Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

Q2 Social Sciences
Robert J. Lowe, A. Almer, C. Balkenius
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引用次数: 6

Abstract

Abstract Connectionist architectures constitute a popular method for modelling animal associative learning processes in order to glean insights into the formation of cognitive capacities. Such approaches (based on purely feedforward activity) are considered limited in their ability to capture relational cognitive capacities. Pavlovian learning value-based models, being not based purely on fully connected feedforward structure, have demonstrated learning capabilities that often mimic those of ‘higher’ relational cognition. Capturing data using such models often reveals how associative mechanisms can exploit structure in the experimental setting, so that ‘explicit’ relational cognitive capacities are not, in fact, required. On the other hand, models of relational cognition, implemented as neural networks, permit formation and retrieval of relational representations of varying levels of complexity. The flexible processing capacities of such models are, however, are subject to constraints as to how offline relational versus online (real-time, real-world) processing may be mediated. In the current article, we review the potential for building a connectionist-relational cognitive architecture with reference to the representational rank view of cognitive capacity put forward by Halford et al. Through interfacing system 1-like (connectionist/associative learning) and system 2-like (relational-cognition) computations through a bidirectional affective processing approach, continuity between Halford et al’s cognitive systems may be operationalized according to real world/online constraints. By addressing i) and ii) in this manner, this paper puts forward a testable unifying framework for system 1-like and system 2-like cognition.
通过双向情感联想加工桥接联结主义与关系认知
连接主义架构构成了一种流行的方法,用于模拟动物联想学习过程,以收集对认知能力形成的见解。这种方法(基于纯粹的前馈活动)在捕捉关系认知能力方面被认为是有限的。巴甫洛夫基于价值的学习模型,并非纯粹基于完全连接的前馈结构,已经证明了模仿“更高”关系认知的学习能力。使用这样的模型获取数据通常揭示了联想机制如何在实验环境中利用结构,因此实际上并不需要“明确的”关系认知能力。另一方面,作为神经网络实现的关系认知模型允许形成和检索不同复杂程度的关系表示。然而,这些模型的灵活处理能力受制于如何协调离线关系处理与在线(实时、真实)处理的约束。在本文中,我们参考Halford等人提出的认知能力表征等级观,回顾了构建连接主义-关系认知架构的潜力。通过双向情感处理方法将类系统1(连接主义/联想学习)和类系统2(关系认知)计算连接起来,Halford等人的认知系统之间的连续性可以根据现实世界/在线约束进行操作。通过这种方式处理i)和ii),本文提出了一个可测试的类系统1和类系统2认知的统一框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Open Information Science
Open Information Science Social Sciences-Library and Information Sciences
CiteScore
1.40
自引率
0.00%
发文量
7
审稿时长
8 weeks
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