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

Artikel i vetenskaplig tidskrift
Författare Robert Lowe
Alexander Almér
Christian Balkenius
Publicerad i Open Information Science
Volym 3
Nummer/häfte 1
Sidor 235-260
ISSN 2451-1781
Publiceringsår 2019
Publicerad vid Institutionen för tillämpad informationsteknologi (GU)
Sidor 235-260
Språk en
Länkar https://doi.org/10.1515/opis-2019-0...
Ämnesord System 1-System 2; relational cognition; associative learning; representational rank; affective computation; habits
Ämneskategorier Interaktionsteknik, Psykologi, Data- och informationsvetenskap

Sammanfattning

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.

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