Perceiving Abstract Concepts Via Evolving Computational Cognitive Modeling



Any cognizable thing (object, or idea) can be
recognized as a concept, and they are usually described by
modality-specific, experience-dependent, or localist-distributed
representations. There has been a significant effort in studying
concrete concepts, along with their underlying cognitive and
psychological processes through conceptual representations pro-
duced by computational approaches (like Artificial Neural Net-
works). However, the notion of abstract concepts, though debated,
is seldom explored. In this article, we propose an approach
to learn abstract representation of concepts with Regulated
Activation Networks (RANs) evolving computational modeling.
RANs methodology is illustrated using a toy-data problem, and
evaluated for metrics Precision, Recall, F1-score, and Accuracy,
along with ROC curve analysis. RANs comparison is presented
using a benchmark data from human activity recognition domain,
yielding Precision= 98.20% (ca.), Recall= 97.62% (ca.), F1-
score= 97.8 (ca.), and Accuracy= 97.62% (ca.). Sleep Detection
data from SOCIALITE project is used to model active and
inactive students, showing a convincing performance. Further,
the model is used in studying 3 students in order to deduce
biomarkers for understanding their psychological conditions.


Unsupervised Machine Learning, Evolutionary Computational Modeling, Contextual Modeling, Computational Cognitive Modeling


Computational Cognitive Modeling

Related Project

SOCIALITE - Social-Oriented Internet of Things Architecture, Solutions and Environment. PTDC/EEI-SCR/2072/2014


IEEE World Congress on Computational Intelligence (IEEE WCCI), July 2018


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