This paper examines some characteristics of the learning process in a model of skill learning(Miyata, 1987) in which performance of executing sequential actions becomes increasingly more efficient as a skill is practiced. The model is a hierarchy of sequential PDP networks which was designed to model a shift from a slow, serial performance of a novice to a fast,parallel performance of an expert in tasks such as typing. The network develops representation of a set of sequences as it tries to produce the sequences faster. The model was found to yield the power law of learning (Newell and Rosenbloom, 1981). In addition, it exhibited a frequency effect on substitution errors similar to what was found in typing(Grudin, 1983).

This paper presents a connectionist model of motor learning in which performance becomes more and more efficient by "chunking" output sequences, organizing small action components into increasingly large structures. The model consists cf two sequential networks: one that maps a stationary representation of an intention to a sequence of action specifications or action plans, and one that maps an action plan to a sequence of action components. As the network is trained to produce output sequences faster and faster, the units that represent the action plans gradually discover representational formats that can encode larger and larger chunks of subsequences.The model also shows digraph frequency effects similar to that observed in typewriting, and it generates capture errors similar to that observed in human actions.Organization of

We have developed principles integrating connectionist and symbolic computation by establishing mathematical relationships between two levels of description of a single computational system: at the lower level, the system is formally described in terms of highly distributed patterns of activity over connectionist units, and the dynamics of these units; at the higher level, the same system is formally described by symbolic structures and symbol manipulation. In this pa- per, w e propose a specific treatment of recur- sion where complex symbolic operations on re- cursive structures are mapped to massively par- allel manipulation of distributed representations in a connectionist network.

We describe harmonic grammar, a connectionist-based approach to formal theories of linguistic well-formedness. The general approach can be applied to various kinds of linguistic well-formedness, e.g., phonological and syntactic. Here, we address a syntactic problem: unaccusativity. Harmonic grammar is a two-lcvcl theory, involving a distributed, lower level connectionist network whose relevant aggregate computational behavior is described by a local, higher level network. The central hypothesis is that the connectionist well-formedness measure called "harmony"^ can be used to model linguistic well-formedness; what is crucial about the relation between the lower and higher level networks is that there is a harmony-preserving mapping between them: they are isoharmonic (at least approximately). A companion paper (Legendre, Miyata, & Smolensky, 1990; henceforth "LMS2") describes the theoretical basis for the two level approach, starting from general connectionist principles. In this paper, we discuss the problem of unaccusativity, give a high level characterization of harmonic syntax, and present a higher level network to account for unaccusativity data in French. W e interpret this network as a fragment of the grammar and lexicon of French expressed in "soft rules." Of the 760 sentence types represented in our data, the network correctly predicts the acceptability in all but two cases. This coverage of real, problematic syntactic data greatly exceeds that of any other formal account of unaccusativity of which we are aware.

In this paper, we derive the formalism of harmonic grammar, a connectionist-based theory of linguistic well formedness. Harmonic grammar is a two-level theory, involving a low level connectionist network using a particular kind of distributed representation, and a second, higher level network that uses local representations and which approximately and incompletely describes the aggregate computational behavior of the lower level network. The central hypothesis is that the connectionist well-formedness measure Harmon)^ can be used to model linguistic well-formedness; what is crucial about the relation between the lower and higher level networks is that there is a harmony-preserving mapping between them: they are isoharmonic (at least approximately). In a companion paper (Legendre, Miyata, & Smolensky, 1990; henceforth, "LMSi"), we apply harmonic grammar to a syntactic problem, unaccusativity, and show that the resulting network is capable of a degree of coverage of difficult data that is unparallelled by symbolic approaches of which we are aware: of the 760 sentence types represented in our data, the network correctly predicts the acceptability in all but two cases. In the present paper, we describe the theoretical basis for the two level approach, illustrating the general theory through the derivation from first principles of the unaccusativity network of LMSj.