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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/11464

Title: Quantitative Linking Hypotheses for Infant Eye Movements
Authors: Yurovsky, Daniel
Hidaka, Shohei
Wu, Rachel
Keywords: Eye Movements
Bayesian Statistical Modeling
Issue Date: 2012-10-26
Publisher: PLOS
Magazine name: PLoS One
Volume: 7
Number: 10
Start page: e47419
DOI: 10.1371/journal.pone.0047419
Abstract: The study of cognitive development hinges, largely, on the analysis of infant looking. But analyses of eye gaze data require the adoption of linking hypotheses: assumptions about the relationship between observed eye movements and underlying cognitive processes. We develop a general framework for constructing, testing, and comparing these hypotheses, and thus for producing new insights into early cognitive development. We first introduce the general framework – applicable to any infant gaze experiment – and then demonstrate its utility by analyzing data from a set of experiments investigating the role of attentional cues in infant learning. The new analysis uncovers significantly more structure in these data, finding evidence of learning that was not found in standard analyses and showing an unexpected relationship between cue use and learning rate. Finally, we discuss general implications for the construction and testing of quantitative linking hypotheses. MATLAB code for sample linking hypotheses can be found on the first author's website.
Rights: Yurovsky D, Hidaka S, Wu R (2012) Quantitative Linking Hypotheses for Infant Eye Movements. PLoS ONE 7(10): e47419. doi:10.1371/journal.pone.0047419 (c) 2012 Yurovsky et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
URI: http://hdl.handle.net/10119/11464
Material Type: publisher
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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