cognitive modeling with hybrid symbolic/nn models

Here are a few models of psychological processes using hybrid models (involving reinforcement learning models): I. R. Sun and C. Terry, Implicit learning of serial reaction time tasks: Connectionist vs. symbolic models. Proceedings of the 24th Annual Conference of the Cognitive Science Society, Fairfax, VA. Lawrence Erlbaum Associates, Mahwah, NJ. 2002. Abstract: This paper describes simulations of implicit learning experiments. It compares simulations using connectionist models with existing simulations using symbolic models. It addresses an interesting issue raised by proponents of symbolic models, namely, the claim that implicit learning is better modeled by symbolic rule learning programs. This paper revisits such an issue by quantitatively comparing connectionist simulations with symbolic ones, in the context of the serial reaction time task of Lewicki et al (1987). This comparison is interesting because it helps to clarify, to some extent, some long standing confusions compounded by many claims and counter-claims. It also points to the idea of hybrid connectionist and symbolic models. II. R. Sun and X. Zhang, Top-down versus bottom-up learning in skill acquisition. Proceedings of the 24th Annual Conference of the Cognitive Science Society, Fairfax, VA. Lawrence Erlbaum Associates, Mahwah, NJ. 2002. Abstract: This paper studies the interaction between implicit and explicit processes in skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) vs. bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we highlight the interaction between the two types of processes, especially in terms of one type giving rise to another. The work presents an integrated model of skill learning that takes into account both implicit and explicit processes and both top-down and bottom-up learning. We examine and simulate human data in the Tower of Hanoi task. The paper shows how the quantitative data in this task may be captured using either top-down or bottom-up approaches, although top-down learning is a more apt explanation of the human data currently available. The results demonstrate the difference between the two different directions of learning (top-down vs. bottom-up), and also provide a new perspective on skill learning in the Tower of Hanoi task. III. An earlier paper: P. Slusarz and R. Sun, The interaction of explicit and implicit learning: An integrated model. Proceedings of the 23rd Cognitive Science Society Conference, Edinburgh, 2001. pp.952-957. Lawrence Erlbaum Associates, Mahwah, NJ. Abstract: This paper explicates the interaction between the implicit and explicit learning processes in skill acquisition, contrary to the common tendency in the literature of studying each type of learning in isolation. It highlights the interaction between the two types of processes and its various effects on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit processes; moreover, it embodies a bottom-up approach (first learning implicit knowledge and then explicit knowledge on its basis) towards skill learning. The paper shows that this approach accounts for various effects in the process control task data, in addition to accounting for other data reported elsewhere. IV. See also: R. Sun, E. Merrill, and T. Peterson, " From implicit skills to explicit knowledge: a bottom-up model of skill learning " Cognitive Science, Vol.25, No.2, pp.203-244. 2001. These papers are downloadable from my web page: http://www.cecs.missouri.edu/~rsun Cheers, ----Ron =========================================================================== Prof. Ron Sun http://www.cecs.missouri.edu/~rsun CECS Department phone: (573) 884-7662 University of Missouri-Columbia fax: (573) 882 8318 201 Engineering Building West Columbia, MO 65211-2060 email: rsun(a)cecs.missouri.edu http://www.cecs.missouri.edu/~rsun http://www.cecs.missouri.edu/~rsun/journal.html http://www.elsevier.com/locate/cogsys ===========================================================================