ライフサイエンスコーパス: prediction error

1 gh efficiency of lexical classification (low prediction error).

2 in lexically classifying these stimuli (high prediction error).

3 o learning-irrelevant variables (i.e. reward prediction error).

4 earning-relevant variables (i.e. probability prediction error).

5 re, and to oversensitivity to feedback (i.e. prediction errors).

6 ment, and this integration is gated by local prediction error.

7 ns between pre-cataract surgery data and the prediction error.

8 e dopamine system signals a multidimensional prediction error.

9 onoisotopic mass to handle the off-by-one-Da prediction error.

10 plained by a quartic polynomial model of the prediction error.

11 resentations and signal their deviation as a prediction error.

12 l value to reward predictive cues as well as prediction error.

13 th the actual refractive outcome to give the prediction error.

14 t can feed this activity back to S1 as value prediction error.

15 mproved spatial CV R(2) of 0.81, and a lower prediction error.

16 rning and the reinforcement learning model's prediction error.

17 operative refractive astigmatism to give the prediction error.

18 pain in relation to the stepped increases in prediction errors.

19 ive prediction errors, but not with positive prediction errors.

20 n to predict the new samples with the lowest prediction errors.

21 ong with those of cortex, influence striatal prediction errors.

22 tent with monitoring for, and updating from, prediction errors.

23 ove that explained by learning from negative prediction errors.

24 y, accompanied by an attenuation of positive prediction errors.

25 tracking, with higher correlations and lower prediction errors.

26 the discontinuation of reinforcement through prediction errors.

27 ng as disturbed weighting of predictions and prediction errors.

28 Associative learning is driven by prediction errors.

29 ntially from positive, relative to negative, prediction errors.

30 neutral cues, neutral outcomes, and neutral prediction errors.

31 terfering with its key computation of reward prediction errors.

32 of automatic, implicit correction of sensory prediction errors.

33 dopamine transients are well established as prediction errors.

34 rcuitry underlying the neural computation of prediction errors.

35 reasons-such as novelty, surprise, or reward prediction errors [20-24]-and to date, precisely which s

36 archical learning model to formally quantify prediction errors about cue-outcome contingencies and ch

37 hold that the mind's core aim is to minimize prediction-error about its experiences.

38 Specifically, prediction-error activation in the nucleus accumbens was

39 Prediction error (adjusted for using both eyes) at 7 yea

40 However, in addition to prediction error against DFT-computed properties, such p

41 PFC and ventral striatum, representations of prediction error also depend on task structure.

42 w that the same signal that codes for reward prediction errors also codes the animal's certainty abou

43 e learning methods, MTL-SGL achieved a lower prediction error and higher accuracy, indicating that gr

44 search can benefit from critically examining prediction error and incomplete reminders.

45 mbinations showed great promise, in terms of prediction error and interphone variation reduction, out

46 the role of temporal correspondence between prediction error and memoranda presentation and (iv) det

47 ximal ESN performance, expressed in terms of prediction error and memory capacity.

48 sychotic symptoms in terms of alterations in prediction error and precision signaling using Bayesian

49 of NA also eliminated the phasic effects of prediction error and volatility on pupil size, consisten

50 s dopaminergic inputs that can signal reward prediction errors and also behavioral transitions and mo

51 gmental area (VTA) to striatum encode reward prediction errors and reinforce specific actions; howeve

52 etween nonrewarding events is also driven by prediction errors and that, contrary to existing canon,

53 ehaviour of the best models, with respect to prediction errors and the impact of used features, to co

54 d uncertainty are updated to minimize future prediction errors and to increase the precision of the p

55 Both the reinforced (no prediction error) and non-reinforced retrieval sessions

56 hat learning depends on the computation of a prediction error, and that reinforcing value, whether in

57 of neurons encoding choice outcomes, outcome prediction errors, and outcome history in their firing r

58 are activated by both positive and negative prediction errors, and thus report signals similar to th

59 al yet rigorous scheme using the accumulated prediction error (APE) metric from information theory, w

60 Prediction errors are critical for associative learning

61 We propose that such prediction errors are mediated by cortico-LC connections

62 Prediction errors are thought to drive associative fear

63 Prediction errors are thought to drive the extinction le

64 influence pain perception despite increasing prediction errors arising in pain pathways.

65 opamine neurons have been proposed to signal prediction errors as defined in model-free reinforcement

66 ain perception, which suggests that aversive prediction error-associated regions, such as the anterio

67 The main outcome measure was prediction error at 7 years of age.

68 ritic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of

69 ean and confidence interval of drug response prediction errors based on ensemble approaches with vari

70 emerge, suggesting that the role of sensory prediction error-based adaptation may be limited to the

71 The persistence of sensory-prediction error-based learning effectively suppresses a

72 inescapable obligatory dependence on sensory-prediction error-based learning-even when this system is

73 elling indicated that precision weighting of prediction errors benefits learning in health and is imp

74 for multiple functions, including the reward prediction error but also motivation and locomotion.

75 Thus, bursting neurons do not convey value-prediction errors but do signal surprise associated with

76 r showed a linear relationship with negative prediction errors, but not with positive prediction erro

77 ayer 2/3, but not layer 5/6, neurons compute prediction errors by subtracting predicted and actual vi

78 circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary

79 lasticity can generate different variants of prediction-error circuits, which can be distinguished by

80 It has been suggested that social prediction errors-coding discrepancies between the predi

81 interaction is a unique neural signature of prediction error computations and is apparent in neural

82 Using fMRI, we asked if prediction error computations in the human striatum, a k

83 Using functional MRI, we show these social prediction errors correlate with activity in ventral str

84 ingly unable to compensate for their sensory-prediction error deficits by spontaneously switching to

85 asure of uncertainty in the form of absolute prediction errors determined how long participants looke

86 ward expectancy (expected outcome value) and prediction error (difference between expected and actual

87 reacquisition, suggesting that engagement of prediction error does not influence the occurrence of re

88 tuations appropriate for the appearance of a prediction error, dopamine transients support associativ

89 earning theory has provided insight into how prediction errors drive updates in beliefs but less atte

90 This cerebellar region also responded to prediction error during the outcome of the trial.

91 siological states, the paper argues that the prediction error encoded in the dopaminergic activity ne

92 action planning involve processes minimizing prediction errors encoded by dopaminergic neurons.

93 e switches were most likely (strong negative prediction error), especially in subjects who obtained a

94 strengthened encoding scaled with the reward prediction error experienced when memoranda were present

95 The resulting prediction errors for both models were compared using th

96 expected, indicating greater sensitivity to prediction errors for negative outcomes.

97 Model-free prediction errors for others relative to self were track

98 t difference existed among the mean absolute prediction errors for the Abulafia-Koch, Barrett, and EV

99 Therefore, the consequences of prediction error go beyond memory weakening.

100 only after outcome, when they encoded reward prediction errors graded by confidence, influencing subs

101 While neural hallmarks of prediction errors have been found throughout the brain,

102 Although reward prediction errors have been mapped to midbrain dopamine

103 Consistent with the "prediction error" hypothesis, activation was significant

104 ue is decreased because of a negative reward prediction error (i.e., the animal receives less than ex

105 These predictions are adjusted after prediction errors, i.e., after surprising events.

106 ne neurons are proposed to signal the reward prediction error in model-free reinforcement learning al

107 li and food outcomes and computes a negative prediction error in Pavlovian conditioning.

108 view that the LHb promotes a negative reward prediction error in Pavlovian conditioning.SIGNIFICANCE

109 leave-one-out cross validation, the overall prediction error in the onset of epidemics was within 1

110 improve the estimation of corneal height and prediction error in two settings, the Hadassah Hospital,

111 esentations in prefrontal cortex with reward prediction errors in basal ganglia support exploration o

112 ted in the BOLD response elicited by sensory prediction errors in human midbrain.

113 al correlates of such deficits in processing prediction errors in people with depression.

114 een corneal asphericity and Haigis-L formula prediction errors in routine cataract surgery after refr

115 Moreover, individuals with attenuated prediction errors in stable conditions were found to mak

116 he neural network processing predictions and prediction errors in the emotional domain.

117 with other lines of evidence suggesting that prediction errors in the mesostriatal dopamine system in

118 We observed a decreased response to positive prediction errors in the putamen in the MT group compare

119 f the signal associated with negative reward prediction errors in the striatum following execution fa

120 that the PFC can suppress the expression of prediction errors in the VS.

121 e anterior cingulate cortex signalled social prediction errors in typically developing individuals, t

122 perty of the LC that translates state-action prediction errors into an optimal balance between plasti

123 ration between pain-related expectations and prediction errors is crucial for pain perception, which

124 In contrast, a history of sensory prediction errors is neither sufficient nor obligatory f

125 On the basis of the mean absolute prediction error (MAE), the formulas were ranked as foll

126 ndings provide a mechanism by which mnemonic prediction errors may drive memory updating-by biasing h

127 ng distance), as well as the analysis of the prediction error (median and mean absolute errors; stand

128 But prediction-error minimization can be 'hacked', by placin

129 Generally, most reward prediction errors models learn the average expected amou

130 Mean prediction error (MPE) and root mean squared prediction

131 ultiple Phenotypes based on cross-validation Prediction Error (MultP-PE).

132 To adjust expectations efficiently, prediction errors need to be associated with the precise

133 s the development and refinement of negative prediction-error neurons in a computational model of mou

134 The brain generates negative prediction error (NPE) signals to trigger extinction, a

135 The most ubiquitous prediction error occurred for the reward-relevant dimens

136 rrelation of r2 = 0.52 (P = 0.02) and a mean prediction error of 10.3% (+/-8.9).

137 single shelled beans (R(2) = 0.84, external prediction error of 2.4%).

138 For both in-shell beans a slightly lower prediction error of 4.0% and R(2) = 0.52 was achieved, b

139 The mean spherical equivalent prediction error of the back-calculator was 0.54 +/- 0.5

140 rnal neural activation pattern may reflect a prediction error of the brain, where rewards at unexpect

141 sensitivity by a factor of 16, decrease the prediction error of the concentration of an unknown anal

142 in, immunoglobulin, and hemoglobin, giving a prediction error of the spiked concentration of 23 ppm.

143 ons in the mammalian midbrain and the reward prediction errors of reinforcement learning algorithms,

144 A) responses are synonymous with the 'reward prediction error' of reinforcement learning (RL), and ar

145 ltivars at both growing sites) gave relative prediction errors on anthocyanin content less than 14.1%

146 he precision that increases the influence of prediction errors on belief updating.

147 digm to (i) estimate the influence of reward prediction errors on the formation of episodic memories,

148 ial effects of positive and negative outcome prediction errors on the two pathways and a weak decay o

149 Thus, although neurons carrying sensory prediction error or feedback signals show attenuated mod

150 g questions, including the relative roles of prediction error or precision signaling in the developme

151 as in the OFC it correlated with an unsigned prediction error or salience signal.

152 Converging evidence implicates prediction error, or surprise, as a key mechanism that r

153 tistically significantly lower mean absolute prediction error (P < 0.001) and a significantly lower v

154 1) and a significantly lower variance of the prediction error (P < 0.01) compared with all other form

155 vision of the hand, which eliminates sensory-prediction errors-patients could be induced to preferent

156 whether the representation of reinforcement prediction error (PE) (the difference between received a

157 Training alters the agent-specificity of prediction error (PE) circuits for at least 24 h, modula

158 The refractive prediction error (PE) was calculated as the difference b

159 ergic midbrain neurons is thought to reflect prediction errors (PE) that depend on the difference bet

160 d less by predictions themselves and more by prediction errors per se, and this relationship scales w

161 (as Gilead et al. suggest), many systematic prediction errors persist despite substantial experience

162 ect of bias on pain ratings was reduced when prediction errors (PEs) increased, but pain perception w

163 show parallel encoding of effort and reward prediction errors (PEs) within distinct brain regions, w

164 f minimizing hierarchical precision-weighted prediction errors (PEs), and disturbances of this putati

165 ccording to these theories, the precision of prediction errors plays a key role in learning and decis

166 Average prediction error (postoperative SE refraction minus targ

167 t this is associated with the suppression of prediction error processing in the ventral striatum by t

168 tal conceptual processes, which can suppress prediction error processing in the VS and lead to reduce

169 that this is mediated by the suppression of prediction error processing through the prefrontal corte

170 We asked whether mnemonic prediction errors promote hippocampal encoding versus re

171 ll nonmodified formulas had a hyperopic mean prediction error ranging from 1.72 to 3.02 D.

172 eward amount surprise (i.e., a scalar reward prediction error), rare reward surprise, and visuospatia

173 d transients function as temporal-difference prediction errors rather than reward predictions.

174 dopamine neurons themselves calculate reward prediction error, rather than inherit it passively from

175 re quickly by feeding back this state-action prediction error-reflected in LC firing and noradrenalin

176 roup exhibited greater brain response 1) for prediction error regression within the caudate, ventral

177 erapy response was specifically predicted by prediction-error related vmPFC activation during early e

178 o drive the extinction learning process, and prediction error-related vmPFC activation specifically p

179 These findings suggest that aversive prediction-error-related regions interact with pain-proc

180 aptation of the learning rate, and coding of prediction errors relative to reward variability.

181 Tg responses to aversive cues, outcomes, and prediction errors, respectively.

182 Prediction error response could be a neurobiological mar

183 heir predictive model, reflected in a larger prediction error response to unexpected sounds, and decr

184 Intriguingly, ventral striatum encodes prediction error responses but not the full RL- or stati

185 Vulnerability modulated the expression of prediction error responses in anterior insula and insula

186 We measured prediction error responses to sound sequences with elect

187 ies have used incomplete reminders to elicit prediction error; retrieval cues that partially replicat

188 Importantly, we can dissociate scalar reward prediction errors-rewards that deviated from the average

189 nd quantification of grape-must caramel (low prediction errors, RMSEP ~ 0.24) and the effects that gr

190 prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78

191 It had lower root mean square prediction error (RMSPE) than when using no tool (leavin

192 ypothesis, we relied on the fact that reward prediction error (RPE) is a strong modulator of dopamine

193 second laboratory stress paradigm on reward prediction error (RPE) signaling in the ventral striatum

194 function, specifically activation to reward prediction error (RPE), are impacted by trauma and predi

195 Dopamine (DA) neurons are to encode reward prediction error (RPE), in addition to other signals, su

196 Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward.

197 have been thought to primarily encode reward prediction error (RPE), recent studies have also found m

198 to be modulated by events that signal reward prediction error (RPE).

199 dopamine neurons are known to encode reward prediction errors (RPE) used to update value predictions

200 TA) and substantia nigra (SNc) encode reward prediction errors (RPEs) and are proposed to mediate err

201 eled as a monolithic process in which reward prediction errors (RPEs) are used to update expected val

202 Recent behavioral evidence implicates reward prediction errors (RPEs) as a key factor in the acquisit

203 ous system is hypothesized to compute reward prediction errors (RPEs) to promote adaptive behavior.

204 nt behavioral research has shown that reward prediction errors (RPEs), a key concept of reinforcement

205 on values are updated on the basis of reward prediction errors (RPEs), defined as the discrepancy bet

206 dopamine neurons is thought to signal reward prediction errors (RPEs), resembling temporal difference

207 ociated with deficits in representing reward prediction errors (RPEs), which are the difference betwe

208 at this activity arises from musical "reward prediction errors" (RPEs) that signal the difference bet

209 This exaggerated weighting of prediction errors shapes the dynamic learning rate (asso

210 When facing a sensory prediction error, should we attribute this error to a ch

211 r effect seems related to learning as reward prediction errors showed a positive correlation with cau

212 track cues and rewards to generate a reward prediction error signal during Pavlovian conditioning.

213 suitable to extract payoffs and costs from a prediction error signal if they occur at different momen

214 he image includes changes, consistent with a prediction error signal in CA1.

215 late these expectations, we find a 80-120 Hz prediction error signal that emerges in both visual asso

216 w that sign-trackers exhibit a neural reward prediction error signal that is not detectable in goal-t

217 the cursor, effectively setting the sensory-prediction error signal to zero.

218 e demonstrate that dopamine encodes a safety prediction error signal, which illustrates that ventral

219 exhibit a stronger model-based neural state prediction error signal.

220 The results describe a novel mode of prediction error signaling by ACC neurons that is associ

221 reward learning behaviour and ventrostriatal prediction error signalling (measured using functional M

222 el, baseline reward learning (P = 0.001) and prediction error signalling (P = 0.004) were both associ

223 treatment reward learning and reward-related prediction error signalling improved most.

224 ally and temporally specific role for reward prediction error signalling in memory formation.

225 Importantly, the degree to which social prediction error signalling was aberrant correlated with

226 bility of reward and concomitantly corrupted prediction error signalling.

227 .02), a trend towards blunted reward-related prediction error signals (P = 0.07), and a trend towards

228 To test for correlations with prediction error signals a Rescorla-Wagner reinforcement

229 Feature-specific prediction error signals a) emerge on average shortly af

230 Whereas local reward prediction error signals are early and phasic in the PH

231 of pain modulation engage different aversive prediction error signals but are dependently regulated b

232 are associated with blunted striatal reward prediction error signals in a large community-based samp

233 se results demonstrate parallel but distinct prediction error signals in NAc and OFC during learning,

234 Precision weighting of cortical prediction error signals is a key mechanism through whic

235 Amplifying ascending sensory prediction error signals may optimize stimulus detection

236 trinsic activity to generate sensory cue and prediction error signals that are essential for reward-b

237 Dopamine is thought to provide reward prediction error signals to temporal lobe memory systems

238 emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anter

239 Prediction-error signals consistent with formal models o

240 different from expectations, suggesting that prediction error size has an immediate functional role i

241 dual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family

242 This temporal trend in prediction error suggests that the current statistical t

243 The state-value-prediction error (SVPE), which is independent of actions

244 The median of prediction errors (symmetric mean absolute percent error

245 the anterior fronto-striatal networks encode prediction errors that are specific to feature values of

246 signals are widely thought to report reward prediction errors that drive learning in the basal gangl

247 timized to dissociate the subtypes of reward-prediction errors that function as the key computational

248 n-outcome chain often introduces mismatch or prediction errors that strongly correlate with the sense

249 ught to encode novelty in addition to reward prediction error (the discrepancy between actual and pre

250 f learning is typically thought to depend on prediction error, the difference between expected and ex

251 omentary happiness is associated with reward prediction error, the difference between experienced and

252 t the magnitude of high level 'state-action' prediction errors, then both tonic and phasic modes of f

253 and updating of beliefs brings the classical prediction error theory into alignment with more recent

254 Since its introduction, the reward prediction error theory of dopamine has explained a weal

255 An extension of the prediction error theory of dopamine, imported from artif

256 underlies the ability of DA to encode reward prediction error, thereby driving motivation, attention,

257 nterval duration, and doesn't reflect reward prediction error, timing, or value as single factors alo

258 nfluences how dopamine neurons convey reward prediction errors to guide learning.

259 ate contributions of task errors and sensory prediction errors to latent sensorimotor memories, we pe

260 s the contributions of positive and negative prediction errors to learning.

261 These prediction errors trigger learning about rewards and hab

262 ckers, model-free phasic dopaminergic reward-prediction errors underlie learning, which renders stimu

263 Using sensory preconditioning, we show that prediction errors underlying stimulus-stimulus learning

264 tween predicted and obtained rewards (reward prediction errors) update these variables, but they are

265 lus processing and during extinction-related prediction errors (US omissions) in regions of interest

266 putational model that was adapted to test if prediction error valence influences learning.

267 ier methods that either minimize the mean of prediction error variance or maximize the mean of genera

268 l cortex) and positive reinforcement-related prediction errors (ventral striatum), but also aversive

269 In eyes with posterior Q > 0, the mean prediction error was +0.50 D higher than in eyes with ne

270 The requirement for prediction error was assessed by using a reinforced or n

271 Prediction error was defined as refractive error minus e

272 tinction effect on memory destabilization or prediction error was investigated in pavlovian cued-fear

273 In contrast, a history of sensory prediction errors was neither sufficient nor obligatory

274 Specifically, mean accuracy (prediction error) was 12.2% versus 78% and mean precisio

275 decision-making process compares the models' prediction errors, weighted by their precisions, to sele

276 TSD symptoms, suggesting that both increased prediction-error weights and decreased striatal tracking

277 ly mediated the positive correlation between prediction-error weights and PTSD symptoms, suggesting t

278 signatures of exploration, exploitation and prediction error were unaffected.

279 Prediction errors were obtained, with estimations of +/-

280 the opposite valence-induced bias: negative prediction errors were preferentially taken into account

281 The smallest prediction errors were provided by OPS low-level data fu

282 in which feedback indicated whether negative prediction errors were, or were not, associated with exe

283 g the response of the LC as a correlate of a prediction error when inferring states for action planni

284 sual and frontoparietal cortices signaling a prediction error when presented at unexpected locations.

285 n signal in the NAc correlated with a reward prediction error, whereas in the OFC it correlated with

286 attribute this finding to a positive reward prediction error, whereby the animal perceives they rece

287 pared with outgroup learning signals (action prediction errors), which formally captured deficits in

288 Dopamine cell spiking can encode prediction errors, which are vital learning signals in c

289 musical pleasure comes from positive reward prediction errors, which arise when what is heard proves

290 FMRI revealed coding of unsigned prediction errors, which signal surprise, relative to th

291 ve significant precision-weighting of signed prediction errors, which signal valence, in the midbrain

292 nomy assumptions are valid by calculation of prediction error-which we show gives a measure of autono

293 stimulus intensity, which generates a large prediction error, will have a weaker influence on the pe

294 updates to associations are proportional to prediction error, with an approximate Bayesian rule, for

295 primarily countered by learning from sensory-prediction errors, with secondary contributions from oth

296 showed the highest proportion of eyes with a prediction error within +/-0.50 D with 65.6%, followed b

297 .5% of eyes, respectively, showed refractive prediction errors within +/-0.5 diopter (D); in eyes wit

298 earning under conditions where an endogenous prediction error would occur.

299 on, developments regarding the estimation of prediction errors would derive in the calculation of oth

300 e predicted that destabilization, induced by prediction error, would be critical for observing the ef