Quantitative modeling of the neural representation of objects: how semantic feature norms can account for fMRI activation.
about
Understanding What We See: How We Derive Meaning From VisionUsing Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experimentsDiscriminating taxonomic categories and domains in mental simulations of concepts of varying concreteness.What makes a pattern? Matching decoding methods to data in multivariate pattern analysisDistributed representations in memory: insights from functional brain imaging.Recent developments in multivariate pattern analysis for functional MRI.The Centre for Speech, Language and the Brain (CSLB) concept property normsPredicting brain states associated with object categories from fMRI data.From perception to conception: how meaningful objects are processed over time.A brain-based account of "basic-level" concepts.Feature Selection Methods for Zero-Shot Learning of Neural Activity.The role of variability in the property listing task.Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.Recognition during recall failure: Semantic feature matching as a mechanism for recognition of semantic cues when recall fails.BLIND: a set of semantic feature norms from the congenitally blind.When are concepts comparable across minds?
P2860
Q26781799-3C0991CE-D3B0-4CB9-BC13-2E6C37300940Q28710361-4F3E28A2-6BC2-4040-B287-4BD21E15FBF0Q30354998-5E31805D-A1C4-41D0-AE11-EA56771BA69DQ30578730-B42998ED-A8B3-4219-B441-593A86D3FD1CQ34029157-5EB7A23E-9EF9-4971-A2E1-B24FCDF59DBCQ34351914-74966F99-0B0E-4CB5-9450-3B6A2A651B9BQ34544975-8E8BE294-0A56-43E7-A786-27886A244485Q35373427-4B0F75C5-936B-4F9E-82AB-FC6A1D9E4C2DQ36748604-79B8BF27-D847-4BBF-8968-791A86939920Q38371856-A873D49E-0648-40C0-A8F3-8A4F9A9B6E5EQ38374308-716D8C2F-CBE3-41D7-AAB2-F6E86B91E3EFQ38375095-8BEF6421-A764-4ABA-8B24-546B4D41410DQ38388906-B20D63CE-368E-4683-A2FE-7D015A30A829Q38405810-1409663C-3FB5-4B2B-99EE-B645E1243687Q38455552-61D5C208-017A-4054-95D6-FC8D175FC869Q58867958-E9022779-00E6-492B-87FA-E1C2CBEF8086
P2860
Quantitative modeling of the neural representation of objects: how semantic feature norms can account for fMRI activation.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
2010年學術文章
@zh
2010年學術文章
@zh-hant
name
Quantitative modeling of the n ...... n account for fMRI activation.
@en
type
label
Quantitative modeling of the n ...... n account for fMRI activation.
@en
prefLabel
Quantitative modeling of the n ...... n account for fMRI activation.
@en
P50
P1433
P1476
Quantitative modeling of the n ...... n account for fMRI activation.
@en
P304
P356
10.1016/J.NEUROIMAGE.2010.04.271
P407
P577
2010-05-05T00:00:00Z