ライフサイエンスコーパス: reinforcement

1 ntral to the mechanism of addiction and drug reinforcement.

2 anufacturing and then modified for pore-wall reinforcement.

3 utamate neuron activity can support positive reinforcement.

4 tion of new evidence even without a targeted reinforcement.

5 g-seeking behavior via processes of negative reinforcement.

6 y impair older adult's ability to learn from reinforcement.

7 ine seeking under a second-order schedule of reinforcement.

8 tide-1 (GLP-1) receptor agonists reduce drug reinforcement.

9 on the same timescale to optimize behavioral reinforcement.

10 relevant, and mostly transient, role in food reinforcement.

11 rotein hnRNP H in methamphetamine reward and reinforcement.

12 ministic vs. probabilistic nature of initial reinforcement.

13 amine system is involved with mediating drug reinforcement.

14 changes through social influence and social reinforcement.

15 ver-press for food under a yoked schedule of reinforcement.

16 ions relative to rats exposed to predictable reinforcement.

17 rocedure to assess sex differences in opioid reinforcement.

18 he ventral medial striatum in mediating drug reinforcement.

19 le of ovarian hormones in promoting nicotine reinforcement.

20 ciations between sensory stimuli and delayed reinforcement.

21 rategies, not for basic reward-driven action reinforcement.

22 icated in nicotine withdrawal, aversion, and reinforcement.

23 n terminals in NAc was sufficient to support reinforcement.

24 or effects were magnified following monetary reinforcement.

25 tion of prosocial preferences from vicarious reinforcement.

26 earning plans to address areas that may need reinforcement.

27 ences are shaped by experience with external reinforcements.

28 preference change in the absence of external reinforcements.

29 it seems to occur independently of apparent reinforcement(1)-young children prefer cues associated w

30 d using a progressive ratio schedule of food reinforcement, (3) effort allocation using a concurrent

31 pecific messages, longitudinal delivery, and reinforcement; 53 clusters); WASH (ventilated, improved

32 separating its established roles in aversive reinforcement and appetitive motivation [5, 6].

33 ing factor (G-CSF) alters cocaine reward and reinforcement and can enhance cognitive flexibility.

34 upling of the two components leads to mutual reinforcement and creates an ultrastrong membrane that s

35 er9Gly variant and measures of both nicotine reinforcement and cue-elicited craving.

36 te gyrus, previously implicated in vicarious reinforcement and empathy, carried less information abou

37 dministration significantly blunted fentanyl reinforcement and increased food reinforcement for 15 we

38 tamine-induced dopamine release, reward, and reinforcement and induces dynamic changes in basal and m

39 ph1 (H1(+/-)) showed reduced methamphetamine reinforcement and intake and dose-dependent changes in m

40 longs to the direct pathway, drives negative reinforcement and is essential for aversive learning in

41 gging force necessary for cell-cell junction reinforcement and maintenance.

42 and reinstatement only in females as well as reinforcement and motivation in males and females.

43 ical role in basal ganglia function, such as reinforcement and motor learning.

44 tin cytoskeleton including stress fiber (SF) reinforcement and realignment.

45 and NOP to elucidate their role in negative reinforcement and relapse.

46 d reinforcement depends upon the schedule of reinforcement and that preclinical opioid vs. food choic

47 tion is driven by both positive and negative reinforcement and that spaced training is more effective

48 utility, which depends on both the objective reinforcement and the motivation.

49 ibit reduced methamphetamine-induced reward, reinforcement, and dopamine release.

50 ce, contingency management and the community-reinforcement approach.

51 s, where complex mechanisms of influence and reinforcement are at work.

52 egative emotional states that drive negative reinforcement are hypothesized to derive from the within

53 ls that are both subliminal and unrelated to reinforcement are processed and exert an influence on VP

54 anisms of psychomotor sensitization and drug reinforcement, as assessed by the conditioned place pref

55 ison groups) were less sensitive to one-back reinforcement, as indicated by a reduced effect on both

56 stress element responsible for the negative reinforcement associated with the "dark side" of alcohol

57 on of the striato-amygdala system engaged in reinforcement-based and emotional learning processes rep

58 explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncert

59 spiny neurons (MSNs) are sufficient to drive reinforcement-based learning.

60 ated the role of the mesoaccumbal circuit in reinforcement-based learning.

61 h-effort motivation but not for all forms of reinforcement-based learning.

62 e dopamine 1 (D1) receptor on the reward and reinforcement behavior of GBP.

63 still present when controlling for secondary reinforcement but absent when social information exchang

64 Removal of CAN(THC) reinforcement (but not CAN(CBD)) resulted in a robust ex

65 Curli not only provides structural reinforcement, but also facilitates surface adhesion.

66 the importance of initial asymmetry and its reinforcement by mechanical feedback within the inner ce

67 hermal and mechanical properties rely on the reinforcement by the high specific strength ceramic nano

68 re reveal further complexity of dopaminergic reinforcement circuits between and within MB compartment

69 modulation during specific tests of cocaine reinforcement, demand, and relapse-related behaviors in

70 the expression of sex differences in opioid reinforcement depends upon the schedule of reinforcement

71 is resource, and proposes how the utility of reinforcements depends on the motivation.

72 a safe/small reward, with the odds of risky reinforcement descending or ascending throughout the tes

73 f global and local attachment as well as tie reinforcement due to social interactions between people.

74 ct DNA methylation maintenance and epigenome reinforcement events that occur in specialized cell type

75 about how humans and animals learn from such reinforcement feedback comes from experiments that invol

76 Reinforcement feedback is thus critical for learning-rel

77 , it is less understood how we learn through reinforcement feedback when sampling from a continuous s

78 ed fentanyl reinforcement and increased food reinforcement for 15 weeks in non-opioid dependent rats.

79 that adolescent nicotine exposure increases reinforcement for cocaine and other drugs.

80 d after an "exposure" intervention (monetary reinforcement for looking at disgusting images).(7)(,)(8

81 We then decreased the likelihood of reinforcement for trained mice, requiring them to modify

82 sponsiveness negatively correlated with food reinforcement, FRR, and zWFL.

83 This local reinforcement has a 20 s lifetime, and requires the micr

84 p a paradigm for studying rapid sensorimotor reinforcement in a circuit that is right at the interfac

85 d by stimulus features that are unrelated to reinforcement in a given sensory context.

86 ls have been extensively used as a matrix or reinforcement in many applications.

87 schedule of fentanyl and diluted Ensure((R)) reinforcement in Sprague-Dawley male and female rats.

88 First, mice acquire positive and negative reinforcement in the presence of discrete discriminative

89 servations indicate short-lived, specialized reinforcement in the spindle center.

90 te release from VTA is sufficient to promote reinforcement independent of concomitant DA co-release,

91 t = 0.100, 95% CI: 0.041, 0.187) and nonfood reinforcement (indirect effect = 0.076, 95% CI: 0.025, 0

92 release might be responsible for behavioral reinforcement induced by VTA glutamate neuron activity.

93 AH compresses reinforcement information across episodes, updating the

94 athways that are critical for motivation and reinforcement integrate information from these "early" d

95 hanisms that transform reward-based positive reinforcement into maladaptive drug seeking.

96 anhedonia, the failure to translate positive reinforcements into future actions, requires multiple se

97 f-initiated action, and that thalamostriatal reinforcement is constrained by mGlu(2) activation.

98 iatal glutamatergic afferents and behavioral reinforcement is not well understood.

99 Classical reinforcement learning (CRL) has been widely applied in

100 This paper presents the deep reinforcement learning (DRL) framework to estimate the o

101 euroscience and psychology; however, quantum reinforcement learning (QRL), which shows superior perfo

102 To test this assumption, we simulated a reinforcement learning (RL) agent equipped with a perfec

103 The computational framework of reinforcement learning (RL) has allowed us to both under

104 We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control p

105 emical spaces were used as training sets for reinforcement learning (RL) in combination with differen

106 Reinforcement learning (RL) is a framework of particular

107 Deep reinforcement learning (RL) methods have driven impressi

108 ping paradigm, where subjects solve multiple reinforcement learning (RL) problems differing in struct

109 Reinforcement learning (RL) refers to the behavioral pro

110 Contemporary reinforcement learning (RL) theory suggests that potenti

111 Reinforcement learning (RL) theory suggests two classes

112 In this paper, we combine Reinforcement Learning (RL) with Agent Based Modeling (A

113 nymous with the 'reward prediction error' of reinforcement learning (RL), and are thought to update n

114 e capacities and, thanks to progress in deep reinforcement learning (RL), it is now possible to apply

115 profound neuroscientific implications: deep reinforcement learning (RL).

116 al, and computational processes that support reinforcement learning (RL).

117 ehavior can be characterized as hierarchical reinforcement learning (RL).

118 rameworks such as animal learning theory and reinforcement learning [3-7].

119 formalizing our predictions as parameters in reinforcement learning accounts of behaviour.

120 We confirm that feedback via a trained reinforcement learning agent can be used to maintain pop

121 A model-free reinforcement learning algorithm revealed that rats with

122 midbrain and the reward prediction errors of reinforcement learning algorithms, which express the dif

123 Explore/exploit decisions were modeled using reinforcement learning algorithms.

124 al the reward prediction error in model-free reinforcement learning algorithms.

125 Reinforcement learning allows organisms to predict futur

126 We then use both optimal control theory and reinforcement learning alongside a combination of analys

127 he curse of dimensionality plagues models of reinforcement learning and decision making.

128 Uncertainty plays a critical role in reinforcement learning and decision making.

129 ershman review concepts and advances in deep reinforcement learning and discuss how these can inform

130 an remember a reward-baited location through reinforcement learning and do so quickly and without req

131 that depression has the strongest effects on reinforcement learning and expectations about the future

132 prefrontal cortex-basal ganglia circuit for reinforcement learning and is ultimately reflected in do

133 Analyses combined computational reinforcement learning and mixed-effects models of decis

134 n the dorsal striatum has been implicated in reinforcement learning and regulation of motivation, but

135 Here, we investigate how reinforcement learning and selective attention interact

136 emonstrator's value function through inverse reinforcement learning and uses it to bias action select

137 se time (RT) distributions that arise during reinforcement learning and value-based decision-making.

138 In particular, we aim to test the role of reinforcement learning as the microscopic mechanism used

139 Finally, we show that reinforcement learning can directly optimise the output

140 uterized conformity task, assumed to rely on reinforcement learning circuits, to 32 patients with sch

141 t multiple sources of uncertainty impinge on reinforcement learning computations: uncertainty about t

142 changes were not associated with deficits in reinforcement learning during an object discrimination r

143 lling can be brought together under a common reinforcement learning framework.

144 n to overcome the key technical challenge of reinforcement learning from imperfect data, which has pr

145 rch on artificial neural networks trained by reinforcement learning has made it possible to model fun

146 the mesoaccumbal circuit for motivation and reinforcement learning have not yet been examined in pri

147 imply that older adults are only impaired in reinforcement learning if they additionally need to lear

148 hoice performance for values learned through reinforcement learning in older adults.

149 Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial int

150 ork focused on agent imitation and show that reinforcement learning is a good candidate to explain ma

151 In computer science, reinforcement learning is a powerful framework with whic

152 Overall, reinforcement learning is a promising technique for the

153 These results demonstrate that reinforcement learning is an effective solution to real-

154 An extensive reinforcement learning literature shows that organisms a

155 sional action and state spaces than existing reinforcement learning methods to model real-life comple

156 behavior was analyzed using both a standard reinforcement learning model and analysis of choice swit

157 We fit a dual-controller reinforcement learning model and obtained a computationa

158 slot machine game play as well as a simpler reinforcement learning model based on the Rescorla-Wagne

159 ask performance was described using a simple reinforcement learning model that dissociates the contri

160 icipants' decisions were best explained by a reinforcement learning model that independently learned

161 correlated with the rate of learning and the reinforcement learning model's prediction error.

162 Values of choices, estimated by a reinforcement learning model, were regressed against BOL

163 Reinforcement learning models capture many behavioral an

164 Reinforcement learning models postulate that neurons tha

165 Reinforcement learning models treat the basal ganglia (B

166 een implemented primarily as state-dependent reinforcement learning models with bias parameters to qu

167 ecting 1) Pavlovian biases in the context of reinforcement learning or 2) hyperprecise prior beliefs

168 We used a reinforcement learning paradigm with compound rewards an

169 thin two hundred trials and errors, as their reinforcement learning processes interact with metacogni

170 vior-reduces positive and increases negative reinforcement learning rates.

171 p to predict expert actions, and (ii) a deep reinforcement learning step to estimate the long-term va

172 gests an imbalance in the influence of these reinforcement learning systems on behavior in individual

173 his, we used a modified version of a classic reinforcement learning task in which feedback indicated

174 We tested subjects in a reinforcement learning task in which reward size and pro

175 e and female) completed multiple blocks of a reinforcement learning task that contained a global hier

176 supports the transition to exploitation on a reinforcement learning task with a spatially structured

177 N = 44) featuring a new variant of a social reinforcement learning task.

178 ) while monkeys performed a two-armed bandit reinforcement learning task.

179 knowledge of chemistry and state-of-the-art reinforcement learning techniques (double Q-learning and

180 ices of older adults are better predicted by reinforcement learning than Bayesian inference, and that

181 Our results consolidate reinforcement learning theory and striatal RPEs as key f

182 re abnormal with respect to predictions from reinforcement learning theory were associated with lower

183 d prediction errors (RPEs), a key concept of reinforcement learning theory, are crucial to the format

184 The circuits modified during such reinforcement learning to support decision-making are no

185 In turn, selective attention biases reinforcement learning towards relevant dimensions of th

186 The combination of reinforcement learning with deep learning is a promising

187 physiological (pupil dilation) signatures of reinforcement learning with eligibility trace across mul

188 n a single task, adapting a standard task of reinforcement learning with incidental episodic encoding

189 Here we describe the use of reinforcement learning(4,5) to create a high-performing

190 cial intelligence research on distributional reinforcement learning(4-6).

191 erse functions, including reward processing, reinforcement learning, and cognitive control.

192 Dopamine is implicated in reward processing, reinforcement learning, and cognitive control.

193 omputer vision, natural language processing, reinforcement learning, and generalized methods.

194 and food consumption through its effects on reinforcement learning, motivation, and hedonic experien

195 ventral tegmental area (VTA) contributes to reinforcement learning, rodent evidence suggests that sl

196 librium, level-k cognition, fictitious play, reinforcement learning, selective payoff-biased imitatio

197 In the framework of reinforcement learning, the probability of performing an

198 is no doubt that social signals affect human reinforcement learning, there is still no consensus abou

199 By adapting computational models of reinforcement learning, we assessed the influence of con

200 of accumulation-to-bound decision models and reinforcement learning, we modeled the performance of hu

201 domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer

202 nference, and that older adults rely more on reinforcement learning-based predictions than younger ad

203 of an approach from artificial intelligence-reinforcement learning-for the control of co-cultures wi

204 a model that combines signal detection with reinforcement learning.

205 clei that contribute to action selection and reinforcement learning.

206 n striatum, a central node in feedback-based reinforcement learning.

207 recognized to play an important role beyond reinforcement learning.

208 e for a neural realization of distributional reinforcement learning.

209 s, including motivation, motor learning, and reinforcement learning.

210 basal ganglia are important for movement and reinforcement learning.

211 ereas choice bias results from supervised or reinforcement learning.

212 separation and conjunctive representation in reinforcement learning.

213 s to reveal their separable contributions to reinforcement learning.

214 gnment with more recent theories of Bayesian reinforcement learning.

215 e algorithmic implementation of imitation in reinforcement learning.

216 y aging does not significantly impair simple reinforcement learning.

217 s: dynamical systems, Bayesian inference and reinforcement learning.

218 riatum where they contribute to movement and reinforcement learning.

219 nisms underlying adaptive imitation in human reinforcement learning.

220 oss, which is associated with impairments in reinforcement learning.

221 h opacities, based on deep learning and deep reinforcement learning.

222 from regression to image classification and reinforcement learning.

223 Training is achieved with Deep Reinforcement Learning.

224 alance between two dissociable strategies of reinforcement learning: model-free and model-based.

225 eralization of two fundamental operations in reinforcement learning: policy improvement and policy ev

226 seamlessly accommodated within the standard reinforcement-learning formalism.

227 emale Long-Evans rats are linked to specific reinforcement-learning mechanisms and are predictive of

228 tational framework was used to elucidate the reinforcement-learning mechanisms that change in adolesc

229 h prediction error signals a Rescorla-Wagner reinforcement-learning model was applied.

230 of tasks previously solved, we can reduce a reinforcement-learning problem to a simpler linear regre

231 reduce the amount of data needed to solve a reinforcement-learning problem.

232 indings, we observed no group differences in reinforcement-learning related fMRI activation.

233 ng in dynamic environments requires multiple reinforcement-learning steps that may be implemented by

234 older adults rely more heavily on suboptimal reinforcement-learning strategies supported by the ventr

235 showed worse performance and relied more on reinforcement-learning strategies than younger adults, w

236 proaches for independently quantifying these reinforcement-learning strategies.

237 al variability in behavioural responses to a reinforcement-learning task encompassing a novelty manip

238 umbens activation in a simple unidimensional reinforcement-learning task was not significantly affect

239 ey behavioural responses associated with the reinforcement-learning task.

240 and decision speed in a two-stage sequential reinforcement-learning task.

241 Reinforcement-learning theory distinguishes between stim

242 A novel reinforcement-learning-based approach is applied to acce

243 urn, a combinatorial model driven by a self-reinforcement mechanism, which relies on a family of nul

244 Reinforcement modeling suggested that this was driven by

245 nactive) phase, cocaine self-administration, reinforcement, motivation and extinction responding were

246 ific performance outcome signals may support reinforcement motor learning of skilled behavior.

247 device for intercalated placement of aligned reinforcement nanofibres.

248 This suggests that reinforcement occurs along preferential force directions

249 eurotransmitter dopamine is required for the reinforcement of actions by rewarding stimuli(1).

250 nd this co-activation is required for robust reinforcement of behavior.

251 As the principal structural reinforcement of biomass giving wood its mechanical prop

252 Such mechanism affects reinforcement of CHK1 phosphorylation and causes persist

253 Reinforcement of current effective strategies and develo

254 e show that neutrophil swarms require mutual reinforcement of damage signaling at the wound core.

255 potentially reprogramming vs maintenance or reinforcement of epigenetic states.

256 Reinforcement of laboratory capacity ensures rapid detec

257 included a forced choice session (to measure reinforcement of nicotine containing vs. denicotinized c

258 Nicotine enhances the reinforcement of non-drug rewards by increasing nucleus

259 One is model-free learning, i.e., simple reinforcement of rewarded actions, and the other is mode

260 ve activity against collagen degradation and reinforcement of the anchoring dentin matrix.

261 atements related to medication efficacy, and reinforcements of the recommendation to take AIs.

262 er, they do not have a selective response to reinforcement omission (the unexpected absence of an eve

263 rontoparietal connectivity, impulsivity, and reinforcement on choice quality (p < 0.001).

264 aversion, movement suppression, and negative reinforcement once activated, and they receive a distinc

265 t associated with responding for conditioned reinforcement or a marker of goal/sign-tracking, suggest

266 nhancing reconsolidation or synaptic reentry reinforcement, or by inhibiting extinction-memory consol

267 sing, rather than motivated responses or the reinforcement process itself.

268 erates in parallel with a model-free operant reinforcement process.

269 We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an

270 desire and intention to drink" and "negative reinforcement"; r = 0.72-0.94).

271 h has shown that infants with a greater food reinforcement ratio (FRR) have higher obesity risk.

272 l factors to investigate the neural basis of reinforcement-related behavior in normal adolescent deve

273 Reinforcement-related cognitive processes, such as rewar

274 ventromedial prefrontal cortex) and positive reinforcement-related prediction errors (ventral striatu

275 neural activation profiles across different reinforcement-related processes might differentiate indi

276 Variation of error- and reinforcement-related spike rates in L2/3 but not L5/6 p

277 These dynamics depend on differential reinforcement representations in the PH and AH.

278 Cannabis vapor reinforcement resulted in strong discrimination between

279 zWFL, but negatively correlated with nonfood reinforcement; satiety responsiveness negatively correla

280 In addition, the context (or reinforcement schedule under) in which stimuli are encou

281 striatum in head-fixed mice on a fixed time reinforcement schedule.

282 n previous demonstrations that probabilistic reinforcement schedules can enhance psychostimulant-indu

283 ration under a progressive-ratio schedule of reinforcement, shifted the oxycodone dose-response curve

284 input to behavioral output requires a strong reinforcement signal, which is also modulated by interna

285 t, which is known to function in relation to reinforcement signaling.

286 Upon such reinforcement, striatal stimulus encoding gives rise to

287 branes was tested using different "backbone" reinforcement structures.

288 esus monkeys (Macaca mulatta) on a vicarious reinforcement task before and after they sustained ACC l

289 ) who have complete data on the food/nonfood reinforcement task, Baby Eating Behavior Questionnaire,

290 ained at 4 months using the mobile conjugate reinforcement task.

291 ing approaches in hydrogel design and bioink reinforcement techniques are critically evaluated.

292 ses/hydrolases may have a role in coleorhiza reinforcement through cell wall remodelling to confer co

293 Probing learning by omitting reinforcement thus uncovers latent knowledge and identif

294 ies were formed despite the lack of explicit reinforcement to do so.

295 18-month supply of free LPG, and behavioral reinforcements to the control arm.

296 into synthetic fibrous materials will allow reinforcement under mechanical load, the potential for m

297 rials (RCTs) that compared prophylactic mesh reinforcement versus conventional suture closure of midl

298 ing sugar sensing by the gut into behavioral reinforcement via midbrain dopamine neuron responses.

299 CeA->SNL terminal activation elicited reinforcement when linked to voluntary actions but faile

300 ivation of GLP-1 receptors attenuated opioid reinforcement without reducing the thermal antinocicepti