"Catastrophic" set size limits on infants’ capacity to represent objects: A systematic review and Bayesian meta-analysis

贝叶斯概率 荟萃分析 集合(抽象数据类型) 计量经济学 计算机科学 统计 人工智能 数学 医学 内科学 程序设计语言
作者
Jenny Wang,Melissa M. Kibbe
标识
DOI:10.31234/osf.io/2gtjv
摘要

Decades of research has revealed that humans can concurrently represent small quantities of three-dimensional objects as those objects move through space or into occlusion. For infants (but not older children or adults), this ability apparently comes with a significant limitation: when the number of occluded objects exceeds three, infants experience what has been characterized as a “catastrophic” set size limit, failing to represent even the approximate quantity of the hidden array. Infants’ apparent catastrophic representational failures suggest a significant information processing limitation in the first years of life, and the evidence has been used as support for prominent theories of the development of object and numerical cognition. However, the evidence for catastrophic failure consists of individual small-n experiments that use null hypothesis significance testing to obtain null results (i.e., p > .05). Whether catastrophic representational failures are robust or reliable across studies, methods, and labs is not known. Here we report a systematic review and Bayesian meta-analysis to examine the strength of the evidence in favor of catastrophic representational failures in infancy. Our analysis of 22 experiments across 12 reports, with a combined total of n = 367 infants aged 10-20 months, revealed strong support for the evidence for catastrophic set size limits. A complementary analysis found moderate support for infants’ success when representing fewer than four objects. We discuss the implications of our findings for theories of object and numerical cognitive development.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
无名欧文完成签到,获得积分10
刚刚
2秒前
虚心海燕发布了新的文献求助10
2秒前
黄啊涛关注了科研通微信公众号
2秒前
2秒前
JamesPei应助Rainbow采纳,获得10
3秒前
一只科研狗完成签到,获得积分10
3秒前
pp0118完成签到 ,获得积分10
3秒前
余呀余完成签到 ,获得积分10
4秒前
5秒前
善良易文关注了科研通微信公众号
5秒前
5秒前
瑶一瑶发布了新的文献求助10
6秒前
yhy完成签到,获得积分10
6秒前
纯真雁菱完成签到,获得积分10
6秒前
sun发布了新的文献求助10
6秒前
w.h完成签到,获得积分10
7秒前
7秒前
Schmoo发布了新的文献求助10
7秒前
赘婿应助Zxc采纳,获得10
7秒前
明理雨筠完成签到,获得积分10
8秒前
Ava应助Chen采纳,获得10
9秒前
9秒前
9秒前
Xing发布了新的文献求助10
9秒前
w.h发布了新的文献求助10
10秒前
搜集达人应助狼来了aas采纳,获得10
11秒前
12秒前
点点发布了新的文献求助10
12秒前
14秒前
14秒前
blingbling完成签到,获得积分10
14秒前
14秒前
黄啊涛发布了新的文献求助10
14秒前
14秒前
嘻嘻发布了新的文献求助30
14秒前
15秒前
15秒前
18秒前
18秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
Luis Lacasa - Sobre esto y aquello 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527998
求助须知:如何正确求助?哪些是违规求助? 3108225
关于积分的说明 9288086
捐赠科研通 2805889
什么是DOI,文献DOI怎么找? 1540195
邀请新用户注册赠送积分活动 716950
科研通“疑难数据库(出版商)”最低求助积分说明 709849