Kevin J Miller

AI-discovered tuning laws explain neuronal population code geometry

Reilly Tilbury, Ali Haydaroğlu, Jacob M Ratliff, Valentin Schmutz et autres

The activity of visual cortical neurons forms a population code representing image stimuli. There is, however, a discrepancy between our understanding of this code at the single-cell and population levels: direct measurements indicate the population code is high-dimensional, but established models of …

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Discovering Symbolic Cognitive Models from Human and Animal Behavior

Pablo Samuel Castro, Nenad Tomašev, Ankit Anand, Rishika Mohanta et autres

Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cognitive process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, …

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Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

Sarah Jo C Venditto, Kevin J Miller, Carlos D. Brody, Nathaniel D. Daw

Abstract Different brain systems have been hypothesized to subserve multiple “experts” that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture …

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Author response: Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

Sarah Jo C Venditto, Kevin J Miller, Carlos D. Brody, Nathaniel D. Daw

Different brain systems have been hypothesized to subserve multiple “experts” that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences …

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Hybrid Neural-Cognitive Models Reveal How Memory Shapes Human Reward Learning

Maria Katharina Eckstein, Christopher Summerfield, Nathaniel D. Daw, Kevin J Miller

Human reward-guided learning is typically modeled with simple reinforcement learning algorithms. These models assume that choices depend on a handful of incrementally learned variables that summarize previous outcomes. Here, we scrutinize this account by collecting and modeling a large dataset of human …

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Reviewer #1 (Public Review): Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

Sarah Jo C Venditto, Kevin J Miller, Carlos D. Brody, Nathaniel D. Daw

Different brain systems have been hypothesized to subserve multiple “experts” that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences …

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

Sarah Jo C Venditto, Kevin J Miller, Carlos D. Brody, Nathaniel D. Daw

Abstract Different brain systems have been hypothesized to subserve multiple “experts” that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture …

us, gb (code pays fourni par la source)

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

Sarah Jo C Venditto, Kevin J Miller, Carlos D. Brody, Nathaniel D. Daw

Abstract Different brain systems have been hypothesized to subserve multiple “experts” that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture …

us, gb (code pays fourni par la source)

Reviewer #2 (Public Review): Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

Sarah Jo C Venditto, Kevin J Miller, Carlos D. Brody, Nathaniel D. Daw

Different brain systems have been hypothesized to subserve multiple “experts” that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences …

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

Sarah Jo C Venditto, Kevin J Miller, Carlos D. Brody, Nathaniel D. Daw

Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences …

us, gb (code pays fourni par la source)

Cognitive Model Discovery via Disentangled RNNs

Kevin J Miller, Maria Katharina Eckstein, Matthew Botvinick, Zeb L. Kurth-Nelson

Abstract Computational cognitive models are a fundamental tool in behavioral neuroscience. They instantiate in software precise hypotheses about the cognitive mechanisms underlying a particular behavior. Constructing these models is typically a difficult iterative process that requires both inspiration from the literature and …

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Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making

Maria Katharina Eckstein, Christopher Summerfield, Nathaniel D. Daw, Kevin J Miller

Abstract Quantitative models of behavior are a fundamental tool in cognitive science. Typically, models are hand-crafted to implement specific cognitive mechanisms. Such “classic” models are interpretable by design, but may provide poor fit to experimental data. Artificial neural networks (ANNs), on the …

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