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Decoding Eye Movements in Cross-Situ...

Amatuni, Andrei.

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Decoding Eye Movements in Cross-Situational Word Learning via Tensor Component Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Decoding Eye Movements in Cross-Situational Word Learning via Tensor Component Analysis./
Author:	Amatuni, Andrei.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	32 p.
Notes:	Source: Masters Abstracts International, Volume: 82-12.
Contained By:	Masters Abstracts International82-12.
Subject:	Developmental psychology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28490365
ISBN:	9798516001024

Decoding Eye Movements in Cross-Situational Word Learning via Tensor Component Analysis.
Amatuni, Andrei.

Decoding Eye Movements in Cross-Situational Word Learning via Tensor Component Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 32 p.

Source: Masters Abstracts International, Volume: 82-12.

Thesis (M.A.)--Indiana University, 2021.

This item must not be sold to any third party vendors.

Statistical learning is an active process wherein information is actively selected from the learning environment. As current information is integrated with existing knowledge, it shapes attention in subsequent learning, placing biases on which new information will be sampled. One statistical learning task that has been studied recently is cross-situational word learning (CSL). In CSL, statistical learners are able to learn the correct mappings between novel visual objects and spoken labels after watching sequences where the two are paired together in referentially ambiguous contexts. In the present paper, we use a computational method called Tensor Component Analysis (TCA) to analyze real-time gaze data collected from a set of CSL studies. We applied TCA to learners' gaze data in order to derive latent variables related to real-time statistical learning and to examine how selective attention is organized in time. Our method allows us to address two specific questions: a) the similarity in attention behavior across strong vs. weak learners as well as across learned vs. not-learned items and b) how the structure of attention relates to word learning. We measured learners' knowledge of label-object pairs at the end of a training session, and demonstrate how their real-time gaze data could be used to predict item-level learning outcomes as well as decode pretrained item knowledge.

ISBN: 9798516001024Subjects--Topical Terms:

516948
Developmental psychology.
Subjects--Index Terms:

Attention

Decoding Eye Movements in Cross-Situational Word Learning via Tensor Component Analysis.
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245 1 0 $a Decoding Eye Movements in Cross-Situational Word Learning via Tensor Component Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 32 p.
500 $a Source: Masters Abstracts International, Volume: 82-12.
500 $a Advisor: Yu, Chen.
502 $a Thesis (M.A.)--Indiana University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Statistical learning is an active process wherein information is actively selected from the learning environment. As current information is integrated with existing knowledge, it shapes attention in subsequent learning, placing biases on which new information will be sampled. One statistical learning task that has been studied recently is cross-situational word learning (CSL). In CSL, statistical learners are able to learn the correct mappings between novel visual objects and spoken labels after watching sequences where the two are paired together in referentially ambiguous contexts. In the present paper, we use a computational method called Tensor Component Analysis (TCA) to analyze real-time gaze data collected from a set of CSL studies. We applied TCA to learners' gaze data in order to derive latent variables related to real-time statistical learning and to examine how selective attention is organized in time. Our method allows us to address two specific questions: a) the similarity in attention behavior across strong vs. weak learners as well as across learned vs. not-learned items and b) how the structure of attention relates to word learning. We measured learners' knowledge of label-object pairs at the end of a training session, and demonstrate how their real-time gaze data could be used to predict item-level learning outcomes as well as decode pretrained item knowledge.
590 $a School code: 0093.
650 4 $a Developmental psychology. $3 516948
650 4 $a Cognitive psychology. $3 523881
650 4 $a Psychology. $3 519075
650 4 $a Educational psychology. $3 517650
650 4 $a Quantitative psychology. $3 2144748
653 $a Attention
653 $a Data mining
653 $a Language learning
653 $a Statistical learning
653 $a Gaze data
653 $a Label-object pairs
653 $a Learning outcomes
690 $a 0620
690 $a 0633
690 $a 0621
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690 $a 0525
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773 0 $t Masters Abstracts International $g 82-12.
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791 $a M.A.
792 $a 2021
793 $a English
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