Pierre Perruchet

DR

LEAD
CNRS - Université Bourgogne Franche-Comté - UMR 5022
Pôle AAFE
11 Esplanade Erasme
21000 Dijon

Thèmes de recherche

Main research interests

The conventional cognitive framework rests on the existence of a powerful cognitive unconscious. Indeed, most psychological models heavily rely on the possibility of performing manipulations and transformations of unconscious representations using algorithms that are unable to operate while accommodating the functional constraints of conscious thought. However, a few researchers, from William James to Don Dulany (1991, 1997 ; see also Searle, 1992) have postulated that the only representations people create and manipulate are those which form the momentary phenomenal experience. Most of my research is aimed at exploring the power and the limits of this thought-provoking view.

A problem with this alternative view is that it leaves unexplained why the phenomenal experience of adult people consists of perceptions and representations of the world that are generally isomorphic with the world structure. Our proposal, with Annie Vinter, a Professor of developmental psychology in Dijon, is that this isomorphism is the end-product of a progressive organization that emerges thanks to elementary associative processes, which take the conscious representations themselves as the stuff on which they operate. We summarize this thesis in the concept of Self-Organizing Consciousness (SOC). The most comprehensive presentation of this thesis is in a BBS target paper dating back from 2002, but more "digestible" presentations have been published since then (for instance in Psychological Research, 2005, and in several book’s chapters).

We have provided evidence of self-organization in the context of an experimental situation that concerns the progressive extraction of words from an artificial language presented as an unsegmented speech flow. Our approach is supported by a computer-implemented model, PARSER, the details of which are presented in the Journal of Memory and Language, 1998. A remarkable feature of PARSER is that the only representations generated by the model closely match the conscious representations people may have when performing the task. Provided that one accepts a few simple assumptions about the properties of the world that are likely to capture subjects’ attention, the rationale underlying PARSER may be extended to the discovery of the relevant units which form natural language and the physical world, and also accounts for word-object mapping.

The same principles may be applied to more complex aspects of the world structure. The SOC framework can account for some forms of behavior seemingly based on the unconscious knowledge of the syntactical structure of the surrounding environment. This claim, which was originally stimulated by the literature on implicit learning of arbitrary structures, finds some echoes in the literature on language processing (notably in the so-called distributional approaches), problem solving (for instance in the computation/ representation trade-off proposed by Clark & Thornton, 1997), incubation (e.g. Mandler, 1994), decision making, and automatism (notably in the instance-based models, as proposed by Logan, e.g.:1988, and Tzelgov, e.g. : 1997). The SOC framework, in conjunction with simple additional hypotheses, also accounts for transfer between event patterns across surface features.

Our analysis opens to the surprising conclusion that there is no need for the concepts of unconscious representations and knowledge and, a fortiori, for the notion of unconscious inferences. Our dynamical framework is more parsimonious than the prevalent conceptions in cognitive and developmental sciences because it manages to account for very sophisticated adaptive functions while respecting (and even taking advantage of ) the constraints inherent to the conscious/attentional system, such as limited capacity, seriality of processing, and quick forgetting.

For an on-line introduction to this approach, click here.

For downloading PARSER, click here.

 

Publications

  • Update (2011). PUBLICATIONS SINCE 2010: see my new website. . Détails ›
  • Destrebecqz, A., Perruchet, P., Cleeremans, A., Laureys, S., Maquet, P., & Peigneux, P. (2010). The influence of temporal factors on automatic priming and conscious expectancy in a simple reaction time task. Quarterly Journal of Experimental Psychology, 63(2), 291-309. Détails ›
  • Perruchet, P., & Tillmann, B. (2010). Exploiting multiple sources of information in learning an artificial language: Human data and modeling. Cognitive Science, 34(2), 255-285. Détails ›
  • Pitel, A. L., Perruchet, P., Vabret, F., Desgranges, B., Eustache, F., & Beaunieux, H. (2010). The advantage of errorless learning for the acquisition of new concepts' labels in alcoholics. Psychological Medicine, 40(3), 497-502. Détails ›
  • Desmet, C., Poulin-Charronnat, B., Lalitte, P., & Perruchet, P. (2009). Implicit learning of nonlocal musical rules: A comment on Kuhn and Dienes (2005). Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(1), 299-305. Détails ›
  • French, R. M., & Perruchet, P. (2009). Generating constrained randomized sequences: Item frequency matters. Behavior Research Methods, 41(4), 1233-1241. Détails ›
  • Rey, A., Goldstein, R. M., & Perruchet, P. (2009). Does unconscious thought improve complex decision making?. Psychological Research, 73(3), 372-379. Détails ›
  • Chambaron, S., Ginhac, D., & Perruchet, P. (2008). Variations méthodologiques dans une tâche de Temps de Réaction Sériel : Quel est l'impact sur l'apprentissage ?. L'Année Psychologique, 108(3), 465-486. Détails ›
  • Chambaron, S., Ginhac, D., & Perruchet, P. (2008). gSRT-Soft: A generic software application and some methodological guidelines to investigate implicit learning through visual-motor sequential tasks. Behavior Research Methods, 40(2), 493-502. Détails ›
  • Pacton, S., & Perruchet, P. (2008). An attention-based associative account of adjacent and nonadjacent dependency learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(1), 80-96. Détails ›

Voir les 110 publications ›

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