Hostname: page-component-76fb5796d-wq484 Total loading time: 0 Render date: 2024-04-25T20:34:22.695Z Has data issue: false hasContentIssue false
  • English
  • Français

The role of prediction in construction-learning

Published online by Cambridge University Press:  14 June 2005

ADELE E. GOLDBERG ,
DEVIN M. CASENHISER  and
NITYA SETHURAMAN
ADELE E. GOLDBERG
Affiliation:
Princeton University
DEVIN M. CASENHISER
Affiliation:
Princeton University
NITYA SETHURAMAN
Affiliation:
Indiana University
Get access Rights & Permissions [Opens in a new window]

Abstract

It is well-established that (non-linguistic) categorization is driven by a functional demand of prediction. We suggest that prediction likewise may well play a role in motivating the learning of semantic generalizations about argument structure constructions. We report corpora statistics that indicate that the argument frame or construction has roughly equivalent cue validity as a predictor of overall sentence meaning as the morphological form of the verb, and has greater category validity. That is, the construction is at least as reliable and more available than the verb. Moreover, given the fact that many verbs have quite low cue validity in isolation, attention to the contribution of the construction is essential.

Type
Note
Information
Journal of Child Language , Volume 32 , Issue 2 , May 2005 , pp. 407 - 426
Copyright
© 2005 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The authors are grateful for helpful discussions with Giulia Bencini, Cindy Fisher, Lynn Gale, Gregory Murphy, Mike Tomasello, Yasuhiro Shirai, and to two anonymous reviewers for helpful comments on an earlier draft. We are also grateful to Liz Bates, for being a wonderful teacher, mentor and friend. This research was funded in part by the first author's NSF Grant SBR-9873450 and fellowship at the Center for Advanced Study in the Behavioral Sciences.