基于深度学习算法的认知过程和信息处理模型。

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2025-03-01 Epub Date: 2024-12-02 DOI:10.1016/j.neunet.2024.106999
DongCai Zhao
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引用次数: 0

摘要

根据幼儿的发展过程,认知能力可分为四个阶段:探索阶段(ES)、映射阶段(MS)、现象-因果阶段(PCS)和本质-因果阶段(ECS)。MS是对事件连续特征的训练,类似于深度学习模型;PCS是将映射训练的输入和输出进行符号化,并将这些符号再次作为映射训练的输入或输出的过程。经过训练,可以预测下一个可能出现的符号,相当于识别本质。在ECS中以功能表达本质本身代表着进入了科学的范畴。为了说明上述过程,以仅具有视觉和成分检测能力的类昆虫的进化历程为例。不需要额外的学习算法编程,仿虫根据认知过程和信息处理模型进化,可以发展自己独立的符号系统。发展自己独特的符号系统的能力实际上标志着代理人的诞生。
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Cognitive process and information processing model based on deep learning algorithms.

According to the developmental process of infants, cognitive abilities are divided into four stages: the Exploration Stage (ES), the Mapping Stage (MS), the Phenomena-causality Stage (PCS), and the Essence-causality Stage (ECS). The MS is a training of the consecutive characteristics of events, similar to a deep learning model; the PCS is a process that symbolizes the input and output of the mapping training, and uses these symbols as the input or output of the mapping training again. After training, the next possible symbol can be predicted, which is equivalent to recognizing the essence. Expressing the essence itself with a function in the ECS represents entering the scope of science. To illustrate the above process, take the evolution journey of an insectoid with only visual and compositional detection capabilities as an example. Without the need for additional learning algorithm programming, the insectoid evolves according to the Cognitive Process and Information Processing Model and can develop its own independent symbol system. The ability to develop its own unique symbolic system actually indicates the birth of an agent.

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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
期刊最新文献
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