語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Decoding Eye Movements in Cross-Situ...
~
Amatuni, Andrei.
FindBook
Google Book
Amazon
博客來
Decoding Eye Movements in Cross-Situational Word Learning via Tensor Component Analysis.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Decoding Eye Movements in Cross-Situational Word Learning via Tensor Component Analysis./
作者:
Amatuni, Andrei.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:
32 p.
附註:
Source: Masters Abstracts International, Volume: 82-12.
Contained By:
Masters Abstracts International82-12.
標題:
Developmental psychology. -
電子資源:
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.
LDR
:02633nmm a2200421 4500
001
2283140
005
20211022115653.5
008
220723s2021 ||||||||||||||||| ||eng d
020
$a
9798516001024
035
$a
(MiAaPQ)AAI28490365
035
$a
AAI28490365
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Amatuni, Andrei.
$3
3562056
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
690
$a
0632
690
$a
0525
710
2
$a
Indiana University.
$b
Psychological & Brain Sciences.
$3
2104218
773
0
$t
Masters Abstracts International
$g
82-12.
790
$a
0093
791
$a
M.A.
792
$a
2021
793
$a
English
856
4 0
$u
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28490365
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9434873
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入