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The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality
Open accessAbstract
An integral part of human language is the capacity to extract meaning from spoken and written words, but the precise relationship between brain representations of information perceived by listening versus reading is unclear. Prior neuroimaging studies have shown that semantic information in spoken language is represented in multiple regions in the human cerebral cortex, while amodal semantic information appears to be represented in a few broad brain regions. However, previous studies were too insensitive to determine whether semantic representations were shared at a fine level of detail rather than merely at a coarse scale. We used fMRI to record brain activity in two separate experiments while participants listened to or read several hours of the same narrative stories, and then created voxelwise encoding models to characterize semantic selectivity in each voxel and in each individual participant. We find that semantic tuning during listening and reading are highly correlated in most semantically selective regions of cortex, and models estimated using one modality accurately predict voxel responses in the other modality. These results suggest that the representation of language semantics is independent of the sensory modality through which the semantic information is received. SIGNIFICANCE STATEMENT Humans can comprehend the meaning of words from both spoken and written language. It is therefore important to understand the relationship between the brain representations of spoken or written text. Here, we show that although the representation of semantic information in the human brain is quite complex, the semantic representations evoked by listening versus reading are almost identical. These results suggest that the representation of language semantics is independent of the sensory modality through which the semantic information is received.
Overview
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Written from the paper’s full text.
TL;DR
Using fMRI and voxelwise encoding models, the authors compared semantic selectivity while the same participants listened to or read the same narrative stories. They found that semantic tuning is highly correlated across modalities in most semantically selective cortex, and models fit in one modality accurately predict responses in the other.
Key findings
- Semantic model prediction was broadly distributed across lateral temporal, ventral temporal, lateral parietal, medial parietal, and prefrontal cortices in both listening and reading, with maximum MNI-space prediction accuracy of 0.27 ± 0.03 for listening and 0.28 ± 0.03 for reading.
- Listening and reading semantic principal component projections were significantly correlated for at least the first five semantic PCs; correlations weakened after the sixth PC but remained above chance until the seventh.
- Voxelwise semantic model weights estimated during listening and reading were strongly correlated in many bilateral temporal, parietal, and prefrontal regions, especially for voxels semantically selective in both modalities.
- Cross-modal prediction was accurate throughout most of the semantic system: models fit to listening predicted reading responses, and models fit to reading predicted listening responses, while sensory-cortex voxels were not well predicted cross-modally.
Methods
- Nine participants (six male, three female) listened to and read more than two hours of narrative stories from The Moth Radio Hour; reading used rapid serial visual presentation with each word timed to match its spoken duration.
- fMRI BOLD responses were modeled voxelwise with banded ridge regression using a 985-dimensional word co-occurrence semantic feature space plus nuisance regressors (motion-energy, spectral, phoneme, letter, word rate, syntax, etc.), with FIR hemodynamic modeling and cross-validated regularization.
- Model performance was assessed on a held-out story using Pearson correlation, permutation tests with 10,000 iterations, and FDR correction; cross-modality prediction used weights from one modality to predict responses in the other.
Limitations
- The paper states that its semantic feature space is only one possible representation of semantics and may miss long-timescale conceptual knowledge, metaphors, humor, sarcasm, and narrative information, which could have modality-specific representations.
- The paper notes that RSVP reading differs from natural reading because readers cannot control which word to read at each point in time, though timing was matched to listening.
- The paper also notes that within-modality data were collected largely within sessions while between-modality data were collected across sessions, so within-modality prediction accuracy is likely somewhat higher for that reason alone; an evident additional limitation is the small sample of nine participants, two of whom were authors.
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40 Own result · 23 Not checked yet · 18 Own interpretation · 17 Own method · 8 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“models estimated using one modality accurately predict voxel responses in the other modality”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find that semantic tuning during listening and reading are highly correlated in most semantically selective regions of cortex”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Outside the semantic system, some voxels on the border to visual cortex and voxels surrounding the temporal parietal junction are well predicted within modality but not across modality (orange voxels in Fig. 9).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Can a voxelwise model fit to one modality predict responses to the other modality?
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In the paper
“Specifically, we show amodal semantic representation in bilateral precuneus, temporal parietal junction (TPJ), angular gyrus (AG), anterior to posterior STS, sPMv, Broca's area and inferior frontal gyrus (IFG).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Discussion
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In the paper
“Semantic tuning is more similar for voxels that are semantically selective in both modalities (red) than for those that are well predicted in one modality only (blue or green).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Is semantic tuning consistent across modalities at the single-voxel level?
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In the paper
“Our study shows that semantically amodal voxels are bilaterally distributed across many regions of the temporal, parietal and prefrontal cortices (Figs. 3, 4, 5, 6).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Discussion
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In the paper
“Most of the voxels in the semantic system are significantly predicted by all participants in both modalities.”
- Also stated in: § Discussion · plain
“Most of the voxels in the semantic system are significantly predicted by all participants in both modalities.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Model estimation and evaluation
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the semantic model accurately predicts activity in most of the semantic system independent of the presentation modality.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Does the cortical distribution of semantically selective voxels depend on stimulus modality?
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“Voxels that are not well predicted cross-modally are located in sensory cortices.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Can a voxelwise model fit to one modality predict responses to the other modality?
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Most voxels that are well predicted within and across modality are located in the semantic system (white voxels in Fig. 9).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Can a voxelwise model fit to one modality predict responses to the other modality?
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 13
- Probably not a substantial claim. 32
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In the paper
“Correlations of the sixth PC projection and beyond are relatively weaker, but remain above chance level until the seventh PC projection.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Does the representation of semantic information vary with sensory modality?
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“(maximum prediction accuracy across all MNI voxels for listening 0.27 Ϯ 0.03, maximum prediction accuracy across all MNI voxels for reading: 0.28 Ϯ 0.03)”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Does the cortical distribution of semantically selective voxels depend on stimulus modality?
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In the paper
“Figure 2a shows that our semantic model predicts brain activity in a broadly distributed semantic system when participants listen to natural stories, replicating our previous study (Huth et al., 2016).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Does the cortical distribution of semantically selective voxels depend on stimulus modality?
What the sources say
- Not checked yetcites Huth et al., 2016
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“One striking difference between our results and those reported in earlier studies is that we find a large network of semantically selective regions that are independent of the presentation modality, whereas previous studies reported a few amodal semantic regions located mostly in the left hemisphere (Petersen et al., 1989;Chee et al., 1999;Jobard et al., 2007;Buchweitz et al., 2009;Liuzzi et al.,”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Discussion
What the sources say
- Not checked yetcites Petersen et al., 1989
- Not checked yetcites Chee et al., 1999
- Not checked yetcites Liuzzi et al., 2017
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In the paper
“Negatively correlated voxels are mostly in sensory regions and are not well predicted by the semantic model in either modality.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Is semantic tuning consistent across modalities at the single-voxel level?
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, due to averaging across participants voxel prediction accuracies are lower than in individual participant results”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Does the cortical distribution of semantically selective voxels depend on stimulus modality?
plaindirectn = 9- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Prior neuroimaging studies have shown that semantic information in spoken language is represented in multiple regions in the human cerebral cortex”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Abstract
boosted- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the demonstration of some common activation during listening and reading is necessary but not sufficient evidence of a common amodal semantic representation.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Introduction
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“while amodal semantic information appears to be represented in a few broad brain regions”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Abstract
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
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In the paper
“Although the early sensory processing pathways for listening and reading are distinct”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Introduction
What the sources say
- Not checked yetcites Rubin et al., 2000
- Not checked yetcites Diakidoy et al., 2005
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, previous studies were too insensitive to determine whether semantic representations were shared at a fine level of detail rather than merely at a coarse scale.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Listening and reading presentations were counterbalanced across participants.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Participants
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In the paper
“This model was selected based on an earlier study showing that it outperforms other low level acoustical models”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Spectral model construction
What the sources say
- Not checked yetcites de Heer et al., 2017
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In the paper
“The eye tracker was calibrated before the first run of data acquisition.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Natural reading stimuli
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In the paper
“Banded ridge regression assigns a different regularization parameter for every feature space and so reduces bias caused by correlations between feature spaces.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Voxelwise model fitting
What the sources say
- Not checked yetcites Nunez-Elizalde et al., 2019
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The motion-energy, spectral, word rate, phoneme rate, phonemes, letter rate, letters, and word length variation features were used to explain away low-level parameters that might otherwise contaminate the semantic model weights.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Feature spaces
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In the paper
“The regularization parameter for each feature space was estimated empirically via cross-validation on a held-out set.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Banded ridge regression
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In the paper
“Most of these cross-modality studies observed activity in left lateralized regions such as the left anterior temporal lobe, left superior temporal sulcus (STS), left middle temporal gyrus (MTG), and left inferior frontal gyrus (IFG).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Introduction
What the sources say
- Not checked yetcites Chee et al., 1999
- Not checked yetcites Michael et al., 2001
- Not checked yetcites Buchweitz et al., 2009
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Banded ridge regression is a special case of the well-established statistical approach called Tikhonov regression”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Banded ridge regression
What the sources say
- Not checked yetcites Tikhonov and Arsenin, 1977
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In the paper
“However, the stories were performed by different speakers and were not uniformly mastered, so some differences in total loudness remain.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Natural speech stimuli
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In the paper
“Statistical significance was computed by a permutation test with 10,000 iterations and comparing estimated correlations to the empirical null distribution of correlations for each participant and modality separately.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Model estimation and evaluation
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In the paper
“To find out whether the semantic projections could be correlated by chance, a permutation test with 10,000 iterations was performed for each individual participant separately.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Correlating the semantic principal components
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In the paper
“Statistical significance was defined as any correlation coefficient that exceeded 95% of all of the permuted correlations.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Correlating the semantic principal components
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In the paper
“The corrected aligned transcript was then spot-checked for accuracy by a different listener.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Story transcription and preprocessing
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In the paper
“The regularization parameter is optimized based on prediction accuracy on a held-out dataset.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Banded ridge regression
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“(Note, however, that within-modality data were collected largely within sessions, and between-modality data were collected across sessions.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Can a voxelwise model fit to one modality predict responses to the other modality?
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Before surface reconstruction, anatomical surface segmentations were carefully handchecked and corrected using Blender software and pycortex”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Cortical surface reconstruction and visualization
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In the paper
“These automatic alignments were manually checked and adjusted as necessary to improve accuracy.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Stimulus down-sampling
plain- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, to make listening and reading more comparable we matched the timing of the words presented during RSVP to the rate at which the words occurred during listening.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Natural reading stimuli
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- Rests on an assumption: RSVP reading differs from natural reading in that the reader has no control over which word to read at each point in time.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Each story was manually transcribed by one listener, and this transcription was checked by a second listener.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Story transcription and preprocessing
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Relatively few have studied cross-modal representations by presenting the same stimuli in both modalities”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Introduction
What the sources say
- Not checked yetcites Petersen et al., 1989
- Not checked yetcites Chee et al., 1999
- Not checked yetcites Michael et al., 2001
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Two of the participants were authors on the paper (A.G.H. and A.O.N.-E.).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Participants
plainn = 2- Asserted: passes — The authors put it forward as true.
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In the paper
“Resulting p-values were corrected for multiple comparisons within each participant using the false discovery rate (FDR) procedure (Benjamini and Hochberg, 1995).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Model estimation and evaluation
What the sources say
- Not checked yetcites Benjamini and Hochberg, 1995
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In the paper
“However, that study did not model semantic information, but only showed that voxel activations in these regions tend to be correlated across these two modalities.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Introduction
What the sources say
- Not checked yetcites Regev et al., 2013
plaincorrelational- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To validate the voxelwise models, estimated model weights were used to predict responses to a validation story that was not used for model estimation.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Model estimation and evaluation
plainn = 9- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Previous studies have shown that complex linguistic stimuli such as narrative stories activate many more brain regions than single words or short sentences (Mazoyer et al., 1993;Xu et al., 2005;Jobard et al., 2007;Lerner et al., 2011).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Discussion
What the sources say
- Not checked yetcites Mazoyer et al., 1993
- Not checked yetcites Xu et al., 2005
- Not checked yetcites Lerner et al., 2011
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“For example, when people listen to or read a story they likely employ conceptual knowledge at long time scales beyond those using for computing semantic features (Yeshurun et al., 2017).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Discussion
What the sources say
- Not checked yetcites Yeshurun et al., 2017
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Therefore, studies performing intersubject averaging might average away meaningful signal and fail to find significant relationships (Fedorenko and Kanwisher, 2009).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Discussion
What the sources say
- Not checked yetcites Fedorenko and Kanwisher, 2009 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“That study used as stimuli twenty-four single words derived from only six animate categories, and showed cross-modal representations within left pars triangularis.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Discussion
What the sources say
- Not checked yetcites Liuzzi et al., 2017
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“To our knowledge, only one previous study of cross-modal representation has used explicit semantic features to model brain activity patterns related to semantics (Liuzzi et al., 2017).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Discussion
What the sources say
- Not checked yetcites Liuzzi et al., 2017
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
RSVP reading differs from natural reading in that the reader has no control over which word to read at each point in time.
“RSVP reading is different than natural reading because during RSVP the reader has no control over which word to read at each point in time.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This language network supports both comprehension (spoken, written, and signed) [30, 31, 32, 33] and production [34, 35]”
From Dissociating language and thought in large language models (2024) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Does the representation of semantic information vary with sensory modality?
“Each voxel was then colored according to a simple RGB color scheme, where the color red represents the first semantic PC, the color green represents the second semantic PC, and blue the third semantic PC.”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Results
“Each voxel is colored according to its projection onto the first (red), second (blue) or third (green) semantic PC.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Feature spaces
“The feature spaces were motion-energy features (39 parameters), spectral features (80 parameters), word rate (1 parameter), phoneme rate (1 parameter), phonemes (39 parameters), letter rate (1 parameter), letters (26 parameters), word length variation per repetition time (1 parameter), syntactic features (56 parameters), and co-occurrence semantics (985 parameters).”
§ The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality > Does the cortical distribution of semantically selective voxels depend on stimulus modality?
“Several low-level features (low-level visual, spectral, word rate, letter rate, word length variation, phonemes, phoneme rate, and pauses) and syntactic features were included alongside the semantic features as nuisance regressors (see Materials and Methods)”
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · cited 1×
“Resulting p-values were corrected for multiple comparisons within each participant using the false discovery rate (FDR) procedure (Benjamini and Hochberg, 1995).”
MethodThe assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900 · cited 2×
“One participant was left handed, all other participants were right handed or ambidextrous according to the Edinburgh handedness inventory (Oldfield, 1971) (laterality quotient of 100: entirely left- handed, 100: entirely right-handed).”
MethodAdvances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Indexing by Latent Semantic Analysis
Deerwester, S., Dumais, S., Landauer, T., et al. (1990) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199009)41:6%3c391::aid-asi1%3e3.0.co;2-9 · Cited by 12,359 · cited 2×
“Semantic model construction To account for response variance caused by the semantic content of the story stimuli a 985-parameter semantic feature space based on word co-occurrence statistics in a large corpus of text (Deerwester et al., 1990; Lund and Burgess, 1996; Mitchell et al., 2008; Huth et al., 2016) was used.”
MethodJenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888 · cited 1×
“Each functional run was motion-corrected using the FMRIB Linear Image Registration Tool (FLIRT) from FSL 5.0 (Jenkinson and Smith, 2001; Jenkinson et al., 2002).”
MethodInfluentialCortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · cited 1×
“Cortical surface reconstruction and visualization Cortical surface meshes were generated from the T1-weighted anatomical scans using Freesurfer software (Dale et al., 1999).”
MethodSolutions of Ill-Posed Problems.
Bell, J. B., Tikhonov, A. N., & Arsenin, V. Y. (1978) · Mathematics of Computation · doi:10.2307/2006360 · Cited by 8,096 · cited 1×
“Banded ridge regression is a special case of the well-established statistical approach called Tikhonov regression (Tikhonov and Arsenin, 1977).”
MethodThe fusiform face area: a module in human extrastriate cortex specialized for face perception
Kanwisher, N., McDermott, J., & Chun, M. M. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-11-04302.1997 · Cited by 7,738 · cited 1×
“The contrast between faces and objects was used to define the fusiform face area (Kanwisher et al., 1997) and occipital face area (Halgren et al.”
MethodA global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018 · cited 1×
“Each functional run was motion-corrected using the FMRIB Linear Image Registration Tool (FLIRT) from FSL 5.0 (Jenkinson and Smith, 2001; Jenkinson et al., 2002).”
MethodInfluentialPraat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417
Research works printed but not linked: 5
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- 10.1002/(SICI)1097-0193(1999)7:1<29::AID-HBM3>3.0.CO;2-R · DOI
- 10.1016/S1053-8119(02)91132-8 · DOI · Paper page
- Oliphant TE (2006) A guide to NumPy, Vol 1, p 85. USA: Trelgol Publishing.
Cited by
Sorted by citations
The default mode network: where the idiosyncratic self meets the shared social world
Yeshurun, Y., Nguyen, M., & Hasson, U. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-00420-w · Cited by 682
Semantic reconstruction of continuous language from non-invasive brain recordings
Tang, J., LeBel, A., Jain, S., & Huth, A. G. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01304-9 · Cited by 442
“Although previous studies have demonstrated that most parts of cortex are active during language processing 5,22–24 , it is unclear which regions represent language at the granularity of words and phrases 25 , which regions are consistently engaged in language processing 26 and whether different…”
ResultSelf-citationDissociating language and thought in large language models
Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.011 · Cited by 345
“Below we briefly summarize a body of evidence from cognitive science and neuroscience that supports this dissociation.”
The language network as a natural kind within the broader landscape of the human brain
Fedorenko, E. G., Ivanova, A. A., & Regev, T. I. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00802-4 · Cited by 374
“Second, the language areas are modality-independent: they respond to diverse kinds of input, including spoken, written and signed language 19,64–67 (Fig.”
BackgroundKeep it real: rethinking the primacy of experimental control in cognitive neuroscience
Nastase, S. A., Goldstein, A., & Hasson, U. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117254 · Cited by 380
“…Heusser et al., 2018), comparing natural language across modalities and contexts (Stephens et al., 2010; Regev et al., 2013; Yeshurun et al., 2017; Deniz et al., 2019), and using data-driven modeling to capture the complexity of naturalistic neural responses (Haxby et al., 2011, 2020; Baldassano…”
MethodVisual and linguistic semantic representations are aligned at the border of human visual cortex
Popham, S. F., Huth, A. G., Bilenko, N. Y., Deniz, F., Gao, J. S., Núñez-Elizalde, A. O., & Gallant, J. L. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00921-6 · Cited by 181
Self-citationDiachek, E., Blank, I., Siegelman, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/744094 · Cited by 55
The neural architecture of language: Integrative modeling converges on predictive processing
(2020) · doi:10.1101/2020.06.26.174482 · Cited by 129
“1) that responds robustly and selectively to linguistic input (23, 24), whether auditory or visual (25, 26).”
BackgroundFeature-space selection with banded ridge regression
la Tour, T. D., Eickenberg, M., Núñez-Elizalde, A. O., & Gallant, J. L. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119728 · Cited by 118
Self-citationNo evidence for differences among language regions in their temporal receptive windows
Blank, I., & Fedorenko, E. G. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116925 · Cited by 74
“a memory probe task), and modality of presentation (visual and auditory: Fedorenko et al., 2010; Braze et al., 2011; Vagharchakian et al., 2012; Deniz et al., 2019).”
Hu, J., Small, H., Kean, H. H., et al. (2021) · bioRxiv · doi:10.1101/2021.09.10.459596 · Cited by 77
“25 combinatorial processing during comprehension, regardless of the input modality (e.g., Fedorenko et al., 2010, 2016; Vagharchakian et al., 2012; Regev et al., 2013; Scott et al., 2017; Deniz et al., 2019).”
BackgroundDriving and suppressing the human language network using large language models
Tuckute, G., Sathe, A., Srikant, S., Taliaferro, M., Wang, M., Schrimpf, M., Kay, K., & Fedorenko, E. G. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01783-7 · Cited by 106
“As additional effects, we modeled other variables that could modulate the BOLD response but that were not our critical variables of interest, including item (sentence), run order within a session (1-10), and sentence order within a run (1-50):”
Brain encoding models based on multimodal transformers can transfer across language and vision
Tang, J., Du, M., Vo, V. A., et al. (2023) · Neural Information Processing Systems · doi:10.48550/arxiv.2305.12248 · Cited by 76
“These findings are consistent with previous studies that mapped how different concepts in language are represented across cortex [30, 43].”
ResultSelf-citationComprehension of computer code relies primarily on domain-general executive brain regions
Ivanova, A. A., Srikant, S., Sueoka, Y., Kean, H., Dhamala, R., O’Reilly, U.-M., Bers, M. U., & Fedorenko, E. G. (2020) · eLife · doi:10.7554/elife.58906 · Cited by 120
“These regions respond robustly to linguistic input, both visual and auditory (Deniz et al., 2019; Fedorenko et al., 2010; Nakai et al., 2020; Regev et al., 2013; Scott et al., 2017).”
BackgroundHu, J., Small, H., Kean, H., Takahashi, A., Zekelman, L., Kleinman, D., Ryan, E., Nieto-Castañón, A., Ferreira, V., & Fedorenko, E. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhac350 · Cited by 73
Doedens, W., & Meteyard, L. (2022) · Neuropsychology Review · doi:10.1007/s11065-021-09531-2 · Cited by 96
“For example, the comprehension of narratives activates large areas of the cerebral cortex, and not simply those canonically viewed as ‘language’ areas (Deniz et al., 2019; Huth et al., 2016).”
BackgroundAnderson, A. J., Kiela, D., Binder, J. R., Fernandino, L., Humphries, C. J., Conant, L. L., Raizada, R. D. S., Grimm, S., & Lalor, E. C. (2021) · The Journal of Neuroscience · doi:10.1523/jneurosci.1152-20.2021 · Cited by 70
Wehbe, L., Blank, I., Shain, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.15.043844 · Cited by 11
Szewczyk, J., & Federmeier, K. D. (2021) · Journal of Memory and Language · doi:10.1016/j.jml.2021.104311 · Cited by 83
“For example, according to the hub-and-spoke theory (Lambon Ralph et al., 2017; Patterson et al., 2007), the activated network includes both common areas (i.e., the ATL) and meaning-specific areas that are different for each word (see, e.g., Deniz et al., 2019; Huth et al., 2016).”
BackgroundVoxelwise Encoding Models Show That Cerebellar Language Representations Are Highly Conceptual
LeBel, A., Jain, S., & Huth, A. G. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0118-21.2021 · Cited by 68
“The word-level semantic model uses a 985-dimensional conceptual feature space that is based on word co-occurrence statistics across a large corpus of written English(de Heer et al., 2017; Deniz et al., 2019; Huth et al., 2016).”
MethodSelf-citationZada, Z., Goldstein, A., Michelmann, S., Simony, E., Price, A. R., Hasenfratz, L., Barham, E., Zadbood, A., Doyle, W., Friedman, D. J., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.06.025 · Cited by 86
An fMRI dataset in response to “The Grand Budapest Hotel”, a socially-rich, naturalistic movie
Castello, M. V. D. O., Chauhan, V. S., Jiahui, G., & Gobbini, M. I. (2020) · Scientific Data · doi:10.1038/s41597-020-00735-4 · Cited by 50
Computational Language Modeling and the Promise of In Silico Experimentation
Jain, S., Vo, V. A., Wehbe, L., & Huth, A. G. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00101 · Cited by 43
“To reap the benefits of both interpretable controlled experiments and generalizable naturalistic stimuli, some studies have deployed a hybrid experimental design (Chien & Honey, 2020; Deniz et al., 2019; Lerner et al., 2011; Overath et al., 2015; Yeshurun et al., 2017).”
BackgroundSelf-citationA natural language fMRI dataset for voxelwise encoding models
LeBel, A., Wagner, L., Jain, S., Adhikari-Desai, A., Gupta, B., Morgenthal, A., Tang, J., Xu, L., & Huth, A. G. (2023) · Scientific Data · doi:10.1038/s41597-023-02437-z · Cited by 52
Self-citationCombining computational controls with natural text reveals aspects of meaning composition
Toneva, M., Mitchell, T. M., & Wehbe, L. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00354-6 · Cited by 66
“Note that this is similar to methods that directly compare the encoding model weights estimated for two voxels (Çukur et al., 2013; Huth et al., 2016; Deniz et al., 2019), but spatial generations looks not only at how much the weights are different, but also at how much this difference affects the ability to predict h…”
MethodComprehension of computer code relies primarily on domain-general executive resourcesPreprint
Ivanova, A. A., Srikant, S., Sueoka, Y., Kean, H., Dhamala, R., O’Reilly, U.-M., Bers, M. U., & Fedorenko, E. G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17605/osf.io/9jfn5 · Cited by 5
“These regions respond robustly to linguistic input (visual or auditory; Bemis & Pylkkänen, 2013; Deniz et al., 2019; Fedorenko et al., 2010) but show little or no response to tasks in non-linguistic domains, such as executive functions, math, logic, music, action observation, or non-linguistic…”
BackgroundSchilling, A., Tomasello, R., Henningsen‐Schomers, M. R., Zankl, A., Surendra, K., Haller, M., Karl, V., Uhrig, P., Maier, A., & Krauß, P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.21.052720 · Cited by 3
“Remarkable, it turned out that the representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality [4].”
BackgroundNaturalistic stimuli: A paradigm for multiscale functional characterization of the human brain
Zhang, Y., Kim, J., Brang, D., & Liu, Z. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100298 · Cited by 49
Zakaj je pomembno branje na višji ravni
Schüller-Zwierlein, A., Mangen, A., Kovač, M., & van der Weel, A. (2024) · Knjižnica revija za področje bibliotekarstva in informacijske znanosti · doi:10.55741/knj.68.1.3 · Cited by 9
Improving semantic understanding in speech language models via brain-tuning
Moussa, O., Klakow, D., & Toneva, M. (2024) · International Conference on Learning Representations · doi:10.48550/arxiv.2410.09230 · Cited by 42
“Given that most large public brain recordings datasets are of speech-evoked language (LeBel et al., 2023; Nastase et al., 2021; Deniz et al., 2019; Momenian et al., 2024), having access to speech models with improved brain-relevant semantics is important and will provide better model organisms for…”
The Brain Connectome for Chinese Reading
Guo, W., Geng, S., Cao, M., & Feng, J. (2022) · Neuroscience Bulletin · doi:10.1007/s12264-022-00864-3 · Cited by 41
“have already been used to expand the understanding of the neural basis for language processing, particularly for natural language comprehension [139, 140].”
Audiobooks, Print, and Comprehension: What We Know and What We Need to Know
Singh, A., & Alexander, P. A. (2022) · Educational Psychology Review · doi:10.1007/s10648-021-09653-2 · Cited by 41
Speech language models lack important brain-relevant semantics
Oota, S., Çelik, E., Deniz, F., et al. (2023) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2311.04664 · Cited by 37
“We also use the articulation and power spectrum (PowSpec) feature vectors provided in Deniz et al. (2019).”
InfluentialSelf-citationBrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity
Luo, A. F., Henderson, M. M., Tarr, M. J., et al. (2023) · International Conference on Learning Representations · doi:10.48550/arxiv.2310.04420 · Cited by 37
“…in vision (Naselaris et al., 2011; Huth et al., 2012; Yamins et al., 2014; Eickenberg et al., 2017; Wen et al., 2018; Kubilius et al., 2019; Conwell et al., 2023; Wang et al., 2022), language (Huth et al., 2016; Deniz et al., 2019), and at the interface of vision and language (Popham et al., 2021).”
BackgroundDisentangling Syntax and Semantics in the Brain with Deep NetworksPreprint
Caucheteux, C., Gramfort, A., & King, J.-R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.01620 · Cited by 44
“Following others (Huth et al., 2016; Deniz et al., 2019; Shain et al., 2020), we align the word-times features X , of length M , to the dynamics of the fMRI signals applying a finite impulse response (FIR) model g (cf.”
MethodThe Cortical Representation of Language Timescales is Shared between Reading and ListeningPreprint
Chen, C., la Tour, T. D., Gallant, J. L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.06.522601 · Cited by 5
“At each permutation iteration, the timecourse of the held-out test dataset was permuted by blockwise shuffling (shuffling was performed in blocks of 10 TRs in order to account for autocorrelations in voxel responses (Deniz et al., 2019; Jain et al., 2020)).”
BackgroundMethodInfluentialSelf-citationListening Ears or Reading Eyes: A Meta-Analysis of Reading and Listening Comprehension Comparisons
Clinton‐Lisell, V. (2021) · Review of Educational Research · doi:10.3102/00346543211060871 · Cited by 51
“…assumed nature of comprehension is supported by findings from brain imaging research comparing listening and reading stories indicating that although sensory processes clearly differ by modality, the representations of the meaning in the verbal information are similar (Deniz et al., 2019).”
BackgroundResultAcunzo, D. J., Low, D. M., & Fairhall, S. L. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119005 · Cited by 38
Szubielska, M., & Imbir, K. K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250924 · Cited by 38
“At the same time, the representation of the language semantics in the brain is independent of whether meaning is extracted from spoken or written words [91].”
BackgroundQin, L., Lyu, B., Shu, S., Yin, Y., Wang, X., Ge, J., Siok, W. T., & Gao, J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2771-20.2021 · Cited by 27
“…resemblance has been demonstrated between two sets of semantic representations recalled by spoken and written stories during language comprehension (Deniz et al., 2019), and the left anterodorsal pars triangularis (Brodmann area 45, BA 45) is significantly correlated with cross-modal semantic…”
BackgroundSymbolicAI: A framework for logic-based approaches combining generative models and solvers
Dinu, M.-C., Leoveanu-Condrei, C., Holzleitner, M., Zellinger, W., & Hochreiter, S. (2024) · CoLLAs · doi:10.48550/arxiv.2402.00854 · Cited by 30
Can fMRI reveal the representation of syntactic structure in the brain?Preprint
Reddy, A. J., & Wehbe, L. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.16.155499 · Cited by 26
“We choose a common value of 10TRs [25].”
MethodPhonemic segmentation of narrative speech in human cerebral cortex
Gong, X. L., Huth, A. G., Deniz, F., Johnson, K. A., Gallant, J. L., & Theunissen, F. E. (2023) · Nature Communications · doi:10.1038/s41467-023-39872-w · Cited by 19
“…effect binomial (semantic versus phonemic) or multinomial (single phonemes vs diphone vs triphone) statistical models in the main text and, just as for d 0 , this calculation is repeated with the “ equal weight" per subject and shown in the supplementary tables for Supplementary Table 2 and 5.”
BackgroundMethodInfluentialSelf-citationNeural Synchronization as a Function of Engagement with the Narrative
Ohad, T., & Yeshurun, Y. (2023) · bioRxiv · doi:10.1101/2023.01.01.522416 · Cited by 25
“The DMN is known to be involved in narrative comprehension and interpretation (Deniz et al., 2019; Nguyen et al., 2019; Simony et al., 2016; Yeshurun et al., 2021).”
BackgroundInfluentialSensory Modality-Independent Activation of the Brain Network for Language
Arana, S., Marquand, A. F., Hultén, A., Peter Hagoort, A., & Schoffelen, J. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2271-19.2020 · Cited by 27
“Some previous studies using narratives and fMRI reported that supramodal activation was not restricted to the left hemisphere (Jobard et al., 2007; Regev et al., 2013; Deniz et al., 2019).”
BackgroundInfluentialEEG-based BCI Dataset of Semantic Concepts for Imagination and Perception Tasks
Wilson, H., Golbabaee, M., Proulx, M. J., Charles, S., & O'Neill, E. J. (2023) · Scientific Data · doi:10.1038/s41597-023-02287-9 · Cited by 23
“The advantage of decoding semantic information, as opposed to sensory based information such as visual details, is that semantic representation is partially invariant across modalities 9–13 .”
BackgroundA Meta-Analysis of fMRI Studies of Semantic Cognition in Children
Enge, A., Rahman, R. A., & Skeide, M. A. (2021) · bioRxiv · doi:10.1101/2021.05.17.442947 · Cited by 24
Behavioral correlates of cortical semantic representations modeled by word vectors
Nishida, S., Blanc, A., Maeda, N., Kado, M., & Nishimoto, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009138 · Cited by 19
“, sentences and speech [14,21,26]; however, it was not applicable when the response is evoked by nonlinguistic stimuli, e.”
BackgroundDTBVis: An interactive visual comparison system for digital twin brain and human brain
Li, Y., Li, X.-H., Shen, S., et al. (2023) · Visual Informatics · doi:10.1016/j.visinf.2023.02.002 · Cited by 20
Combining Concepts Across Categorical Domains: A Linking Role of the Precuneus
Rabini, G., Ubaldi, S., & Fairhall, S. L. (2021) · Neurobiology of Language · doi:10.1162/nol_a_00039 · Cited by 22
“…in the human brain has also been highlighted through datadriven approaches, by assessing brain responses to naturally spoken narrative stories (Deniz et al., 2019; Huth et al., 2016), further mapping single word-related semantic selectivity on the whole brain surface (see also Pereira et al.,…”
BackgroundFunctions of the posterior cingulate cortex and default network
Foster, B. L., & Koslov, S. R. (2025) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2025.101560 · Cited by 12
“These findings, which have benefited from more precise functional mapping approaches, link with prior work suggesting broad semantic coding within PCC/DMN [72,73].”
Zhang, L., & Xing, H.-B. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1076810 · Cited by 17
“Only when the connection of various representations reached a particular strength could mutual activation and diffusion be realized, and the output of orthography could be realized efficiently (Deniz et al., 2019).”
BackgroundDissociating language and thought in large language models
Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2023) · arXiv · doi:10.48550/arxiv.2301.06627 · Cited by 80
“This ‘language network’ supports both comprehension (spoken, written, and signed; e.g., Deniz et al., 2019, Fedorenko et al., 2010, MacSweeney et al., 2002, Regev et al., 2013, Scott et al., 2017) and production Menenti et al., 2011, Hu et al., 2022b.”
la Tour, T. D., Castello, M. V. D. O., & Gallant, J. L. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00575 · Cited by 18
“…a, *, Matteo Visconti di Oleggio Castello a,b, *, Jack L. Gallant a,b Imaging Neuroscience, Volume 3, 2025 Wehbe et al., 2014), and narrative language ( Caucheteux & King, 2022; de Heer et al., 2017; Deniz et al., 2019; Huth et al., 2016; Jain & Huth, 2018; Jain et al., 2020; Toneva & Wehbe, 2019).”
Self-citationWolna, A., Wright, A., Casto, C., Hutchinson, S., Lipkin, B., & Fedorenko, E. G. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.0638-25.2026 · Cited by 18
“…language areas, responds to both written and auditory linguistic inputs (Fedorenko et al., 2010; Vagharchakian et al., 2012; Regev et al., 2013; Deniz et al., 2019), its computations plausibly have to do with the content of the linguistic signals rather than their surface (visual or auditory)…”
Chen, C., Gong, X. L., Tseng, C., Klein, D. L., Gallant, J. L., & Deniz, F. (2025) · bioRxiv · doi:10.1101/2024.06.24.600505 · Cited by 18
“In each permutation, the test responses were shuffled in blocks of 10 TRs (19, 44, 71, 81–85).”
InfluentialSelf-citationConvergent Representations of Computer Programs in Human and Artificial Neural Networks
Srikant, S., Lipkin, B., Ivanova, A. A., et al. (2022) · Neural Information Processing Systems · doi:10.52202/068431-1368 · Cited by 16
“This method is similar to a variance-partitioning analysis which is often employed in encoding models, e.g. Deniz et al. [2019].”
MethodThe cortical representation of language timescales is shared between reading and listening
Chen, C., la Tour, T. D., Gallant, J. L., Klein, D. B., & Deniz, F. (2024) · Communications Biology · doi:10.1038/s42003-024-05909-z · Cited by 27
“On the other hand, many parts of temporal, parietal, and prefrontal cortex process both written and spoken language (e.g., Booth et al., 2002; Buchweitz et al., 2009; Liuzzi et al., 2017; Regev et al., 2013; Deniz et al., 2019; Nakai et al., 2021).”
BackgroundMethodInfluentialSelf-citationMulti-modal brain encoding models for multi-modal stimuli
Oota, S., Pahwa, K., Marreddy, M., et al. (2025) · International Conference on Learning Representations · doi:10.48550/arxiv.2505.20027 · Cited by 15
“The choice of these specific permutation test configurations is based on established methodologies in previous research (Deniz et al., 2019; Reddy & Wehbe, 2021; Oota et al., 2024a).”
Evaluating the impact of short educational videos on the cortical networks for mathematics
Amalric, M., Roveyaz, P., & Dehaene, S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2213430120 · Cited by 16
Wu, W., Morales, M., Patel, T., et al. (2022) · bioRxiv · doi:10.1101/2022.03.07.483336 · Cited by 14
Cooney, C., Folli, R., & Coyle, D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104783 · Cited by 16
“However, it is possible to ensure that text and audio are presented for a precisely equal time period to ensure faithful comparison (Deniz et al., 2019).”
BackgroundMethodResolving the time course of visual and auditory object categorization
Iamshchinina, P., Karapetian, A., Kaiser, D., et al. (2021) · bioRxiv · doi:10.1101/2021.11.25.470008 · Cited by 14
“Future studies could employ tasks, such as category verification or story listening/reading (Huth et al., 2016; Deniz et al., 2019; Popham et al., 2021) that encourage deep processing of words and their context in modality-independent rather than modality-focused details.”
MethodConvergence of Modality Invariance and Attention Selectivity in the Cortical Semantic Circuit
Nakai, T., Yamaguchi, H. Q., & Nishimoto, S. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab125 · Cited by 13
“Researchers have adopted this approach to comprehensively examine semantic representations (de Heer et al. 2017; Deniz et al. 2019; Huth et al. 2016), visual object category representations (Çukur et al. 2013a; Çukur et al. 2016; Çelik et al. 2019; Huth et al. 2012), and how attention modulates…”
InfluentialModelling brain representations of abstract concepts
KAISER, D. H., Jacobs, A. M., & Cichy, R. M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009837 · Cited by 13
“Other studies have probed correspondences between language processing models and the brain through the use of encoding models [2,3,6,48,50].”
BackgroundMorton, N. W., Zippi, E. L., & Preston, A. R. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad179 · Cited by 20
“2012) that represent distinct features of stimuli (Huth et al. 2012; Deniz et al. 2019; Morton et al. 2021).”
BackgroundNeural correlates of linguistic collocations during continuous speech perception
Garibyan, A., Schilling, A., Boehm, C., et al. (2022) · bioRxiv · doi:10.1101/2022.03.25.485771 · Cited by 12
“[26] Fatma Deniz, Anwar O Nunez-Elizalde, Alexander G Huth, and Jack L Gallant.”
BackgroundThe cerebellar components of the human language network
Casto, C., Poliak, M., Tuckute, G., Small, H., Sherlock, P., Wolna, A., Lipkin, B., D’Mello, A. M., & Fedorenko, E. G. (2026) · Neuron · doi:10.1016/j.neuron.2025.12.030 · Cited by 30
Zheng, Y., Zhang, J., Yang, Y., & Xu, M. (2025) · Communications Biology · doi:10.1038/s42003-025-07466-5 · Cited by 16
“Existing studieshaveexploredneuralactivationpatternsbetweenspeechandread-ing,emphasizingdistinctionsbetweenunimodalversusmultimodal regions,andmodality-speci fi c effects in language comprehension 61 – 65 .”
Geng, S., Guo, W., Rolls, E. T., Xu, K., Jia, T., Zhou, W., Blakemore, C. B., Tan, L. H., Cao, M., & Feng, J. (2023) · Communications Biology · doi:10.1038/s42003-023-04446-5 · Cited by 13
Ufer, C., & Blank, H. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2023.2166679 · Cited by 10
“This is underpinned for example by a semantic model forming accurate predictions irrespective of modality (Deniz et al., 2019) and an MEG study showing word-speci fi c neural activity shared between modalities and subjects (Arana et al., 2020).”
Unveiling A Core Linguistic Region in Large Language Models
Zhao, J., Zhang, Z.-H., Ma, Y.-D., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2310.14928 · Cited by 10
“This region underpins both the comprehension (Deniz et al., 2019; Scott et al., 2017; Regev et al., 2013; Fedorenko et al., 2010) and production (Menenti et al., 2011; Hu et al., 2021) of language across spoken, written, and signed modalities.”
BackgroundMeschke, E. X., Castello, M. V. D. O., la Tour, T. D., & Gallant, J. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.17.549356 · Cited by 12
“Significance was evaluated for each network in each participant by comparing the N -1 group’s network-predicted activity to a null distribution 40,42 .”
BackgroundMethodSelf-citationThye, M. D., Hoffman, P., & Mirman, D. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120204 · Cited by 11
“, 2016), regions within a distributed, bilateral semantic network display functional specialization for conceptual categories presented in narratives and this semantic selectivity appears regardless of sensory input modality (Deniz et al., 2019; Zhang et al., 2020).”
BackgroundMethodText to Speech: Transportation-Imagery Theory and Outcomes of Narrative Delivery Format
Gregg, P. B. (2020) · Journal of Radio & Audio Media · doi:10.1080/19376529.2020.1801689 · Cited by 14
Fan, G., Carlson, K. D., & Thomas, R. D. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.614280 · Cited by 10
“Despite its old age, the EHI is still extensively used in the field of psychology, especially cognitive neuroscience, as a valid measure of handedness (Voss et al., 2010; Erickson et al., 2011; Stoodley et al., 2012; Barch et al., 2013; Frolov et al., 2017; Deniz et al., 2019).”
BackgroundBrain-Informed Fine-Tuning for Improved Multilingual Understanding in Language Models
Negi, A., Oota, S., Nunez-Elizalde, A. O., et al. (2025) · bioRxiv · doi:10.1101/2025.07.07.662360 · Cited by 9
“…models can predict brain responses to written and spoken stimuli with high accuracy (Wehbe et al., 2014a; Jain & Huth, 2018; Toneva & Wehbe, 2019; Deniz et al., 2019; Abdou et al., 2021; Toneva et al., 2022; Antonello et al., 2021; Oota et al., 2022; Aw & Toneva, 2023; Oota et al., 2024b;…”
Self-citationKosch, L., Stocker, G., Ahrens-Schwabe, A., & Boomgaarden, H. (2025) · Poetics · doi:10.1016/j.poetic.2025.102021 · Cited by 9
Brains and language models converge on a shared conceptual space across different languagesPreprint
Zada, Z., Nastase, S. A., Li, J., & Hasson, U. (2025) · PubMed · doi:10.48550/arxiv.2506.20489 · Cited by 1
“Brain regions supporting language comprehension are engaged by both listening and reading (Regev et al., 2013; Deniz et al., 2019), as well as speaking and listening (Stephens et al., 2014; Zada et al., 2024, 2025).”
Zada, Z., Nastase, S. A., Speer, S. P. H., et al. (2025) · bioRxiv · doi:10.1101/2025.02.14.638276 · Cited by 9
“Many recent studies have applied them during passive language comprehension (Caucheteux & King, 2022; de Heer et al., 2017; Deniz et al., 2019; Goldstein et al., 2022; Heilbron et al., 2022; Huth et al., 2016; Kumar et al., 2024; Schrimpf et al., 2021).”
Language modality influences risk perception: Innovations read well but sound even better
Geipel, J., Hadjichristidis, C., Savadori, L., & Keysar, B. (2022) · Risk Analysis · doi:10.1111/risa.13917 · Cited by 8
“Indeed, neuroimaging studies show that hearing or reading a given description evokes identical semantic representations in the human brain (Arana et al., 2020; Deniz et al., 2019).”
BackgroundMatricciani, E., & Caro, L. D. (2020) · J — Multidisciplinary Scientific Journal · doi:10.3390/j3010010 · Cited by 9
Functional Gradient of the Fusiform Cortex for Chinese Character Recognition
Guo, W., Geng, S., Cao, M., & Feng, J. (2022) · eNeuro · doi:10.1523/eneuro.0495-21.2022 · Cited by 8
“RSA is powerful for integrating different level/scale/ modality (e.g., neural, behavioral, physical, theoretical) activities to identify cognitive manipulation (FischerBaum et al., 2017; Wang et al., 2018; Deniz et al., 2019).”
BackgroundSemantic representations during language comprehension are affected by context
(2021) · doi:10.1101/2021.12.15.472839 · Cited by 8
“…sentences, the development and validation of the voxelwise encoding model framework for language processing (Huth et al., 2016; de Heer et al., 2017; Deniz et al., 2019; Popham et al., 2021) has made it possible to rigorously test hypotheses about semantic processing with natural language stimuli.”
BackgroundMethodInfluentialSelf-citationLinguistics and human brain: a perspective of computational neuroscience
Zhang, F., Chai, B., Wu, Y., Siok, W. T., & Wang, N. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-026-10496-2 · Cited by 5
“LLMs provide computational platforms for examining modern linguistic phenomena such as semantic integration, long-distance dependencies, and predictive processing.”
Conversational content is organized across multiple timescales in the brain
Yamashita, M., Kubo, R., & Nishimoto, S. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02231-4 · Cited by 11
“…semantics to sentence meaning, ultimately culminating in a coherent narrative (Leaner et al., 2011; Regev et al., 2013; de Heer et al., 2017; Deniz et al., 2019; Popham et al., 2021, Deniz et al., 2023; Caucheteux et al., 2023; Huth et al., 2016; Schrimpf et al., 2021; Goldstein et al.,…”
Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)Preprint
Oota, S. R., Jindal, A., Mondal, I., Pahwa, K., Satya Sai Srinath Namburi, G. N. V. V., Shrivastava, M., Singh, M., Bapi, R. S., & Gupta, M. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2505.20029 · Cited by 3
“Specifically, variance partitioning separates the brain response variance that can be attributed to two models based on their unique and overlapping contributions (Vaidya et al., 2022; Deniz et al., 2019).”
BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human BrainPreprint
Wasserman, N., Cosarinsky, M., Golbari, Y., Oliva, A., Torralba, A. B., Shaham, T. R., & Irani, M. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2512.08560 · Cited by 4
“Stimulus-driven decompositions for interpretability have also been explored, in domains such as speech and audio [14, 42, 57] and visual stimuli [7, 17, 28].”
López-Cardona, Á., Idesis, S., Masias-Bruns, M., Abadal, S., & Arapakis, I. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.17833 · Cited by 1
Development and validation of a rapid and precise online sentence reading efficiency assessment
Yeatman, J. D., Tran, J. E., Burkhardt, A. K., Ma, W. A., Mitchell, J. L., Yablonski, M., Gijbels, L., Townley-Flores, C., & Richie-Halford, A. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1494431 · Cited by 7
Thye, M. D., Hoffman, P., & Mirman, D. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02228 · Cited by 8
“These results are consistent with prior naturalistic investigations of semantic processing (Thye et al., 2023; Wu et al., 2022; Deniz et al., 2019; Huth et al., 2012, 2016) and with the language comprehension network identified across diverse languages, especially in the left hemisphere…”
ResultRicciardi, E., & Pietrini, P. (2023) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2023.2218502 · Cited by 7
“…knowledge relies on speci fi c hubs, but explain how semantic information integrates between brain areas and progresses from a modality-based towards a modality-independent conceptual representation (Deniz et al., 2019; Handjaras et al., 2016; Mahon & Caramazza, 2011; Popham et al., 2021).”
Chung, A., Jin, P., Kamboukos, D. M., Robbins, R., Blanc, J., Jean‐Louis, G., & Seixas, A. A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159416 · Cited by 7
“However, in a study that compared brain activation between audio-only and text-based narratives, researchers found that the same parts of the brain were activated with both audio-only and reading books [46].”
BackgroundFrom Analog to Digital Computing: Is Homo sapiens’ Brain on Its Way to Become a Turing Machine?
Danchin, A., & Fenton, A. A. (2022) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2022.796413 · Cited by 12
“…(meaningless) and information with “value” has been discussed for a long time under the name “semantic” information [(Bar-Hillel and Carnap, 1953; Deniz et al., 2019; Lundgren, 2019; Miłkowski, 2021), see also emphasis on the requirement for recursive modeling to account for information in the…”
BackgroundDeep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)Preprint
Oota, S. R., Chen, Z., Gupta, M., Bapi, R. S., Jobard, G., Alexandre, F., & Hinaut, X. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.10246 · Cited by 34
Ramirez, D. C. O., Salazar, D. A. R., Muñoz, Á. M. V., Betancur, L. M. Q., & Fletscher, L. A. (2025) · Education Sciences · doi:10.3390/educsci15081075 · Cited by 7
Verbal Encoding Strategies in Visuo-Spatial Working Memory
Seabra, J. P., Chopurian, V., Souza, A. S., et al. (2025) · Journal of Cognition · doi:10.5334/joc.406 · Cited by 6
Wang, Y., Gong, X., Meng, L.-W., et al. (2024) · Interspeech · doi:10.48550/arxiv.2407.10376 · Cited by 6
“…(as shown in Figure 2): (1) these vectors are resampled at times corresponding to the fMRI acquisitions using a three-lobe Lanczos filter [13, 24]; (2) vectors are reduced to d = 90 dimensions using PCA for computational efficiency; (3) finally, based on the linearized finite impulse…”
MethodLearning in Wilson-Cowan model for metapopulation
Marino, R., Buffoni, L., Chicchi, L., et al. (2024) · Neural Computation · doi:10.48550/arxiv.2406.16453 · Cited by 6
“This language network supports both comprehension (spoken, written, and signed) (Deniz et al., 2019; Terri L. Scott and Fedorenko, 2017) and production (Menenti et al., 2011; Hu et al., 2022).”
BackgroundHuang, C., Li, A., Pang, Y., Yang, J., Zhang, J., Wu, X., & Mei, L. (2024) · Brain Imaging and Behavior · doi:10.1007/s11682-024-00849-y · Cited by 6
“We found that the regions in the semantic network spanned most of the bilateral semantic system, consistent with prior findings that bilateral activation was observed in response to complex stimuli, such as narratives (Deniz et al., 2019).”
Metadata
- Authors
- Deniz, F., Nunez-Elizalde, A. O., Huth, A. G., & Gallant, J. L.
- Affiliations
- International Computer Science Institute (US)
- University of California, Berkeley (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2019-08-19
- Volume
- 39
- Issue
- 39
- Pages
- 7722-7736
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6764208
- OpenAlex
- W2968395892
- Semantic Scholar
- 44731d68e7e65d7bd6807b53b348ee93c107411f
- Cited by
- 214
- References
- 78
- Citations by year
- 2026: 19 · 2025: 44 · 2024: 24 · 2023: 37 · 2022: 28 · 2021: 36 · 2020: 25 · 2019: 1
- FWCI
- 12.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-sa)
- Funders
- National Science Foundation (CCF0-939370, 0939370, 1208203)
- Deutscher Akademischer Austauschdienst
- National Institutes of Health (5r01ey019684-08)
- Advanced Research Projects Agency
- National Eye Institute (R01-EY019684, R01EY022454, EY022454, EY019684)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- semantic representation, spoken language, written language, narrative stories, language comprehension, cerebral cortex
- MeSH terms
- Acoustic Stimulation, Adult, Auditory Perception, Cerebral Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Photic Stimulation, Reading, Semantics, Visual Perception, Comprehension
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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