The computational neurobiology of learning and reward.

By Nathaniel D. Daw, Kenji Doya. Current Opinion in Neurobiology, 2006

503 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/eb96fc97bbae7b5653173293438e2f4682affd34

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Habits, rituals, and the evaluative brain. (2008, methodology): “Experiments on goal-directed behavior in animals, including some with reward devaluation protocols, are increasingly being interpreted within the general framework of reinforcement learning (Daw & Doya 2006, Niv et al. 2006).”
Policy Gradient Coagent Networks (2011, background): “Methods for solving sequential decision problems with delayed reward, where the problems are formulated as Markov decision processes (MDPs), have been compared to the learning mechanisms of animal brains [3, 4, 9, 10, 13, 20, 22].”
The Effects of Acute Exercise on Mood, Cognition, Neurophysiology, and Neurochemical Pathways: A Review (2016, unknown): “learning [271, 272], and motor functions [273].”
Behavioral and neural properties of social reinforcement learning (2011, background): “Measuring differences in reaction times to cues to index learning differs from reinforcement studies that use modulated choice behavior as an indicator of learning (Tanaka et al., 2004; Daw and Doya, 2006; Schönberg et al., 2007).”
Go and no-go learning in reward and punishment: Interactions between affect and effect (2012, result): “Reconciling these results with those showing prediction errors (Daw and Doya, 2006) is an important task for the future.”
How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis (2012, background): “Activity and plasticity in striatal neurons, a major target of dopaminergic efferents, are dynamically sensitive to these dopaminergic prediction error signals, which enable the striatum to represent RL values (O’Doherty et al. , 2004; Frank, 2005; Daw & Doya, 2006).”
Cognitive bias as an indicator of animal emotion and welfare: Emerging evidence and underlying mechanisms (2009, background): “The role of the basal ganglia in initiating action consequent on different types of incoming information about relative decisions is currently an important area of study (e.g. Redgrave et al., 1999; McHaffie et al., 2005; Daw and Doya, 2006; Bogacz, 2007).”
Trial-by-trial data analysis using computational models (2011, methodology): “One practical point is that, in our experience (Daw et al., 2006; Schonberg et al., 2007), multisubject fMRI results from analyses of this sort are, in practice, more robust if a single α (and a single set of any other parameters of the learning model) is used to generate regressors for all…”
Uncertainty in perception and the Hierarchical Gaussian Filter (2014, methodology): “The notation chosen here emphasizes the role of precisions in the updating process more than that used in (Mathys et al., 2011); they are, however, equivalent.”
Free Energy, Value, and Attractors (2011, background): “Perhaps the most important thing to come out of these modelling initiatives is that phasic dopamine discharges in the brain are a prime candidate for reporting reward prediction error [3, 70, 71].”
Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice (2015, unknown): “In theories of reinforcement learning based on the TD model, these dopamine responses are important for driving higher-order acquisition of approach behavior to potential reward 23,37,38,49,50 .”
Heuristics, learning and the business angel investment decision-making process (2015, background): “If we accept the neuroeconomics emphasis on the embedding of learning as routinized knowledge derived from prior experience (Daw and Doya 2006; Sutton and Barto 1998; Juliusson et al. 2005), heuristics represent a residual outcome of learning, the hard-wiring of experiential learning for more…”
The anatomy of choice: active inference and agency (2013, background): “The question then reduces to how the brain maximizes value (Camerer, 2003; Daw and Doya, 2006; Dayan and Daw, 2008).”