The acquisition of English noun and verb morphology is modeled using a single-system connectionist network. The network is trained to produce the plurals and past tense forms of a large corpus of monosyllabic English nouns and verbs. The developmental trajectory of network performance is analyzed in detail and is shown to mimic a number of important features of the acquisition of English noun and verb morphology in young children. These include an initial error-free period of performance on both nouns and (...) verbs followed by a period of intermittent over-regularization of irregular nouns and verbs. Errors in the model show evidence of phonological conditioning and frequency effects. Furthermore, the network demonstrates a strong tendency to regularize denominal verbs and deverbal nouns and masters the principles of voicing assimilation. Despite their incorporation into a single-system network, nouns and verbs exhibit some important differences in their profiles of acquisition. Most importantly, noun inflections are acquired earlier than verb inflections. The simulations generate several empirical predictions that can be used to evaluate further the suitability of this type of cognitive architecture in the domain of inflectional morphology. (shrink)
What mechanism implements the mutual exclusivity bias to map novel labels to objects without names? Prominent theoretical accounts of mutual exclusivity (e.g., Markman, 1989, 1990) propose that infants are guided by their knowledge of object names. However, the mutual exclusivity constraint could be implemented via monitoring of object novelty (see Merriman, Marazita, & Jarvis, 1995). We sought to discriminate between these contrasting explanations across two preferential looking experiments with 22-month-olds. In Experiment 1, infants viewed three objects: one name-known, two name-unknown. (...) Of the two name-unknown objects, one was novel, and the other had been previously familiarized. The infants responded to hearing a novel label by increasing attention only to the novel, name-unknown object. In a second experiment in which the name-known object was absent, a novel label increased infants’ attention to a novel object beyond baseline preference for novelty. The experiments provide clear evidence for a novelty-based mechanism. However, differences in the time course of disambiguation across experiments suggest that novelty processing may be influenced by contextual factors. (shrink)
A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorization processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. We distinguish between two competing accounts: supervised name‐based categorization and unsupervised feature‐based categorization. We describe a neurocomputational model of infant visual categorization, based on self‐organizing maps, that implements the unsupervised feature‐based approach. The model successfully reproduces experiments demonstrating the impact of labeling on infant visual categorization reported in (...) Plunkett, Hu, and Cohen (2008). It mimics infant behavior in both the familiarization and testing phases of the procedure, using a training regime that involves only single presentations of each stimulus and using just 24 participant networks per experiment. The model predicts that the observed behavior in infants is due to a transient form of learning that might lead to the emergence of hierarchically organized categorical structure and that the impact of labels on categorization is influenced by the perceived similarity and the sequence in which the objects are presented. The results suggest that early in development, say before 12 months old, labels need not act as invitations to form categories nor highlight the commonalities between objects, but they may play a more mundane but nevertheless powerful role as additional features that are processed in the same fashion as other features that characterize objects and object categories. (shrink)
Comments on G. Marcus' criticisms (see record 1996-24670-001) of K. Plunkett's and V. Marcham's (see record 1994-35650-001) connectionist account of the acquisition of the English past tense (verb morphology). The original model is reviewed. Graphing, overregularization, and other criticisms are addressed (PsycINFO Database Record (c) 2000 APA, all rights reserved).
The nature–nurture controversy has been with us since it was first outlined by Plato and Aristotle. Nobody likes it anymore. All reasonable scholars today agree that genes and environment interact to determine complex cognitive outcomes. So why does the controversy persist? First, it persists because it has practical implications that cannot be postponed (i.e., what can we do to avoid bad outcomes and insure better ones?), a state of emergency that sometimes tempts scholars to stake out claims they cannot defend. (...) Second, the controversy persists because we lack a precise, testable theory of the process by which genes and the environment interact. In the absence of a better theory, innateness is often confused with (1) domain specificity (outcome X is so peculiar that it must be innate), (2) species specificity (we are the only species who do X, so X must lie in the human genome), (3) localization (outcome X is mediated by a particular part of the brain, so X must be innate), and (4) learnability (we cannot figure out how X could be learned, so X must be innate). We believe that an explicit, plausible theory of interaction is now around the corner, and that many of the classic maneuvers to defend or attack innateness will soon disappear. In the interim, some serious errors can be avoided if we keep these confounded issues apart. That is the major goal of this chapter: not to attack innateness but to clarify what claims about innateness are (and are not) about. (shrink)
Connectionist networks have been used to model a wide range of cognitivephenomena, including developmental, neuropsychological and normal adultbehaviours. They have offered radical alternatives to traditional accounts ofwell-established facts about cognition. The primary source of the success ofthese models is their sensitivity to statistical regularities in their trainingenvironment. This paper provides a brief description of the connectionisttoolbox and how this has developed over the past 2 decades, with particularreference to the problem of reading aloud.
We add to the constructivist approach of Quartz & Sejnowski (Q&S) by outlining a specific classification of sources of constraint on the emergence of representations from Elman et al. (1996). We suggest that it is important to consider behavioral constructivism in addition to neural constructivism.