ライフサイエンスコーパス: representational

1 which they must traverse, and that different representational abilities underlie travel across differ

2 of some of the ways that representation and representational abstraction are involved with, and expl

3 or another utility function, itself requires representational abstraction.

4 perspective moves away from the traditional representational account of memory toward a functional a

5 r timed behavior, supporting the model-based representational account of temporal risk assessment.

6 undamental distinction between dynamical and representational accounts of enactivism.

7 We tested a novel hypothesis, generated from representational accounts of medial temporal lobe (MTL)

8 Recent "representational" accounts suggest a key role for the hi

9 to transform perceptual representations into representational actions.

10 cognition depends on synergism between both representational and motivational factors and is unlikel

11 processing as a foundation for the proposed representational architectures of the mind.

12 red construal level elicited by abstract vs. representational art and asked subjects to assign abstra

13 hey do not typically question the linguists' representational assumptions.

14 Thus, our findings reveal the representational basis of ensemble processing, its fine-

15 Reduced representational bisulfite sequencing was then used to c

16 We propose such a theory of the abstract representational capacities that allow humans to transce

17 These findings demonstrate that the representational capacity of A1 depends strongly on cort

18 Our results demonstrate the high representational capacity of multivariate statistical me

19 Thus, PL has greater representational capacity.

20 r (BC) learning, as well as learning-related representational change for indirectly related memory el

21 urrent stimulation (tDCS) to facilitate such representational change.

22 learned constraints, leading to a successful representational change.

23 and form an empirical framework to study the representational changes in learning, attention, and spe

24 Here we examine the relationship between representational changes in mPFC and behavioural strateg

25 from decision rules, attention learning, or representational changes, and small differences in the t

26 the generalization accuracy as a function of representational complexity.

27 ngth of retrieval of one, but not the other, representational component correlated with generalizatio

28 or ensembles to increase stability in a core representational component.

29 een auditory cortical stages, illustrating a representational consequence of hierarchical organizatio

30 owever, such reactivation may have different representational consequences within the hippocampal cir

31 ncy of retrieval processes also is linked to representational consistency.

32 coding processes has recently been linked to representational consistency: the reactivation of a repr

33 ncephalographic data to map out the specific representational constraints that are constructed as eac

34 ultivariate decoding techniques to probe the representational content of neural signals in a time-res

35 lly, we used fMRI to investigate whether the representational content of prestimulus activity in earl

36 ial frequency (low/high), and found that the representational content of the animal- and tool-selecti

37 ive contribution of these two factors in the representational content of visual areas is unclear.

38 h top-down processes for the analysis of its representational content, and possibly regulate subseque

39 files in terms of both temporal dynamics and representational content.

40 Here, we modelled the three-dimensional representational contents of 4 faces that were familiar

41 The representational contents were created by reverse-correl

42 ) generalized across qualitatively different representational correlates (location and direction).

43 rough synchronous activation of sensorimotor representational cortices.

44 tive polymerase chain reaction (qPCR), and a representational deep-sequencing method (biome represent

45 g amplified fragment length polymorphism and representational difference analysis.

46 Again, representational differences between expected and unexpe

47 sembled IT in terms of their within-category representational dissimilarities.

48 pal computational model, revealing increased representational dissimilarity for unexpected highly sim

49 in encoding analysis and population-response representational dissimilarity in RSA) and have specific

50 We compared the representational dissimilarity matrices (RDMs) of the mo

51 trained faces exhibited greater magnitude in representational distance change relative to untrained f

52 First, representational distance changes were more selective fo

53 Second, there was a difference in where the representational distance changes were observed; for fac

54 mputational models, each of which predicts a representational distance matrix and a regional-mean act

55 The representational distance matrix encapsulates what disti

56 A model is tested by comparing the representational distance matrix it predicts to that of

57 We support our argument for representational diversity by explaining how the element

58 ion at the heart of "mental travel" under a "representational diversity" perspective that embraces fo

59 d on an extensive literature on abstraction, representational diversity, neurocognition, and psychopa

60 ve processing (PP) must accommodate its rich representational diversity.

61 Here, we measure and model the rapid representational dynamics across multiple stages of the

62 neuroanatomical, cognitive-mechanistic, and representational (e.g., conscious contents) processes as

63 They argue that the social benefits of representational exchange are at least as important as i

64 The result is a theory of rationalization as representational exchange both wider in scope and better

65 Representational exchange enables us to represent any in

66 ionalization belongs to the broader class of representational exchange mechanisms, which transfer inf

67 motivated reasoning" model, and the proposed representational exchange model - and show that they hav

68 They describe how representational exchange occurs not only by post hoc ra

69 The theory of representational exchange reveals connections between ra

70 ividually and collectively - as a variety of representational exchange.

71 concepts to consider collective instances of representational exchange.

72 mmodate many of the interpersonal aspects of representational exchange.

73 ive dissonance does not have the function of representational exchange.

74 ed to cover another part of his portrayal of representational exchange: thought experiments that lead

75 itted from the learning sequence showed that representational fidelity is primarily determined by the

76 increased fine spatial discriminability and representational fidelity near the attended target.

77 Using representational fMRI analyses, we then examined how thi

78 in the blind than the sighted group, using a representational format closer to the one found in visio

79 Episodic memories are shaped by the representational format of their contents.

80 veridical beliefs about the past nor to its representational format.

81 The representational formats of medial temporal lobe and neo

82 & Bargh (H&B) to consider a wider variety of representational forms with motivational force and to en

83 of knowledge among communicators can create representational gaps (rGaps).

84 Critically, such common representational geometries were formed despite the lack

85 Representational geometries were significantly correlate

86 We characterized the representational geometry by the dissimilarity matrix of

87 The representational geometry remained stable across scannin

88 n of activity profiles, which determines the representational geometry, and thus how well any feature

89 ance and their ability to account for the IT representational geometry.

90 g views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-

91 work-generates the kind of (de)compositional representational hierarchy that is crucial for human lan

92 n anterior temporal lobe (ATL) is a semantic representational hub.

93 Our results suggest that hIT brain representational idiosyncrasies accessible to fMRI are e

94 ia to Southeast Asia, e.g., the emergence of representational imagery and the colonization of arctic

95 representations is exactly the hierarchy of representational impairment in autism, making autism a n

96 presentational deep-sequencing method (biome representational in silico karyotyping [BRiSK]) were app

97 -evaluative situations may induce high-level representational incoherence between one's own intention

98 These findings indicate that very early representational information can be preserved in the mem

99 e over time, in contrast to prior reports of representational instability in the dlPFC.

100 , causal role for DLPFC in norm enforcement: representational integration of the distinct information

101 tible to neuroscience, despite the fact that representational interpretations pose several challenges

102 s, but our understanding of population-level representational invariance remains coarse.

103 propose that these networks reveal the major representational joints in how actions are processed by

104 memory, the ability to temporarily maintain representational knowledge, is a foundational cognitive

105 It has been suggested that we might drop representational language altogether and seek to underst

106 s this feat will include three components: a representational language for characterizing modality-in

107 ut; (2) as the bottleneck recurs at each new representational level, the language system must build a

108 each level of the movement hierarchy at the representational level.

109 by obtaining critical empirical data at the representational level: the level where this computation

110 ason is that prime sentences affect multiple representational levels driving syntactic choice.

111 l was to acquire a comprehensive M1 forelimb representational map of movement endpoints elicited with

112 onality by introducing inaccuracies into the representational map required for planning and effective

113 The computational and representational mechanisms underlying these observation

114 What kind of causal and representational model of the brain does the metaphor en

115 In an experimental context, representational models can be defined as hypotheses abo

116 Representational models specify how activity patterns in

117 duction to the motivation and mathematics of representational models, a critical discussion of their

118 the methods to adjudicate between competing representational models.

119 Next, the distinction between "process" and "representational" models of temporal lobe function is ch

120 As such, they cannot be informed by representational ontology based on mental objects and st

121 The representational organization of stimulus-general action

122 roactive task encoding, governed by distinct representational organizations in specific brain regions

123 d "pretty-good measurements" with negligible representational overhead.

124 Second, we show that lower representational overlap in the hippocampus benefits sub

125 there is limited empirical evidence linking representational overlap in the hippocampus to memory in

126 elationship between fast spindle density and representational overlap in vmPFC was mediated by the de

127 sociated with greater pattern similarity, or representational overlap, across individual object-word

128 al art and asked subjects to assign abstract/representational paintings by the same artist to a situa

129 tant situation significantly more often than representational paintings, indicating that abstract art

130 With an analysis of representational pattern similarity between the acquisit

131 l) event, we derived time-frequency resolved representational patterns in the hippocampus and compare

132 tational patterns to distributed neocortical representational patterns in the suppression of aversive

133 ate how previous category experience governs representational patterns of exemplar learning as well a

134 show a shift away from hippocampal-dependent representational patterns to distributed neocortical rep

135 that models that perform well on measures of representational performance also perform well on measur

136 at possibility cannot be ruled out merely on representational performance grounds.

137 It remains unclear, however, whether the representational performance of DNNs rivals that of the

138 o-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual

139 We thus explored their representational power and dynamical properties after tr

140 tant open question is what characterizes the representational power of deep and shallow neural networ

141 Can selective attention improve the representational precision with which objects are held i

142 nd Chinese music and the form in which these representational processes are preserved across differen

143 ains, each DNN has a unique connectivity and representational profile.

144 sites of attentional control on the basis of representational properties by demonstrating that simple

145 processing in primates, is able to generate representational properties similar to those observed in

146 Our results advance the understanding of the representational properties that give rise to subsequent

147 Dance has representational properties that rely on the dancers' ab

148 sensory processing networks on the basis of representational properties.

149 sidered (e.g., adding stimulus intensity and representational quality).

150 models of cognitive development emphasizing representational redescription and the role of culturall

151 re basic cognitive ability such as recursive representational redescription.

152 To evaluate the idea that representational reliability was the key difference betw

153 provides a potential mechanism for the broad representational repertoire of our semantic system.

154 hippocampal place cells and providing a rich representational repertoire to support complex navigatio

155 completely new information sources into its representational repertoire, and use this information in

156 But any theory that recognizes the representational requirements of agency can do likewise.

157 ons, consistent with allocation of a limited representational resource.

158 These findings support a representational role of spontaneous brain activity.

159 nding to the longitudinal axis of increasing representational scale.

160 neural mechanisms that underlie such dynamic representational shifts.

161 reachable-scale environments have a distinct representational signature from both scene and object vi

162 spersion, a powerful, multivariate metric of representational similarity (precision).

163 us studies have identified similar drifts in representational similarity across space or time over th

164 ard exerted a general effect, increasing the representational similarity among different instructions

165 Functional MRI combined with representational similarity analyses (RSA) of spatial pa

166 Across-species representational similarity analyses (RSAs) revealed str

167 Representational similarity analyses and classification

168 Representational similarity analyses further revealed th

169 Using representational similarity analyses of functional magne

170 Bayesian variational representational similarity analyses on fMRI data assess

171 Representational similarity analyses showed that amygdal

172 Two fMRI studies used representational similarity analyses to demonstrate that

173 Representational similarity analyses, which investigated

174 ps, then combined Bayesian modelling and EEG representational similarity analyses.

175 bution as well as their interactions through representational similarity analyses.

176 We propose Bayesian Representational Similarity Analysis (BRSA), an alternat

177 Using representational similarity analysis (Kriegeskorte et al

178 Here, we applied representational similarity analysis (RSA) [17] to intra

179 Here we used representational similarity analysis (RSA) of fMRI data

180 In this study, we employed representational similarity analysis (RSA) to reveal the

181 Representational similarity analysis (RSA) was used to e

182 Multivariate pattern analysis (MVPA) and representational similarity analysis (RSA) were adopted

183 One approach to this problem is representational similarity analysis (RSA), which charac

184 lysis, pattern component modeling (PCM), and representational similarity analysis (RSA).

185 tuents, we ran a time-resolved topographical representational similarity analysis (tRSA) comparing th

186 We used representational similarity analysis in primary somatose

187 combination with spatiotemporal searchlight representational similarity analysis in source space, we

188 A representational similarity analysis in this parietal re

189 Using multiple regression representational similarity analysis of fMRI data, in wh

190 ions onto brain activity using inter-subject representational similarity analysis of fMRI data, we fi

191 s, or coarse grip with five digits) and used representational similarity analysis of functional magne

192 We investigated this problem using representational similarity analysis of human fMRI data

193 Using representational similarity analysis of male and female

194 By contrast, representational similarity analysis of population activ

195 Representational similarity analysis on item pairs from

196 Using time-resolved representational similarity analysis on the EEG data, we

197 Representational similarity analysis revealed a clear di

198 Representational similarity analysis revealed a dissocia

199 epresentations to computational models using representational similarity analysis revealed a relation

200 Representational similarity analysis revealed that a com

201 Representational similarity analysis revealed that neura

202 tudy we used multivoxel pattern analysis and representational similarity analysis to characterize the

203 f real-time brain activity with multivariate representational similarity analysis to determine direct

204 Here, we used representational similarity analysis to determine what d

205 We used representational similarity analysis to examine how top-

206 We applied representational similarity analysis to human electroenc

207 Finally, we used representational similarity analysis to identify mesolim

208 onses to incomplete scene fragments and used representational similarity analysis to reconstruct thei

209 Here we use fMRI with representational similarity analysis to search for a neu

210 Here, we applied time-resolved representational similarity analysis to the spectral-tem

211 including: MVPA (pattern classification and representational similarity analysis); parallelized sear

212 We used representational similarity analysis, a type of multivar

213 roencephalography (EEG), in combination with representational similarity analysis, to seek neural evi

214 Using representational similarity analysis, we combined human

215 Using representational similarity analysis, we compared activa

216 fMRI adaptation and searchlight model-based representational similarity analysis, we found evidence

217 First, using representational similarity analysis, we found that faci

218 Using representational similarity analysis, we quantified the

219 neuroimaging methods, including multivariate representational similarity analysis.

220 ocial relationship information using EEG and representational similarity analysis.

221 This apparent representational similarity between the posterior-dorsal

222 mPFC, loneliness was associated with reduced representational similarity between the self and others.

223 In most of these regions, the representational similarity of action categories was con

224 As the resulting change in representational similarity predicts interindividual dif

225 If tilted coins bear a representational similarity to elliptical objects, then

226 performance also perform well on measures of representational similarity to IT, and on measures of pr

227 ions in bias and allows estimation of neural representational similarity with previously unattained l

228 (BRSA), an alternative method for computing representational similarity, in which we treat the covar

229 - or electroencephalography (M/EEG) based on representational similarity.

230 here was a decrease in dimensionality of the representational space after training on YUFOs, but not

231 related variables that might orchestrate the representational space for novel instructions.

232 nts, thus suggesting that it operates in the representational space of decisions, rather than motoric

233 so demonstrate that the brain leverages a 3D representational space to make these predictions.

234 e regarding the organization of objects, the representational space underlying the organization of ob

235 , both current and out of sight, in a common representational space.

236 well as the underlying dimensionality of the representational space.

237 tance changes occurred more uniformly across representational space.

238 task in the motor cortex into a common model representational space.

239 d neural representational spaces with target representational spaces based on behavioral judgments an

240 revealed multiple dissociations between the representational spaces for those learning faces and tho

241 neral semantic representation and/or possess representational spaces linked to particular semantic fe

242 n parameters that were used to map subjects' representational spaces of the motor imagery task in the

243 al vocalizations into low dimensional latent representational spaces that are directly learned from t

244 We also find that in the high-dimensional representational spaces these methods generate, simple p

245 We compared neural representational spaces with target representational spa

246 at the MTL forms cognitive maps of arbitrary representational spaces, helping to reconcile longstandi

247 ding, and whether motivation modulated these representational spaces.

248 nized around the concept of high-dimensional representational spaces.

249 iently these mechanisms can support memory's representational specificity in a way that cannot simply

250 These data provide evidence in humans that representational stability in hippocampus across time ma

251 Global Proteome Machine and Database (GPMDB) representational state transfer (REST) service was desig

252 ess protocol (SOAP) and PUG-REST, which is a Representational State Transfer (REST)-style interface.

253 t a Web service to access Ensembl data using Representational State Transfer (REST).

254 This API, which uses a RESTful (Representational State Transfer) implementation, is comp

255 New REST (REpresentational State Transfer) web services have been

256 pecies in European Nucleotide Archive, using Representational State Transfer.

257 ing memory (WM) might not all be in the same representational state.

258 te analysis technique to uncover the elusive representational states during the first few implementat

259 ediate shifts of information among different representational states in STM.

260 at can nevertheless consist of contradictory representational states, which are reflected by mismatch

261 mals has a reduced "vocabulary" of available representational states.

262 t behavioral costs associated with different representational states.

263 information may indeed be afforded a unique representational status in the brain.

264 Peaks in representational strength-indexed through decoding accur

265 tegories in sighted and blind people using a representational structure and connectivity partially si

266 erties of language, including its multilevel representational structure and duality of patterning.

267 But we are worried about the representational structure assumed by Cushman, particula

268 of formal and mechanistic alignment between representational structure building and "cortical" oscil

269 noise ratio map that shows where the learned representational structure is supported more strongly.

270 Instead, the representational structure of body maps in OTC appears s

271 Second, normal topography, magnitude, and representational structure of category-selective organiz

272 MRI analyses revealed normal selectivity and representational structure of category-selective regions

273 ifferent events are used to characterize the representational structure of cognitive states in a neur

274 The organization and representational structure of sensorimotor features unde

275 ptual knowledge can systematically alter the representational structure of social categories at multi

276 nverging accounts of the visual content, the representational structure, and the neural dynamics of f

277 observed a temporal evolution in the brain's representational structure.

278 detect both widely distributed and localized representational structure.

279 rospective decisions share a common, dynamic representational structure.

280 layed a relatively minor role in driving the representational structures of early visual and ventral

281 vance both contributing significantly to the representational structures of the dorsal regions.

282 nectivity profiles associated with different representational subspaces of FFA: the first principal c

283 d et al. raise some fascinating issues about representational substrates and structures in the predic

284 ve cognition literature, we suggest that the representational substrates of the mind are built as a h

285 theoretical result that maximally efficient representational systems assign shorter codes to statist

286 is discontinuity as evidence of two distinct representational systems.

287 nsive characterization of episodic memory in representational terms, and propose a novel functional a

288 These findings inform representational theories on how the prefrontal cortex s

289 itions as merely possible part of our innate representational toolbox or does it await development, p

290 Our results suggest a general representational transformation between auditory cortica

291 We observe substantial representational transformations during the first 300 ms

292 non-primary auditory cortex, the associated representational transformations have remained elusive.

293 change in reward criterion, strategy-related representational transitions in mPFC occurred after anim

294 animals made their own strategic decisions, representational transitions in mPFC preceded changes in

295 in the stability and plasticity of different representational types, and

296 ain activity should be analyzed to achieve a representational understanding.

297 computational goal of the visual system is 'representational untangling' (RU): representing increasi

298 cial recurrent retrieval was associated with representational variability, such that the pattern simi

299 ernatively, recurrent retrieval might foster representational variability--the altering or adding of

300 sts of decision-making through a distributed representational warping in decision-making circuits.