The role of the right hemisphere in metaphor comprehension: A meta-analysis of functional magnetic resonance imaging studies

Yang, Jie

doi:10.1002/hbm.22160

Cited by 78 publications

(77 citation statements)

References 73 publications

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“…Several studies indicate that processing novel metaphors shows stronger effects only in the left brain regions compared with literal processing (e.g., Mashal et al, 2009;Rapp et al, 2004;Schibata et al, 2007). A recent metaanalysis using activation likelihood estimation found that the significant effects in the right hemisphere during metaphor processing occur when the metaphorical meaning is novel (Yang, 2012). This result supports the right hemisphere hypothesis on processing broad semantic fields and integrating distant concepts (Beeman, 2005).…”

Section: Figurative Language Comprehensionsupporting

confidence: 50%

Language Processing, Functional Magnetic Resonance Imaging of

Yang¹,

Small²

2015

International Encyclopedia of the Social &Amp; Behavioral Sciences

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Section: Figurative Language Comprehensionsupporting

confidence: 50%

Language Processing, Functional Magnetic Resonance Imaging of

Yang¹,

Small²

2015

International Encyclopedia of the Social &Amp; Behavioral Sciences

Self Cite

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“…Therefore, individual concepts are less specific; some of the discriminating factors between concepts are forfeited in exchange for access to a larger semantic network. Coarse semantic processing is thought to play an important role in inference making (Beeman, 1993;Beeman, Bowden, & Gernsbacher, 2000), metaphor processing (Yang, 2014), reading (St. George, Kutas, Martinez, & Sereno, 1999;Sandak, Mencl, Frost, & Pugh, 2004), idiom processing (Yang et al, 2016), and insight (Bowden & Beeman, 1998).…”

Section: Narrow Versus Coarse Semantic Processingmentioning

confidence: 99%

Disentangling narrow and coarse semantic networks in the brain: The role of computational models of word meaning

Schloss

2016

Behav Res

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There has been a recent boom in research relating semantic space computational models to fMRI data, in an effort to better understand how the brain represents semantic information. In the first study reported here, we expanded on a previous study to examine how different semantic space models and modeling parameters affect the abilities of these computational models to predict brain activation in a datadriven set of 500 selected voxels. The findings suggest that these computational models may contain distinct types of semantic information that relate to different brain areas in different ways. On the basis of these findings, in a second study we conducted an additional exploratory analysis of theoretically motivated brain regions in the language network. We demonstrated that data-driven computational models can be successfully integrated into theoretical frameworks to inform and test theories of semantic representation and processing. The findings from our work are discussed in light of future directions for neuroimaging and computational research.Keywords LSA . HAL . Semantic space models . Coarse semantic coding . fMRI Latent semantic analysis (LSA; Landauer & Dumais, 1997) and the hyperspace analogue to language (HAL; Lund & Burgess, 1996) are among the most influential computational models of word meaning. LSA and HAL, among other socalled Bsemantic space models^or Bdistributional semantic models,^use word co-occurrence frequencies as the basic building blocks for word meaning (see Jones, Willits, & Dennis, 2015, for a recent review). In these models, the cooccurrence frequencies of a word with all the other documents (as in LSA) or all other words with which the word occurs (as in HAL) are used to build the vector representation for that word, typically based on a very large-scale text corpus. The resulting representation of any target word is a highdimensional vector with each dimension denoting either a word (word-to-word matrix) or a document (word-to-document matrix). The raw vectors may consist of thousands or tens of thousands of dimensions and are usually very sparse. Dimension reduction methods are often used to reduce the number of dimensions in these models. These standard methods used by LSA and HAL have since been further developed or expanded. For example, probabilistic LSA (Hoffman, 2001) and its fully Bayesian extension the Topic model (Griffiths, Steyvers, & Tenenbaum, 2007) can identify lexemes with multiple senses (Tomar et al., 2013) and generate semantic representations as probability distributions rather than points in a high-dimension space. Positive pointwise mutual information (PPMI) has been used in place of raw cooccurrence frequencies (Bullinaria & Levy, 2007). Zhao, Li, and Kohonen (2011) integrated these models into a selforganizing map framework, and Fyshe, Talukdar, Murphy, and Mitchell (2013) discussed how different types of constraints on what counts as a co-occurrence qualitatively affect semantic information. Evaluation of semantic space modelsComputational models...

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“…These masks were mirror-projected onto the right hemisphere (RH) to create six homologous masks (the masks cover significant parts of the cortex, so their mirrored version is likely to encompass the righthemispheric homologue of the left-hemispheric language network, despite possible hemispheric asymmetries in their precise locations). These RH homologues were included because they are activated during many language-processing tasks (54,(114)(115)(116)(117) and they show strong functional correlations with the LH language network during naturalistic cognition (59). In each of the resulting 12 masks, a fROI was defined using the top 10% method described in the Methods section of the main text.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Expanding the language network: Domain-specific hippocampal recruitment during high-level linguistic processing

Blank

Duff²,

Brown‐Schmidt³

et al. 2016

Preprint

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Language processing requires us to encode linear relations between acoustic forms and map them onto hierarchical relations between meaning units. Such relational binding of linguistic elements might recruit the hippocampus given its engagement by similar operations in other cognitive domains. Historically, hippocampal engagement in online language use has received little attention because patients with hippocampal damage are not aphasic. However, recent studies have found that these patients exhibit language impairments when the demands on flexible relational binding are high, suggesting that the hippocampus does, in fact, contribute to linguistic processing. A fundamental question is thus whether language processing engages domain-general hippocampal mechanisms that are also recruited across other cognitive processes or whether, instead, it relies on certain language-selective areas within the hippocampus. To address this question, we conducted the first systematic analysis of hippocampal engagement during comprehension in healthy adults (n=150 across three experiments) using fMRI. Specifically, we functionally localized putative "language-regions" within the hippocampus using a language comprehension task, and found that these regions (i) were selectively engaged by language but not by six non-linguistic tasks; and (ii) were coupled in their activity with the cortical language network during both "rest" and especially story comprehension, but not with the domain-general "multiple-demand (MD)" network. This functional profile did not generalize to other hippocampal regions that were localized using a non-linguistic, working memory task. These findings suggest that some hippocampal mechanisms that maintain and integrate information during language comprehension are not domain-general but rather belong to the language-specific brain network. Significance statementAccording to popular views, language processing is exclusively supported by neocortical mechanisms. However, recent patient studies suggest that language processing may also require the hippocampus, especially when relations among linguistic elements have to be flexibly integrated and maintained. Here, we address a core question about the place of the hippocampus in the cognitive architecture of language: are certain hippocampal operations language-specific rather than domain-general? By extensively characterizing hippocampal recruitment during language comprehension in healthy adults using fMRI, we show that certain hippocampal subregions exhibit signatures of language specificity in both their response profiles and their patterns of activity synchronization with known functional regions in the neocortex. We thus suggest that the hippocampus is a satellite constituent of the language network.

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The role of the right hemisphere in metaphor comprehension: A meta-analysis of functional magnetic resonance imaging studies

Cited by 78 publications

References 73 publications

Language Processing, Functional Magnetic Resonance Imaging of

Language Processing, Functional Magnetic Resonance Imaging of

Disentangling narrow and coarse semantic networks in the brain: The role of computational models of word meaning

Expanding the language network: Domain-specific hippocampal recruitment during high-level linguistic processing

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