
    The direct and indirect pathways of the dorsal striatum play indispensable roles in value-dependent action selection and value learning, respectively.

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