Understanding Human Navigation Using Network Analysis

Iyengar, S. R. S.; Madhavan, C. E. Veni; Zweig, Katharina Anna; Natarajan, Abhiram

doi:10.1111/j.1756-8765.2011.01178.x

Cited by 37 publications

(29 citation statements)

References 20 publications

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“…The present finding adds to an increasing amount of evidence that suggests that the structure of the lexical network—as described in Vitevitch (2008) and Arbesman et al (2010)— influences the speed and accuracy of various aspects of lexical processing (e.g., Chan & Vitevitch, 2009, 2010; Iyengar et al, 2012; Vitevitch et al, 2011; Vitevitch, Chan & Goldstein, 2013). It has been recognized for some time that the structure of the lexicon influences lexical processing.…”

Section: Discussionsupporting

confidence: 61%

“…The time it took to find a solution dropped from 10–18 minutes in the first 10 games, to about 2 minutes after playing 15 games, to about 30 seconds after playing 28 games, because participants would “morph” the start-word (e.g., bay ) into one of the landmark words that were high in closeness centrality (e.g., aid ), then morph the landmark-word into the desired end-word (e.g., egg ). Although this task is a contrived word-game rather than a task that assesses on-line lexical processing, the results of Iyengar et al (2012) nevertheless support the idea that (orthographic) word-forms may indeed be organized like a network in the mental lexicon, and that the tools of network science can be used to provide insights about cognitive processes and representations.…”

mentioning

confidence: 72%

“…Given the influence of closeness centrality that Iyengar et al (2012) found in the word-morph game we also controlled the closeness centrality of the foils and keywords. Closeness assesses how far away other nodes in the network are from a given node.…”

Section: Methodsmentioning

confidence: 99%

“…Using a game called word-morph, in which participants were given a word, and asked to form a disparate word by changing one letter at a time, Iyengar et al (2012) demonstrated the importance of words with high closeness centrality in a network of the orthographic lexicon. For example, asked to “morph” the word bay into the word egg participants might have changed bay into bad-bid-aid-add-ado-ago-ego and finally into egg .…”

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confidence: 99%

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Keywords in the mental lexicon

Vitevitch

Goldstein

2014

Journal of Memory and Language

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Network science draws from a number of fields to examine complex systems using nodes to represent individuals and connections to represent relationships between individuals to form a network. This approach has been used in several areas of Psychology to illustrate the influence that the structure of a network has on processing in that system. In the present study the concept of keyplayers in a network (Borgatti, 2006) was examined in the domain of Psycholinguistics. Keyplayers are nodes in a network that, when removed, result in the network fracturing into several smaller components. A set of such nodes was found in a network of phonological word-forms as was another set of foil words, comparable to the “keywords” on a number of lexical and network characteristics. In three conventional psycholinguistic tasks keywords were responded to more quickly and accurately than the foils. A similar trend was observed in an analysis of the keywords and foils (and another set of foils) in the English Lexicon Project. These results open avenues for further exploration of keywords in various areas of language processing, and demonstrate the utility of the network science approach to psycholinguistics and psychology more generally.

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Section: Discussionsupporting

confidence: 61%

mentioning

confidence: 72%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Keywords in the mental lexicon

Vitevitch

Goldstein

2014

Journal of Memory and Language

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“…It has also been suggested that generating and deciphering language can be usefully modelled as navigation on language networks by means of various strategies [47] such as random walks [48], switching random walks [49], random walks with memory [50], and using ‘landmarks’ with high network closeness centralities [51]. We further propose that linguistic networks contain shortcuts that optimizes their ease of navigation and access.…”

Section: Syntactic Network Of Languagesmentioning

confidence: 99%

Functional shortcuts in language co-occurrence networks

2018

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Human language contains regular syntactic structures and grammatical patterns that should be detectable in their co-occurence networks. However, most standard complex network measures can hardly differentiate between co-occurence networks built from an empirical corpus and a body of scrambled text. In this work, we employ a motif extraction procedure to show that empirical networks have much greater motif densities. We demonstrate that motifs function as efficient and effective shortcuts in language networks, potentially explaining why we are able to generate and decipher language expressions so rapidly. Finally we suggest a link between motifs and constructions in Construction Grammar as well as speculate on the mechanisms behind the emergence of constructions in the early stages of language acquisition.

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Word morph and topological structures: A graph generating algorithm

2016

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An algorithm of generating graph structures for the ordering of datasets is proposed. It operates with the key concept of ''sphere neighborhood'' and generates the graph structured ordering of datasets, provided the elements of such sets can be related by some similarity relation. The algorithm always allows determining the shortest path between two nodes in a constructive way. It is demonstrated by an application to the famous Word Morph game and in addition to the ordering of logfiles with respect to the detection of errors. Therefore, the algorithm can be very useful for the dealing with Big Data problems.

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Understanding Human Navigation Using Network Analysis

Cited by 37 publications

References 20 publications

Keywords in the mental lexicon

Keywords in the mental lexicon

Functional shortcuts in language co-occurrence networks

Word morph and topological structures: A graph generating algorithm

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