Implicit and explicit learning were originally distinguished in terms of accessibility to verbal report. We identify evidence for the proposal that the implicit/explicit contrast corresponds to a divide between connectionist and symbolic representations. We show that explicit learning shows marked improvement between 4 and 8 years of age. This finding contrasts against very early implicit learning abilities, and concurs with other evidence on the progressive development of symbolic reasoning abilities.

In a guessing game, Ss reconstruct a sequence by guessing each successive element of the sequence from a finite set of alternatives, receiving feedback after each guess. A n upper bound on Ss knowledge of the sequence is given by H, the estimated entropy of the numbers of guesses. The method provides a measure of learning independent of material type and distractors, and the resulting data set is very rich. Here, the method is apK plied to artificial grammar learning; Ss were exposed to strings from a finite state grammar and subsequently distinguished between strings that followed or violated the grammar reliably better than Ss who had not seen the learning strings (but who themselves performed at above chance levels). Ss knowledge of the strings, H, reflected both grammaticality and exposure to learning strings, and was correlated with overall judgement performance. For non-grammatical strings, the strings that Ss knew most about were those they found most difficult to c\assify correctly. These results support the hypothesis that fragment knowledge plays an important part in artificial grammar learning, and we suggest that the guessing game paradigm is a useful tool for studies of learning and memory in general.

Distributional information, in the form of simple, lo- cally computed statistics of an input corpus, provi- des a potential m e a n s of establishing initial syntac- tic categories (noun, verb, etc.). Finch and Chater (1991, 1992) clustered words hierarchically, accor- ding to the distribution of locad contexts in which they appeared in large, written English corpora, obtaining clusters that corresponded well with the standard syntactic categories. Here, a stronger de- monstration of their method is provided, using 'real' data, that to which children are exposed during ca- tegory acquisition, taken from the childes corpus. For 2-5 million words of aulult speech, clustering on syntsu:tic and semantic bases was observed, with a high degree of cleai differentiation between syntac- tic categories. For child data, s o m e noun and verb clusters emerged, with s o m e evidence of other ca- tegories, but the data set was too small for reliable trends to emerge. S o m e initial results investigating the possibility of classifying novel words using only the immediate context of a single instance are also presented. These results demonstrate that statisti- cal information m a y play an important role in the processes of early language 2u:quisition.

Artificial grammar leeiming is noted for the claim that subjects are unaware of their knowledge. Chan (1992) and Dienes et al. (in press) have demonstrated that subjects are unawcire in the sense that they lack metaknowledge. Dissociations between subjects' performance and their confidence in their decisions suggest that the learning mechanism may be in some sense encapsulated from the "confidence system". Here we tested the alternative hypothesis that the confidence system is initially poorly calibrated, or does not know which aspects of the learning mechanism to attend to, by training and testing subjects over four weekly sessions. On all four weeks we found a strong, near-perfect association between confidence and performance for trained subjects, but a dissociation for untrained control subjects. We discuss possible explanations for these results, and previously observed dissociations.

In a major theoretical paper, Gigerenzer and Goldstein (1996a) argue that classical rationality should be rejected as a norm of good reasoning, and that this thesis undermines both rational models of human thought and the alternative heuristics-and-biases program. They illustrate their argument by proposing that a specific cognitive estimation problem may be carried out by the "Take the Best" algorithm, which is "fast and frugal," but not rational. We argue: (1) that "fast and frugal" cognitive algorithms may approximate rational norms, and only in this way can their success be explained; and (2) that new computer simulations, and considerations of speed and generality, suggest that other algorithms are at least as psychologically plausible as Take the Best.