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.
AI snapshot
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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Claims the paper makes
0 of 42 cited sources checked · 0 supported · 0 flagged
- Findingown data
Models estimated using one modality accurately predict voxel responses in the other modality.
“models estimated using one modality accurately predict voxel responses in the other modality”
Quote in the paperSubstantial claim - Findingown data
Semantic tuning during listening and reading are highly correlated in most semantically selective regions of cortex.
“We find that semantic tuning during listening and reading are highly correlated in most semantically selective regions of cortex”
Quote in the paperSubstantial claim - Findingown data
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.
“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).”
Quote in the paperSubstantial claim - Findingown data
Amodal semantic representation is present in bilateral precuneus, temporal parietal junction (TPJ), angular gyrus (AG), anterior to posterior STS, sPMv, Broca's area and inferior frontal gyrus (IFG).
“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).”
Quote in the paperSubstantial claim - Findingown data
Semantic tuning is more similar for voxels that are semantically selective in both modalities than for those that are well predicted in one modality only.
“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).”
Quote in the paperSubstantial claim
- Findingown data
Semantically amodal voxels are bilaterally distributed across many regions of the temporal, parietal and prefrontal cortices.
“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).”
Quote in the paperSubstantial claim - Findingown data
Most of the voxels in the semantic system are significantly predicted by all participants in both modalities.
“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.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Findingown data
Overall, the semantic model accurately predicts activity in most of the semantic system independent of the presentation modality.
“Overall, the semantic model accurately predicts activity in most of the semantic system independent of the presentation modality.”
Quote in the paperSubstantial claim - Findingown data
Voxels that are not well predicted cross-modally are located in sensory cortices.
“Voxels that are not well predicted cross-modally are located in sensory cortices.”
Quote in the paperSubstantial claim - Findingown data
Most voxels that are well predicted within and across modality are located in the semantic system.
“Most voxels that are well predicted within and across modality are located in the semantic system (white voxels in Fig. 9).”
Quote in the paperSubstantial claim - Findingown data
These voxels are also significantly well predicted by the semantic model in both modalities.
“These voxels are also significantly well predicted by the semantic model in both modalities.”
Quote in the paperSubstantial claim - Findingown data
The semantic system for listening extends across much of lateral temporal cortex (LTC), ventral temporal cortex (VTC), lateral parietal cortex (LPC), medial parietal cortex (MPC), medial PFC, superior PFC, and inferior PFC.
“This system extends across much of lateral temporal cortex (LTC), ventral temporal cortex (VTC), lateral parietal cortex (LPC), medial parietal cortex (MPC), medial PFC, superior PFC, and inferior PFC.”
Quote in the paperSubstantial claim - Findingown data
Voxels in bilateral temporal, parietal, and prefrontal cortices are well predicted across modalities.
“In both cases, voxels in bilateral temporal, parietal, and prefrontal cortices are well predicted across modalities.”
Quote in the paperSubstantial claim - Findingown data
Across all participants, estimated semantic model weights in the listening modality accurately predict BOLD responses in many brain regions in the semantic system, including LTC, VTC, LPC, MPC, and PFC.
“Across all participants the estimated semantic model weights in the listening modality accurately predict BOLD responses in many brain regions in the semantic system, including LTC, VTC, LPC, MPC, and PFC.”
Quote in the paperSubstantial claim - Findingown data
Across all participants, estimated semantic model weights in the reading modality accurately predict BOLD responses in the semantic system.
“Across all participants, estimated semantic model weights in the reading modality accurately predict BOLD responses in the semantic system.”
- Also stated in: § Discussion · plain
“Across all participants, estimated semantic model weights in the reading modality accurately predict BOLD responses in the semantic system.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Findingown data
The listening and reading semantic maps are very similar.
“Inspection of the listening and reading semantic maps shown in Figure 4 reveals that the semantic representations in both modalities are very similar.”
Quote in the paperSubstantial claim - Findingown data
Most voxels are approximately equally well predicted in both modalities.
“Most voxels are approximately equally well predicted in both modalities.”
Quote in the paperSubstantial claim - Findingown data
Semantic representations do not differ between listening and reading in left DLPFC.
“In our study, we focused solely on semantic representations and our results suggest that semantic representations do not differ between listening and reading in left DLPFC.”
Quote in the paperSubstantial claim - Findingown data
The first five semantic PC projections are significantly correlated between listening and reading modalities.
“Inspection of Figure 5 reveals that the first five semantic PC projections are significantly correlated between listening and reading modalities.”
Quote in the paperSubstantial claim - Findingown data
Voxels whose model weights are not correlated are located in few scattered voxels in the bilateral sensory cortex, intraparietal sulcus, and in PFC.
“Voxels whose model weights are not correlated are located in few scattered voxels in the bilateral sensory cortex, intraparietal sulcus, and in PFC (white voxels).”
Quote in the paperSubstantial claim - Findingown data
The semantic model predicts brain activity within the semantic system in all participants.
“These results show that our semantic model predicts brain activity within the semantic system in all participants.”
Quote in the paperSubstantial claim - Findingown data
When participants read natural stories, the same network of brain regions is similarly well predicted by the semantic model.
“Figure 2b shows that when participants read natural stories this network of brain regions are similarly well predicted by the semantic model.”
Quote in the paperSubstantial claim - Findingown data
The study demonstrates modality-independent semantic selectivity in most of the bilateral semantic system.
“In sum, we demonstrate modality-independent semantic selectivity in most of the bilateral semantic system.”
Quote in the paperSubstantial claim - Findingown data
Across all participants the estimated semantic model weights in the listening modality accurately predict BOLD responses in many brain regions in the semantic system, including LTC, VTC, LPC, MPC, and PFC.
“Across all participants the estimated semantic model weights in the listening modality accurately predict BOLD responses in many brain regions in the semantic system, including LTC, VTC, LPC, MPC, and PFC.”
Quote in the paperSubstantial claim - Findingown data
Semantically selective voxels are tuned for similar semantic concepts during both listening and reading.
“Comparison of panels a and b reveals that semantically selective voxels are tuned for similar semantic concepts during both listening and reading.”
Quote in the paperSubstantial claim - Findingown data
The semantic model estimated in reading accurately predicts voxel responses in listening within the semantic system including bilateral temporal (LTC, VTC), parietal (LPC, MPC), and prefrontal cortices (PFC).
“The semantic model estimated in reading accurately predicts voxel responses in listening within the semantic system including bilateral temporal (LTC, VTC), parietal (LPC, MPC), and prefrontal cortices (PFC).”
Quote in the paperSubstantial claim - Findingown data
Modality invariant semantic representations are not isolated in a few left-lateralized regions, but are instead present in many bilaterally distributed regions of the semantic system.
“Furthermore, our results reveal that modality invariant semantic representations are not isolated in a few left-lateralized regions, but are instead present in many bilaterally distributed regions of the semantic system.”
Quote in the paperSubstantial claim - Findingown data
At least the first five semantic PC projections are significantly correlated between listening and reading.
“At least the first five semantic PC projections are significantly correlated between listening and reading.”
Quote in the paperSubstantial claim - Findingown data
Voxels in the early sensory regions such as the primary AC and early visual cortex are not well predicted.
“In contrast, voxels in the early sensory regions such as the primary AC and early visual cortex are not well predicted.”
Quote in the paperNot a substantial claim - Findingown data
The semantic model estimated in listening accurately predicts voxel responses in reading within the semantic system including bilateral temporal (LTC, VTC), parietal (LPC, MPC), and prefrontal cortices (PFC).
“The semantic model estimated in listening accurately predicts voxel responses in reading within the semantic system including bilateral temporal (LTC, VTC), parietal (LPC, MPC), and prefrontal cortices (PFC).”
Quote in the paperSubstantial claim - Findingown data
Estimated semantic model weights accurately predict BOLD responses in many brain regions in the semantic system, including LTC, VTC, LPC, MPC, and PFC in both modalities.
“Estimated semantic model weights accurately predict BOLD responses in many brain regions in the semantic system, including LTC, VTC, LPC, MPC, and PFC in both modalities.”
Quote in the paperSubstantial claim - Findingown data
Semantic tuning is more similar for voxels that are semantically selective in both modalities than for those that are selective in one modality only.
“Semantic tuning is more similar for voxels that are semantically selective in both modalities (red) than for those that are selective in one modality only (blue and green).”
Quote in the paperSubstantial claim - Findingown data
Voxels in the semantic system have similar semantic tuning across all the semantic features.
“Voxels in the semantic system have similar semantic tuning across all the semantic features.”
Quote in the paperSubstantial claim - Findingown data
Projecting the results into a standard brain space and averaging across individuals reduces prediction performance within modality across much of the brain.
“Indeed, projecting our results into a standard brain space and averaging across individuals reduces prediction performance within modality across much of the brain (cf.Figs.3a, 2).”
Quote in the paperSubstantial claim - Findingown data
Most voxels are equally well predicted in listening and reading.
“Most voxels are equally well predicted in listening and reading indicating that these voxels represent semantic information independent of the presentation modality.”
Quote in the paperSubstantial claim - Premisereasoning
The human brain represents semantic information in an amodal form that is independent of input modality.
“This suggests that the human brain represents semantic information in an amodal form that is independent of input modality”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Vigneau et al., 2006
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- cites Binder et al., 2009
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- cites Price, 2010
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- Premisecitation
Listeners and readers appear to extract very similar information about the meaning of a narrative story.
“listeners and readers appear to extract very similar information about the meaning of a narrative story”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Rubin et al., 2000
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- cites Diakidoy et al., 2005
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- Interpretationown data
The representation of language semantics is independent of the sensory modality through which the semantic information is received.
“These results suggest that the representation of language semantics is independent of the sensory modality through which the semantic information is received.”
Quote in the paperSubstantial claim - Premisecitation
Several cortical regions are activated during both listening and reading.
“There is evidence that several cortical regions are activated during both listening and reading”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Price, 2010
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- cites Price, 2012
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- Interpretationown data
The cortical representation of semantic information is consistent across input modalities.
“Together, these results indicate that the cortical representation of semantic information is consistent across input modalities.”
Quote in the paperSubstantial claim - Premisecitation
Most cross-modality studies used tightly controlled stimuli, such as a set of single isolated words, sentences or curated passages, and an explicit lexical semantic task.
“Most of these studies used tightly controlled stimuli, such as a set of single isolated words, sentences or curated passages, and an explicit lexical semantic task”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Chee et al., 1999
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- cites Michael et al., 2001
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- cites Buchweitz et al., 2009
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- Interpretationown data
Voxels that are semantically selective in listening and reading modalities represent similar semantic information.
“This suggests that voxels that are semantically selective in listening and reading modalities (red) represent similar semantic information.”
Quote in the paperSubstantial claim - Premisereasoning
The evidence available currently is insufficient to determine whether semantic information obtained during listening and reading are represented in the same way.
“In sum, the evidence available currently is insufficient to determine whether semantic information obtained during listening and reading are represented in the same way.”
Quote in the paperNot a substantial claim - Premisecitation
Most imaging studies of the semantic system have examined only one input modality, either spoken or written words.
“Most imaging studies of the semantic system have examined only one input modality, either spoken or written words”
Quote in the paperSubstantial claim0 of 3 sources checked- cites De ´monet et al., 1994
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- cites Vandenberghe et al., 1996
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- cites Scott et al., 2000
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- Premisecitation
A low-dimensional semantic subspace was created in a previous study from the authors' laboratory (Huth et al., 2016) by applying principal component analysis to an aggregated set of estimated semantic model weights from seven participants.
“That study recovered a low-dimensional semantic subspace from an aggregated set of estimated semantic model weights using principal components analysis.”
- Also stated in: § Does the representation of semantic information vary with sensory modality? · plain
“This semantic subspace was created by applying principal component analysis to an aggregated set of estimated semantic model weights from seven participants included in a previous study from our laboratory (Huth et al., 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Huth et al., 2016
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- Also stated in: § Does the representation of semantic information vary with sensory modality? · plain
- Interpretationown data
The distribution of semantically selective voxels in most of the semantic system is independent of the modality.
“This result demonstrates that the distribution of semantically selective voxels in most of the semantic system is independent of the modality.”
Quote in the paperSubstantial claim - Interpretationown data
Individual voxels located within the semantic system are selective to similar semantic features during both listening and reading.
“In general, individual voxels located within the semantic system are selective to similar semantic features during both listening and reading.”
Quote in the paperSubstantial claim - Interpretationown data
Individual voxels within the semantic system are tuned for the same semantic concepts regardless of presentation modality.
“The similarity between the listening and reading semantic maps indicate that individual voxels within the semantic system are tuned for the same semantic concepts regardless of presentation modality.”
Quote in the paperSubstantial claim - Interpretationown data
Semantically amodal voxels span most of the bilateral semantic system.
“Our results demonstrate in a single study that semantically amodal voxels span most of the bilateral semantic system.”
Quote in the paperSubstantial claim - Premisenone given
To date no study has performed a direct comparison of semantic selectivity maps obtained during listening and reading of natural text in single participants.
“However, to date no study has performed this crucial comparison.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Within-modality prediction accuracy is likely to be somewhat higher than between-modality accuracy for this reason alone.
“Thus, within-modality prediction accuracy is likely to be somewhat higher than between-modality accuracy for this reason alone.)”
Quote in the paperNot a substantial claim - Interpretationown data
The experiments provide three lines of evidence in support of the hypothesis that semantic representations throughout most of the semantic system are invariant to presentation modality.
“Our experiments provide three lines of evidence in support of the hypothesis that semantic representations throughout most of the semantic system are invariant to presentation modality.”
Quote in the paperSubstantial claim - Premisereasoning
Previous studies were too coarse grained to determine whether listening and reading shared semantic representations at the level of a single voxel.
“Furthermore, previous studies were too coarse grained to determine whether listening and reading shared semantic representations at the level of a single voxel.”
Quote in the paperSubstantial claim - Interpretationown data
Across the 985 semantic features semantic information is represented similarly in both modalities in the semantic system.
“This suggests that across the 985 semantic features semantic information is represented similarly in both modalities in the semantic system.”
Quote in the paperSubstantial claim - Interpretationreasoning
The most likely reason that the Liuzzi et al. (2017) study only identified one region as semantically amodal is that single word presentations elicit little brain activity.
“However, the most likely reason that the Liuzzi et al. (2017) study only identified one region as semantically amodal is that single word presentations elicit little brain activity.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Liuzzi et al., 2017
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- Interpretationcitation
Differences in signal-to-noise ratio can account for fewer number of amodal regions identified in previous cross-modality studies that use single words or short sentences.
“Hence, differences in signal-to-noise ratio can account for fewer number of amodal regions identified in previous cross-modality studies that use single words or short sentences.”
Quote in the paperSubstantial claim - Premisenone given
Most previous neuroimaging studies of language perform comparisons at the group level after transforming individual participant data into a standardized brain space.
“In contrast, most previous neuroimaging studies of language perform comparisons at the group level after transforming individual participant data into a standardized brain space (e.g., MNI or Talairach space).”
Quote in the paperSubstantial claim - Premisecitation
Anatomical normalization procedures used in previous studies tend to smooth and mask the substantial individual variability in language processing.
“However, the anatomical normalization procedures used in these studies tend to smooth and mask the substantial individual variability in language processing (Caramazza, 1986;Steinmetz and Seitz, 1991;Fedorenko and Kanwisher, 2009).”
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- cites Steinmetz and Seitz, 1991
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- cites Fedorenko and Kanwisher, 2009 source
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- Interpretationreasoning
Voxels that are well predicted in both modalities represent similar semantic information.
“This suggests that voxels that are well predicted in both modalities represent similar semantic information.”
Quote in the paperSubstantial claim - Interpretationown data
Voxels within the semantic system represent semantic information independent of modality.
“Voxels within the semantic system represent semantic information independent of modality.”
Quote in the paperSubstantial claim - Interpretationown data
These voxels represent semantic information independent of the presentation modality.
“Most voxels are equally well predicted in listening and reading indicating that these voxels represent semantic information independent of the presentation modality.”
Quote in the paperSubstantial claim - Interpretationreasoning
Sensory regions process unimodal information related to low-level processing of spoken or written language.
“Our findings are consistent with the view that sensory regions process unimodal information related to low-level processing of spoken or written language, whereas high-level regions process modality invariant semantic information.”
Quote in the paperNot a substantial claim - Interpretationnone given
The naturalistic experiment and voxelwise modeling (VM) provide a method for identifying amodal representations in individual human brains.
“Our naturalistic experiment and VM provides a powerful and efficient method for identifying amodal representations in individual human brains.”
Quote in the paperSubstantial claim - Interpretationown data
The individual dimensions of the semantic maps are similar across the two modalities.
“This shows that the individual dimensions of the semantic maps in Figure4where the first three semantic PCs are displayed are similar across the two modalities.”
Quote in the paperSubstantial claim - Interpretationown data
High-level regions process modality invariant semantic information.
“Our findings are consistent with the view that sensory regions process unimodal information related to low-level processing of spoken or written language, whereas high-level regions process modality invariant semantic information.”
Quote in the paperSubstantial claim - Premiseown data
The present study is the first that reveals the amodal representation of semantic information during listening and reading in single participants.
“Third, the present study is the first that reveals the amodal representation of semantic information during listening and reading in single participants (Fig. 1).”
Quote in the paperSubstantial claim
- May combine two claims. 13
- Classified as probably not a substantial claim. 32
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Findingown dataCorrelations of the sixth PC projection and beyond are relatively weaker, but remain above chance level until the seventh PC projection.
“Correlations of the sixth PC projection and beyond are relatively weaker, but remain above chance level until the seventh PC projection.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe maximum prediction accuracy across all MNI voxels was 0.27 ± 0.03 for listening and 0.28 ± 0.03 for reading.
“(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)”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe semantic model predicts brain activity in a broadly distributed semantic system when participants listen to natural stories, replicating the previous study (Huth et al., 2016).
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Huth et al., 2016
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Findingown dataThe present study finds 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.
“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.,”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Petersen et al., 1989
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- cites Chee et al., 1999
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- cites Liuzzi et al., 2017
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Findingown dataNegatively correlated voxels are mostly in sensory regions and are not well predicted by the semantic model in either modality.
“Negatively correlated voxels are mostly in sensory regions and are not well predicted by the semantic model in either modality.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataAveraging across participants lowers voxel prediction accuracies relative to individual participant results.
“However, due to averaging across participants voxel prediction accuracies are lower than in individual participant results”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenPrior neuroimaging studies have shown that semantic information in spoken language is represented in multiple regions in the human cerebral cortex.
“Prior neuroimaging studies have shown that semantic information in spoken language is represented in multiple regions in the human cerebral cortex”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenThe demonstration of some common activation during listening and reading is necessary but not sufficient evidence of a common amodal semantic representation.
“However, the demonstration of some common activation during listening and reading is necessary but not sufficient evidence of a common amodal semantic representation.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Premisenone givenAmodal semantic information appears to be represented in a few broad brain regions.
“while amodal semantic information appears to be represented in a few broad brain regions”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe early sensory processing pathways for listening and reading are distinct.
“Although the early sensory processing pathways for listening and reading are distinct”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Rubin et al., 2000
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- cites Diakidoy et al., 2005
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Premisenone givenPrevious studies were too insensitive to determine whether semantic representations were shared at a fine level of detail rather than merely at a coarse scale.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenListening and reading presentations were counterbalanced across participants.
“Listening and reading presentations were counterbalanced across participants.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe cochleogram model outperforms other low level acoustical models.
“This model was selected based on an earlier study showing that it outperforms other low level acoustical models”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites de Heer et al., 2017
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Study design and conductnone givenThe eye tracker was calibrated before the first run of data acquisition.
“The eye tracker was calibrated before the first run of data acquisition.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductcitationBanded ridge regression assigns a different regularization parameter for every feature space and so reduces bias caused by correlations between feature spaces.
“Banded ridge regression assigns a different regularization parameter for every feature space and so reduces bias caused by correlations between feature spaces.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Nunez-Elizalde et al., 2019
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Study design and conductnone givenThe 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 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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe regularization parameter for each feature space was estimated empirically via cross-validation on a held-out set.
“The regularization parameter for each feature space was estimated empirically via cross-validation on a held-out set.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMost 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).
“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).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Chee et al., 1999
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- cites Michael et al., 2001
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- cites Buchweitz et al., 2009
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PremisecitationBanded ridge regression is a special case of the well-established statistical approach called Tikhonov regression.
“Banded ridge regression is a special case of the well-established statistical approach called Tikhonov regression”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Tikhonov and Arsenin, 1977
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Study design and conductreasoningThe stories were performed by different speakers and were not uniformly mastered, so some differences in total loudness remain.
“However, the stories were performed by different speakers and were not uniformly mastered, so some differences in total loudness remain.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataStatistical 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.
“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.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataTo 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.
“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.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenStatistical significance was defined as any correlation coefficient that exceeded 95% of all of the permuted correlations.
“Statistical significance was defined as any correlation coefficient that exceeded 95% of all of the permuted correlations.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe corrected aligned transcript was spot-checked for accuracy by a different listener.
“The corrected aligned transcript was then spot-checked for accuracy by a different listener.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe regularization parameter is optimized based on prediction accuracy on a held-out dataset.
“The regularization parameter is optimized based on prediction accuracy on a held-out dataset.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenWithin-modality data were collected largely within sessions, and between-modality data were collected across sessions.
“(Note, however, that within-modality data were collected largely within sessions, and between-modality data were collected across sessions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAnatomical surface segmentations were carefully handchecked and corrected using Blender software and pycortex.
“Before surface reconstruction, anatomical surface segmentations were carefully handchecked and corrected using Blender software and pycortex”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe automatic alignments were manually checked and adjusted as necessary to improve accuracy.
“These automatic alignments were manually checked and adjusted as necessary to improve accuracy.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningTo make listening and reading more comparable, the timing of the words presented during RSVP was matched to the rate at which the words occurred during listening.
“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.”
Quote in the paperSubstantial claim- 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.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenEach story was manually transcribed by one listener, and this transcription was checked by a second listener.
“Each story was manually transcribed by one listener, and this transcription was checked by a second listener.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationRelatively few studies have studied cross-modal representations by presenting the same stimuli in both modalities.
“Relatively few have studied cross-modal representations by presenting the same stimuli in both modalities”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Petersen et al., 1989
Not checked in the free run.
- cites Chee et al., 1999
Not checked in the free run.
- cites Michael et al., 2001
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenTwo of the participants were authors on the paper.
“Two of the participants were authors on the paper (A.G.H. and A.O.N.-E.).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductcitationResulting p-values were corrected for multiple comparisons within each participant using the false discovery rate (FDR) procedure.
“Resulting p-values were corrected for multiple comparisons within each participant using the false discovery rate (FDR) procedure (Benjamini and Hochberg, 1995).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Benjamini and Hochberg, 1995
Not checked in the free run.
May combine two claims.
PremisecitationThat study did not model semantic information, but only showed that voxel activations in these regions tend to be correlated across the two modalities.
“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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Regev et al., 2013
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductnone givenTo validate the voxelwise models, estimated model weights were used to predict responses to a validation story that was not used for model estimation.
“To validate the voxelwise models, estimated model weights were used to predict responses to a validation story that was not used for model estimation.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationPrevious studies have shown that complex linguistic stimuli such as narrative stories activate many more brain regions than single words or short sentences.
“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).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Mazoyer et al., 1993
Not checked in the free run.
- cites Xu et al., 2005
Not checked in the free run.
- cites Lerner et al., 2011
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWhen people listen to or read a story, they likely employ conceptual knowledge at long time scales beyond those used for computing semantic features.
“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).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Yeshurun et al., 2017
Not checked in the free run.
May combine two claims.
InterpretationcitationStudies performing intersubject averaging might average away meaningful signal and fail to find significant relationships.
“Therefore, studies performing intersubject averaging might average away meaningful signal and fail to find significant relationships (Fedorenko and Kanwisher, 2009).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Fedorenko and Kanwisher, 2009 source
Not checked in the free run.
May combine two claims.
PremisecitationLiuzzi et al. (2017) used as stimuli twenty-four single words derived from only six animate categories, and showed cross-modal representations within left pars triangularis.
“That study used as stimuli twenty-four single words derived from only six animate categories, and showed cross-modal representations within left pars triangularis.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Liuzzi et al., 2017
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOnly one previous study of cross-modal representation has used explicit semantic features to model brain activity patterns related to semantics.
“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).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Liuzzi et al., 2017
Not checked in the free run.
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?
The language network supports both comprehension (spoken, written, and signed) and production.
“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.
Second and third semantic PCs have conflicting color assignments
MinorThe Results assign green to the second semantic PC and blue to the third, while the Figure 4 caption assigns blue to the second and green to the third.
“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.”
“Each voxel is colored according to its projection onto the first (red), second (blue) or third (green) semantic PC.”
Pauses are included as nuisance regressors but omitted from the feature-space list
MinorThe stated list of feature spaces omits pauses, although the Results say pauses were included as nuisance regressors.
“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).”
“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)”
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“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
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MethodIndexing by latent semantic analysis
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“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× · see citing works
“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× · see citing works
“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× · see citing works
“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× · see citing works
“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 6,806 · cited 1× · see citing works
“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 · see citing works
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“Weight maps for the foot, hand, and mouth responses were used to define primary motor and somatosensory areas for the feet (M1F, S1F), hands (M1H, S1H), and mouth (M1M, S1M); supplementary motor areas for the feet and hands; secondary somatosensory area for the feet, and, in some participants, the hands; and, in some…”
BackgroundMethodBinder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp055 · Cited by 4,060 · cited 1× · see citing works
“This suggests that the human brain represents semantic information in an amodal form that is independent of input modality (Vigneau et al., 2006; Binder et al., 2009; Price, 2010, 2012).”
BackgroundIPython: A System for Interactive Scientific Computing
Pérez, F., & Granger, B. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.53 · Cited by 3,927 · cited 1× · see citing works
“Analysis and visualizations were developed using iPython (Pérez and Granger, 2007) and the interactive programming and visualization environment jupyter notebook (Kluyver et al., 2016).”
MethodWord association norms, mutual information, and lexicography
Church, K., & Hanks, P. (1990) · doi:10.5555/89086.89095 · Cited by 3,644 · see citing works
Spatiotemporal energy models for the perception of motion
Adelson, E. H., & Bergen, J. R. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000284 · Cited by 3,442 · cited 2× · see citing works
“Motion-energy model construction A spatiotemporal Gabor pyramid was used to extract low-level visual features from the sequence of word frames used in the reading experiment (Adelson and Bergen, 1985; Watson and Ahumada, 1985).”
MethodStatistical Parametric Mapping: The Analysis of Functional Brain Images
Penny, W. D., Friston, K. J., Ashburner, J., Kiebel, S. J., & Thomas, N. (2007) · UCL Discovery (University College London) · Cited by 3,339 · see citing works
Python for scientific computing
Oliphant, T. E. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.58 · Cited by 3,303 · cited 2× · see citing works
“Voxelwise model fitting and analysis was performed using custom software tikreg (Nunez-Elizalde et al., 2019) written in Python, making heavy use of NumPy (Oliphant, 2006) and SciPy (Oliphant, 2007).”
MethodInfluentialJupyter Notebooks – a publishing format for reproducible computational workflows
Kluyver, T. A., Ragan-Kelley, B., Fernando, P., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J. B., Grout, J., Corlay, S., et al. (2016) · IOS Press eBooks · doi:10.3233/978-1-61499-649-1-87 · Cited by 3,191 · cited 1× · see citing works
“Analysis and visualizations were developed using iPython (Pérez and Granger, 2007) and the interactive programming and visualization environment jupyter notebook (Kluyver et al., 2016).”
MethodA cortical representation of the local visual environment
Epstein, R. A., & Kanwisher, N. (1998) · Nature · doi:10.1038/33402 · Cited by 2,960 · cited 1× · see citing works
“The contrast between places and objects was used to define the parahippocampal place area (Epstein and Kanwisher, 1998), occipital place area (Nakamura et al.”
MethodFrom Frequency to Meaning: Vector Space Models of Semantics
Turney, P. D., & Pantel, P. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2934 · Cited by 2,845 · cited 1× · see citing works
“The semantic content of the stories was estimated continuously by projecting the narrative into a word embedding space based on word co-occurrence statistics (Church and Hanks, 1990; Lund and Burgess, 1996; Mitchell et al., 2008; Turney and Pantel, 2010; Wehbe et al., 2014).”
MethodWhere do you know what you know? The representation of semantic knowledge in the human brain
Patterson, K., Nestor, P. J., & Rogers, T. T. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2277 · Cited by 2,706 · cited 2× · see citing works
“…by listening or reading converge in semantically selective regions (Chee et al., 1999; Carpentier et al., 2001; Booth et al., 2002; Cohen et al., 2004; Constable et al., 2004; Spitsyna et al., 2006; Jobard et al., 2007; Patterson et al., 2007; Buchweitz et al., 2009; Liuzzi et al., 2017).”
BackgroundPrice, C. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.062 · Cited by 2,159 · cited 2× · see citing works
“There is evidence that several cortical regions are activated during both listening and reading (for reviews, see Price, 2010, 2012).”
BackgroundA cortical area selective for visual processing of the human body
Downing, P. E., Jiang, Y., Shuman, M., & Kanwisher, N. (2001) · Science · doi:10.1126/science.1063414 · Cited by 2,043 · see citing works
Meta-analyzing left hemisphere language areas: phonology, semantics, and sentence processing.
Vigneau, M., Beaucousin, V., Hervé, P. Y., Duffau, H., Crivello, F., Houdé, O., Mazoyer, B., & Tzourio-Mazoyer, N. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.11.002 · Cited by 1,851 · cited 1× · see citing works
“This suggests that the human brain represents semantic information in an amodal form that is independent of input modality (Vigneau et al., 2006; Binder et al., 2009; Price, 2010, 2012).”
BackgroundProducing high-dimensional semantic spaces from lexical co-occurrence
Lund, K., & Burgess, C. (1996) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03204766 · Cited by 1,808 · cited 2× · see citing works
“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.”
MethodA Common Network of Functional Areas for Attention and Eye Movements
Corbetta, M., Akbudak, E., Conturo, T. E., Snyder, A. Z., Ollinger, J. M., Drury, H. A., Linenweber, M. R., Petersen, S. E., Raichle, M. E., Essen, D. C. V., et al. (1998) · Neuron · doi:10.1016/s0896-6273(00)80593-0 · Cited by 1,590 · cited 1× · see citing works
“The weight map for saccade responses was used to define the frontal eye field (Paus, 1996), frontal operculum eye movement area (Corbetta et al., 1998), intraparietal sulcus visual areas, and, in some participants, the supplementary eye field (Grosbras et al., 1999).”
MethodNatural speech reveals the semantic maps that tile human cerebral cortex
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016) · Nature · doi:10.1038/nature17637 · Cited by 1,504 · cited 16× · see citing works
“Natural speech stimuli The speech stimuli consisted of 10- to 15 min stories taken from The Moth Radio Hour and used previously (Huth et al., 2016).”
BackgroundMethodInfluentialSelf-citationFunctional analysis of human MT and related visual cortical areas using magnetic resonance imaging
Tootell, R. B. H., Reppas, J. B., Kwong, K. K., Malach, R., Born, R. T., Brady, T., Rosen, B. R., & Belliveau, J. (1995) · Journal of Neuroscience · doi:10.1523/jneurosci.15-04-03215.1995 · Cited by 1,412 · cited 1× · see citing works
“The contrast between continuous and temporally scrambled natural movies was used to define visual motion area MT (Tootell et al., 1995).”
MethodThe anatomy of language: a review of 100 fMRI studies published in 2009
Price, C. J. (2010) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2010.05444.x · Cited by 1,387 · see citing works
Positron Emission Tomographic Studies of the Processing of Singe Words
Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M. A., & Raichle, M. E. (1989) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1989.1.2.153 · Cited by 1,284 · cited 2× · see citing works
“…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., 2017).”
BackgroundPredicting Human Brain Activity Associated with the Meanings of Nouns
Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K.-M., Malave, V. L., Mason, R. A., & Just, M. A. (2008) · Science · doi:10.1126/science.1152876 · Cited by 1,180 · see citing works
Functional anatomy of a common semantic system for words and pictures
Vandenberghe, R. R., Price, C. J., Wise, R., Josephs, O., & Frackowiak, R. S. J. (1996) · Nature · doi:10.1038/383254a0 · Cited by 1,172 · cited 2× · see citing works
“Most imaging studies of the semantic system have examined only one input modality, either spoken or written words (Démonet et al., 1994, 1992; Vandenberghe et al., 1996; Scott et al., 2000; Booth et al., 2002; Rissman et al., 2003; Devlin et al., 2004; Nakamura et al., 2005).”
BackgroundModel of human visual-motion sensing
Watson, A. B., & Ahumada, A. J. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000322 · Cited by 1,170 · see citing works
THE ANATOMY OF PHONOLOGICAL AND SEMANTIC PROCESSING IN NORMAL SUBJECTS
Démonet, J., Chollet, F., Ramsay, S. C., Cardebat, D., Nespoulous, J., WISE, R., Rascol, A., & Frackowiak, R. S. J. (1992) · Brain · doi:10.1093/brain/115.6.1753 · Cited by 1,145 · cited 1× · see citing works
“Most imaging studies of the semantic system have examined only one input modality, either spoken or written words (Démonet et al., 1994, 1992; Vandenberghe et al., 1996; Scott et al., 2000; Booth et al., 2002; Rissman et al., 2003; Devlin et al., 2004; Nakamura et al., 2005).”
BackgroundIdentification of a pathway for intelligible speech in the left temporal lobe
Scott, S. K. (2000) · Brain · doi:10.1093/brain/123.12.2400 · Cited by 1,143 · cited 2× · see citing works
“Most imaging studies of the semantic system have examined only one input modality, either spoken or written words (Démonet et al., 1994, 1992; Vandenberghe et al., 1996; Scott et al., 2000; Booth et al., 2002; Rissman et al., 2003; Devlin et al., 2004; Nakamura et al., 2005).”
BackgroundWord association norms, mutual information, and lexicography
Church, K. W., & Hanks, P. (1989) · Proceedings of the 27th annual meeting on Association for Computational Linguistics - · doi:10.3115/981623.981633 · Cited by 1,094 · cited 1× · see citing works
“The semantic content of the stories was estimated continuously by projecting the narrative into a word embedding space based on word co-occurrence statistics (Church and Hanks, 1990; Lund and Burgess, 1996; Mitchell et al., 2008; Turney and Pantel, 2010; Wehbe et al., 2014).”
MethodReconstructing Visual Experiences from Brain Activity Evoked by Natural Movies
Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant, J. L. (2011) · Current Biology · doi:10.1016/j.cub.2011.08.031 · Cited by 933 · cited 3× · see citing works
“…to characterize the semantic selectivity of each voxel in each presentation modality and for each individual participant (see Materials and Methods and Naselaris et al., 2011; Nishimoto et al., 2011; Huth et al., 2012, 2016; Çukur et al., 2013; Stansbury et al., 2013; Lescroart et al., 2015).”
BackgroundSelf-citationHuth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.014 · Cited by 901 · cited 2× · see citing works
“…to characterize the semantic selectivity of each voxel in each presentation modality and for each individual participant (see Materials and Methods and Naselaris et al., 2011; Nishimoto et al., 2011; Huth et al., 2012, 2016; Çukur et al., 2013; Stansbury et al., 2013; Lescroart et al., 2015).”
BackgroundMethodSelf-citationNaselaris, T., Kay, K., Nishimoto, S., & Gallant, J. L. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.073 · Cited by 894 · cited 1× · see citing works
“…to characterize the semantic selectivity of each voxel in each presentation modality and for each individual participant (see Materials and Methods and Naselaris et al., 2011; Nishimoto et al., 2011; Huth et al., 2012, 2016; Çukur et al., 2013; Stansbury et al., 2013; Lescroart et al., 2015).”
BackgroundSelf-citationTopographic mapping of a hierarchy of temporal receptive windows using a narrated story
Lerner, Y., Honey, C. J., Silbert, L. J., & Hasson, U. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3684-10.2011 · Cited by 834 · cited 1× · see citing works
“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).”
BackgroundLocation and function of the human frontal eye-field: A selective review
Paus, T. (1996) · Neuropsychologia · doi:10.1016/0028-3932(95)00134-4 · Cited by 746 · cited 2× · see citing works
“The weight map for saccade responses was used to define the frontal eye field (Paus, 1996), frontal operculum eye movement area (Corbetta et al.”
MethodThe Cortical Representation of Speech
Mazoyer, B. M., Tzourio‐Mazoyer, N., Frak, V., Syrota, A., Murayama, N., Levrier, O., Salamon, G., Dehaene, S., Cohen, L., & Mehler, J. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.4.467 · Cited by 712 · cited 1× · see citing works
“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).”
BackgroundStatistical Parametric Mapping
Friston, K. J. (2003) · Springer eBooks · doi:10.1007/978-1-4615-1079-6_16 · Cited by 690 · cited 1× · see citing works
“The temporal prior was constructed from a set of HRF basis functions (Penny et al., 2007).”
MethodCaramazza, A. (1986) · Brain and Cognition · doi:10.1016/0278-2626(86)90061-8 · Cited by 639 · cited 1× · see citing works
“However, the anatomical normalization procedures used in these studies tend to smooth and mask the substantial individual variability in language processing (Caramazza, 1986; Steinmetz and Seitz, 1991; Fedorenko and Kanwisher, 2009).”
MethodSpeaker identification on the SCOTUS corpus
Yuan, J., & Liberman, M. (2008) · The Journal of the Acoustical Society of America · doi:10.1121/1.2935783 · Cited by 600 · cited 2× · see citing works
“5 kHz and the Penn Phonetics Lab Forced Aligner (P2FA; Yuan and Liberman, 2008) was used to automatically align the audio to the transcript.”
MethodInfluentialXIII—The Development of the CIE 1976 (L* a* b*) Uniform Colour Space and Colour‐difference Formula
McLaren, K. R. (1976) · Journal of the Society of Dyers and Colourists · doi:10.1111/j.1478-4408.1976.tb03301.x · Cited by 509 · cited 1× · see citing works
“The word frames were then converted to the CIE L*A*B* color space (McLaren, 1976) and the color information was discarded.”
MethodLanguage in context: Emergent features of word, sentence, and narrative comprehension
Xu, J., Kemeny, S., Park, G., Frattali, C., & Braun, A. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.013 · Cited by 502 · cited 1× · see citing works
“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).”
BackgroundGlobally Normalized Transition-Based Neural Networks
Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S., & Collins, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1231 · Cited by 494 · cited 1× · see citing works
“A pretrained neural network was used to create a parse tree for each sentence of the stories (Andor et al., 2016).”
MethodBroca's Region: Novel Organizational Principles and Multiple Receptor Mapping
Amunts, K., Lenzen, M., Friederici, A. D., Schleicher, A., Morosan, P., Palomero-Gallagher, N., & Zilles, K. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000489 · Cited by 357 · cited 1× · see citing works
“The weight map for speech production responses was used to define Broca’s area (Amunts et al., 2010; Zilles and Amunts, 2018) and the superior ventral premotor speech area (sPMv).”
MethodAttention during natural vision warps semantic representation across the human brain
Çukur, T., Nishimoto, S., Huth, A. G., & Gallant, J. L. (2013) · Nature Neuroscience · doi:10.1038/nn.3381 · Cited by 351 · cited 2× · see citing works
“…to characterize the semantic selectivity of each voxel in each presentation modality and for each individual participant (see Materials and Methods and Naselaris et al., 2011; Nishimoto et al., 2011; Huth et al., 2012, 2016; Çukur et al., 2013; Stansbury et al., 2013; Lescroart et al., 2015).”
BackgroundSelf-citationVisual perception of rapidly presented word sequences of varying complexity
Forster, K. I. (1970) · Perception & Psychophysics · doi:10.3758/bf03210208 · Cited by 345 · cited 2× · see citing works
“The words of each story were presented one-by-one at the center of the screen using a rapid serial visual presentation (RSVP) procedure (Forster, 1970; Buchweitz et al., 2009).”
MethodNakamura, K. (2000) · Brain · doi:10.1093/brain/123.9.1903 · Cited by 325 · see citing works
Toward a universal decoder of linguistic meaning from brain activation
Pereira, F., Lou, B., Pritchett, B., Ritter, S., Gershman, S. J., Kanwisher, N., Botvinick, M., & Fedorenko, E. (2018) · Nature Communications · doi:10.1038/s41467-018-03068-4 · Cited by 324 · cited 1× · see citing works
“However, the semantic feature space that we used here is only one possible way of representing semantics (Mitchell et al., 2008; Huth et al., 2016; Pereira et al., 2018), and it has some limitations.”
BackgroundConverging language streams in the human temporal lobe
Spitsyna, G., Warren, J. E., Scott, S. K., Turkheimer, F. E., & Wise, R. J. S. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.0559-06.2006 · Cited by 312 · cited 5× · see citing works
“…by listening or reading converge in semantically selective regions (Chee et al., 1999; Carpentier et al., 2001; Booth et al., 2002; Cohen et al., 2004; Constable et al., 2004; Spitsyna et al., 2006; Jobard et al., 2007; Patterson et al., 2007; Buchweitz et al., 2009; Liuzzi et al., 2017).”
BackgroundInfluentialSame Story, Different Story: The Neural Representation of Interpretive Frameworks
Yeshurun, Y., Swanson, S., Simony, E., Chen, J., Lazaridi, C., Honey, C. J., & Hasson, U. (2017) · Psychological Science · doi:10.1177/0956797616682029 · Cited by 300 · cited 1× · see citing works
“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).”
BackgroundThe Hierarchical Cortical Organization of Human Speech Processing
de Heer, W. A., Huth, A. G., Griffiths, T. L., Gallant, J. L., & Theunissen, F. E. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3267-16.2017 · Cited by 281 · cited 2× · see citing works
“A cochleogram model that accounts for the logarithmic filtering of the mammalian coclea described in (de Heer et al., 2017) was used to create the low level auditory features (80 parameters).”
MethodSelf-citationAn Event-Related fMRI Investigation of Implicit Semantic Priming
Rissman, J., Eliassen, J., & Blumstein, S. (2003) · Journal of Cognitive Neuroscience · doi:10.1162/089892903322598120 · Cited by 280 · cited 2× · see citing works
“Most imaging studies of the semantic system have examined only one input modality, either spoken or written words (Démonet et al., 1994, 1992; Vandenberghe et al., 1996; Scott et al., 2000; Booth et al., 2002; Rissman et al., 2003; Devlin et al., 2004; Nakamura et al., 2005).”
BackgroundDistinct unimodal and multimodal regions for word processing in the left temporal cortex
Cohen, L., Jobert, A., Bihan, D. L., & Dehaene, S. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.052 · Cited by 266 · see citing works
Location and spatial profile of category‐specific regions in human extrastriate cortex
Spiridon, M., Fischl, B. R., & Kanwisher, N. (2005) · Human Brain Mapping · doi:10.1002/hbm.20169 · Cited by 264 · cited 1× · see citing works
“Localizers for known ROIs Known ROIs were localized separately in each participant using standard techniques (Spiridon et al., 2006; Hansen et al., 2007).”
MethodSentence complexity and input modality effects in sentence comprehension: an fMRI study
Constable, R. T., Pugh, K. R., Berroya, E., Mencl, W. E., Westerveld, M., Ni, W., & Shankweiler, D. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.01.001 · Cited by 263 · cited 2× · see citing works
“…by listening or reading converge in semantically selective regions (Chee et al., 1999; Carpentier et al., 2001; Booth et al., 2002; Cohen et al., 2004; Constable et al., 2004; Spitsyna et al., 2006; Jobard et al., 2007; Patterson et al., 2007; Buchweitz et al., 2009; Liuzzi et al., 2017).”
BackgroundWehbe, L., Murphy, B., Talukdar, P. P., Fyshe, A., Ramdas, A., & Mitchell, T. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112575 · Cited by 256 · cited 1× · see citing works
“The semantic content of the stories was estimated continuously by projecting the narrative into a word embedding space based on word co-occurrence statistics (Church and Hanks, 1990; Lund and Burgess, 1996; Mitchell et al., 2008; Turney and Pantel, 2010; Wehbe et al., 2014).”
MethodModality independence of word comprehension
Booth, J. R., Burman, D. D., Meyer, J. R., Gitelman, D. R., Parrish, T. B., & Mesulam, M. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10054 · Cited by 253 · cited 4× · see citing works
“…pathways for processing information by listening or reading converge in semantically selective regions (Chee et al., 1999; Carpentier et al., 2001; Booth et al., 2002; Cohen et al., 2004; Constable et al., 2004; Spitsyna et al., 2006; Jobard et al., 2007; Patterson et al., 2007; Buchweitz et al.,…”
BackgroundInfluentialDiakidoy, I. N., Stylianou, P., KAREFILLIDOU, C., et al. (2005) · Reading Psychology · doi:10.1080/02702710590910584 · Cited by 236 · cited 1× · see citing works
“Although the early sensory processing pathways for listening and reading are distinct, listeners and readers appear to extract very similar information about the meaning of a narrative story (Rubin et al., 2000; Diakidoy et al., 2005).”
BackgroundSelective and invariant neural responses to spoken and written narratives
Regev, M., Honey, C. J., Simony, E., & Hasson, U. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.1580-13.2013 · Cited by 215 · cited 7× · see citing works
“Therefore, it is unclear whether the differences they identified within left DLPFC are due to differences in semantic representation or some other aspect of linguistic information (e.g., syntax).”
BackgroundInfluentialFunctional anatomy of language processing: Neuroimaging and the problem of individual variability
Steinmetz, H., & Seitz, R. J. (1991) · Neuropsychologia · doi:10.1016/0028-3932(91)90030-c · Cited by 214 · cited 1× · see citing works
“However, the anatomical normalization procedures used in these studies tend to smooth and mask the substantial individual variability in language processing (Caramazza, 1986; Steinmetz and Seitz, 1991; Fedorenko and Kanwisher, 2009).”
MethodFunctional MRI of Language Processing: Dependence on Input Modality and Temporal Lobe Epilepsy
Carpentier, A. C., Pugh, K. R., Westerveld, M., Studholme, C., Škrinjar, O. M., Thompson, J. L., Spencer, D. D., & Constable, R. T. (2001) · Epilepsia · doi:10.1046/j.1528-1157.2001.35500.x · Cited by 206 · cited 4× · see citing works
“…sensory processing, the pathways for processing information by listening or reading converge in semantically selective regions (Chee et al., 1999; Carpentier et al., 2001; Booth et al., 2002; Cohen et al., 2004; Constable et al., 2004; Spitsyna et al., 2006; Jobard et al., 2007; Patterson et…”
BackgroundInfluentialMorphology and the internal structure of words
Devlin, J. T., Jamison, H. L., Matthews, P. M., & Gonnerman, L. M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403766101 · Cited by 198 · cited 2× · see citing works
“Most imaging studies of the semantic system have examined only one input modality, either spoken or written words (Démonet et al., 1994, 1992; Vandenberghe et al., 1996; Scott et al., 2000; Booth et al., 2002; Rissman et al., 2003; Devlin et al., 2004; Nakamura et al., 2005).”
Démonet, J., Price, C. J., Wise, R., & Frackowiak, R. S. J. (1994) · Neuroscience Letters · doi:10.1016/0304-3940(94)90196-1 · Cited by 197 · cited 1× · see citing works
“Most imaging studies of the semantic system have examined only one input modality, either spoken or written words (Démonet et al., 1994, 1992; Vandenberghe et al., 1996; Scott et al., 2000; Booth et al., 2002; Rissman et al., 2003; Devlin et al., 2004; Nakamura et al., 2005).”
BackgroundGrosbras, M. (1999) · Cerebral Cortex · doi:10.1093/cercor/9.7.705 · Cited by 179 · cited 2× · see citing works
“, 1998), intraparietal sulcus visual areas, and, in some participants, the supplementary eye field (Grosbras et al., 1999).”
BackgroundMethodPycortex: an interactive surface visualizer for fMRI
Gao, J. S., Huth, A. G., Lescroart, M. D., & Gallant, J. L. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00023 · Cited by 171 · cited 2× · see citing works
“Before surface reconstruction, anatomical surface segmentations were carefully handchecked and corrected using Blender software and pycortex (Gao et al., 2015) (http://pycortex.org).”
MethodSelf-citationBuchweitz, A., Mason, R. A., Tomitch, L. M. B., & Just, M. A. (2009) · Psychology & Neuroscience · doi:10.3922/j.psns.2009.2.003 · Cited by 171 · cited 7× · see citing works
“In the reading condition we used an RSVP method (Forster, 1970; Buchweitz et al., 2009) to present the stories at precisely the same rate as they occurred during listening.”
BackgroundMethodInfluentialTopographic Organization in and near Human Visual Area V4
Hansen, K. A., Kay, K., & Gallant, J. L. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.2991-07.2007 · Cited by 170 · cited 2× · see citing works
“Localizers for known ROIs Known ROIs were localized separately in each participant using standard techniques (Spiridon et al., 2006; Hansen et al., 2007).”
MethodSelf-citationMichael, E. B., Keller, T. A., Carpenter, P. A., & Just, M. A. (2001) · Human Brain Mapping · doi:10.1002/hbm.1036 · Cited by 168 · cited 2× · see citing works
“Most of these studies used tightly controlled stimuli, such as a set of single isolated words, sentences or curated passages, and an explicit lexical semantic task (Chee et al., 1999; Michael et al., 2001; Buchweitz et al., 2009; Liuzzi et al., 2017).”
BackgroundNatural Scene Statistics Account for the Representation of Scene Categories in Human Visual Cortex
Stansbury, D. E., Naselaris, T., & Gallant, J. L. (2013) · Neuron · doi:10.1016/j.neuron.2013.06.034 · Cited by 161 · cited 1× · see citing works
“…to characterize the semantic selectivity of each voxel in each presentation modality and for each individual participant (see Materials and Methods and Naselaris et al., 2011; Nishimoto et al., 2011; Huth et al., 2012, 2016; Çukur et al., 2013; Stansbury et al., 2013; Lescroart et al., 2015).”
BackgroundSelf-citationNakamura, K., Dehaene, S., Jobert, A., Bihan, D. L., & Kouider, S. (2005) · Journal of Cognitive Neuroscience · doi:10.1162/0898929054021166 · Cited by 155 · cited 2× · see citing works
“Most imaging studies of the semantic system have examined only one input modality, either spoken or written words (Démonet et al., 1994, 1992; Vandenberghe et al., 1996; Scott et al., 2000; Booth et al., 2002; Rissman et al., 2003; Devlin et al., 2004; Nakamura et al., 2005).”
BackgroundImpact of modality and linguistic complexity during reading and listening tasks
Jobard, G., Vigneau, M., Mazoyer, B., & Tzourio-Mazoyer, N. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.06.067 · Cited by 134 · cited 7× · see citing works
“…by listening or reading converge in semantically selective regions (Chee et al., 1999; Carpentier et al., 2001; Booth et al., 2002; Cohen et al., 2004; Constable et al., 2004; Spitsyna et al., 2006; Jobard et al., 2007; Patterson et al., 2007; Buchweitz et al., 2009; Liuzzi et al., 2017).”
BackgroundInfluentialNeuroimaging of Language: Why Hasn't a Clearer Picture Emerged?
Fedorenko, E., & Kanwisher, N. (2009) · Language and Linguistics Compass · doi:10.1111/j.1749-818x.2009.00143.x · Cited by 117 · cited 2× · see citing works
“However, the anatomical normalization procedures used in these studies tend to smooth and mask the substantial individual variability in language processing (Caramazza, 1986; Steinmetz and Seitz, 1991; Fedorenko and Kanwisher, 2009).”
BackgroundMethodVoxelwise encoding models with non-spherical multivariate normal priors
Nunez-Elizalde, A. O., Huth, A. G., & Gallant, J. (2018) · bioRxiv · doi:10.1101/386318 · Cited by 107 · cited 7×
“BOLD responses were modeled as a linear combination of all the feature spaces using linear regression with a non-spherical spatiotemporal multivariate normal prior on the weights (Nunez-Elizalde et al., 2019).”
MethodInfluentialSelf-citationLescroart, M. D., Stansbury, D. E., & Gallant, J. L. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00135 · Cited by 105 · cited 2× · see citing works
“…to characterize the semantic selectivity of each voxel in each presentation modality and for each individual participant (see Materials and Methods and Naselaris et al., 2011; Nishimoto et al., 2011; Huth et al., 2012, 2016; Çukur et al., 2013; Stansbury et al., 2013; Lescroart et al., 2015).”
BackgroundSelf-citationVoxelwise encoding models with non-spherical multivariate normal priors
Núñez-Elizalde, A. O., Huth, A. G., & Gallant, J. L. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.012 · Cited by 86 · see citing works
Self-citationReading and listening to oral‐based versus literate‐based discourse
Rubin, D. L., Hafer, T., & Arata, K. (2000) · Communication Education · doi:10.1080/03634520009379200 · Cited by 80 · cited 1× · see citing works
“Although the early sensory processing pathways for listening and reading are distinct, listeners and readers appear to extract very similar information about the meaning of a narrative story (Rubin et al., 2000; Diakidoy et al., 2005).”
BackgroundCross-modal representation of spoken and written word meaning in left pars triangularis
Liuzzi, A. G., Bruffaerts, R., Peeters, R. R., Adamczuk, K., Keuleers, E., Deyne, S. D., Storms, G., Dupont, P. J., & Vandenberghe, R. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.032 · Cited by 53 · cited 7× · see citing works
“…by listening or reading converge in semantically selective regions (Chee et al., 1999; Carpentier et al., 2001; Booth et al., 2002; Cohen et al., 2004; Constable et al., 2004; Spitsyna et al., 2006; Jobard et al., 2007; Patterson et al., 2007; Buchweitz et al., 2009; Liuzzi et al., 2017).”
BackgroundInfluentialCytoarchitectonic and receptorarchitectonic organization in Broca's region and surrounding cortex
Zilles, K., & Amunts, K. (2018) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.02.011 · Cited by 51 · cited 1× · see citing works
“The weight map for speech production responses was used to define Broca’s area (Amunts et al., 2010; Zilles and Amunts, 2018) and the superior ventral premotor speech area (sPMv).”
Method
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- 10.2307/2346101 · DOI
- Bergstra, 2011, Algorithms for hyper-parameter optimization, 24, 2546
- 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 · see citing works
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 · see citing works
“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 · see citing works
“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 327 · see citing works
“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 349 · see citing works
“…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 · see citing works
Self-citationDiachek, E., Blank, I., Siegelman, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/744094 · Cited by 55 · see citing works
The neural architecture of language: Integrative modeling converges on predictive processing
(2020) · doi:10.1101/2020.06.26.174482 · Cited by 129 · see citing works
“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 111 · see citing works
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 72 · see citing works
“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 85 · see citing works
“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 111 · see citing works
“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 · see citing works
Doedens, W., & Meteyard, L. (2022) · Neuropsychology Review · doi:10.1007/s11065-021-09531-2 · Cited by 86 · see citing works
“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 · see citing works
Wehbe, L., Blank, I., Shain, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.15.043844 · Cited by 11 · see citing works
Szewczyk, J., & Federmeier, K. D. (2021) · Journal of Memory and Language · doi:10.1016/j.jml.2021.104311 · Cited by 69 · see citing works
“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 64 · see citing works
“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 77 · see citing works
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 54 · see citing works
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 37 · see citing works
“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 · see citing works
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 65 · see citing works
“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 · see citing works
“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 · see citing works
“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 43 · see citing works
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 4 · see citing works
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 39 · see citing works
“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 · see citing works
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 41 · see citing works
“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 · see citing works
“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 43 · see citing works
“…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 37 · see citing works
Szubielska, M., & Imbir, K. K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250924 · Cited by 38 · see citing works
“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 26 · see citing works
“…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., et al. (2024) · CoLLAs · doi:10.48550/arxiv.2402.00854 · Cited by 29
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 25 · see citing works
“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 · see citing works
“…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 · see citing works
“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 · see citing works
“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 20 · see citing works
“, 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 · see citing works
“…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 · see citing works
“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 · see citing works
“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 · see citing works
“…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 · see citing works
“…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 17 · see citing works
“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 · see citing works
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 17 · see citing works
“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 · see citing works
“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 · see citing works
“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 18 · see citing works
“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 27 · see citing works
Zheng, Y., Zhang, J., Yang, Y., & Xu, M. (2025) · Communications Biology · doi:10.1038/s42003-025-07466-5 · Cited by 13 · see citing works
“Existing studieshaveexploredneuralactivationpatternsbetweenspeechandread-ing,emphasizingdistinctionsbetweenunimodalversusmultimodal regions,andmodality-speci fi c effects in language comprehension 61 – 65 .”
Geng, S., Guo, W., Rolls, E., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04446-5 · Cited by 11
Ufer, C., & Blank, H. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2023.2166679 · Cited by 10 · see citing works
“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 10 · see citing works
“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 · see citing works
“, 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 12 · see citing works
Fan, G., Carlson, K. D., & Thomas, R. D. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.614280 · Cited by 10 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
“…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 1 · see citing works
“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 10 · see citing works
“…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)
Oota, S., Jindal, A., Mondal, I., et al. (2025) · International Conference on Learning Representations · doi:10.48550/arxiv.2505.20029 · Cited by 8
“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 Brain
Wasserman, N., Cosarinsky, M., Golbari, Y., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2512.08560 · Cited by 7
“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., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2510.17833 · Cited by 7
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 · see citing works
Thye, M. D., Hoffman, P., & Mirman, D. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02228 · Cited by 8 · see citing works
“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 · see citing works
“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 11 · see citing works
“…(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 17 · see citing works
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 · see citing works
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 · see citing works
“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).”
- 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
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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