Connectivity and thought: The influence of semantic network structure in a neurodynamical model of thinking

Marupaka, Nagendra; Iyer, Laxmi; Minai, Ali A.

doi:10.1016/j.neunet.2012.02.004

Cited by 52 publications

(26 citation statements)

References 64 publications

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“…Other studies, however, have revealed a small-world structure of semantic memory is related to more creative achievements and the facilitation of unique conceptual combinations (Kenett et al, 2016a;Marupaka, Iyer, & Minai, 2012). Moreover, shorter associative pathways have been associated with better performance on divergent thinking tasks (Rossmann & Fink, 2010).…”

Section: Divergent Thinking Divergent Thinking (Dt) Is a Common Proxmentioning

confidence: 98%

“…Overall, these results complement the network structure of the high openness group, which had shorter paths between concepts and decreased rigidity of categorizations. Thus, people high in openness are more likely to be creative in part because they are better able to access more remote responses for conceptual recombination (Marupaka, Iyer, & Minai, 2012).…”

Section: Unique Responsesmentioning

confidence: 99%

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Remotely Close Associations: Openness to Experience and Semantic Memory Structure

et al. 2018

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Section: Divergent Thinking Divergent Thinking (Dt) Is a Common Proxmentioning

confidence: 98%

Section: Unique Responsesmentioning

confidence: 99%

Remotely Close Associations: Openness to Experience and Semantic Memory Structure

et al. 2018

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“…The ANSWER model draws upon this work for its theoretical underpinnings. It is based on a neurodynamical model of thinking called IDEA (itinerant dynamics with emergent attractors) that we have described previously [45], [46], [47], [48], and our earlier work on computational models of ideation and priming [49], [50], [51], [52].…”

Section: Previous Workmentioning

confidence: 99%

“…Elsewhere, we have suggested that these might represent latent ideas that could be the basis of subsequent innovation [53]. In ANSWER, the sampling of ideas from the ASN occurs using the attractor network approach derived from our previously described IDEA model [45], [46], [47], [48]. In that model, the ASN is constructed from the text corpus as a recurrent neural network with neural units representing words and weights encoding associations between them.…”

Section: Description Of Ov Erall Approachmentioning

confidence: 99%

ANSWER: An unsupervised attractor network method for detecting salient words in text corpora

Candadai

Vanarase

Mei

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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The availability of unstructured text as a source of data has increased by orders of magnitude in the last few years, triggering extensive research in the automated processing and analysis of electronic texts. An especially important and difficult problem is the identification of salient words in a corpus, so that further processing can focus on these words without distraction by uninformative words. Standard lists of stop words are used to remove common words such as articles, pronouns and prepositions, but many other words that should be removed are much harder to identify because word salience is highly context-dependent. In this paper, we describe a neu rodynamical approach for the context-dependent identification of salient words in large text corpora. The method, termed the Attractor Network-based Salient Word Extraction Rule (ANSWER)is modeled as a cognitive mechanism that identifies salient words based on their participation in coherent multi-word ideas.These ideas are, in turn, extracted via attractor dynamics in a recurrent neural network modeling the associative semantic graph of the corpus. The corpus used in this paper comprises the abstracts of all papers published in the proceedings of IJCNN 2009IJCNN , 2011IJCNN and 2013. The list of salient words that the system generates is compared with those generated by other standard metrics, and is found to outperform all of them in almost all cases.

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“…The second approach is based on a neurodynamical model of thinking that we have described previously [13], [14], [15]. In this model, the epistemic network corresponding to the text corpus is represented as a recurrent neural network with nodes (neural units) representing words and weights encoding associations between them.…”

Section: Background and Motivationmentioning

confidence: 99%

Chunks of thought: Finding salient semantic structures in texts

Mei

Vanarase

Minai

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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As the availability of large, digital text corpora increases, so does the need for automatic methods to analyze them and to extract significant information from them. A number of algorithms have been developed for these applications, with topic modeling-based algorithms such as latent Dirichlet allocation (LDA) enjoying much recent popularity. In this paper, we focus on a specific but important problem in text analysis: Identifying coherent lexical combinations that represent "chunks of thought" within the larger discourse. We term these salient semantic chunks (SSCs), and present two complimentary approaches for their extraction. Both these approaches derive from a cognitive rather than purely statistical perspective on the generation of texts. We apply the two algorithms to a corpus of abstracts from IJCNN 2009, and show that both algorithms find meaningful chunks that elucidate the semantic structure of the corpus in complementary ways.

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Connectivity and thought: The influence of semantic network structure in a neurodynamical model of thinking

Cited by 52 publications

References 64 publications

Remotely Close Associations: Openness to Experience and Semantic Memory Structure

Remotely Close Associations: Openness to Experience and Semantic Memory Structure

ANSWER: An unsupervised attractor network method for detecting salient words in text corpora

Chunks of thought: Finding salient semantic structures in texts

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