TY - GEN
T1 - Evidence of error-driven cross-situational word learning
AU - Grimmick, Chris
AU - Gureckis, Todd
AU - Kachergis, George
N1 - Publisher Copyright:
© Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019.All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - cross-situational word learning
KW - error-driven associative learning model
KW - word learning
UR - http://www.scopus.com/inward/record.url?scp=85120067930&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120067930&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85120067930
T3 - Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
SP - 373
EP - 379
BT - Proceedings of the 41st Annual Meeting of the Cognitive Science Society
PB - The Cognitive Science Society
T2 - 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
Y2 - 24 July 2019 through 27 July 2019
ER -