| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Dongbin Zhao, Derong Liu, F L Lewis, Jose C Principe, Stefanoi Squarti. Editorial Special Issue on Deep Reinforcement Learning and Adaptive Dynamic Programming. IEEE transactions on neural networks and learning systems. 2019-11-20. PMID:29993895. |
deep rl is able to output control signal directly based on input images, which incorporates both the advantages of the perception of deep learning (dl) and the decision making of rl or adaptive dynamic programming (adp). |
2019-11-20 |
2023-08-13 |
human |
| Vincent Moens, Alexandre Zéno. Learning and forgetting using reinforced Bayesian change detection. PLoS computational biology. vol 15. issue 4. 2019-05-28. PMID:30995214. |
overall, the proposed method provides a general framework to study learning flexibility and decision making in rl contexts. |
2019-05-28 |
2023-08-13 |
Not clear |
| Wei James Chen, Ian Krajbic. Computational modeling of epiphany learning. Proceedings of the National Academy of Sciences of the United States of America. vol 114. issue 18. 2018-04-24. PMID:28416682. |
models of reinforcement learning (rl) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of rl. |
2018-04-24 |
2023-08-13 |
human |
| Sigurd Ziegler, Mads L Pedersen, Athanasia M Mowinckel, Guido Biel. Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning. Neuroscience and biobehavioral reviews. vol 71. 2017-10-17. PMID:27608958. |
attention deficit hyperactivity disorder (adhd) is characterized by altered decision-making (dm) and reinforcement learning (rl), for which competing theories propose alternative explanations. |
2017-10-17 |
2023-08-13 |
Not clear |
| Wing Chung Chang, James A Waltz, James M Gold, Tracey Chi Wan Chan, Eric Yu Hai Che. Mild Reinforcement Learning Deficits in Patients With First-Episode Psychosis. Schizophrenia bulletin. vol 42. issue 6. 2017-07-24. PMID:27179125. |
our findings of preserved abilities to use representations of expected value to guide decision making, and our mixed results regarding rapid rl, may reflect a lesser degree of prefrontal cortical functional impairment in fep than in chronic samples. |
2017-07-24 |
2023-08-13 |
Not clear |
| Samuel J Gershman, Nathaniel D Da. Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework. Annual review of psychology. vol 68. 2017-05-24. PMID:27618944. |
we review the psychology and neuroscience of reinforcement learning (rl), which has experienced significant progress in the past two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. |
2017-05-24 |
2023-08-13 |
Not clear |
| Gregory P Strauss, Nicholas S Thaler, Tatyana M Matveeva, Sally J Vogel, Griffin P Sutton, Bern G Lee, Daniel N Alle. Predicting psychosis across diagnostic boundaries: Behavioral and computational modeling evidence for impaired reinforcement learning in schizophrenia and bipolar disorder with a history of psychosis. Journal of abnormal psychology. vol 124. issue 3. 2016-12-13. PMID:25894442. |
each participant's trial-by-trial decision-making behavior was fit to 3 computational models of rl: (a) a standard actor-critic model simulating pure basal ganglia-dependent learning, (b) a pure q-learning model simulating action selection as a function of learned expected reward value, and (c) a hybrid model where an actor-critic is "augmented" by a q-learning component, meant to capture the top-down influence of orbitofrontal cortex value representations on the striatum. |
2016-12-13 |
2023-08-13 |
human |
| Stacey Emmonds, John OʼHara, Kevin Till, Ben Jones, Amy Brightmore, Carlton Cook. Physiological and Movement Demands of Rugby League Referees: Influence on Penalty Accuracy. Journal of strength and conditioning research. vol 29. issue 12. 2016-06-30. PMID:25970494. |
to date, no studies have considered decision making in rl referees. |
2016-06-30 |
2023-08-13 |
Not clear |
| Pragathi P Balasubramani, V Srinivasa Chakravarthy, Balaraman Ravindran, Ahmed A Moustaf. A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making. Frontiers in computational neuroscience. vol 9. 2015-07-03. PMID:26136679. |
we have previously proposed a reinforcement learning (rl)-based model of the bg that simulates the interactions between dopamine (da) and serotonin (5ht) in a diverse set of experimental studies including reward, punishment and risk based decision making (balasubramani et al., 2014). |
2015-07-03 |
2023-08-13 |
Not clear |
| Sivaramakrishnan Kaveri, Hiroyuki Nakahar. Dual reward prediction components yield Pavlovian sign- and goal-tracking. PloS one. vol 9. issue 10. 2015-06-29. PMID:25310184. |
reinforcement learning (rl) has become a dominant paradigm for understanding animal behaviors and neural correlates of decision-making, in part because of its ability to explain pavlovian conditioned behaviors and the role of midbrain dopamine activity as reward prediction error (rpe). |
2015-06-29 |
2023-08-13 |
rat |
| Michael J Frank, Chris Gagne, Erika Nyhus, Sean Masters, Thomas V Wiecki, James F Cavanagh, David Badr. fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. The Journal of neuroscience : the official journal of the Society for Neuroscience. vol 35. issue 2. 2015-04-07. PMID:25589744. |
two largely separate literatures have examined dynamics of reinforcement learning (rl) as a function of experience but assuming a static choice process, or conversely, the dynamics of choice processes in decision making but based on static decision values. |
2015-04-07 |
2023-08-13 |
human |
| Michael J Frank, Chris Gagne, Erika Nyhus, Sean Masters, Thomas V Wiecki, James F Cavanagh, David Badr. fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. The Journal of neuroscience : the official journal of the Society for Neuroscience. vol 35. issue 2. 2015-04-07. PMID:25589744. |
here we show that human choice processes during rl are well described by a drift diffusion model (ddm) of decision making in which the learned trial-by-trial reward values are sequentially sampled, with a choice made when the value signal crosses a decision threshold. |
2015-04-07 |
2023-08-13 |
human |
| Pragathi P Balasubramani, V Srinivasa Chakravarthy, Balaraman Ravindran, Ahmed A Moustaf. An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning. Frontiers in computational neuroscience. vol 8. 2014-05-05. PMID:24795614. |
in this study, we present a model of risk based decision making in a modified reinforcement learning (rl)-framework. |
2014-05-05 |
2023-08-13 |
Not clear |
| Vladislav D Veksler, Wayne D Gray, Michael J Schoelle. Goal-proximity decision-making. Cognitive science. vol 37. issue 4. 2013-12-17. PMID:23551486. |
reinforcement learning (rl) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. |
2013-12-17 |
2023-08-12 |
human |
| Mehdi Khamassi, Pierre Enel, Peter Ford Dominey, Emmanuel Procy. Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters. Progress in brain research. vol 202. 2013-11-19. PMID:23317844. |
converging evidence suggest that the medial prefrontal cortex (mpfc) is involved in feedback categorization, performance monitoring, and task monitoring, and may contribute to the online regulation of reinforcement learning (rl) parameters that would affect decision-making processes in the lateral prefrontal cortex (lpfc). |
2013-11-19 |
2023-08-12 |
human |
| Mehdi Khamassi, Pierre Enel, Peter Ford Dominey, Emmanuel Procy. Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters. Progress in brain research. vol 202. 2013-11-19. PMID:23317844. |
here, we analyze the sensitivity to rl parameters of behavioral performance in two monkey decision-making tasks, one with a deterministic reward schedule and the other with a stochastic one. |
2013-11-19 |
2023-08-12 |
human |
| B Ravindra. Relativized hierarchical decomposition of Markov decision processes. Progress in brain research. vol 202. 2013-11-19. PMID:23317845. |
reinforcement learning (rl) is a popular paradigm for sequential decision making under uncertainty. |
2013-11-19 |
2023-08-12 |
Not clear |
| Darrell A Worthy, Melissa J Hawthorne, A Ross Ott. Heterogeneity of strategy use in the Iowa gambling task: a comparison of win-stay/lose-shift and reinforcement learning models. Psychonomic bulletin & review. vol 20. issue 2. 2013-09-03. PMID:23065763. |
reinforcement learning (rl) models such as the expectancy valence (ev) model have often been used to characterize choice behavior in this work, and accordingly, parameter differences from these models have been used to examine differences in decision-making processes between different populations. |
2013-09-03 |
2023-08-12 |
human |
| M A J Apps, R Green, N Ramnan. Reinforcement learning signals in the anterior cingulate cortex code for others' false beliefs. NeuroImage. vol 64. 2013-06-04. PMID:22982355. |
in rl, unexpected decision-making outcomes constitute prediction errors (pe), which are coded for by neurons in the anterior cingulate cortex (acc). |
2013-06-04 |
2023-08-12 |
human |
| Peter Daya. How to set the switches on this thing. Current opinion in neurobiology. vol 22. issue 6. 2013-05-22. PMID:22704797. |
reinforcement learning (rl) has become a dominant computational paradigm for modeling psychological and neural aspects of affectively charged decision-making tasks. |
2013-05-22 |
2023-08-12 |
human |