Dialogues on prediction errors.

By Yael Niv, Geoffrey Schoenbaum. 2008

352 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning (2015, background): “…have also been developed as models of animal learning (e.g., Sutton & Barto 1990, Klopf 1988, Ludvig, Sutton & Kehoe 2012) and as models of the brain’s reward systems (Schultz, Dayan & Montague 1997), where they have been particularly influential (e.g., Niv & Schoenbaum 2008, O’Doherty 2012).”
A Neural Computational Model of Incentive Salience (2009, background): “To the extent that computational models aim to capture real psychological cognition, retasting may remain an important feature of many model-based systems [17,18,21,22,28,39,40,44].”
Free Energy, Value, and Attractors (2011, background): “…Article Free Energy, Value, and Attractors Karl Friston1 and Ping Ao2, 3 1 The Wellcome Trust Centre for Neuroimaging, UCL, Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK 2 Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine of…”
Attention, learning and the value of information (2012, background): “Importantly, however, the reward prediction errors that have been traditionally considered in dopamine cells are model-free quantities that only register changes in value between consecutive time steps (Niv and Schoenbaum, 2008).”
Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms (2015, background): “…with probabilistic rewards, we better emulated real-world learning (Kruschke, 2006), prolonged the learning process so as to allow a detailed computational analysis of the dynamics of learning (see below), and ensured that prediction errors occur on every trial (Niv and Schoenbaum, 2008).”
Model-based predictions for dopamine (2017, neutral): “The striking correspondence between the phasic responses of midbrain dopamine neurons and the tempo-ral-difference reward prediction error posited by rein-forcement-learning theory is by now well established [1–5].”
Reinforcement Learning and Causal Models (2017, background): “…has figured so prominently in neuroscience is that the phasic firing of midbrain dopamine neurons appears to correspond closely with the theoretical prediction error (Bayer & Glimcher, 2005; Glimcher, 2011; Niv & Schoenbaum, 2008; Schultz, Dayan, & Montague, 1997; Schultz & Dickinson, 2000).”
From prediction error to incentive salience: mesolimbic computation of reward motivation (2012, background): “…cached learned value, Zhang et al. adopted the temporal difference model of reinforcement learning (Sutton & Barto, 1981; Schultz & Dickinson, 2000; Dayan & Balleine, 2002; McClure et al., 2003b; O’Doherty et al., 2003; Schultz, 2006; Niv & Schoenbaum, 2008; Redish et al., 2008b; Glimcher, 2011).”
How Prediction Errors Shape Perception, Attention, and Motivation (2012, background): “Predictions and PEs are also key concepts in models of reward learning, motivational control, and decisionmaking (e.g., Rescorla and Wagner, 1972; Pearce and Hall, 1980; Behrens et al., 2007; Niv and Schoenbaum, 2008).”