Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

Crangle, Colleen E.; Perreau-Guimaraes, Marcos; Suppes, Patrick

doi:10.1371/journal.pone.0065366

Cited by 7 publications

(8 citation statements)

References 41 publications

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“…Taxonomic models. WordNet is the most influential representational model based on taxonomic information, having been used in several neuroimaging studies to successfully model semantic content (2,22,54,55). It is organized as a knowledge graph in which words are grouped into sets of synonyms ("synsets"), each expressing a distinct concept.…”

Section: Resultsmentioning

confidence: 99%

“…Whether, and to what extent, each of these types of information plays a role in the neural representation of conceptual knowledge is a topic of intense research and debate. A large body of evidence has emerged from behavioral studies, functional neuroimaging experiments, and neuropsychological assessment of patients with semantic deficits, with results typically interpreted in terms of taxonomic (19)(20)(21)(22)(23)(24), experiential (13,(25)(26)(27)(28)(29)(30)(31)(32)(33)(34), or distributional (2,3,5,35,36) accounts. However, the extent to which each of these representational systems plays a role in the neural representation of conceptual knowledge remains controversial (23,37,38), in part because their representations of common lexical concepts are strongly inter-correlated: patterns of word co-occurrence in natural language are driven in part by taxonomic and experiential similarities between the concepts to which they refer, and the taxonomy of natural categories is systematically related to the experiential attributes of the exemplars (39)(40)(41).…”

Section: Introductionmentioning

confidence: 99%

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Decoding the Information Structure Underlying the Neural Representation of Concepts

Fernandino

Conant

Humphries

et al. 2021

Preprint

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The nature of the neural code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. Three main types of information have been proposed as candidates for the neural representations of lexical concepts: taxonomic (i.e., information about category membership and inter-category relations), distributional (i.e., information about patterns of word co-occurrence in natural language use), and experiential (i.e., information about sensory-motor, affective, and other features of phenomenal experience engaged during concept acquisition). In two experiments, we investigated the extent to which these three types of information are encoded in the neural activation patterns associated with hundreds of English nouns from a wide variety of conceptual categories. Participants made familiarity judgments on the meaning of written nouns while undergoing functional MRI. A high-resolution, whole-brain activation map was generated for each noun in each participant′s native space. These word-specific activation maps were used to evaluate different representational spaces corresponding to the three types of information described above. In both studies, we found a striking advantage for experience-based models in most brain areas previously associated with concept representation. Partial correlation analyses revealed that only experiential information successfully predicted concept similarity structure when inter-model correlations were taken into account. This pattern of results was found independently for object concepts and event concepts. Our findings indicate that the neural representation of conceptual knowledge primarily encodes information about features of experience, and that - to the extent that it is represented in the brain - taxonomic and distributional information may rely on such an experience-based code.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decoding the Information Structure Underlying the Neural Representation of Concepts

Fernandino

Conant

Humphries

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Whether, and to what extent, each of these types of information plays a role in the neural representation of conceptual knowledge is a topic of intense research and debate. A large body of evidence has emerged from behavioral studies, functional neuroimaging experiments, and neuropsychological assessments of patients with semantic deficits, with results typically interpreted in terms of taxonomic ( 19 – 24 ), experiential ( 13 , 25 – 34 ), or distributional ( 2 , 3 , 5 , 35 , 36 ) accounts. However, the extent to which each of these representational systems plays a role in the neural representation of conceptual knowledge remains controversial ( 23 , 37 , 38 ), in part, because their representations of common lexical concepts are strongly intercorrelated.…”

mentioning

confidence: 99%

Decoding the information structure underlying the neural representation of concepts

Fernandino

Tong

Conant

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional “semantic hubs” contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

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“…We end with a brief discussion of what these methods offer. Quantitative text analysis has been used to do text data mining in the scientific literature ( Crangle et al, 2007 ) and detect patterns in language for studies of the brain ( Crangle, Perreau-Guimaraes & Suppes, 2013 ; Crangle, 2014 ). An alternative to qualitative methods such as those described in Barney, Griffiths & Banfield (2011) and Griffiths & Crisp (2012) , quantitative methods applied to language also allow common themes to be identified.…”

Section: Discussionmentioning

confidence: 99%

A questions-based investigation of consumer mental-health information

Crangle

2015

PeerJ

Self Cite

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Despite the wealth of mental-health information available online to consumers, research has shown that the mental-health information needs of consumers are not being met. This study contributes to that research by soliciting consumer questions directly, categorizing them, analyzing their form, and assessing the extent to which they can be answered from a trusted and vetted source of online information, namely the website of the US National Institute of Mental Health (NIMH). As an alternative to surveys and analyses of online activity, this study shows how consumer questions provide new insight into what consumers do not know and how they express their information needs. The study crowdsourced 100 consumer questions through Amazon Inc.'s Mechanical Turk. Categorization of the questions shows broad agreement with earlier studies in terms of the content of consumer questions. It also suggests that consumers' grasp of mental health issues may be low compared to other health topics. The majority of the questions (74%) were simple in form, with the remainder being multi-part, multifaceted or narrative. Even simple-form questions could, however, have complex interpretations. Fifty four questions were submitted to the search box at the NIMH website. For 32 questions, no answer could be found in the top one to three documents returned. Inadequacies in the search and retrieval technology deployed at websites account for some of the failure to find answers. The nature of consumer questions in mental health also plays a role. A question that has a false presupposition is less likely to have an answer in trusted and vetted sources of information. Consumer questions are also expressed with a degree of specificity that makes the retrieval of relevant information difficult. The significance of this study is that it shows what an analysis of consumer mental-health questions can tell us about consumer information needs and it provides new insight into the difficulties facing consumers looking for answers to their questions in online resources.

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Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

Cited by 7 publications

References 41 publications

Decoding the Information Structure Underlying the Neural Representation of Concepts

Decoding the Information Structure Underlying the Neural Representation of Concepts

Decoding the information structure underlying the neural representation of concepts

A questions-based investigation of consumer mental-health information

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