Paper announcements

[Dr. Morten H. Christiansen]

The following two papers may be of interest to the readers of this list. 
Both papers involve connectionist modeling of psycholinguistic data.


Christiansen, M.H. & Chater, N. (1999).  Toward a connectionist model
of recursion in human linguistic performance.  Cognitive Science, 23,
157-205.

			   Abstract

Naturally occurring speech contains only a limited amount of complex
recursive structure, and this is reflected in the empirically documented
difficulties that people experience when processing such structures. We
present a connectionist model of human performance in processing recursive
language structures. The model is trained on simple artificial languages.
We find that the qualitative performance profile of the model matches
human behavior, both on the relative difficulty of center-embedded and
cross-dependency, and between the processing of these complex recursive
structures and right-branching recursive constructions. We analyze how
these differences in performance are reflected in the internal
representations of the model by performing discriminant analyses on these
representation both before and after training. Furthermore, we show how a
network trained to process recursive structures can also generate such
structures in a probabilistic fashion. This work suggests a novel
explanation of people's limited recursive performance, without assuming
the existence of a mentally represented competence grammar allowing
unbounded recursion. 


The paper was published in the current issue of Cognitive Science. A 
preprint version can be downloaded from:

	http://www-rcf.usc.edu/~mortenc/nn-rec.html

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Christiansen, M.H. & Curtin, S.L. (1999). The power of statistical
learning: No need for algebraic rules. In The Proceedings of the 21st
Annual Conference of the Cognitive Science Society (pp. 114-119). Mahwah,
NJ: Lawrence Erlbaum Associates. 

			   Abstract

Traditionally, it has been assumed that rules are necessary to explain
language acquisition. Recently, Marcus, Vijayan, Rao & Vishton (1999) have
provided behavioral evidence which they claim can only be explained by
invoking algebraic rules. In the first part of this paper, we show that
contrary to these claims an existing simple recurrent network model of
word segmentation can fit the relevant data without invoking any rules. 
Importantly, the model closely replicates the experimental conditions, and
no changes are made to the model to accommodate the data. The second part
provides a corpus analysis inspired by this model, demonstrating that
lexical stress changes the basic representational landscape over which
statistical learning takes place. This change makes the task of word
segmentation easier for statistical learning models, and further obviates
the need for lexical stress rules to explain the bias towards trochaic
stress patterns in English. Together the connectionist simulations and the
corpus analysis show that statistical learning devices are sufficiently
powerful to eliminate the need for rules in an important part of language
acquisition. 

The paper was published in the most recent Cognitive Science Society 
proceedings. An HTML version of the paper can be viewed at:

	http://www.siu.edu/~psycho/faculty/morten/statlearn.html

And a hardcopy can be downloaded from:

	http://www-rcf.usc.edu/~mortenc/no-rules.html


Best regards,
		Morten Christiansen

PS: Apologies if you receive two copies of this message.

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Morten H. Christiansen
Assistant Professor	       	   Phone: +1 (618) 453-3547
Department of Psychology      	   Fax:   +1 (618) 453-3563
Southern Illinois University	   Email: morten at siu.edu
Carbondale, IL 62901-6502   	   Office: Life Sciences II, Room 271A
URL: http://www.siu.edu/~psycho/faculty/mhc.html
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