Award Abstract #0741666
CAREER: Automaton Theories of Human Sentence Comprehension

NSF Org:	BCS Division of Behavioral and Cognitive Sciences
Initial Amendment Date:	September 18, 2008
Latest Amendment Date:	June 1, 2009
Award Number:	0741666
Award Instrument:	Continuing grant
Program Manager:	Eric H. Potsdam BCS Division of Behavioral and Cognitive Sciences SBE Directorate for Social, Behavioral & Economic Sciences
Start Date:	September 15, 2008
Expires:	August 31, 2010 (Estimated)
Awarded Amount to Date:	$201140
Investigator(s):	John Hale jthale@cornell.edu (Principal Investigator)
Sponsor:	Cornell University 373 Pine Tree Road ITHACA, NY 14850 607/255-5014
NSF Program(s):	LINGUISTICS
Field Application(s):
Program Reference Code(s):	OTHR, 1311, 1187, 1045, 0000
Program Element Code(s):	1311

ABSTRACT

The mental process humans use to comprehend a sentence, like this one,

can be simulated by automata. These automata are specific kinds of computer programs that, when run, mimic the steps cognitive scientists think occur between the point at which people read or hear individual words and when they know what whole sentences mean.

In this project, the research team is constructing automata in which three factors, usually studied singly, are combined. The first factor is the linguistic grammar, which characterizes what people know when they know a language. The second is the control strategy, which determines the particular order in which comprehenders deploy this knowledge in time. The third factor is the theory of memory for words and phrases. The researchers expect these triply-endowed automata to match human performance in a variety of sentence types. In addition, since automata can be mathematically altered to take into account alternative grammars, memories and control strategies, they can be used to gain insights that would not come easily from behavioral experiments with real people. For example, behavioral experiments with elderly adults might show the effects of reduced memory capacity on the sentence comprehension process. However, using automata allows the researchers to selectively alter not only memory but also control and grammar, revealing the role of each in ways that human experiments could not. Similarly, mathematically rendering the automaton's grammar more Spanish-like (as opposed to English-like), for example, could also yield a deeper understanding of how the three factors interact and, more broadly, how people understand one another.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Marisa Ferrara Boston and John Hale and Reinhold Kliegl and Umesh Patil and Shravan Vasishth. "Parsing costs as predictors of reading difficulty: An evaluation using the Potsdam Sentence Corpus," Journal of Eye Movement Research, v.2, 2008, p. 1.

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