Cerebral Hemispheric Mechanisms Linking Ambiguous Word Meaning Retrieval and Creativity

Atchley, Ruth Ann; Keeney, Maureen; Burgess, Curt

doi:10.1006/brcg.1999.1080

Cited by 68 publications

(36 citation statements)

References 49 publications

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“…Previous work validated the existence of coarse versus narrow semantic processing of meaning both computationally (Jones, Willits, & Dennis 2015;Rubin et al, 2014;Sahlgren, 2006) and neuropsychologically (Atchley et al, 1999;Beeman, 1998;Burgess & Simpson, 1988;JungBeeman, 2005;Virtue et al, 2006). Our results suggest some degree of similarity between the computational models and the brain in how these distinct types of meaning are represented.…”

Section: Discussionsupporting

confidence: 78%

“…Early behavioral studies linked coarse semantic processing to the RH using hemi-field experiments, in which stimulus information was manipulated to enter the brain through either the right or left hemisphere by presenting stimuli exlusively to the left or right visual field, respectively (Atchley, Keeney, & Burgess, 1999;Burgess & Simpson, 1988;Faust, Ben-Artzi, & Harel, 2008). Other methods have examined individuals with RH brain damage .…”

Section: Narrow Versus Coarse Semantic Processingmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 78%

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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“…The second possibility is that the difference between the two solution types is not a matter of different brain areas, but of different processing within the same areas [Atchley et al, 1999;Bogen and Bogen, 1969;Kwong et al, 1992]. For example, the main difference might be interhemispheric interconnectivity, the timing of activation in different brain areas, or a difference in the pattern of activation for a given brain area.…”

Section: Introductionmentioning

confidence: 99%

“Aha!”: The neural correlates of verbal insight solutions

Aziz‐Zadeh

Kaplan

Iacoboni

2008

Human Brain Mapping

166

119

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What are the neural correlates of insight solutions? To explore this question we asked participants to perform an anagram task while in the fMRI scanner. Previous research indicates that anagrams are unique in that they can yield both insight and search solutions in expert subjects. Using a single-trial fMRI paradigm, we utilized the anagram methodology to explore the neural correlates of insight versus search solutions. We used both reaction time measures and subjective reports to classify each trial as a search or insight solution. Data indicate that verbal insight solutions activate a distributed neural network that includes bilateral activation in the insula, the right prefrontal cortex, and the anterior cingulate. These areas are discussed with their possible role in evaluation and metacognition of insight solutions, as well as attention and monitoring during insight.

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“…Divergent and creative thinking may be characterized by increased cooperation of the two hemispheres (Atchley et al, 1999;Bekhtereva et al, 2000;Carlsson et al, 2000), and reduced hemispheric dominance has been linked to creativity (Claridge and Broks, 1984). Schizophrenia is associated with reduced functional and structural laterality.…”

Section: Introductionmentioning

confidence: 99%

Verbal creativity and schizotypal personality in relation to prefrontal hemispheric laterality: A behavioral and near-infrared optical imaging study

Folley

Park

2005

Schizophrenia Research

179

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Although anecdotal and correlational results have suggested a reliable relationship between creativity and psychosis, few studies have examined this relationship using empirical methods. In addition, little is known about the neural substrates of creative thinking. We investigated the creative thinking process in relation to schizotypal personality, schizophrenia and prefrontal hemispheric laterality using behavioral and near-infrared optical spectroscopy (NIRS) methods. Schizophrenic, psychometrically ascertained schizotypal, and healthy control subjects (all right-handed) participated in a novel "alternate uses" task designed to assess divergent thinking (DT) ability. The DT task required subjects to generate "uses" for conventional and ambiguous objects. Prefrontal activity was measured using NIRS while subjects were engaged in DT vs. a cognitive control task in a subset of the subjects. Behavioral data indicated that schizotypes had enhanced DT ability compared with schizophrenic and control subjects, who showed similar performance overall. NIRS data showed that DT was associated with bilateral prefrontal cortex (PFC) activation, but the right PFC particularly contributed to the enhanced creative thinking in psychometric schizotypes compared with the other two groups. Thus, creative thinking seems to robustly recruit bilateral PFC, but it is the right PFC that is preferentially activated in schizotypes in relation to their enhanced DT.

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Cerebral Hemispheric Mechanisms Linking Ambiguous Word Meaning Retrieval and Creativity

Cited by 68 publications

References 49 publications

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

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

“Aha!”: The neural correlates of verbal insight solutions

Verbal creativity and schizotypal personality in relation to prefrontal hemispheric laterality: A behavioral and near-infrared optical imaging study

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