Discovering hidden time patterns in behavior: T-patterns and their detection

Article

Abstract

This article deals with the definition and detection of particular kinds of temporal patterns in behavior, which are sometimes obvious or well known, but other times difficult to detect, either directly or with standard statistical methods. Characteristics of well-known behavior patterns were abstracted and combined in order to define a scale-independent, hierarchical time pattern type, called aT-pattern. A corresponding detection algorithm was developed and implemented in a computer program, called Theme. The proposed pattern typology and detection algorithm are based on the definition and detection of a particular relationship between pairs of events in a time series, called acritical interval relation. The proposed bottom-up, level-by-level (or breadth-first) search algorithm is based on a binary tree of such relations. The algorithm first detects simpler patterns. Then, more complex and complete patterns evolve through the connection of simpler ones, pattern completeness competition, and pattern selection. Interindividual T-patterns in a quarter-hour interaction between two children are presented, showing that complex hidden T-patterns may be found by Theme in such behavioral streams. Finally, implications for studies of complexity, self-organization, and dynamic patterns are discussed.

References

  1. Bakeman, R., &Gottman, J. M. (1997).Observing interaction: An introduction to sequential analysis. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  2. Bakeman, R., &Quera, V. (1995).Analyzing interaction: Sequential analysis with SIDS and GSEQ. New York: Cambridge University Press.Google Scholar
  3. Beaudichon, J., Legros, S., &Magnusson, M. S. (1991). Organization des régulations inter et intrapersonnelles dans la transmission d’informations complexes organisées.Bulletin de Psychologie,44, (Whole No. 399), 110–120.Google Scholar
  4. Beaudichon, J., &Magnusson, M. S. (1999).Children’s problem solving and context: Role symmetry, task complexity and communication patterns. Manuscript submitted for publication.Google Scholar
  5. Bensalah, L. (1992).Effets de la relation amicale sur les comportements interactifs en situation dyadique de résolution de problemes. Unpublished doctoral thesis, Université Paris V-René Descartes, Sorbonne.Google Scholar
  6. Blackmore, S. (1999).The meme machine. New York: Oxford University Press.Google Scholar
  7. Blanchet, A., &Magnusson, M. S. (1988). Processus cognitifs et programmation discursive dans l’entretien de recherche.Psychologie Fran?aise,33, 91–98.Google Scholar
  8. Casagrande, C. (1995).Organisation des interactions sociales dyadiques de nourissons de 4–5 mois: Contribution à une nouvelle méthode d’étude. Unpublished doctoral thesis, Université de Franche-Comté, Besançon.Google Scholar
  9. Chomsky, N. (1959). Review of Skinner (1957).Language,35, 26–58.CrossRefGoogle Scholar
  10. Chomsky, N. (1965).Aspects of the theory of syntax. Cambridge, MA: M.I.T. Press.Google Scholar
  11. Colgan, P. W. (Ed.) (1978).Quantitative ethology. New York: Wiley.Google Scholar
  12. Cosnier, J. (1971).Clefs pour la psychologie. Paris: Seghers.Google Scholar
  13. Dawkins, R. (1976). Hierarchical organisation: A candidate principle for ethology. In P. P. Bateson & R. A. Hinde (Eds.),Growing points in ethology (pp. 7–54). Cambridge: Cambridge University Press.Google Scholar
  14. Dickins, D.,Kwint, M. A. C . G.,Magnusson, M. S.,Neads, C. M.,Noldus, L. P. J. J., &Quera, V. (in press). OBSERVE: A multimedia course on the observational analysis of behavior.Behavior Research Methods, Instruments, & Computers.Google Scholar
  15. Duncan, S. (1998, August).Analyzing individual differences in face-to- face interaction. Paper presented at 2nd International Conference on Methods and Techniques in Behavioral Research, Groningen, The Netherlands. Abstract published at http://www.noldus.comGoogle Scholar
  16. Duncan, S., &Fiske, D.W. (1977).Face-to-face interaction: Research, methods and theory. Hillsdale NJ: Erlbaum.Google Scholar
  17. Eibl-Eibesfeldt, I. (1970).Ethology: The biology of behavior. New York: Holt, Rinehart & Winston.Google Scholar
  18. Ekman, P., &Friesen, W. V. (1978).Facial action coding system: A technique for the measurement of facial movement. Palo Alto, CA: Consulting Psychologists Press.Google Scholar
  19. Feron, C. (1992).Les comportements socio-sexuels des souris staggerers males: Caracteristiques et effets de l’experience sociale. Unpublished doctoral thesis, Université de Paris XIII, Paris.Google Scholar
  20. Gardner, M. (1970, October). Mathematical games: The fantastic combinations of John Conway’s new solitaire game, Life.Scientific American, pp. 120–123.Google Scholar
  21. Grammer, K., Kruck, K. B., &Magnusson, M. S. (1998). The courtship dance: Patterns of nonverbal synchronization in opposite-sex encounters.Journal of Nonverbal Behavior,22, 3–29.CrossRefGoogle Scholar
  22. Hayes-Roth, F., Waterman, D. A., &Lenat, D. B. (Eds.) (1983).Building expert systems. London: Addison-Wesley.Google Scholar
  23. Holland, J. H. (1998).Emergence: From chaos to order. Reading, MA: Addison-Wesley.Google Scholar
  24. Jonsson, G. K. (1997). Self-esteem, friendship and verbal and nonverbal interaction [Abstract]. In A. Schmitt, K. Atzwanger, K. Grammer, & K. Schafer (Ed.),New aspects of human ethology (p. 206). New York: Plenum.Google Scholar
  25. Jonsson, G. K. (1998). Detecting patterns in complex behavioral processes with The Observer and Theme. Abstract in L. P. J. J. Noldus (Ed.),Proceedings of Measuring Behavior ’98: 2nd International Workshop on Methods and Techniques in Behavioral Research (p. 176). Wageningen: Noldus Information Technology. Abstract retrieved October 25, 1999 from the World-Wide Web: http://www.noldus.webaxxs.net/events/index.htmlGoogle Scholar
  26. Kelso, J. A. S. (1997).Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press.Google Scholar
  27. Köhler, W. (1947).Gestalt psychology: An introduction to new concepts in modern psychology. New York: Liveright.Google Scholar
  28. Lyon, M., Lyon, N., &Magnusson, M. S. (1994). The importance of temporal structure in analyzing schizophrenic behavior: Some theoretical and diagnostic implications.Schizophrenia Research,13, 45–56.CrossRefPubMedGoogle Scholar
  29. Lyon, M., &Magnusson, M. S. (1982). Central stimulant drugs and the learning of abnormal behavioral sequences. In M. Y. Spiegelstein & A. Levy (Ed.),Behavioral models and the analysis of drug action (pp. 135–153). Amsterdam: Elsevier.Google Scholar
  30. Magnusson, M. S. (1982, April).Temporal configuration analysis: Detection of an underlying meaningful structure through artificial categorization of a real-time behavioral stream. Paper presented at Workshop on Artificial Intelligence, University of Uppsala.Google Scholar
  31. Magnusson, M. S. (1983).Theme and syndrome: Two programs for behavior research. In D. Edwards & A. Høskuldsson (Eds.),Proceedings of symposium in applied statistics (pp. 17–42). Copenhagen: NEUCC, RECKU & RECAU.Google Scholar
  32. Magnusson, M. S. (1988). Le temps et les patterns syntaxiques du comportement humain: Modèle, méthode et programme Thème.Revue des Conditions de Travail, 284–314.Google Scholar
  33. Magnusson, M. S. (1989). Structure syntaxique et rythmes comportementaux: Sur la détection de rythmes cachés.Sciences et Techniques de l’Animal du Laboratoire,14, 143–147.Google Scholar
  34. Magnusson, M. S. (1996). Hidden real-time patterns in intra- and interindividual behavior: Description and detection.European Journal of Psychological Assessment,12, 112–123.CrossRefGoogle Scholar
  35. Magnusson, M. S. (1998, August).Real-time pattern detection versus standard sequential and time series analysis. Paper presented at Measuring Behavior ’98, 2nd International Conference on Methods and Techniques in Behavioral Research, Groningen, The Netherlands. Abstract retrieved October 25, 1999, from the World-Wide Web: http://www.noldus.webaxxs.net/events/index.htmlGoogle Scholar
  36. Magnusson, M. S., &Beaudichon, J. (1997). Détection de “marqueurs” dans la communication référentielle entre enfants. In J. Bernicot, J. Caron-Pargue, A. Trognon (Eds.),Conversation, interaction et fonctionnement cognitif (pp. 315–335). Nancy: Presse Universitaire de Nancy.Google Scholar
  37. Martaresche, M.,Le Fur, C.,Magnusson, M. S.,Faure, J. M., &Picard, M. (1999).Time patterns of feed pecking in chicks. Manuscript submitted for publication.Google Scholar
  38. McGrew, W. C. (1972).An ethological study of children’s behavior. London: Academic Press.Google Scholar
  39. Monge, P. R., &Cappella, J. N. (Eds.) (1980).Multivariate techniques in human communication research. New York: Academic Press.Google Scholar
  40. Montagner, H. (1978).L’enfant et la communication. Paris: Stock/Pernoud.Google Scholar
  41. Montagner, H., Magnusson, M. S., Casagrande, C., Restoin, A., Bel, J.-P., Hoang, P. N. M., Ruiz, V., Delcout, S., Gauffier, G., &Epoulet, B. (1990). Une nouvelle méthode pour l’étude des organisateurs de comportement et systemes d’interaction du jeune enfant.Psychiatrie de l’Enfant,33, 391–456.PubMedGoogle Scholar
  42. Noldus, L. P. J. J. (1991). The Observer: A software system for the collection and analysis of observational data.Behavior Research Methods, Instruments, & Computers,23, 415–429.Google Scholar
  43. Noldus, L. P. J. J., Trienes, R. J. H., Hendriksen, A. H. M., Jansen, H., &Jansen, R. G. (2000). The Observer Video-Pro: New software for the collection, management, and presentation of time-structured data from videotapes and digital media files.Behavior Research Methods, Instruments, & Computers,32, 197–206.CrossRefGoogle Scholar
  44. Pike, K. L. (1960).Language: In relation to a unified theory of the structure of human behavior. Glendale, CA: Summer Institute of Linguistics.Google Scholar
  45. Sackett, G. P. (Ed.) (1978).Observing behavior: Data collection and analysis methods (Vol. 2). Baltimore: University Park Press.Google Scholar
  46. Scherer, K. R., &Ekman, P. (Eds.) (1982).Handbook of methods in nonverbal behavior research. Cambridge: Cambridge University Press.Google Scholar
  47. Schwab, F. (1999, September).Smiling patterns in face to face interactions. Oral presentation at the 8th European Conference on Facial Expression: Measurement and Meaning, University of the Saarland, Saarbruecken, Germany. Abstract retrieved October 31, 1999 from the World-Wide Web: http://emotions.psychologie.uni-sb.de/facs/abstract.htmGoogle Scholar
  48. Sevre-Rousseau, S. (1999).Les competences sociales des enfants sourds-aveugles: Influences de l’interlocuteur et du contexte sur les échanges interpersonnels. Unpublished Doctoral thesis, Université Paris V-René Descartes, Sorbonne.Google Scholar
  49. Sigurdsson, T. (2000).La relation de tutelle entre parents et enfants handicappés mentaux de 4 à 6 ans. Lille: Presses Universitaires du Septentrion.Google Scholar
  50. Skinner, B. F. (1957).Verbal behavior. New York: Appleton-Century-Crofts.CrossRefGoogle Scholar
  51. Skinner, B. F. (1969).Contingencies of reinforcement: A theoretical analysis. New York: Appleton-Century-Crofts.Google Scholar
  52. Tardif, C. (1996).Contribution à l’étude des interactions observées dans des études de dyades adulte/enfant autiste. Unpublished doctoral thesis, Université Paris V-René Descartes, Sorbonne.Google Scholar
  53. Tinbergen, N. (1963). On the aims and methods of Ethology.Zeitschrift für Tierpsychologie,20, 410–433.CrossRefGoogle Scholar
  54. Todd, P. M., &Gigerenzer, G. (1999).Simple heuristics that make us smart. New York: Oxford University Press.Google Scholar
  55. Watt, J. A., &Vanlear, C. A. (Eds.) (1996).Dynamic patterns in communication processes. New York: Sage.Google Scholar

Copyright information

© Psychonomic Society, Inc 2000

Authors and Affiliations

  1. 1.Human Behavior LaboratoryUniversity of IcelandReykjavikIceland

Personalised recommendations