Abstract
The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with five Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.
Highlights
Concepts are of great value to humans because they are one of the building blocks of our recognition process
Though the aforementioned computational approaches contribute toward abstract concept modeling and representation, they have a fixed topology
Besides abstract representation of underlying categories, the activation of nodes in newly created layer discloses the degree of confidence (DoC) (Calculating DoC of a node is explained in detail with upward activation propagation operation). indicating the certainty of identification of a class by its representative node in the new layer
Summary
Concepts are of great value to humans because they are one of the building blocks of our recognition process. Computational models provide us algorithmic specificity, conceptual clarity, and precision They empower us to perform simulations that can either be useful to test and validate psychological theories or to generate new hypotheses about how the mind works—this has turned them into an indispensable tool to study the human brain. Several computational modeling techniques (or tools) simulate cognitive states and represent concepts at symbolic and connectionist levels. Connectionist Learning with Adaptive Rule Induction Online (CLARION) [12] is a methodology that is hybrid, and capable of simulating scenarios related to cognitive and social psychology All these methodologies either require a predefined structure or have a fixed topology that imposes a limitation of having supervision, and inflexibility while modeling the concepts. The model generation process with RAN and the three cognitive functions (i.e., concept creation, learning and activation propagation) are simulated using a Toy-data problem.
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