recent papers on cognitive and language development

[Thomas R. Shultz]

Recent papers on cognitive and language development that may be of interest 
to readers of this list

Shultz, T. R., & Bale, A. C. (2001, in press). Neural network simulation of 
infant familiarization to artificial sentences: Rule-like behavior without 
explicit rules and variables. Infancy.

A fundamental issue in cognitive science is whether human cognitive 
processing is better explained by symbolic rules or by sub-symbolic neural 
networks. A recent study of infant familiarization to sentences in an 
artificial language claims to have produced data that can only be explained 
by symbolic rule learning and not by unstructured neural networks. Here we 
present successful unstructured neural network simulations of the infant 
data, showing that these data do not uniquely support a rule-based account. 
In contrast to other simulations of these data, the present simulations 
cover more aspects of the data with fewer assumptions about prior knowledge 
and training, using a more realistic coding scheme based on sonority of 
phonemes. The networks show exponential decreases in attention to a 
repeated sentence pattern, more recovery to novel sentences inconsistent 
with the familiar pattern than to novel sentences consistent with the 
familiar pattern, occasional familiarity preferences, more recovery to 
consistent novel sentences than to familiarized sentences, and 
extrapolative generalization outside the range of the training patterns. A 
variety of predictions suggest the utility of the model in guiding future 
psychological work. The evidence, from these and other simulations, 
supports the view that unstructured neural networks can account for the 
existing infant data.

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Sirois, S., Buckingham, D., & Shultz, T. R. (2000). Artificial grammar 
learning by infants: An auto-associator perspective. Developmental Science, 
4, 442-456.

This paper reviews a recent article suggesting infants use a system of 
algebraic rules to learn an artificial grammar (Marcus, Vijayan, Bandi Rao, 
& Vishton, 1999). In three reported experiments, infants exhibited 
increased responding to auditory strings that violated the pattern of 
elements they were habituated to. We argue that a perceptual interpretation 
is more parsimonious, as well as more consistent with a broad array of 
habituation data. We report successful neural network simulations that 
implement a lower-level interpretation and capture the empirical 
regularities reported by Marcus and colleagues (1999). The discussion puts 
the simulation results in the context of the broader debate about 
interpreting infant habituation. Other neural network models of habituation 
in general, and of the Marcus et al. (1999) task specifically, are discussed.

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Buckingham, D., & Shultz, T. R. (2000). The developmental course of 
distance, time, and velocity concepts: A generative connectionist model. 
Journal of Cognition and Development, 1, 305-345.

Connectionist simulations of children's acquisition of distance (d), time 
(t), and velocity (v) concepts using a generative algorithm, 
cascade-correlation, are reported. Rules that correlated most highly with 
network responses during training were consistent with the developmental 
course of children's concepts. Networks integrated the defining dimensions 
of the concepts first by identity rules (e.g., v = d), then additive rules 
(e.g., v = d  t), and finally multiplicative rules (e.g., v = d / t). The 
results are discussed in terms of similarity to children's development, the 
contribution of connectionism to the study of cognitive development, 
contrasts with alternate models, and directions for future research.

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Oshima-Takane, Y., Takane, Y., & Shultz, T. R. (1999). The learning of 
first and second pronouns in English: Network models and analysis. Journal 
of Child Language, 26, 545-575.

Although most English-speaking children master the correct use of first and 
second person pronouns by three years, some children show persistent 
reversal errors in which they refer to themselves as you and to others as 
me. Recently, such differences have been attributed to the relative 
availability of overheard speech during the learning process. The present 
study tested this proposal with feed-forward neural networks learning these 
pronouns. Network learning speed and analysis of their knowledge 
representations confirmed the importance of exposure to shifting reference 
provided by overheard speech. Errorless pronoun learning was linked to the 
amount of overheard speech, interactions with a greater number of speakers, 
and prior knowledge of the basic-level kind PERSON.

Preprints and reprints can be found at 
http://www.psych.mcgill.ca/perpg/fac/shultz/default.htm

Cheers,
Tom

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Thomas R. Shultz, Professor, Department of Psychology,
McGill University, 1205 Penfield Ave., Montreal, Quebec,
Canada H3A 1B1.
E-mail: shultz at psych.mcgill.ca
Updated 23 March 2001: http://www.psych.mcgill.ca/perpg/fac/shultz/default.htm
Phone: 514 398-6139
Fax: 514 398-4896
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