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Using complex networks to understand the mental lexicon

Using complex networks to understand the mental lexicon. Michael S. Vitevitch Department of Psychology University of Kansas. Presented at Poznan Linguistic Meeting 29-August-2013. A network is one way to represent a complex system. Complex System. Comprised of many independent units

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Using complex networks to understand the mental lexicon

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  1. Using complex networksto understand the mental lexicon Michael S. Vitevitch Department of Psychology University of Kansas Presented at Poznan Linguistic Meeting 29-August-2013

  2. A network is one way to represent a complex system.

  3. Complex System • Comprised of many independent units • Units interact in simple ways at the local level • System exhibits unexpected behaviors at global level • Coordinated behavior at KU Basketball games • The Band (not a complex system) • Students behind the backboards (a complex system)

  4. Network • Vertices or Nodes • an entity • Edges or Links/Connections • a relationship between entities • More abstract than an “artificial neural network” • They do NOT learn, have activation levels, etc. • They describe structures, not processes • Structure affects processing

  5. Small-World NetworkWatts & Strogatz (1998)

  6. Small-World NetworkWatts & Strogatz (1998) • Small path length • Network is very large, but “random” shortcuts allow one to traverse the network very quickly. • Milgram (1967) • “Six Degrees of Separation” • High clustering coefficient • “Probability” of my two friends being friends with each other. • Example: networks of actors, collaborators, etc.

  7. The Mental Lexicon--a complex cognitive system--

  8. Mental LexiconVitevitch (2008) J of Speech-Language-Hearing Research • Mental dictionary, or words that you know • Semantic relationships • Steyvers & Tenenbaum (2003) • FerreriCancho & Solé (2001) • Word-forms • Phoneme co-occurrences • Mukherjee, Choudhury, Basu & Ganguly (2008) • Understanding the structure might provide insights about language processing

  9. The structure of the lexicon • Nodes = 19,340 word-forms in database • Nusbaum, Pisoni & Davis (1984) • Links = phonologically related • One phoneme metric (Luce & Pisoni, 1998)

  10. Links in the phonological network • One phoneme metric • cat has as neighbors • scat, at, hat, cut, can, etc. • dog is NOT a phonological neighbor of cat

  11. The Phonological Network 12

  12. The Phonological Network Unique constellation of network features: • A large component, islands, and hermits • Large component = small-world • Small Average path length (Llex= Lrand) • High clustering coefficient (Clex >> Crand) • Assortative mixing by degree • Degree-distribution ≠ power-law

  13. The Phonological Network • Constellation not found in other systems • Constellation is found in other languages • English • Spanish • Mandarin • Hawaiian • Basque • Arbesman, Strogatz & Vitevitch (2010) Internat. J of Bifurcation and Chaos

  14. Does the structure of the lexicon (at various scales) influence processing?

  15. Clustering Coefficient High C Low C

  16. Implications for Lexical ProcessingClustering Coefficient Lexical retrieval • High C • Activation restricted to a small region of network • Few nodes with a large amount of activation. • Slow retrieval due to “competition” • Low C • Activation broadly dispersed across the network • Many nodes with a small amount of activation. • Rapid retrieval; word-form stands out.

  17. Clustering Coefficient • Word Recognition • Chan & Vitevitch (2009) J of Exper. Psych:Human Perception & Performance • Vitevitch, Ercal & Adagarla (2011) Frontiers in Lang. Sci. • Word Production • Chan & Vitevitch (2010) Cognitive Science • LTM & STM • Vitevitch, Chan & Roodenrys (2012) J of Memory & Language • Word-learning • Goldstein & Vitevitch (submitted)

  18. Clustering Coefficient • Micro-level structure influences speed and/or accuracy of responses. • Current models of spoken word production and recognition make no predictions about C, and can not account for these findings.

  19. Clustering coefficient is a measure of the micro-structure of the network. Does the macro-structure of the lexicon influence processing?

  20. AssortativeMixing by Degree Mixing • Preference for how nodes in a network tend to connect to each other. • Examples from a social network • Age • Gender • Race

  21. Assortative Mixing by Degree • Assortative mixing • a.k.a. homophily • “like goes with like” • Disassortative mixing • Dissimilar entities will tend to be connected

  22. Assortative Mixing by Degree • Degree • Number of connections incident to a node

  23. Assortative Mixing by Degree • A node with many connections tends to be connected to nodes with many connections • A node with few connections tends to be connected to nodes with few connections • Overall in the network, there is a positive correlation between the degree of a node and the degree of its neighbors.

  24. Assortative Mixing by Degree • Often observed in social networks with values ranging from .1-.3 • Arbesman et al. (2010) observed values as high as .7 in phonological networks.

  25. Assortative Mixing by Degree • Assortativity & Network Resilience (Newman, 2002) • Networks with assortative mixing by degree are 5-10x better able to maintain processing pathways than disassortative networks in the face of targeted attacks to the system (remove high-degree nodes). • Arbesman et al. (2010) found that the phonological networks were more resilient to such attacks.

  26. Is there evidence of assortative mixing by degree when lexical retrieval fails?

  27. Implications for Lexical ProcessingAssortative Mixing by Degree Failed lexical retrieval • If assortativity influences lexical retrieval, then when you fail to retrieve a word, the “next best thing” will be similar in degree to the target word. • That is, there should be a positive correlation between the degree of the target word and the degree of what is actually retrieved.

  28. Assortative Mixing by Degree(Vitevitch, Chan & Goldstein, submitted) • Computer simulation • TRACE (McClelland & Elman, 1986) • Naturalistic observation • Analysis of slips of the ear (Bond, 1999) • Psycholinguistic experiments • Perceptual identification task • Phonological associate task • Verbal Transformation Illusion

  29. Assortative Mixing by Degree(Vitevitch, Chan & Goldstein, submitted) • In all cases, there was a significant, positive correlation between the degree of the “target” word and the degree of the response. • Macro-level structure influences lexical processing. • Current models of spoken word recognition make no predictions about this, and never explicitly address the question of what is retrieved when lexical retrieval fails.

  30. The micro- and macro-structure of the network influence processing. Are some words more “important” than others for keeping the network together? Do those words influence processing?

  31. Keyplayers(Borgatti, 2006) 14 5 6 10 9 7 13 1 8 11 4 2 12 15 3

  32. Keyplayers(Borgatti, 2006) 14 5 6 10 9 7 13 8 11 4 2 12 15 3

  33. Keyplayers(Borgatti, 2006) 14 5 6 10 9 7 13 1 11 4 2 12 15 3 Node 1 has many connections, but Node 8 is a Keyplayer, because its removal partitions the network.

  34. Keyplayers  Keywords • In individuals with acquired language disorders, treatments that focus on the re-acquisition or rehabilitation of such keywords could facilitate language recovery. • In individuals learning a (first or second) language, introducing keywords early in the process could accelerate (or otherwise facilitate) the acquisition of new words.

  35. Keywords • We selected 25 Keywords and 25 foils that were similar in: • Word length (phonemes/syllables) • Frequency of occurrence • Subjective familiarity • Phonological neighborhood density (degree) • Neighborhood frequency • Phonotactic probability • Spoken duration • Initial phoneme

  36. Perceptual Identification Task • Auditory Naming Task • Auditory Lexical Decision Task Keywords were responded to more quickly / accurately than the Foils.

  37. Summary • Micro-structure of the lexicon influences processing (clustering coefficient). • Macro-structure of the lexicon influences processing (assortative mixing by degree). • Some words are more important than others, but maybe not for the reasons you think (keywords). • The relationships among words matter as much as the characteristics of the words themselves.

  38. Conclusion • Network science enables us to examine new questions about language at multiple (time) scales. • Current approaches do not ask these questions. • Current approaches do not make predictions about these characteristics. • Current approaches can not account for these findings.

  39. Dziękuję Thank you For more information on network science and language see the bibliography webpage maintained by Ramon Ferrer I Cancho.

  40. Michael VitevitchDepartment of PsychologyUniversity of Kansas (USA)mvitevit@ku.edu

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