超越可学性:用 DNN 理解人类视觉发展。

IF 16.7 1区 心理学 Q1 BEHAVIORAL SCIENCES
Lei Yuan
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引用次数: 0

摘要

最近,奥尔罕和雷克通过计算证明,儿童可以从自然输入数据中获得复杂的视觉表征,而不会出现固有的偏差,这对人类学习中需要先天限制的观点提出了挑战。这些发现还揭示了早期视觉学习的关键特性,并为人类视觉发展理论提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beyond learnability: understanding human visual development with DNNs.

Recently, Orhan and Lake demonstrated the computational plausibility that children can acquire sophisticated visual representations from natural input data without inherent biases, challenging the need for innate constraints in human learning. The findings may also reveal crucial properties of early visual learning and inform theories of human visual development.

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来源期刊
Trends in Cognitive Sciences
Trends in Cognitive Sciences 医学-行为科学
CiteScore
27.90
自引率
1.50%
发文量
156
审稿时长
6-12 weeks
期刊介绍: Essential reading for those working directly in the cognitive sciences or in related specialist areas, Trends in Cognitive Sciences provides an instant overview of current thinking for scientists, students and teachers who want to keep up with the latest developments in the cognitive sciences. The journal brings together research in psychology, artificial intelligence, linguistics, philosophy, computer science and neuroscience. Trends in Cognitive Sciences provides a platform for the interaction of these disciplines and the evolution of cognitive science as an independent field of study.
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