| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Angela Radulescu, Reka Daniel, Yael Ni. The effects of aging on the interaction between reinforcement learning and attention. Psychology and aging. vol 31. issue 7. 2017-08-02. PMID:27599017. |
reinforcement learning (rl) in complex environments relies on selective attention to uncover those aspects of the environment that are most predictive of reward. |
2017-08-02 |
2023-08-13 |
human |
| 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 |
| Judit Zsuga, Klara Biro, Csaba Papp, Gabor Tajti, Rudolf Gesztely. The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept. Behavioral neuroscience. vol 130. issue 1. 2016-10-17. PMID:26795580. |
reinforcement learning (rl) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. |
2016-10-17 |
2023-08-13 |
Not clear |
| Judit Zsuga, Klara Biro, Csaba Papp, Gabor Tajti, Rudolf Gesztely. The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept. Behavioral neuroscience. vol 130. issue 1. 2016-10-17. PMID:26795580. |
based on the functional connectivity of vs, model-free and model based rl systems center on the vs that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. |
2016-10-17 |
2023-08-13 |
Not clear |
| Matthew A Albrecht, James A Waltz, James F Cavanagh, Michael J Frank, James M Gol. Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients. PloS one. vol 11. issue 4. 2016-08-12. PMID:27044008. |
the negative symptoms of schizophrenia (sz) are associated with a pattern of reinforcement learning (rl) deficits likely related to degraded representations of reward values. |
2016-08-12 |
2023-08-13 |
Not clear |
| Matthew A Albrecht, James A Waltz, James F Cavanagh, Michael J Frank, James M Gol. Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients. PloS one. vol 11. issue 4. 2016-08-12. PMID:27044008. |
however, the rl tasks used to date have required active responses to both reward and punishing stimuli. |
2016-08-12 |
2023-08-13 |
Not clear |
| Courtney A Bryce, John G Howlan. Stress facilitates late reversal learning using a touchscreen-based visual discrimination procedure in male Long Evans rats. Behavioural brain research. vol 278. 2015-12-14. PMID:25251839. |
ru38486 did not block the facilitation of rl by stress, although it dramatically increased response, but not reward, latencies. |
2015-12-14 |
2023-08-13 |
rat |
| Jasmina Bakic, Rudi De Raedt, Marieke Jepma, Gilles Pourtoi. What is in the feedback? Effect of induced happiness vs. sadness on probabilistic learning with vs. without exploration. Frontiers in human neuroscience. vol 9. 2015-11-20. PMID:26578929. |
valence of an emotional experience is pivotal here, as it alters reward and punishment processing, as well as the balance between safety and risk taking, which can be translated into changes in the exploration-exploitation trade-off during reinforcement learning (rl). |
2015-11-20 |
2023-08-13 |
human |
| Chen Zhang, Chao Sun, Liqiang Gao, Nenggan Zheng, Weidong Chen, Xiaoxiang Zhen. Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2013. 2015-09-09. PMID:24111331. |
this paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on reinforcement learning (rl) with the learning intelligence of animals together. |
2015-09-09 |
2023-08-12 |
rat |
| 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 |
| Hasan A A Al-Rawi, Kok-Lim Alvin Yau, Hafizal Mohamad, Nordin Ramli, Wahidah Hashi. Reinforcement learning for routing in cognitive radio ad hoc networks. TheScientificWorldJournal. vol 2014. 2015-05-11. PMID:25140350. |
this paper applies rl in routing and investigates the effects of various features of rl (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. |
2015-05-11 |
2023-08-13 |
Not clear |
| Hasan A A Al-Rawi, Kok-Lim Alvin Yau, Hafizal Mohamad, Nordin Ramli, Wahidah Hashi. Reinforcement learning for routing in cognitive radio ad hoc networks. TheScientificWorldJournal. vol 2014. 2015-05-11. PMID:25140350. |
simulation results show that the rl parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves sus' network performance without significantly jeopardizing pus' network performance, specifically sus' interference to pus. |
2015-05-11 |
2023-08-13 |
Not clear |
| 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 |
| Kok-Lim Alvin Yau, Geong-Sen Poh, Su Fong Chien, Hasan A A Al-Raw. Application of reinforcement learning in cognitive radio networks: models and algorithms. TheScientificWorldJournal. vol 2014. 2015-04-01. PMID:24995352. |
it provides an extensive review on how most schemes have been approached using the traditional and enhanced rl algorithms through state, action, and reward representations. |
2015-04-01 |
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. |
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 |
| Leanne Chukoskie, Joseph Snider, Michael C Mozer, Richard J Krauzlis, Terrence J Sejnowsk. Learning where to look for a hidden target. Proceedings of the National Academy of Sciences of the United States of America. vol 110 Suppl 2. 2013-08-27. PMID:23754404. |
learning trajectories were well characterized by a simple reinforcement-learning (rl) model that maintained and continually updated a reward map of locations. |
2013-08-27 |
2023-08-12 |
human |
| Anne G E Collins, Michael J Fran. How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. The European journal of neuroscience. vol 35. issue 7. 2013-02-05. PMID:22487033. |
this system is typically modeled in the reinforcement learning (rl) framework by incrementally accumulating reward values of states and actions. |
2013-02-05 |
2023-08-12 |
human |
| Amir Dezfouli, Bernard W Ballein. Habits, action sequences and reinforcement learning. The European journal of neuroscience. vol 35. issue 7. 2013-02-05. PMID:22487034. |
to account for habits, theorists have argued that another action controller is required, called model-free rl, that does not form a model of the world but rather caches action values within states allowing a state to select an action based on its reward history rather than its consequences. |
2013-02-05 |
2023-08-12 |
Not clear |
| Kyung Man Kim, Michael V Baratta, Aimei Yang, Doheon Lee, Edward S Boyden, Christopher D Fiorill. Optogenetic mimicry of the transient activation of dopamine neurons by natural reward is sufficient for operant reinforcement. PloS one. vol 7. issue 4. 2012-10-17. PMID:22506004. |
these results provide strong evidence that the transient activation of dopamine neurons provides a functional reward signal that drives learning, in support of rl theories of dopamine function. |
2012-10-17 |
2023-08-12 |
mouse |