ライフサイエンスコーパス: model-free

1 ational learning mechanisms, model-based and model-free.

2 it without estimating stochastic parameters (model-free adaptation processes, such as associative lea

3 Also, we applied a recently developed model-free algorithm called "step transition and state i

4 Model-free algorithms rely on the accumulation of substa

5 has been traditionally analyzed using either model-free algorithms, such as principal components anal

6 of learning propose a key division between "model-free" algorithms that cache outcome values in acti

7 of 2.85; similar results were obtained with model-free analyses (maximum nonparametric linkage [NPL]

8 Results were corroborated by model-free analyses and modeling via more standard appro

9 Targeted as well as model-free analyses convergently indicated that nucleus

10 Model-free analyses in both tasks showed the same two ef

11 of equilibrium peptide N(1)H/N(2)H exchange, model-free analyses of backbone NH relaxation data and r

12 Model-based and model-free analyses of human and monkey behavior show th

13 Reduced spectral density mapping and model-free analyses reveal dynamic characteristics consi

14 or linkage in four families; model-based and model-free analyses showed a heterogeneity LOD (HLOD) of

15 and used the simple model-free and extended model-free analyses to fit the data and estimate the amp

16 ata than conventional model-free or extended model-free analyses with two or three correlation times.

17 Model-free analysis additionally demonstrated chemical e

18 Model-free analysis indicated that while substrate bindi

19 Here, we demonstrate a novel approach to the model-free analysis of critical micellar concentrations

20 analytical and computational approach for a model-free analysis of metabolic data applicable to mamm

21 le, independently reproducing results from a model-free analysis of small-angle neutron and X-ray sca

22 -nanosecond dynamics S(2) values observed by model-free analysis of standard (15) N relaxation of ubi

23 Model-free analysis of the NMR relaxation parameters ind

24 intensity synchrotron source, combined with model-free analysis of the scattering data, to demonstra

25 Model-free analysis shows that the catalytic residues in

26 Model-dependent and model-free analysis techniques provided converging evide

27 A voxel-wise tracer kinetic model and a model-free analysis were applied to the dynamic MR data.

28 he curve for R1 as a function of field and a model-free analysis were used to extract tauc, a correla

29 a combination of NMR relaxation dispersion, model-free analysis, and ligand titration experiments to

30 regulation mechanisms, measurable using the model-free analysis.

31 re obtained for this complex by Lipari-Szabo model-free analysis.

32 Only 4 additional genes were positive in model-free analysis.

33 consists of two distinct phases, an entirely model-free and assumption-free data analysis and a model

34 and of structured loops, and used the simple model-free and extended model-free analyses to fit the d

35 ms can be characterized computationally with model-free and model-based algorithms, but how these pro

36 lfberry pulp was kinetically monitored using model-free and model-based approaches.

37 making (MSDM) task to independently quantify model-free and model-based behavioral mechanisms in rats

38 ng task, which allows discrimination between model-free and model-based behavioural strategies.

39 oral expression and the neural signatures of model-free and model-based control.

40 Computational modelling of behaviour, model-free and model-based functional MRI, and effective

41 These findings provide direct evidence that model-free and model-based learning mechanisms are invol

42 Both model-free and model-based learning were reduced in rats

43 that male rats, similar to humans, use both model-free and model-based learning when making value-ba

44 trategies for reinforcement learning, called model-free and model-based learning.

45 ecisions using different strategies known as model-free and model-based learning; the former is mere

46 Both model-free and model-based methods are involved.

47 y to expectations, the signal reflected both model-free and model-based predictions in proportions ma

48 multistage decision-making task to quantify model-free and model-based processes before and after se

49 evaluations of novel targets are updated via model-free and model-based processes, implicit evaluatio

50 This distinction is well captured by model-free and model-based reinforcement learning algori

51 fference learning models, is compatible with model-free and model-based reinforcement learning, repor

52 show that the hallmark task for dissociating model-free and model-based strategies, as well as severa

53 obtains an accuracy-demand trade-off between model-free and model-based strategies.

54 ential decision-making task that dissociates model-free and model-based strategies.

55 hat choice behavior in rats is influenced by model-free and model-based systems and demonstrate that

56 ip between this architecture and learning in model-free and model-based systems, episodic memory, ima

57 ciable strategies of reinforcement learning: model-free and model-based.

58 relaxation data using both the Lipari-Szabo model-free and reduced spectral density function formali

59 his distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcemen

60 es were undertaken using both nonparametric (model-free) and parametric (model-based) methods.

61 s were undertaken using both non-parametric (model-free) and parametric (model-based) methods.

62 t the validity of our approach, we propose a model-free application that builds on the identification

63 With the advantage of a model free approach and the power of probing multiple su

64 The Lipari-Szabo model-free approach (documented in a 1980 article in Bio

65 etermined that match those from the standard model-free approach applied to (15)N R1, R2 , and {(1)H}

66 Here we use a model-free approach based on measurement of many residua

67 MR relaxation dispersion profiles based on a model-free approach describing the main dynamical proces

68 Here, we propose PC-Relate, a model-free approach for estimating commonly used measure

69 Lastly, a simulation-based model-free approach for obtaining A is proposed.

70 ictions, our results suggest that a flexible model-free approach may be the most promising way forwar

71 yzed molecular dynamics simulations with the model-free approach of nuclear magnetic relaxation.

72 This model-free approach provides a robust and clinically rel

73 Using a "lean" version of the model-free approach S(2) order parameters can be determi

74 We show that the model-free approach strongly correlates with k for whole

75 This indicates a need for a model-free approach to interpret such data.

76 a recently established Bayesian inheritance model-free approach to meta-analyze the data.

77 nalyzed using general time course equations (model-free approach) and mechanistic model equations (me

78 In the model-free approach, the initial rate of signaling is qu

79 In addition to our generalized, model-free approach, we have introduced a mathematical t

80 The two data sets were analyzed by using the model-free approach.

81 We also compare our model with the "extended model-free" approach and discuss possible future develop

82 tter forecast than that obtained using other model-free approaches as well as univariate and multivar

83 ion of subtypes in external cohort using two model-free approaches for multiclass prediction.

84 ears to be more accurate than other existing model-free approaches to estimating coalescent times.

85 d using reduced spectral density mapping and model-free approaches.

86 However, these model-free approximations fall short of comprehensively

87 Multipoint, model-free ARP linkage analysis was performed.

88 urate and their interpretations should be as model-free as possible.

89 e high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be e

90 Model-free-based NMR dynamics studies have been undertak

91 arning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspec

92 etamine self-administration; rats with lower model-free behavior took more methamphetamine than rats

93 al role for human OFC in model-based but not model-free behavior.

94 k more methamphetamine than rats with higher model-free behavior.

95 nguish it from pre-determined, habitual, or "model-free" behavior.

96 In a canine model, free-breathing 3D radial UTE performs better than

97 Model-based RL is more flexible than model-free but requires sophisticated calculations using

98 Individual differences in model-free, but not model-based, learning prior to any d

99 whelming individual chemical identities, and model-free characterizations of chemically exchanging pa

100 cluded in previous models, were critical for modeling free chlorine loss.

101 sed dopamine levels promote model-based over model-free choice.

102 An unbiased, model-free clustering analysis identified distinct group

103 The model-free clustering analysis of the resultant protein

104 entially resulting from enhanced reliance on model-free computations.

105 tes the contribution of model-based, but not model-free, contributions to behavior.

106 mply result from a shift from model-based to model-free control but is instead dependent on the inter

107 on task known to distinguish model-based and model-free control in humans.

108 critical role of OFC in model-based but not model-free control of behavior.SIGNIFICANCE STATEMENT It

109 an imbalance in reliance on model-based and model-free control, and that it may do so in a linear or

110 In contrast, habit control, also known as model-free control, is based on an integration of previo

111 man individuals' reliance on model-based and model-free control.

112 nts manifest a dominance of the less optimal model-free control.

113 making exhibits a mixture of model-based and model-free control.

114 only when potential rewards exceed those of model-free control.

115 ted and habitual behavior as model-based and model-free control.

116 overemphasis on less flexible, maladaptive 'model-free' control systems.

117 w the Koopman mode decomposition can offer a model-free, data-driven approach for analyzing and forec

118 nce for heritability of both model-based and model-free DD measures and suggests that DD is a promisi

119 herefore, we investigated model-based versus model-free decision making and its neural correlates as

120 We found that model-free decision making was prioritized when learning

121 ltiway (here, more specifically multilinear) model-free decomposition methods such as PARAFAC (parall

122 By giving model-free definitions of direct and indirect effects an

123 The extended model free (EMF) approach has been implemented to analyz

124 Incorporating this mechanism into a model free energy for the bilayer, we find that between

125 Molecular modeling, free-energy perturbation calculations, X-ray c

126 parate mechanisms underlying model-based and model-free evaluation and support the hypothesis that mo

127 bilayers have provided the gold standard of model-free evidence to understand the domains' shapes, s

128 bulent convective flow and combine them with model-free, experience-based, reinforcement learning alg

129 We present unequivocal and model-free experimental proof for the presence of streng

130 ed rigorous theory-based deconvolution for a model-free extraction of the energy landscape and local

131 The presented phasor analysis is model-free, fast and accurate.

132 nal dynamics by spectral density mapping and model-free fitting procedures.

133 usion chromatography for the isolation and a model-free fluorescence fluctuation analysis for the inv

134 re is a wide variety of comparatively simple model-free forecasting methods that could be used to pre

135 The relaxation data is analyzed using a model free formalism which takes into account the very h

136 ng of relaxation data using the Lipari-Szabo model-free formalism.

137 clear Overhauser effects were analyzed using model-free formalism.

138 roups were determined using the Lipari-Szabo model-free formalism.

139 750 MHz, and analyze these results using the model-free formalism.

140 olomon-Bloembergen equation incorporating a "model-free" formalism, based on a multiple-structure rep

141 We present a model-free formulism based on the ratio of total areas u

142 lver nanoparticles is presented here using a model-free framework that derives the energy of critical

143 n task, which has been shown to discriminate model-free from model-based control.

144 he Fibromyalgia Family Study and performed a model-free genome-wide linkage analysis of fibromyalgia

145 Phasor diagrams are model-free graphical representations of transformed time

146 of either mechanism, we show a bias towards model-free (habit) acquisition in disorders involving bo

147 sidered to arise out of contributions from a model-free habitual system and a model-based goal-direct

148 e approximations, discerning between innate, model-free, heuristic, and model-based controllers.

149 We use Q-learning as a model-free implementation of Temporal difference learnin

150 ng both model-driven seed-based analysis and model-free independent component analysis and controllin

151 istep decision task in which model-based and model-free influences on human choice behavior could be

152 Model-free internal dynamic analyses of these data provi

153 ic reconstruction (LoTToR) method contains a model-free iteration process under a set of constraints

154 e results challenge the notion of a separate model-free learner and suggest a more integrated computa

155 ural data to argue that both model-based and model-free learners implement a value comparison process

156 he involvement of the dopaminergic system in model-free learning and prefrontal, central executive-de

157 Dopamine is widely thought to support model-free learning by modulating plasticity in striatum

158 ions linking striatal dopamine to putatively model-free learning did not rule out model-based effects

159 Model-free learning enables an agent to make better deci

160 ential choice task for which model-based and model-free learning have distinct and identifiable trial

161 This favoring of model-free learning may underlie the repetitive behavior

162 The model-free learning task induced an increase in corticos

163 motor and sensory cortex parameters after a model-free learning task, i.e. a ballistic motor task, c

164 the magnitude of drug-induced disruptions in model-free learning was not correlated with disruptions

165 One is model-free learning, i.e., simple reinforcement of rewar

166 fferent learning algorithms, model-based and model-free learning, respectively.

167 h work has pointed to a role for dopamine in model-free learning.

168 ions in striatal dopamine function relate to model-free learning.

169 found no effect of disease or medication on model-free learning.

170 prediction error signals thought to underlie model-free learning.

171 onal and model based and not consistent with model-free learning.

172 d their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospe

173 hypothesized to arise computationally from 'model-free' learning.

174 ms and demonstrate that model-based, but not model-free, learning is associated with corticostriatal

175 transients support associative, rather than model-free, learning.

176 DESIGN, SETTING, AND PATIENTS: Model-free linkage analyses of 21 concordant-affected si

177 Model-free linkage analyses, using a dichotomous definit

178 Model-free linkage analysis identified one novel signifi

179 s and false-positive evidence for multipoint model-free linkage analysis of affected sib pair data.

180 A genome-wide model-free linkage analysis was performed, using two sem

181 satellite markers (average spacing 9 cM) and model-free linkage analysis.

182 Harnessing the model-free Lipari-Szabo based formalism for estimation o

183 ellite markers: chromosome 8, with a maximum model-free LOD score of 2.2; chromosome 2, with a LOD sc

184 ility distribution of the peak position in a model-free manner and compare the performance to manual

185 We developed an empirical, model-free measure of centralization that compares infor

186 In contrast, a model-free method based on state-space reconstruction ga

187 Here we introduce a new, noninvasive model-free method called Loss Angle Mapping (LAM).

188 dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships

189 Although the model-free method is not universally applicable (for exa

190 We developed an automated, model-free method to rapidly and exhaustively examine th

191 Here we introduce a model-free method, SpydrPick, whose computational effici

192 ence algorithms face one of two limitations: model-free methods are scalable but suffer from a lack o

193 es, can provide better forecasts than simple model-free methods for ecological systems with noisy non

194 nalyses were undertaken using nonparametric (model-free) methods.

195 thin the framework of an interaction between model-free (MF) and model-based (MB) control systems.

196 ecting reduced decision and state spaces and model-free (MF) architectures.

197 be habitual or goal-directed, also known as model-free (MF) or model-based (MB) control.

198 A paradigmatic model-free (MF) strategy simply repeats actions that hav

199 strategy and a fast, retrospective habitual model-free (MF) strategy.

200 ble systems: a model-based (MB) system and a model-free (MF) system.

201 ory distinguishes between stimulus-response (model-free; MF) learning and deliberative (model-based;

202 Finally, model-free moral learning varied with individual differe

203 e of choice, neural activity consistent with model-free moral learning was observed in subgenual ante

204 Here we define a model-free multipoint method on the basis of dense seque

205 Genomewide, model-free, multipoint linkage analyses were performed s

206 ped with markers spaced by every 10 cM and a model-free nonparametric linkage (NPL-all) analysis was

207 Te in a laser-induced plasma (LIP), using a model free of assumptions regarding local thermodynamic

208 ere, we have employed a network interdiction model free of growth optimality assumptions, a special c

209 NOX4 inhibition was confirmed in an ex vivo model, free of vascular and BBB components.

210 e immediately available optical information (model-free online control mechanisms), or whether intern

211 is would not be expected based upon existing model-free online steering control models, and strongly

212 which adaptation operates in parallel with a model-free operant reinforcement process.

213 ent with experimental data than conventional model-free or extended model-free analyses with two or t

214 s (parametric linkage) and complex diseases (model-free or non-parametric linkage).

215 directed versus habitual, model-based versus model-free or prospective versus retrospective.

216 ly significant elevations (P << .001) in the model-free parameter initial area under the curve and in

217 The fitting of model-free parameters yielded S (2) variability which is

218 ntain populations at target levels, and that model-free performance with bang-bang control can outper

219 A model-free pharmacokinetic measurement based on area und

220 The model-free phase reveals inconsistencies within the data

221 In sign-trackers, model-free phasic dopaminergic reward-prediction errors

222 y encountered imperfect crystals and enables model-free phasing.

223 Whereas model-free prediction error signals were preserved, alco

224 Model-free prediction errors for others relative to self

225 l prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum.

226 s in ventral striatum notably covarying with model-free prediction errors.

227 radigms, that these distinct model-based and model-free processes combine to learn an error-based mot

228 Our approach allows model-free quantitative analysis of the degrees of freed

229 sis defects in WT C. elegans Supporting this model, free radical scavengers suppressed the Rhizobium-

230 Model-based and model-free reinforcement learning (RL) have been suggest

231 A model-free reinforcement learning algorithm revealed tha

232 tionally characterized using model-based and model-free reinforcement learning algorithms, respective

233 ed to signal prediction errors as defined in model-free reinforcement learning algorithms.

234 sed to signal the reward prediction error in model-free reinforcement learning algorithms.

235 We then consider model-free reinforcement learning strategies that exploi

236 such learning is not easily accounted for by model-free reinforcement learning theories such as tempo

237 pecified distinction between model-based and model-free reinforcement learning to investigate the uni

238 ached-value error signal proposed to support model-free reinforcement learning, cached-value errors a

239 wo computational mechanisms, model-based and model-free reinforcement learning, neuronally implemente

240 rbitration mechanism between model-based and model-free reinforcement learning, placing such a mechan

241 k promises to differentiate model-based from model-free reinforcement learning, while generating neur

242 dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signal

243 process can yield choice patterns similar to model-free reinforcement learning; however, samples can

244 the relative reliability of model-based and model-free reinforcement-learning (RL) systems plays a r

245 rning algorithms known as "model-based" and "model-free" reinforcement learning.

246 Decisions may arise via 'model-free' repetition of previously reinforced actions

247 ity of the ventral striatal BOLD signal as a model-free report.

248 ighlight a need for a reconsideration of how model-free representations are formed and regulated acco

249 the same residues show the plasticity in the model-free residual dipolar coupling (RDC) order paramet

250 nal structure between states; DLS implements model-free response learning by learning associations be

251 Whereas model-free RL uses this experience directly, in the form

252 However, they resorted to model-free RL when both uncertainty and task complexity

253 fied with slow, deliberative processing, and model-free RL with fast, automatic processing.

254 Model-free RNA structure comparisons were performed usin

255 describe a dynamic programming approach for model-free sequence comparison that incorporates high-th

256 owing estimation of recombination rates in a model-free setting.

257 Model-free solutions (e.g., soft models) are produced un

258 It is generally assumed that model-free state representations are based on outcome-re

259 A novel model-free statistical approach (self modeling curve res

260 By merging model-free statistical learning with the Viterbi algorit

261 The method uses model-free statistics to identify peak-like distribution

262 Model-free strategies are computationally cheap, but som

263 ugh simulation that under certain conditions model-free strategies can masquerade as being model-base

264 em gamblers (PG) orchestrate model-based and model-free strategies has not been evaluated.

265 l cortex correlate with model-based, but not model-free, strategies, indicating that the biological m

266 tients were relatively better explained by a model-free strategy due to reduced inference on the alte

267 for two types of learning strategy: a narrow model-free strategy that learns an input-output mapping

268 In contrast, model-free subjects tend to ignore model-based aspects o

269 tomated "reverse engineering" approaches for model-free symbolic nonlinear system identification may

270 mption by providing evidence that a putative model-free system assigns credit to task representations

271 nation of a Model-Based system and a revised Model-Free system can account for the development of dis

272 ed or model-based system and the habitual or model-free system in the domain of instrumental conditio

273 These include: (1) a model-free system that uses values cached from the outco

274 Moreover, we show that revising a classical Model-Free system to individually process stimuli by usi

275 n error-like signal arising from a classical Model-Free system, necessary for Pavlovian conditioning.

276 aking is influenced by both a retrospective "model-free" system and a prospective "model-based" syste

277 berative "model-based" and a more reflexive "model-free" system.

278 deliberative "model-based" and a reflexive "model-free" system.

279 of control over behavior by model-based and model-free systems as a function of the reliability of t

280 the relative contribution of model-based and model-free systems during decision-making according to t

281 sure for the contribution of model-based and model-free systems to human choice.

282 task and planning within it, to traditional model-free TD learning.

283 overlapping with those thought to carry out model-free temporal difference (TD) learning.

284 riation of alleles across the chromosome and model-free testing of dependencies between pairs of poly

285 comprises parallel systems, model based and model free, that respectively generate flexible and habi

286 the assumption that learning is exclusively model-free; that animals do not develop a cognitive map

287 that this can be accomplished with a simple, model-free transformation that is general enough to be a

288 This relationship was selective to model-free updating following a rewarded, but not unrewa

289 rning processes that can be characterized as model-free: use-dependent plasticity and operant reinfor

290 over, connectivity between these regions and model-free valuation areas is negatively modulated by th

291 bitration may work through modulation of the model-free valuation system when the arbitrator deems th

292 ue in our procedure does not directly accrue model-free value and further suggest that the cue may no

293 However, it has been suggested that model-free value might accrue directly to the preconditi

294 Model-free value only guides gaze and pupil dilation in

295 ng the view that dopamine transients reflect model-free value.

296 and the interaction between model-based and model-free values, prediction errors, and preferences is

297 In addition, our method provides model-free variable selection of important prognostic ma

298 -step reward-learning task which dissociates model-free versus model-based learning.

299 ompared different motor-learning tasks, i.e. model-free vs. model-based learning tasks, and their pos

300 Using a bi-tensor model, free-water values were found to be increased in t