Representation of Action-Specific Reward Values in the Striatum

By Kazuyuki Samejima, Yasumasa Ueda, Kenji Doya, M. Kimura. Science, 2005

932 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3b06e2bec4d1886a0aff662454e5c29d35ac9990

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In Cognitive Neuroscience

Citations in the corpus

Habits, rituals, and the evaluative brain. (2008, background): “Many projection neurons in the striatum encode action value when monkeys perform in block design paradigms in which action values are experimentally manipulated (Samejima et al. 2005).”
The computational neurobiology of learning and reward. (2006, background): “[9 ,17,27]).”
Trial-by-trial data analysis using computational models (2011, neutral): “…experiments on reward learning and decision making (Platt and Glimcher, 1999; O’Doherty et al., 2003; Sugrue et al., 2004; Barraclough et al., 2004; Samejima et al., 2005; Daw et al., 2006; Li et al., 2006; Frank et al., 2007; Tom et al., 2007; Kable and Glimcher, 2007; Lohrenz et al., 2007;…”
The Neurobiology of Decision: Consensus and Controversy (2009, neutral): “Reprinted with permission from Samejima et al. (2005).”
Human Reinforcement Learning Subdivides Structured Action Spaces by Learning Effector-Specific Values (2009, background): “Although such action–value learning mechanisms work well for experiments involving choice between a few options (Sugrue et al., 2004; Lau and Glimcher, 2005; Samejima et al., 2005; Daw et al., 2006a; Behrens et al., 2007), for movements comprising multiple effectors, they would require unrealistic a”
Choice, uncertainty and value in prefrontal and cingulate cortex (2008, background): “The striatum is one of the main projection regions of the midbrain dopaminergic nuclei, and the activity of its neurons encodes the expected value of potential actions and the difference in the expected values of potential action”
Ten simple rules for the computational modeling of behavioral data (2019, background): “…2015; Donoso et al., 2014; Cohen et al., 2017), but also for electroencephalogram (EEG), electrocorticography (ECOG), electrophysiology and pupillometry among many other data sources (O’Reilly et al., 2013; Collins and Frank, 2018; Samejima et al., 2005; Cavanagh et al., 2014; Nassar et al., 2012).”
FROM REINFORCEMENT LEARNING MODELS OF THE BASAL GANGLIA TO THE PATHOPHYSIOLOGY OF PSYCHIATRIC AND NEUROLOGICAL DISORDERS (2011, background): “) and that the basal ganglia then arbitrate between these actions (likely on the basis of their learned reinforcement probabilitie”
A basal ganglia-forebrain circuit in the songbird biases motor output to avoid vocal errors (2009, background): “Striatal neurons have been shown to be involved in the rapid evaluation of rewarded associations (13) and in the coding of action-specific reward values (24, 25).”
How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis (2012, background): “Further, neural correlates of RL values and prediction errors have consistently implicated the striatum in monkey and rat electrophysiology and human functional imaging (O’Doherty et al. , 2004; Samejima et al. , 2005; Pessiglione et al. , 2006; Lau & Glimcher, 2007; Jocham et al. , 2011).”
The Basal Ganglia and Cortex Implement Optimal Decision Making Between Alternative Actions (2007, background): “In this phase, the main difficulty lies in finding the optimal policy: the mapping between states and actions that maximizesreward(SuttonB Samejima, Ueda, Doya, & Kimura, 2005; Schultz, Dayan, & Montague, 1997), and several models for this process have been ...”
NEUROBIOLOGY OF ECONOMIC CHOICE: A GOOD-BASED MODEL (2011, background): “Consequently, evidence for neuronal encoding of action values gathered using tasks that include a major learning component—instrumental conditioning (Samejima et al. 2005), dynamic matching tasks (Lau & Glimcher 2008, Sugrue et al. 2004), or n-armed bandit tasks—and obtained inferring values from…”
Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. (2014, background): “Neural models suggest that these effects are mediated by differential coding of positive and negative consequences of actions in distinct MSN populations, as observed in electrophysiological studies (Samejima et al., 2005).”