Abstract
Abstract concepts play a vital role in decision-making or recall operations because the associations among them are essential for contextual processing. Abstract concepts are complex and difficult to represent (conceptually, formally, or computationally), leading to difficulties in their comprehension and recall. This contribution reports the computational simulation of the cued recall of abstract concepts by exploiting their learned associations. The cued recall operation is realized via a novel geometric back-propagation algorithm that emulates the recall of abstract concepts learned through regulated activation network (RAN) modeling. During recall operation, another algorithm uniquely regulates the activation of concepts (nodes) by injecting excitatory, neutral, and inhibitory signals to other concepts of the same level. A Toy-data problem is considered to illustrate the RAN modeling and recall procedure. The results display how regulation enables contextual awareness among abstract nodes during the recall process. The MNIST dataset is used to show how recall operations retrieve intuitive and non-intuitive blends of abstract nodes. We show that every recall process converges to an optimal image. With more cues, better images are recalled, and every intermediate image obtained during the recall iterations corresponds to the varying cognitive states of the recognition procedure.
Highlights
Published: 28 February 2021Concepts are an important object of research in cognitive and psychological research.Usually, the conceptual representations are process-oriented, symbolic or distributed, and knowledge-based [1,2,3]
This article uses a computational model, regulated activation network (RAN) [9,10,11], capable of building representation of convex abstract concepts, which are later used in recall simulations
Concepts are normally perceived in a hierarchical form, where the concrete concepts occupy the lower level, and the abstract concepts take up the relatively higher level in the hierarchy
Summary
Published: 28 February 2021Concepts are an important object of research in cognitive and psychological research.Usually, the conceptual representations are process-oriented, symbolic or distributed, and knowledge-based [1,2,3]. Concepts are an important object of research in cognitive and psychological research. A hierarchical structure defines an organization of concepts where the concrete concepts are placed in the lower level, and the abstract. Abstract concepts are seen as the generalization of concrete concepts [4,5]. Abstract concepts are studied mathematically [6] and theoretically [7,8], but computational studies are scarce [1]. This article uses a computational model, regulated activation network (RAN) [9,10,11], capable of building representation of convex abstract concepts, which are later used in recall simulations
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