word2vec Search Results


word2vec  (Merriam Webster Inc)
90
Merriam Webster Inc word2vec
Word2vec, supplied by Merriam Webster Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/word2vec/product/Merriam Webster Inc
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word2vec - by Bioz Stars, 2026-03
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word2vec  (Baidu Inc)
90
Baidu Inc word2vec
Word2vec, supplied by Baidu Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/word2vec/product/Baidu Inc
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word2vec - by Bioz Stars, 2026-03
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word2vec model  (NLP Labs)
90
NLP Labs word2vec model
Word2vec Model, supplied by NLP Labs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/word2vec model/product/NLP Labs
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word2vec model - by Bioz Stars, 2026-03
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word2vec word embedding function  (Curran Associates Inc)
90
Curran Associates Inc word2vec word embedding function
Word2vec Word Embedding Function, supplied by Curran Associates Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/word2vec word embedding function/product/Curran Associates Inc
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word2vec word embedding function - by Bioz Stars, 2026-03
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japanese pre-trained word2vec database  (Hottolink Inc)
90
Hottolink Inc japanese pre-trained word2vec database
Japanese Pre Trained Word2vec Database, supplied by Hottolink Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/japanese pre-trained word2vec database/product/Hottolink Inc
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japanese pre-trained word2vec database - by Bioz Stars, 2026-03
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regular (word2vec) configured medcat models  (MedCaT GmbH)
90
MedCaT GmbH regular (word2vec) configured medcat models
Regular (Word2vec) Configured Medcat Models, supplied by MedCaT GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/regular (word2vec) configured medcat models/product/MedCaT GmbH
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regular (word2vec) configured medcat models - by Bioz Stars, 2026-03
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word2vec  (CH Instruments)
90
CH Instruments word2vec
Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and <t>Word2Vec</t> [W2V]).
Word2vec, supplied by CH Instruments, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/word2vec/product/CH Instruments
Average 90 stars, based on 1 article reviews
word2vec - by Bioz Stars, 2026-03
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word2vec approach  (KNIME GmbH)
90
KNIME GmbH word2vec approach
Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and <t>Word2Vec</t> [W2V]).
Word2vec Approach, supplied by KNIME GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/word2vec approach/product/KNIME GmbH
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word2vec approach - by Bioz Stars, 2026-03
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cbow word2vec  (Fairview Diagnostic Laboratories)
90
Fairview Diagnostic Laboratories cbow word2vec
Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and <t>Word2Vec</t> [W2V]).
Cbow Word2vec, supplied by Fairview Diagnostic Laboratories, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/cbow word2vec/product/Fairview Diagnostic Laboratories
Average 90 stars, based on 1 article reviews
cbow word2vec - by Bioz Stars, 2026-03
90/100 stars
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word2vec algorithm  (BioVec Pharma Inc)
90
BioVec Pharma Inc word2vec algorithm
Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and <t>Word2Vec</t> [W2V]).
Word2vec Algorithm, supplied by BioVec Pharma Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/word2vec algorithm/product/BioVec Pharma Inc
Average 90 stars, based on 1 article reviews
word2vec algorithm - by Bioz Stars, 2026-03
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word2vec algorithm  (INFAP GmbH)
90
INFAP GmbH word2vec algorithm
Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and <t>Word2Vec</t> [W2V]).
Word2vec Algorithm, supplied by INFAP GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/word2vec algorithm/product/INFAP GmbH
Average 90 stars, based on 1 article reviews
word2vec algorithm - by Bioz Stars, 2026-03
90/100 stars
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wikipedia®-based word2vec models  (Wikimedia Foundation)
90
Wikimedia Foundation wikipedia®-based word2vec models
Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and <t>Word2Vec</t> [W2V]).
Wikipedia® Based Word2vec Models, supplied by Wikimedia Foundation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/wikipedia®-based word2vec models/product/Wikimedia Foundation
Average 90 stars, based on 1 article reviews
wikipedia®-based word2vec models - by Bioz Stars, 2026-03
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Image Search Results


Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and Word2Vec [W2V]).

Journal: bioRxiv

Article Title: Creativity and memory: Cortical representational change along with amygdala activation predict the insight memory effect

doi: 10.1101/2023.06.13.544774

Figure Lengend Snippet: Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and Word2Vec [W2V]).

Article Snippet: Those results were replicated when using the Word2Vec instead of the AlexNet conceptual model: the linear trend of the insight-memory-factor was significant ( Chi 2 ( 2) = 12.39, p <.01) over both brain regions (pFusG, iLOC) suggesting that this linear trend is robust.

Techniques: Activation Assay, Generated

Insight memory factor predicts multivariate activity in VOTC, univariate activity in amygdala and Amygdala-VOTC functional connectivity. Note . Values represent estimated marginal means ± (between subject) SEM. Forgotten = subsequently forgotten trials; Rem_LO-I: subsequently remembered trials originally solved with low insight; Rem_HI-I = subsequently remembered trials originally solved with high insight. Panel A: change (Δ) in Multivoxel Pattern Similarity (MVPS). Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B: Representational strength of solution object (post solution) is measured via a conceptual model created out of the penultimate layer of AlexNet. Panel C: Representational strength is measured via a conceptual Word2Vec [W2V] model. Panel D: Amygdala mean activity at post solution divided by insight memory conditions. Panel E: Functional connectivity between Amygdala and VOTC. Values represent averaged correlation coefficients between left and right amygdala and left and right iLOC or pFusG.

Journal: bioRxiv

Article Title: Creativity and memory: Cortical representational change along with amygdala activation predict the insight memory effect

doi: 10.1101/2023.06.13.544774

Figure Lengend Snippet: Insight memory factor predicts multivariate activity in VOTC, univariate activity in amygdala and Amygdala-VOTC functional connectivity. Note . Values represent estimated marginal means ± (between subject) SEM. Forgotten = subsequently forgotten trials; Rem_LO-I: subsequently remembered trials originally solved with low insight; Rem_HI-I = subsequently remembered trials originally solved with high insight. Panel A: change (Δ) in Multivoxel Pattern Similarity (MVPS). Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B: Representational strength of solution object (post solution) is measured via a conceptual model created out of the penultimate layer of AlexNet. Panel C: Representational strength is measured via a conceptual Word2Vec [W2V] model. Panel D: Amygdala mean activity at post solution divided by insight memory conditions. Panel E: Functional connectivity between Amygdala and VOTC. Values represent averaged correlation coefficients between left and right amygdala and left and right iLOC or pFusG.

Article Snippet: Those results were replicated when using the Word2Vec instead of the AlexNet conceptual model: the linear trend of the insight-memory-factor was significant ( Chi 2 ( 2) = 12.39, p <.01) over both brain regions (pFusG, iLOC) suggesting that this linear trend is robust.

Techniques: Activity Assay, Functional Assay