English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Probabilistic language models in cognitive neuroscience: Promises and pitfalls

Armeni, K., Willems, R. M., & Frank, S. (2017). Probabilistic language models in cognitive neuroscience: Promises and pitfalls. Neuroscience and Biobehavioral Reviews, 83, 579-588. doi:10.1016/j.neubiorev.2017.09.001.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-DDA6-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-6239-2
Genre: Journal Article

Files

show Files
hide Files
:
Armeni_etal_2017_probabilistic.pdf (Publisher version), 535KB
Name:
Armeni_etal_2017_probabilistic.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Armeni, Kristijan1, Author
Willems, Roel M.1, 2, Author              
Frank, Stefan3, Author
Affiliations:
1Donders Institute for Brain, Cognition and Behaviour, External Organizations, ou_55236              
2Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792551              
3Center for Language Studies , External Organizations, ou_55238              

Content

show
hide
Free keywords: -
 Abstract: Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We highlight the advantages and potential pitfalls of this approach and indicate avenues for future research

Details

show
hide
Language(s): eng - English
 Dates: 20172017
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neubiorev.2017.09.001
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Neuroscience and Biobehavioral Reviews
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York [etc.] : Pergamon
Pages: - Volume / Issue: 83 Sequence Number: - Start / End Page: 579 - 588 Identifier: ISSN: 0149-7634
CoNE: /journals/resource/954928536106