Networks in Psychology/Linguistics/Education CSE 5339: Topics in - - PowerPoint PPT Presentation
Networks in Psychology/Linguis5cs/Educa5on Networks in Psychology/Linguistics/Education CSE 5339: Topics in Network Data Analysis Networks in Psychology/Linguis5cs/Educa5on Content Cogni5ve Models (3) Associa5onism Four Laws of
Networks in Psychology/Linguis5cs/Educa5on
CSE 5339: Topics in Network Data Analysis
Networks in Psychology/Linguis5cs/Educa5on
Content Cogni5ve Models (3)
Networks in Linguis5cs (7) Sociolinguis5cs (8)
Natural Language Processing (12) Mental Lexicon Networks (13) Networks in Educa5on Bibliography Ques5ons and Comments
Networks in Psychology/Linguis5cs/Educa5on
“Some of our ideas have a natural correspondence and connec5on with one another: it is the office of excellency of our reason to trace these…” –John Locke
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This theory considers the connec5ons between experiences as a model for human cogni5on.
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There is debate over the founder of this theory:
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Some claim it comes from Plato, and is found in the works of Aristotle (Boeree, 2000).
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Others give credit to philosopher John Locke in his second book of “An Essay Concerning Human Understanding” (Warren, 1921).
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These models are limited to linear progressions which are represented by path graphs (Elman, 1996).
Defini5on: A path graph with ver5ces listed in the order v1, v2, …, vn, only has edges {vi, vi+1} where i = 1, 2, …, n − 1.
Networks in Psychology/Linguis5cs/Educa5on
Aristotle considered the following four laws related to associa5ons (Boeree, 2000):
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together in the mind. For example, if you think of a cup, you may think of a saucer; if you think of making coffee, you may then think of
drinking that coffee. ì
associa5on. For example, if you have an éclair with your coffee every day, and have done so for the last twenty years, the
associa5on will be strong indeed -- and you will be fat. ì
For example, if you think of one twin, it is hard not to think of the other. If you recollect one birthday, you may find yourself thinking about others as well. ì
something completely opposite. For example, if you think of the tallest person you know, you may suddenly recall the shortest one as well. If you
are thinking about birthdays, the one that was totally different from all the rest is quite likely to come up.
Networks in Psychology/Linguis5cs/Educa5on
“’The mul5-layer nets connec5onists use are nonlinear dynamical systems, and nonlinear systems can learn rela5onships of considerable complexity.’” (Elman, 1996).
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This theory is ini5ally credited to psychologist Donald Hebb (Elman, 1996). He is well known in computa5onal neuroscience for the Hebbian Learning Rule.
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It considers more complex connec5ons for models for human cogni5on when compared to Associa5onism.
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Many Connec5onists consider biological plausibility when crea5ng models.
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Neural networks are a common tool used by Connec5onists.
Networks in Psychology/Linguis5cs/Educa5on
Assump5ons are made for the Connec5onist theory. They do not completely agree with the following two theories:
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Theory of Na5vism/Inna5sm: These theories consider the possibility of preexis5ng cogni5ve func5ons not developed by the associa5on of experiences.
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This argument is similar to that of nature vs. nature (Pinker, 2002).
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Theory of Systema5city: This theory is difficult to explain, see (Cummins, 1996; or Pullum & Scholz, 2007)
Networks in Psychology/Linguis5cs/Educa5on
Networks in Psychology/Linguis5cs/Educa5on
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This subfield of linguis5cs broadly considers the following ([Editorial], 1997):
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Socially grounded research into linguis5c varie5es;
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textual and discourse processes; and
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linguis5c and other communica5ve aspects of social life.
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There is thought to be overlap with linguis5c anthropolgy (Gumperz & Cook-Gumperz, 2008).
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The use of social networks has played an important role in the development of graph theory.
Networks in Psychology/Linguis5cs/Educa5on
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As an early example of introducing graph theory to the study of social networks, and vice versa, Cartwright and Harary (1956), describe a process for crea5ng networks:
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Considers balances necessary to describe rela5ons (edges) in social networks, referred to as Heider’s Concep5on of Balance theory.
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It uses P-O-X units (i.e. person, another person, and impersonal en5ty). With rela5onal units L and U for awtudes and cogni5ve unit forma5on, respec5vely. e.g. (a) PLO reads “P likes,loves, values, or approves of O…” (b) P~LO reads “P dislikes, nega5vely values, or disapproves of O.” (c) PUX reads “P owns, made, is close to, or is associated with X…” Remark: Of Aristotle’s Four Laws of Associa5on, these rules seem to neglect frequencies.
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The combina5on of nega5ve rela5ons (~L) and unsymmetric rela5ons, requires the use of signed digraphs, (Cartwright & Harary, 1956).
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When considering two rela5onships on persons (L and U), we have graphs of type 2, (Harary & Norman, 1953).
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Addi5onally, they define the degree of balance of an s-digraph. Defini5on: A directed graph (digraph) is a graph in which the edges are ordered pairs. Defini5on: A signed digraph (s-digraph) is a digraph in which every edge is weighted as posi5ve or nega5ve. Defini5on: A graph of type r is a graph in which the edges can be assigned any one dis5nct color from the op5on of r different colors.
Networks in Psychology/Linguis5cs/Educa5on
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Daniel Nexle (1999) uses social networks to explore the evolu5on of language:
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Uses Social Impact Theory (i.e. ones beliefs, axributes, or behavior is influenced by those around them).
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Considers two variants, p and q, of the same gramma5cal item.
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Of 400 individuals, each learn either p or q. These are based on par5cular rules (Nowak, Szamrej, & Latané, 1990).
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Updates the network, based on the previously cited rules.
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From simula5ons, he concludes that some speaking persons (nodes) are more influen5al on the evolu5on of the networks, even when they do not share the majority gramma5cal varia5on. Remark: For a more detailed compila5on of research/methods involving networks of language change before 2005, see Marshall (2004).
Networks in Psychology/Linguis5cs/Educa5on
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The conference paper by Fagyal et al., (2010), is a more modern approach to Nexle’s research:
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Considers social influence as assymetric, and so the networks use directed edges.
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Network forma5on considers the work of Barabási and Albert (1999):
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Social networks have small diameter;
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they have high clustering; and
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they evolve with a scale-free degree distribu5on. ì
Concludes there is a biased adop5on of gramma5cal variants from individuals who are more “popular,” and these persons must be aware
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Addi5onally, they describe the necessity of peripheral members (i.e. those who are less “popular”) as necessary to regulate the diffusion dynamics within the popula5on.
Networks in Psychology/Linguis5cs/Educa5on
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This is a subfield of computa5onal linguis5cs.
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It incorporates the use of ar5ficial intelligence algorithms which are also used by neural networks.
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Some of the many applica5ons include the following:
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Machine transla5on;
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Natural language understanding;
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Natural language genera5on;
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Op5cal character recogni5on; and
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Speech recogni5on.
Remark: This research is very similar to that of neural networks, and I will leave this topic for a later discussion. For more informa5on, read Handbook of Natural Language Processing (Dale, Moisl, & Somers, 2000).
Networks in Psychology/Linguis5cs/Educa5on
Networks in Psychology/Linguis5cs/Educa5on
Networks in Psychology/Linguis5cs/Educa5on
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Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509–512.
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Boeree, C. G. (2000). Psychology: The Beginnings. Retrieved from hxp://webspace.ship.edu/ cgboer/psychbeginnings.html
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Cartwright, D., & Harary, F. (1956). Structural Balance: A Generaliza5on of Heider’s Theory. Psychological Review, 63(5), 277–293. hxp://doi.org/10.1037/h0046049
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Collins, A. M., & Quillian, M. R. (1969). Retrieval Time from Seman5c Memory. Journal of Verbal Learning and Verbal Behavior, 8(2), 240–247. hxp://doi.org/10.1016/S0022-5371(69)80069-1
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Cummins, R. (1996). Systema5city. The Jounral of Philosophy, 93(12), 591–614. hxp://doi.org/ 10.2307/2941118
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Dale, R., Moisl, H. L., & Somers, H. L. (2000). Handbook of Natural Language Processing. New York: Marcel Dekker.
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Dyne, S. D., & Storms, G. (2008). Word associa5ons: Network and seman5c proper5es. Behavior Research Methods, 40(1), 213–231. hxp://doi.org/10.3758/BRM.40.1.213
Networks in Psychology/Linguis5cs/Educa5on
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[Editorial]. (1997). Journal of Sociolinguis5cs, 1(1), 1–5.
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Elman, J. L. (1996). Rethinking Innateness : A Connec5onist Perspec5ve on Development. Cambridge, Mass : A Bradford Book.
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Fagyal, Z., Swarup, S., Escobar, A. M., Gasser, L., & Lakkaraju, K. (2010). Centers, Peripheries, and Popularity: The Emergence of Norms in Simulated Networks of Linguis5c Influence. In NWAV 37 (Vol. 15). University of Pennsylvania Working Papers in Linguis5cs.
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Gumperz, J. J., & Cook-Gumperz, J. (2008). Studying language, culture, and society: Sociolinguis5cs or linguis5c anthropology? Journal of Sociolinguis5cs, 12(4), 532–545. hxp:// doi.org/10.1111/josl.2008.12.issue-4
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Harary, F., & Norman, R. Z. (1953). Graph Theory as a Mathema5cal model in Social Science. Ann Arbor.
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Marshall, J. (2004). Language Change and Sociolinguis5cs: Rethinking Social Networks. Palgrave Macmillan.
Networks in Psychology/Linguis5cs/Educa5on
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Miller, G. A., & Gildea, P. M. (1987). How Children Learn Words. Scien5fic America, 257(3), 94–
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Nelson, D. L., Zhang, N., & McKinney, V. M. (2001). The Ties That Bind What Is Known to the Recogni5on of What Is New. Journal of Experimental Psychology: Learning, Memory, and Cogni5on, 27(5), 1147–1159. hxp://doi.org/10.1037/0278-7393.27.5.1147
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Nexle, D. (1999). Using Social Impact Theory to simulate language change. Lingua, 108(2-3), 95–117.
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Nowak, A., Szamrej, J., & Latané, B. (1990). From Private Awtude to Public Opinion. Psychological Review, 97(3), 362–376. hxp://doi.org/10.1037//0033-295X.97.3.362
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Pinker, S. (2002). The Blank Slate: The Modern Denial of Human Nature. New York: Viking.
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Pullum, G. K., & Scholz, B. C. (2007). Systema5city and Natural Language Syntax. Croa5an Journal of Philosophy, 7(21), 28.
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Warren, W. C. (1921). A History of the Associa5on Psychology. Charles Scribner’s Sons.
Networks in Psychology/Linguis5cs/Educa5on