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

Lowe, Robert ; Almér, Alexander and Balkenius, Christian LU (2019) In Open Information Science 3(1). p.235-260
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Open Information Science
volume
3
issue
1
pages
235 - 260
publisher
De Gruyter
ISSN
2451-1781
DOI
10.1515/opis-2019-0017
language
English
LU publication?
yes
id
cdda1fcd-dafc-45ec-b392-94e2a9f9526d
date added to LUP
2019-11-01 14:24:48
date last changed
2019-11-07 02:20:36
@article{cdda1fcd-dafc-45ec-b392-94e2a9f9526d,
  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.},
  author       = {Lowe, Robert and Almér, Alexander and Balkenius, Christian},
  issn         = {2451-1781},
  language     = {eng},
  number       = {1},
  pages        = {235--260},
  publisher    = {De Gruyter},
  series       = {Open Information Science},
  title        = {Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing},
  url          = {http://dx.doi.org/10.1515/opis-2019-0017},
  doi          = {10.1515/opis-2019-0017},
  volume       = {3},
  year         = {2019},
}