Retrospective revaluation in sequential decision making: a tale of two systems.

By Samuel J. Gershman, Arthur B. Markman, A. Ross Otto. 2014

214 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Strengths, Weaknesses, and Combinations of Model-based and Model-free Reinforcement Learning (2015, background): “The goal of this research ranges from flexible planning in reinforcement learning (Silver et al., 2008) to understanding the mind in cognitive science and introducing the computational models that can account psychological experiments (Daw et al., 2005; Gershman et al., 2014).”
Integrating memories to guide decisions (2015, unknown): “Also, a recent study [50 ] showed that manipulating rest periods in a revaluation design analogous to Figure 1 had effects on humans’ success solving subsequent integration tests, consistent with the idea that this behavior was supported by processes occurring during rest.”
Offline replay supports planning in human reinforcement learning (2018, methodology): “To measure revaluation behavior, we computed a replanning magnitude score for each subject and block (Gershman et al., 2014; Momennejad et al., 2017; Russek et al., 2017), defined as the pro-”
Model-Based Reasoning in Humans Becomes Automatic with Training (2015, background): “Indeed, a recent experiment has suggested that the model-based system can “train” the model-free system by replaying and simulating experience offline, and that this in turn allows for choice under load that appears model-based [36].”
The Tools Challenge: Rapid Trial-and-Error Learning in Physical Problem Solving (2019, background): “Recently, evidence for this sort of knowledge transfer has been found to exist in people (Gershman, Markman, & Otto, 2014; Gershman, Zhou, & Kommers, 2017).”
Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation (2014, background): “…instance, the activation of VTA dopamine neurons to a saltiness-related CS, if observed in salt appetite conditions, could arise from a model-based system as part of the way that this putatively trains model-free predictions (Doll et al., 2009; Foster & Wilson, 2006, 2007; Gershman et al., 2014).”
Reinforcement Learning and Causal Models (2017, background): “For example, Gershman et al. (2014) showed that human subjects can make choices on the basis of model-based knowledge under conditions where the model-free system is ostensibly in control of behavior.”