Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning

By Michael J Frank, Ahmed A. Moustafa, H. Haughey, Tim Curran, Kent Hutchison. Proceedings of the National Academy of Sciences of the United States of America, 2007

701 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4a5794c5aa33d6548c7c3f9c2d05d6602d325d6c

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Citations in the corpus

Ten simple rules for the computational modeling of behavioral data (2019, background): “, 2016), for investigating individual differences (Frank et al., 2007; Starns and Ratcliff, 2010; Collins and Frank, 2012; Gillan et al., 2016; Somerville et al., 2017; Nilsson et al., 2011) and for quantifying the effects of interventions such as drugs, lesions, illness, or experimental conditions ”
Reinforcement learning: the good, the bad and the ugly. (2008, background): “Further, studies examining the relationship between learning from negative and positive consequences and genetic polymorphisms related to the D2 dopaminergic receptor have established a functional link between dopamine and learning when outcomes are worse than expected [41,42].”
How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis (2012, background): “However, we also note that, in contrast to our previous studies (Frank et al. , 2007, 2009b; Doll et al. , 2011), we did not observe an effect of SNPs impacting striatal dopaminergic function (DARPP-32 and DRD2) on RL parameters.”
The Neuropsychopharmacology of Fronto-Executive Function: Monoaminergic Modulation (2009, result): “However, Frank et al. (2007) found on a trial-to-trial basis during discrimination learning that val/val homozygotes were less likely than met/met carriers to switch responses after negative feedback, which appears inconsistent with the hypothesis of Bilder et al. Clearly, further investigations…”
Frontal Theta Links Prediction Errors to Behavioral Adaptation in Reinforcement Learning (2009, positive): “…delay RT effect was not seen on the stimulus pair immediately following feedback but on the next appearance of the same stimulus following some delay, so we interpret these effects as being partially reflective of working memory for the outcome specific to the stimulus chosen (Frank et al., 2007b).”
Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. (2012, methodology): “In the MoE model, we implemented this selection across all levels of actions with the softmax function, commonly used in algorithmic models of this circuitry (Samejima et al. 2005; Daw et al. 2006; Frank et al. 2007; Doll et al. 2009).”
Asymmetric learning from financial information (2013, positive): “A particular variant of this gene, referred to as the COMT Met/Met genotype, has previously been associated with better memory and cognitive performance relative to the other two genotypes, namely V al/Met and V al/V al (Frank et al. (2007), Doll et al. (2011)).”
Cognitive control over learning: creating, clustering, and generalizing task-set structure. (2013, neutral): “S 1 S 2 + + 0 0 0 0 0 0 0 0 0 0 0 0 + + + 0 0 0 0 0 0 + 0 0 0 0 0 0 + C 1 C 2 C 3 C 4 TS 2 TS 3 TS 1 C 1 C 2 C 3 C 4”
Neurophysiology of performance monitoring and adaptive behavior. (2014, background): “Genetic polymorphisms in the DARPP-32 gene, which relates to direct pathway function (as it is selectively expressed in D1 receptor expressing neurons), are related to increased learning from rewards, whereas polymorphisms relating to D2 receptor expression are related to the degree of learning from”
Disentangling the Roles of Approach, Activation and Valence in Instrumental and Pavlovian Responding (2011, background): “A similar effect may be expected from DARPP-32, although its closer relationship to synaptic plasticity would also suggest effects on instrumental learning [90–92].”
Trial-by-trial data analysis using computational models (2011, background): “…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; Schonberg et al., 2007; Wittmann et al., 2008; Hare et al.,…”
Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. (2014, background): “…better learning and choice based on positive outcomes but worse performance in avoiding negative outcomes (Bódi et al., 2009; Cools et al., 2009; Frank, Moustafa, et al., 2007; Frank, Seeberger, & O'Reilly, 2004; Moustafa, Sherman, & Frank, 2008; Palminteri, Boraud, Lafargue, Dubois, &…”
Probing Compulsive and Impulsive Behaviors, from Animal Models to Endophenotypes: A Narrative Review (2010, background): “Unexpected punishment (monetary loss) has been proposed to result in a dip in central dopaminergic activity, reversal learning, and diminished reward seeking (Frank et al, 2007).”