Go and no-go learning in reward and punishment: Interactions between affect and effect
By Marc Guitart-Masip, Quentin J. M. Huys, Lluís Fuentemilla, Peter S. Dayan, Emrah Düzel, R. J. Dolan. NeuroImage, 2012
387 citations recorded by the source corpus.
Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/87eeaa59fdfc55362d76a92cf97c56a67e94d4d1
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In Cellular and Molecular Neuroscience
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Citations in the corpus
- Motivation and cognitive control: from behavior to neural mechanism. (2015, background): “…of positive or appetitive incentives, it has also been extended to settings involving negative incentives or threats (Dreisbach & Fischer 2012; Engelmann et al. 2009; Guitart-Masip et al. 2012a,b; Krawczyk & D’Esposito 2013; Murty et al. 2012; Simoes-Franklin et al. 2010; Taylor et al. 2006).”
- Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package (2016, methodology): “…in cognitive and decision sciences further confirmed the validity and usefulness of HBA and other hierarchical approaches (e.g., Ahn et al., 2014; Guitart-Masip et al., 2012; Huys et al., 2011; Katahira, 2016; Lee, 2011; Raja Beharelle, Polania, Hare, & Ruff, 2015; Shiffrin, Lee, Kim, &…”
- The mysterious motivational functions of mesolimbic dopamine. (2012, neutral): “A recent imaging paper showed that doses of L-DOPA that enhanced the striatal representation of appetitively motivated actions did not affect the neural representation of reinforcement value (Guitart-Masip et al., 2012).”