C E N T E R F O R R E S E a R C H I N L a N G U a G E

A 3-layer back propagation network is used to implement a pattern association task which learns mappings that are analogous to the present and past tense forms of English verbs, i.e., arbitrary, identity, vowel change, and suffixation mappings. The degree of correspondence between connectionist models of tasks of this type (Rumelhart & McClelland, 1986; 1987) and children’s acquisition of inflectional morphology has recently been highlighted in discussions of the general applicability of PDP to the study of human cognition and language (Pinker & Mehler, 1988). In this paper, we attempt to eliminate many of the shortcomings of the R&M work and adopt an empirical, comparative approach to the analysis of learning (i.e., hit rate and error type) in these networks. In all of our simulations, the network is given a constant ’diet’ of input stems -that is, discontinuities are not introduced into the learning set at any point. Four sets of simulations are described in which input conditions (class size and token frequency) and the presence/absence of phonological subregularities are manipulated. First, baseline simulations chart the initial computational constraints of the system and reveal complex "competition effects" when the four verb classes must be learned simultaneously. Next, we explore the nature of these competitions given different type (class sizes) and token frequencies (# of repetitions). Several hypotheses about input to children are tested, from dictionary counts and production corpora. Results suggest that relative class size determines which "default" transformation is employed by the network, as well as the frequency of overgeneralization errors (both "pure" and "blended" overgeneralizations). A third series of simulations manipulates token frequency within a constant class size, searching for the set of token frequencies which results in "adult-like competence" and "child-like" errors across learning. A final series investigates the addition of phonological sub-regularities into the identity and vowel change classes. Phonological cues are clearly exploited by the system, leading to overall improved performance. However, overgeneralizations, U-shaped learning and competition effects continue to be observed in similar conditions. These models establish that input configuration plays a role in determining the types of errors produced by the network including the conditions under which "rulelike" behavior and "U-shaped" development will and will not emerge. The results are discussed with reference to behavioral data on children’s acquisition of the past tense and the validity of drawing conclusions about the acquisition of language from models of this sort. Please send requests for hard copy to: Center for Research in Language C-008 University of California, San Diego La Jolla, CA 92093 Attn. Yvonne or yvonne@ amos.ucsd.edu Virginia Marchman (marchman@ amos.ucsd.edu) Kim Plunkett (psykimp@ dkarh02.bitnet)