Many theorists have dismissed a priori the idea that distributional information could play a significant role in syntactic category acquisition. We demonstrate empirically that such information provides a powerful cue to syntactic category membership, which can be exploited by a variety of simple, psychologically plausible mechanisms. We present a range of results using a large corpus of child-directed speech and explore their psychological implications. While our results show that a considerable amount of information concerning the syntac-tic categories can be obtained from distributional information alone, we stress that many other sources of information may also be potential contributors to the identification of syntactic classes. I.

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects.

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. The role of implicit learning in skill acquisition and the distinction between implicit and explicit learning have been widely recognized in recent years (see, e.g., Cleeremans, Destrebecqz, &

In everyday tasks, selecting actions in the proper sequence requires a continuously updated representation of temporal context. Many existing models address this problem by positing a hierarchy of processing units, mirroring the roughly hierarchical structure of naturalistic tasks themselves. Such an approach has led to a number of difficulties, including a reliance on overly rigid sequencing mechanisms, an inability to account for context sensitivity in behavior, and a failure to address learning. We consider here an alternative framework, according to which the representation of temporal context is facilitated by recurrent connections within a network mapping from environmental inputs to actions. Applying this approach to a specific, and in many ways prototypical, everyday task (coffee-making), we examine its ability to account for several central characteristics of normal and impaired human performance. The model we consider learns to deal flexibly with a complex set of sequencing constraints, encoding contextual information at multiple time-scales within a single, distributed internal representation. Mildly degrading this context representation leads

this article, we have presented evidence that a computational model that instantiates approximate, local learning with graded transfer provides a good account of how subjects learn on-line from outcome feedback in the SPF, a simple dynamic task. We base this conclusion on the model's ability to predict subjects' performance during training and on two subsequent tests of their ability to generalize, the control questions and the transfer task. We now explore the limitations of our efforts and discuss two alternative approaches to understanding human performance before concluding on our own approach 's merits

Children's (Grades 1 to 5) implicit learning of French orthographic regularities were investigated through nonword judgment (Experiments 1 and 2) and completion (Experiments 3a and 3b) tasks. Children were increasingly sensitive to (a) the frequency of double consonants (Experiments 1, 2 and 3a), (b) the fact that vowels can never be doubled (Experiment 2) and (c) the legal position of double consonants (Experiments 2 and 3b). The later effect transferred to never doubled consonants, although with a decrement in performance. Moreover, this decrement persisted without any trend towards fading even after the massive amounts of experience provided by years of practice. This result runs against the idea that transfer to novel material is indicative of abstract rule-based knowledge, and suggests instead the action of mechanisms sensitive to the statistical properties of the material. A connectionist model is proposed as an instantiation of such mechanisms.

The emphasis in the connectionist sentence-processing literature on distributed representation and emergence of grammar from such systems seems to have prevented connectionists and symbolists alike from recognizing the often close relations between their respective systems. This paper argues that simply recurrent network (SRN) models proposed by Jordan (1990) and Elman (1990) are more directly related to stochastic Part-of-Speech (POS) Taggers than to parsers or grammars as such, while recursive auto-associative memory (RAAM) of the kind pioneered by Pollack and incorporated in many hybrid connectionist parsers since may be useful for grammar induction from a network-based conceptual structure as well as for structure-building. These observations suggest some interesting new directions for connectionist sentence processing research, including more efficient devices for representing finite state machines, and acquisition devices based on a distinctively connectionist grounded conceptual...

Implicit learning is generally characterized as learning that proceeds both unintentionally and unconsciously. Here are some examples: 1 Reber (1967), who coined the term ‘implicit learning’, asked participants to study a series of letter strings such as VXVS for a few seconds each. Then he told them that these strings were all constructed according to a particular set of rules (that is, a grammar; see Figure 8.1) and that in the test phase they would see some new strings and would have to decide which ones conformed to the same rules and which ones did not. Participants could make these decisions with better-than-chance accuracy but had little ability to describe the rules. For example, participants could not recall correctly which letters began and ended the strings. Reber described his results as a ‘peculiar combination of highly efficient behavior with complex stimuli and almost complete lack of verbalizable knowledge about them ’ (p. 859). 2 In the 1950s, a number of studies asked people to generate words ad libitum and established that the probability with which they would produce, say, plural nouns was increased if each such word was reinforced by the experimenter saying ‘umhmm ’ (e.g. Greenspoon, 1955). This result occurred in subjects apparently unable to report the reinforcement contingency. 3 Svartdal (1991) presented participants with brief trains of between 4 and 17 auditory clicks. Participants immediately had to press a response button exactly the same number of times and were instructed that feedback would be presented when the number of presses matched the number of clicks. In fact, though, feedback was contingent on speed of responding: for some

Representations of task context play a crucial role in shaping human behavior. While the nature of these representations remains poorly understood, existing theories share a number of basic assumptions. One of these is that task representations are discrete, independent, and non-overlapping. We present here an alternative view, according to which task representations are instead viewed as graded, distributed patterns occupying a shared, continuous representational space. In recent work, we have implemented this view in a computational model of routine sequential action. In the present article, we focus specifically on this model's implications for understanding task representation, considering the implications of the account for two influential concepts: (1) cognitive underspecification, the idea that task representations may be imprecise or vague, especially in contexts where errors occur, and (2) information-sharing, the idea that closely related operations rely on common sets of internal representations.

Many attempts have been made to capture the very nature of knowledge in different fields: philosophy, cognitive science, artificial intelligence, etc. There is now a renewal of the studies on context and several proposals to represent and implement the context in "intelligent" systems. Up to now, there were, as far as we know, few attempts to compare the two concepts of context and knowledge, while they obviously share some common aspects. In this paper, we review the main characteristics of both concepts and, while we note a large overlapping of the two concepts, we also emphasize their differences as regards decision making and action.