THE STUDY OF COGNITIVE PSYCHOLOGY IN CONJUNCTION WITH ARTIFICIAL INTELLIGENCE

KDV Prasad, Sripathi Kalavakolanu

Resumo


The major purpose of this research is to provide a thorough review and analysis of the interplay between artificial intelligence (AI) and psychology. I talk about state-of-the-art computer programs that are able to simulate human cognition and behavior (such as Human-Computer Interfaces, models of the mind, and data mining programs). Applications may be broken down into several sub-categories and have many different aspects. While developing artificially intelligent robots has been and continues to be the major goal of AI research and development, the widespread acceptance and usage of AI systems have resulted in a much broader transfer of technology. The article begins with a brief history of cognitive psychology, a discussion of its fundamental ideas and models, and a look at the ways in which the study is connected to artificial intelligence (AI). The second part of this article takes a closer look at the difficulties encountered by the field of human-computer interaction, along with its aims, duties, applications, and underlying psychological theories. Multiple scientific, pragmatic, and technical obstacles (complexity problems, disturbing coefficients, etc.) stand in the way of extending or overcoming these limits. We also demonstrate the potential use of mental modeling in the areas of diagnosis, manipulation, and education support in this work. Predictions may be made with the use of data mining, knowledge discovery, or expert systems (for instance, the prognoses of children with mental problems based on their settings). The article reviews the missing features and offers an overview of the coefficients used in the system. Finally, we discuss the application of expert systems and life simulation (applied mental model) in virtual reality to benefit autistic people and their loved ones.


Palavras-chave


Artificial intelligence; Cognitive psychology; Children's mental health; Deep learning; Convolutional neural network.

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DOI: http://dx.doi.org/10.18316/rcd.v15i36.10788

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