Reconstructing meaning from bits of information

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Reconstructing meaning from bits of information. / Kivisaari, Sasa L.; van Vliet, Marijn; Hultén, Annika; Lindh-Knuutila, Tiina; Faisal, Ali; Salmelin, Riitta.

In: Nature Communications, Vol. 10, No. 1, 927, 25.02.2019, p. 1-11.

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@article{7f20dc0f37ce469ebedc8b49adc38279,
title = "Reconstructing meaning from bits of information",
abstract = "Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of semantic features (e.g., “dog” would include “barks”). Here, we demonstrate using functional MRI (fMRI) that the brain combines bits of information into meaningful object representations. Participants receive clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We use machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. The recorded brain patterns are best decoded using a combination of not only the three semantic features that were in fact presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to demonstrate that fragmented information is combined into a complete semantic representation of an object and to identify brain regions associated with object meaning.",
author = "Kivisaari, {Sasa L.} and {van Vliet}, Marijn and Annika Hult{\'e}n and Tiina Lindh-Knuutila and Ali Faisal and Riitta Salmelin",
year = "2019",
month = "2",
day = "25",
doi = "10.1038/s41467-019-08848-0",
language = "English",
volume = "10",
pages = "1--11",
journal = "Nature Communications",
issn = "2041-1723",
number = "1",

}

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TY - JOUR

T1 - Reconstructing meaning from bits of information

AU - Kivisaari, Sasa L.

AU - van Vliet, Marijn

AU - Hultén, Annika

AU - Lindh-Knuutila, Tiina

AU - Faisal, Ali

AU - Salmelin, Riitta

PY - 2019/2/25

Y1 - 2019/2/25

N2 - Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of semantic features (e.g., “dog” would include “barks”). Here, we demonstrate using functional MRI (fMRI) that the brain combines bits of information into meaningful object representations. Participants receive clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We use machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. The recorded brain patterns are best decoded using a combination of not only the three semantic features that were in fact presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to demonstrate that fragmented information is combined into a complete semantic representation of an object and to identify brain regions associated with object meaning.

AB - Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of semantic features (e.g., “dog” would include “barks”). Here, we demonstrate using functional MRI (fMRI) that the brain combines bits of information into meaningful object representations. Participants receive clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We use machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. The recorded brain patterns are best decoded using a combination of not only the three semantic features that were in fact presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to demonstrate that fragmented information is combined into a complete semantic representation of an object and to identify brain regions associated with object meaning.

UR - http://www.scopus.com/inward/record.url?scp=85062078026&partnerID=8YFLogxK

U2 - 10.1038/s41467-019-08848-0

DO - 10.1038/s41467-019-08848-0

M3 - Article

VL - 10

SP - 1

EP - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 927

ER -

ID: 32317781