As linguistic competence so clearly illustrates, processing sequences of events is a fundamental aspect of human cognition. For this reason perhaps, sequence learning behavior currently attracts considerable attention in both cognitive psychology and computational theory. In typical sequence learning situations, participants are asked to react to each element of sequentially structured visual sequences of events. An important issue in this context is to determine whether essentially associative processes are sufficient to understand human performance, or whether more powerful learning mechanisms are necessary. To address this issue, we explore how well human participants and connectionist models are capable of learning sequential material that involves complex, disjoint, longdistance contingencies. We show that the popular Simple Recurrent Network model (Elman, 1990), which has otherwise been shown to account for a variety of empirical findings (Cleeremans, 1993), fails to account for ...

this paper, to win is to capture stones as mentioned above. CHAPTER 3. SYSTEM 18 turn and capture one's stones, so one has no time to take opponent's stones. Although only Rule f1 is treated as an exception in this thesis, it is not enough. In many cases the order of applying rules should be considered. Details are in Section (Discussion). 3.5 Rule acquisition

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g. it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. SUSTAIN has expanded the scope of findings that models of human category learning can address. This paper extends SUSTAIN to account for both supervised and unsupervised learning data through a common mechanism. The modified model, uSUSTAIN (unified SUSTAIN), is successfully applied to human learning data that compares unsupervised and supervised learning performances. 18

“The most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka! ' (I found it!) but 'That's funny... ' “ Isaac Asimov (Science fiction novelist & scholar: 1920- 1992) As the quote above illustrates, there are facets of the scientific process that are prompted by feelings of intuition. Although this notion may seem closer to stereotypes of art than science, the notion that scientists can rely upon knowledge that is generated without their awareness has an empirical foundation in the literature on implicit learning (e.g. Reber, 1989; Lewicki, Czyzewska, & Hoffman, 1987; Mathews, et al., 1989). So, although the subjective experience of intuition may seem “magical,” researchers have begun to characterize the characteristics of such knowledge. One major theme of this chapter will be that implicit knowledge (knowledge gained directly from experience) and explicit knowledge (e.g. the explicit facts that are typically acquired in science instruction) may be fruitfully combined (and clearly are in mature scientists). To use the example above, the intuition that alerts one to the detection of an anomaly (e.g., “That’s funny”) is likely to spur a more explicit reasoning process that involves trying to locate the source of the anomaly and understand its implications. Most educational settings focus on teaching conceptual (explicit) knowledge rather than setting