WeChat Mini Program
Old Version Features

Cross-situational Learning from Ambiguous Egocentric Input is a Continuous Process: Evidence Using the Human Simulation Paradigm.

Cognitive Science - A Multidisciplinary Journal(2021)SCI 2区

Univ Texas Austin

Cited 3|Views23
Abstract
Recent laboratory experiments have shown that both infant and adult learners can acquire word-referent mappings using cross-situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real-world contexts in which learning occurs. Thus, the feasibility of generalizing cross-situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of experiments examining cross-situational learning from children's egocentric videos captured during naturalistic play. Focusing on individually ambiguous naming moments that naturally occur during toy play, we asked how statistical learning unfolds in real time through accumulating cross-situational statistics in naturalistic contexts. We found that even when learning situations were individually ambiguous, learners' performance gradually improved over time. This improvement was driven in part by learners' use of partial knowledge acquired from previous learning situations, even when they had not yet discovered correct word-object mappings. These results suggest that word learning is a continuous process by means of real-time information integration.
More
Translated text
Key words
Word learning,Early language acquisition,Statistical learning,Cross-situational learning
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文通过人类仿真范式证明了在自然情境中,即使面对 individually ambiguous 的自我中心输入,跨情境学习依然是一个连续的过程,支持词汇学习的实时信息整合。

方法】:研究采用了人类仿真范式,通过分析儿童在自然玩耍过程中捕捉的自我中心视频,关注在玩具玩耍过程中自然发生的 individually ambiguous 命名时刻。

实验】:实验通过连续观察和统计在自然情境中玩具玩耍的egocentric videos,发现学习者在面对个别模糊的学习情境时,其表现随着时间逐渐提升,这种提升部分得益于学习者利用之前学习情境中获得的局部知识,即使他们尚未发现正确的词-物映射。数据集为自然玩耍情境中捕捉的儿童自我中心视频。