Adaptive Visual Re-Weighting in Children's Postural Control

被引:39
|
作者
Polastri, Paula F. [1 ]
Barela, Jose A. [2 ,3 ]
机构
[1] Univ Estadual Paulista, UNESP, Fac Sci, Dept Phys Educ,Lab Informat Vis & Act, Bauru, SP, Brazil
[2] Cruzeiro do Sul Univ, Inst Phys Activ & Sport Sci, Sao Paulo, Brazil
[3] Univ Estadual Paulista, UNESP, Inst Biosci, Dept Phys Educ, Rio Claro, SP, Brazil
来源
PLOS ONE | 2013年 / 8卷 / 12期
关键词
BODY SWAY; SOMATOSENSORY INFORMATION; SENSORY INTEGRATION; OPTIC FLOW; STANCE; INFANTS; VISION; ENVIRONMENT; ADAPTATION; LOCOMOTION;
D O I
10.1371/journal.pone.0082215
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This study investigated how children's postural control adapts to changes in the visual environment and whether they use previous experience to adjust postural responses to following expositions. Four-, eight-, and twelve-year-old children (10 in each group) and 10 young adults stood upright inside of a moving room during eight trials each lasting one-minute. In the first trial, the room was stationary. In the following seven trials, the room oscillated at 0.2 Hz, amplitude of 0.5 cm, with the exception of the fifth trial, in which the room oscillated with amplitude of 3.2 cm. Body sway responses of young adults and older children down-weighted more to the increased visual stimulus amplitude when compared to younger children. In addition, four- and eight-year-old children quickly up-weighted body responses to visual stimulus in the subsequent two trials after the high amplitude trial. Sway variability decreased with age and was greatest during the high-amplitude trial. These results indicate that four year olds have already developed the adaptive capability to quickly down-weight visual influences. However, the increased gain values and residual variability observed for the younger children suggest that they have not fully calibrated their adaptive response to that of the young adults tested. Moreover, younger children do not carry over their previous experience from the sensorial environment to adapt to future changes.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Re-weighting of Sound Localization Cues by Audiovisual Training
    Kumpik, Daniel P.
    Campbell, Connor
    Schnuppt, Jan W. H.
    King, Andrew J.
    FRONTIERS IN NEUROSCIENCE, 2019, 13
  • [22] Adaptive postural control in children
    Polastri, Paula F.
    Godoi, Daniela
    Weigelt, Matthias
    Kiemel, Tim
    Jeka, John J.
    Barela, Jose A.
    JOURNAL OF SPORT & EXERCISE PSYCHOLOGY, 2008, 30 : S56 - S56
  • [23] A general adaptive ridge regression method for generalized linear models: an iterative re-weighting approach
    Guo, Zijun
    Chen, Mengxing
    Fan, Yali
    Song, Yan
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2023, 52 (18) : 6420 - 6443
  • [24] Re-weighting of GPS baselines for vertical deformation analysis
    de Heus, H
    Martens, M
    van der Marel, H
    GEODESY BEYOND 2000: THE CHALLENGES OF THE FIRST DECADE, 2000, 121 : 381 - 386
  • [25] APTER: Aggregated Prognosis Through Exponential Re-weighting
    Liu, Yang
    Pelckmans, Kristiaan
    COMPUTING AND COMBINATORICS, COCOON 2019, 2019, 11653 : 425 - 436
  • [26] Threshold Re-weighting Attention Mechanism for Speaker Verification
    Li, Bo
    Cai, Xiaodong
    PROCEEDINGS OF 2018 IEEE 4TH INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC 2018), 2018, : 971 - 974
  • [27] Re-weighting Relevance Feedback in HSV Quantization for CBIR
    Pardede, Jasman
    Sitohang, Benhard
    Akbar, Saiful
    Khodra, Masayu Leylia
    2018 19TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), 2018, : 58 - 63
  • [28] Reinforced Sample Re-weighting for Pedestrian Attribute Recognition
    Liu, Yuan
    Lin, Zhiping
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
  • [29] Normal Distribution Re-Weighting for Personalized Web Search
    Liu, Hanze
    Hoeber, Orland
    ADVANCES IN ARTIFICIAL INTELLIGENCE, 2011, 6657 : 281 - 284
  • [30] Learning to Select Pivotal Samples for Meta Re-weighting
    Wu, Yinjun
    Stein, Adam
    Gardner, Jacob
    Naik, Mayur
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 5, 2023, : 6128 - 6136