Cited by 214Neurobiology of Language and BilingualismReading and Literacy DevelopmentTactile and Sensory Interactions

The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality

Open access
Deniz, Fatma, Nunez-Elizalde, Anwar O., Huth, Alexander G., Gallant, Jack L.·2019·The Journal of Neuroscience

Abstract

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1523/jneurosci.0675-19.2019

Cites

85 of 85 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

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
  • May combine two claims. 13
  • Classified as probably not a substantial claim. 32
  • May combine two claims.

    Findingown data

    Correlations 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 claim
  • May combine two claims.

    Findingown data

    The 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 claim
  • May combine two claims.

    Findingown data

    The 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

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    The 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 data

    Negatively 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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Averaging 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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    Prior 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 claim
  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given

    The 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 claim
  • Classified as probably not a substantial claim.

    Premisenone given

    Amodal 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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The 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 given

    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.

    “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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Listening and reading presentations were counterbalanced across participants.

    “Listening and reading presentations were counterbalanced across participants.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The 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”
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    Study design and conductnone given

    The eye tracker was calibrated before the first run of data acquisition.

    “The eye tracker was calibrated before the first run of data acquisition.”
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    Study design and conductcitation

    Banded 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 given

    The motion-energy, spectral, word rate, phoneme rate, phonemes, letter rate, letters, and word length variation features were used to explain away low-level parameters that might otherwise contaminate the semantic model weights.

    “The 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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The 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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Most 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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    Premisecitation

    Banded 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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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    The 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 claim
  • Classified as probably not a substantial claim.

    Study design and conductown data

    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.

    “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 claim
  • Classified as probably not a substantial claim.

    Study design and conductown data

    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.

    “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 claim
  • Classified as probably not a substantial claim.

    Study design and conductnone given

    Statistical 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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The 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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The 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 claim
  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    Within-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 claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Anatomical 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 claim
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    Study design and conductnone given

    The 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 claim
  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    To 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 given

    Each 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 claim
  • Classified as probably not a substantial claim.

    Premisecitation

    Relatively 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

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    • cites Chee et al., 1999

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    • cites Michael et al., 2001

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    Study design and conductnone given

    Two 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 claim
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    Study design and conductcitation

    Resulting 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).”
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    • cites Benjamini and Hochberg, 1995

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    Premisecitation

    That 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

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    Study design and conductnone given

    To 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 claim
  • Classified as probably not a substantial claim.

    Premisecitation

    Previous 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

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    • cites Xu et al., 2005

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    • cites Lerner et al., 2011

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    Premisecitation

    When 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

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  • May combine two claims.

    Interpretationcitation

    Studies 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

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    Premisecitation

    Liuzzi 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.”
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    • cites Liuzzi et al., 2017

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    Premisecitation

    Only 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

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40 of 40
  • 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.”
1 of 1

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

    Minor

    The 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.

  • Pauses are included as nuisance regressors but omitted from the feature-space list

    Minor

    The stated list of feature spaces omits pauses, although the Results say pauses were included as nuisance regressors.

2 of 2

Cites

Flagged first, then by citations

Research works printed but not linked: 5

  1. 10.2307/2346101 · DOI
  2. Bergstra, 2011, Algorithms for hyper-parameter optimization, 24, 2546
  3. 10.1002/(SICI)1097-0193(1999)7:1<29::AID-HBM3>3.0.CO;2-R · DOI
  4. 10.1016/S1053-8119(02)91132-8 · DOI · Paper page
  5. Oliphant TE (2006) A guide to NumPy, Vol 1, p 85. USA: Trelgol Publishing.

Cited by

Sorted by citations

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
Published
2019-08-19
Volume
39
Issue
39
Pages
7722-7736
ISSN
  • 0270-6474 (ISSN-L)
  • 1529-2401
OpenAlex
W2968395892
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
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

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