The Role of Co-Occurrence Statistics in Developing Semantic Knowledge

被引:17
|
作者
Unger, Layla [1 ]
Vales, Catarina [2 ]
Fisher, Anna V. [2 ]
机构
[1] Ohio State Univ, Dept Psychol, 1835 Neil Ave,PS 268, Columbus, OH 43210 USA
[2] Carnegie Mellon Univ, Dept Psychol, Pittsburgh, PA 15213 USA
关键词
Semantic development; Conceptual development; Co-occurrence; Taxonomic; Semantic organization; Visual search; TAXONOMIC RELATIONS; CATEGORIZATION; SIMILARITY; FIXATION; INFANTS; REPRESENTATIONS; INFORMATION; CHILDREN; ORGANIZATION; PREFERENCE;
D O I
10.1111/cogs.12894
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
The organization of our knowledge about the world into an interconnected network of concepts linked by relations profoundly impacts many facets of cognition, including attention, memory retrieval, reasoning, and learning. It is therefore crucial to understand how organized semantic representations are acquired. The present experiment investigated the contributions of readily observable environmental statistical regularities to semantic organization in childhood. Specifically, we investigated whether co-occurrence regularities with which entities or their labels more reliably occur together than with others (a) contribute to relations between concepts independently and (b) contribute to relations between concepts belonging to the same taxonomic category. Using child-directed speech corpora to estimate reliable co-occurrences between labels for familiar items, we constructed triads consisting of a target, a related distractor, and an unrelated distractor in which targets and related distractors consistently co-occurred (e.g., sock-foot), belonged to the same taxonomic category (e.g., sock-coat), or both (e.g., sock-shoe). We used an implicit, eye-gaze measure of relations between concepts based on the degree to which children (N = 72, age 4-7 years) looked at related versus unrelated distractors when asked to look for a target. The results indicated that co-occurrence both independently contributes to relations between concepts and contributes to relations between concepts belonging to the same taxonomic category. These findings suggest that sensitivity to the regularity with which different entities co-occur in children's environments shapes the organization of semantic knowledge during development. Implications for theoretical accounts and empirical investigations of semantic organization are discussed.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] Semantic information retrieval research based on co-occurrence analysis
    Lou, Wen
    Qiu, Junping
    ONLINE INFORMATION REVIEW, 2014, 38 (01) : 4 - 23
  • [22] Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval
    Feng, Linan
    Bhanu, Bir
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (04) : 785 - 799
  • [23] COSTA: Co-Occurrence Statistics for Zero-Shot Classification
    Mensink, Thomas
    Gavves, Efstratios
    Snoek, Cees G. M.
    2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, : 2441 - 2448
  • [24] Utilizing Co-occurrence Patterns for Semantic Concept Detection in Images
    Feng, Linan
    Bhanu, Bir
    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 2918 - 2921
  • [25] Basic Co-Occurrence Latent Semantic Vector Space Model
    Feng gao Niu
    Journal of Classification, 2019, 36 : 277 - 294
  • [26] Associative priming in faces: Semantic relatedness or simple co-occurrence?
    Matei Vladeanu
    Michael Lewis
    Hadyn Ellis
    Memory & Cognition, 2006, 34 : 1091 - 1101
  • [27] Ternary Co-occurrence Latent Semantic Vector Space Model
    Niu Fenggao
    Wang Shichang
    Zhang Yayu
    16TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS (ISSI 2017), 2017, : 1618 - 1626
  • [28] A DSM-Based Co-Occurrence Matrix for Semantic Classification
    Xia, Wang
    Yan, Li
    Xie, Hong
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [29] An in-depth look into the co-occurrence distribution of semantic associates
    Walde, Sabine Schulte im
    Melinger, Alissa
    ITALIAN JOURNAL OF LINGUISTICS, 2008, 20 (01): : 89 - 128
  • [30] Basic Co-Occurrence Latent Semantic Vector Space Model
    Niu, Feng Gao
    JOURNAL OF CLASSIFICATION, 2019, 36 (02) : 277 - 294