Evidence of error-driven cross-situational word learning.
Abstract
One powerful way children can learn word meanings is via cross-situational learning, the ability to discern consistent word-referent mappings from a series of ambiguous scenes and utterances. Various computational accounts of word learning have been proposed, with mechanisms ranging from storing and testing a single hypothesized referent for each word, to tracking multiple graded associations and selectively strengthening some of them. Nearly all word learning models assume storage of some feasible word-referent mappings from each situation, resulting in a degree of learning proportional to the number of co-occurrences. While these accumulative models would generally predict that incorrect co-occurrences would slow learning, recent empirical work suggests these accounts are incomplete: paradoxically, giving learners incorrect mappings early in training was found to boost performance (Fitneva & Christiansen, 2015). We test this finding's generality in a new experiment with more items, consider system - and item-level explanations, and find that a model with error-driven learning best accounts for this benefit of initially-inaccurate pairings.
Keywords
Bibtex entry:
@inproceedings{grimmick2019evidence,
abstract = {One powerful way children can learn word meanings is via cross-situational learning, the ability to discern consistent word-referent mappings from a series of ambiguous scenes and utterances. Various computational accounts of word learning have been proposed, with mechanisms ranging from storing and testing a single hypothesized referent for each word, to tracking multiple graded associations and selectively strengthening some of them. Nearly all word learning models assume storage of some feasible word-referent mappings from each situation, resulting in a degree of learning proportional to the number of co-occurrences. While these accumulative models would generally predict that incorrect co-occurrences would slow learning, recent empirical work suggests these accounts are incomplete: paradoxically, giving learners incorrect mappings early in training was found to boost performance (Fitneva & Christiansen, 2015). We test this finding's generality in a new experiment with more items, consider system - and item-level explanations, and find that a model with error-driven learning best accounts for this benefit of initially-inaccurate pairings.},
address = {Austin, TX},
author = {Grimmick, C. and Gureckis, T.M. and Kachergis, G.},
booktitle = {Proceedings of the 41st Annual Conference of the Cognitive Science Society},
editor = {Goel, A.K. and Seifert, C.M. and Freksa, C.},
pages = {373--379},
publisher = {Cognitive Science Society},
title = {Evidence of error-driven cross-situational word learning.},
year = {2019}}QR Code:
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