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Modeling Semantic Encoding in a Common Neural Representational Space

Overview of attention for article published in Frontiers in Neuroscience, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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16 X users

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22 Dimensions

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83 Mendeley
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Title
Modeling Semantic Encoding in a Common Neural Representational Space
Published in
Frontiers in Neuroscience, July 2018
DOI 10.3389/fnins.2018.00437
Pubmed ID
Authors

Cara E. Van Uden, Samuel A. Nastase, Andrew C. Connolly, Ma Feilong, Isabella Hansen, M. Ida Gobbini, James V. Haxby

Abstract

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual's unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual's fine-grained functional-anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 24%
Researcher 16 19%
Student > Master 15 18%
Professor 5 6%
Student > Bachelor 4 5%
Other 12 14%
Unknown 11 13%
Readers by discipline Count As %
Neuroscience 20 24%
Psychology 19 23%
Engineering 6 7%
Computer Science 5 6%
Agricultural and Biological Sciences 3 4%
Other 9 11%
Unknown 21 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 July 2018.
All research outputs
#3,255,547
of 25,385,509 outputs
Outputs from Frontiers in Neuroscience
#2,397
of 11,542 outputs
Outputs of similar age
#61,920
of 339,365 outputs
Outputs of similar age from Frontiers in Neuroscience
#68
of 235 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 339,365 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 235 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.