Page:The World Within Wikipedia： An Ecology of Mind.pdf/3

Perhaps the most striking evidence of a cognitive-linguistic ecosystem comes from developmental studies. The common experimental paradigm in these studies is to situate a child and adult in a play session with a new toy. The adult then produces a novel name for the toy multiple times in the session, and some time later the adult uses the novel name to ask the child to get the toy. Children at 13 months of age will correctly respond to a paired novel non-word as well as a paired novel word, but at 20 months children lose this ability and can only correctly respond when labels are novel words^[1]. Similarly 20–26 month old children respond to words as labels but only when they are produced by the mouth, rather than by a tape recorder held by the adult^[2]. During development, attention is increasingly focused on words and child-directed words as a cue to naming objects.

Related work in named category learning builds on these effects. In this paradigm, multiple objects/toys belonging to the same category are presented with a word label. When 17 month old children are presented with a label for two toys that are different in all respects except shape, not only do they correctly learn that the label corresponds to shape and generalize it to new objects, but when presented with a new label and new objects with a novel shape, children are able to correctly generalize that the new label refers to the novel shape in a single trial^[3]. In addition, children who participated in the 8 week experiment showed a roughly 250% increase in object name vocabulary growth during this time compared to a control group that was exposed to the same objects without corresponding word labels. Only children exposed to categories and word labels were able to generalize the property of shape to new objects in a single trial. In a related study with 13 month olds, not only were word labels found to increase attention to novel objects of the same category, but word labels were also found to increase attention to the superordinate category (cow–animal), relative to a non-word-label condition^[4]. These studies demonstrate the mutual influence between language and cognition during development: Word labeling focuses attention on category features, attention to discriminating features improves category structure, and improved category structure facilitates the learning of more word labels.

Although the growing body of empirical work above indicates that our cognitive-linguistic environment affects language structure and categorization, it also highlights the difficulty of long duration experiments with human participants. An alternative approach is to provide a comparable cognitive-linguistic environment to a computational cognitive model and observe the similarities between that model’s behavior and human behavior. There is an extensive literature using this approach to model human semantic behavior. One popular approach, known as latent semantic analysis (LSA), represents text meaning as the spatial relationships between words in a vector space^[5],[6]. LSA has been used to model a variety of semantic effects including approximating vocabulary acquisition in children^[7], cohesion detection^[8], grading essays^[9], understanding student contributions in tutorial dialogue^[10],^[11], entailment detection^[12], and dialogue segmentation^[13], amongst many others. LSA is part of a larger family of distributional models. The underlying assumption of distributional models is that the context of use determines the meaning of a word^[14]. Thus doctor and nurse would have a similar meaning, because these words (as well as their referents) typically occur in the same context. In the example of LSA, the contexts associated with a word are represented as vector components, such that the jth component of the word vector is the number of times that word appeared in the jth document in the text collection. Other distributional models vary according to how they define, represent, and learn contexts^[15].

1. Woodward, A.L.; Hoyne, K.L. Infants’ learning about words and sounds in relation to objects. Child Dev. 1999, 70, 65–77.
2. Colunga, E.; Smith, L.B. The emergence of abstract ideas: Evidence from networks and babies. Philos. Trans. R. Soc. Lond. Series B Biol. Sci. 2003, 358, 1205–1214.
3. Smith, L.B.; Jones, S.S.; Landau, B.; Gershkoff-Stowe, L.; Samuelson, L. Object name learning provides on-the-job training for attention. Psychol. Sci. 2002, 13, 13–19.
4. Waxman, S.R.; Markow, D.B. Words as invitations to form categories: Evidence from 12-to 13-month-old infants. Cogn. Psychol. 1995, 29, 257–302.
5. Landauer, T.K.; Dumais, S.T. A solution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychol. Rev. 1997, 104, 211–240.
6. Landauer, T.K. LSA as a Theory of Meaning. In Handbook of Latent Semantic Analysis; Landauer, T., McNamara, D., Dennis, S., Kintsch, W., Eds.; Lawrence Earlbaum: Mahweh, NJ, USA, 2007; pp. 379–400.
7. Landauer, T.K.; Dumais, S.T. A solution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychol. Rev. 1997, 104, 211–240.
8. Foltz, P.W.; Kintsch, W.; Landauer, T.K. The measurement of textual coherence with latent semantic analysis. Discourse Process. 1998, 25, 285–308.
9. Foltz, P.W.; Gilliam, S.; Kendall, S.A. Supporting content-based feedback in on-line writing evaluation with LSA. Interact. Learn. Environ. 2000, 8, 111–127.
10. Graesser, A.C.; Wiemer-Hastings, P.; Wiemer-Hastings, K.; Harter, D.; Tutoring Research Group; Person, N. Using latent semantic analysis to evaluate the contributions of students in autotutor. Interact. Learn. Environ. 2000, 8, 129–147.
11. Olde, B.A.; Franceschetti, D.; Karnavat, A.; Graesser, A.C. The Right Stuff: Do You Need to Sanitize Your Corpus When Using Latent Semantic Analysis? In Proceedings of the 24th Annual Meeting of the Cognitive Science Society, Fairfax, USA, 7–10 August 2002; Erlbaum: Mahwah, NJ, USA, 2002; pp. 708–713.
12. Olney, A.M.; Cai, Z. An Orthonormal Basis for Topic Segmentation in Tutorial Dialogue. In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics: Philadelphia, PA, USA, 2005; pp. 971–978.
13. Olney, A.M.; Cai, Z. An Orthonormal Basis for Entailment. In Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, Clearwater Beach, FL, USA, 15–17 May 2005; AAAI Press: Menlo Park, CA, USA, 2005; pp. 554–559.
14. Harris, Z. Distributional structure. Word 1954, 10, 140–162.
15. McNamara, D.S. Computational methods to extract meaning from text and advance theories of human cognition. Topics Cogn. Sci. 2011, 3, 3–17.