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Learning Object Names at Different Hierarchical Levels Using Cross‐Situational Statistics
Author(s) -
Chen Chihsin,
Zhang Yayun,
Yu Chen
Publication year - 2018
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12516
Subject(s) - object (grammar) , generalization , situational ethics , artificial intelligence , psychology , computer science , natural language processing , statistics , cognitive psychology , mathematics , social psychology , mathematical analysis
Objects in the world usually have names at different hierarchical levels (e.g., beagle , dog , animal ). This research investigates adults' ability to use cross‐situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use co‐occurrence information to learn hierarchical labels in contexts where the labels for individual objects and labels for categories were presented in completely separated blocks, in interleaved blocks, or mixed in the same trial. Temporal presentation schedules significantly affected the learning of individual object labels, but not the learning of category labels. Learners' subsequent generalization of category labels indicated sensitivity to the structure of statistical input.

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