THE STUDY OF COGNITIVE PSYCHOLOGY IN CONJUNCTION WITH ARTIFICIAL INTELLIGENCE

Autores/as

DOI:

https://doi.org/10.18316/rcd.v15i36.10788

Palabras clave:

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

Resumen

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.

Biografía del autor/a

KDV Prasad, Hyderabad Symbiosis International (Deemed University)

Assistant Professor (Research), Symbiosis Institute of Business Management, Hyderabad

Symbiosis International (Deemed University), Pune, Hyderabad

Sripathi Kalavakolanu, Hyderabad Symbiosis International (Deemed University)

Assistant Professor (Human Resources), Symbiosis Institute of Business Management, Hyderabad Symbiosis International (Deemed University), Pune, India

Citas

Cassimatis, N. L. (2006). A Cognitive Substrate for Achieving Human-Level

Intelligence. AI Magazine, 27(2), 45–56.

Chance, F. S., Aimone, J. B., Musuvathy, S. S., Smith, M. R., Vineyard, C. M., & Wang, F.

(2020). Crossing the Cleft: Communication Challenges Between Neuroscience and

Artificial Intelligence. Frontiers in Computational Neuroscience, 14, 1–9.

https://doi.org/10.3389/fncom.2020.00039

Delecraz, S., Eltarr, L., Becuwe, M., Bouxin, H., Boutin, N., & Oullier, O. (2022).

Responsible Artificial Intelligence in Human Resources Technology: An innovative

inclusive and fair by design matching algorithm for job recruitment purposes.

Journal of Responsible Technology, 11, 100041.

Foundations of Classical Artificial Intelligence and Cognitive Science. (2012).

Understanding Intelligence. https://doi.org/10.7551/mitpress/6979.003.0004

Goertzel, B., & Pennachin, C. (n.d.). The Novamente Artificial Intelligence

Engine. Artificial General Intelligence, 63–129. https://doi.org/10.1007/978-3-540-

-4_3

KAYSER, D. A. N. I. E. L. (2013). Artificial Intelligence and cognitive science. Applied

Artificial Intelligence, 5(2), 153–162. https://doi.org/10.1080/08839519108927922

Lieto, A. (2021). Cognitive science and artificial intelligence. Cognitive Design for

Artificial Minds, 1–19. https://doi.org/10.4324/9781315460536-1

Miller, T. (2019). Explanation in artificial intelligence: Insights from the social

sciences. Artificial intelligence, 267, 1-38.

Murphy, J. (2018). Artificial Intelligence, Rationality, and the World Wide Web. IEEE

Intelligent Systems, 33(1), 98–103. https://doi.org/10.1109/MIS.2018.012001557

Pennachin, C., & Goertzel, B. (n.d.). Contemporary approaches to Artificial

General Intelligence. Artificial General Intelligence, 1–30. https://doi.org/10.1007/978-

-540-68677-4_1

Rakover, S. S. (2022). How Can Behavior Be Understood if Its Explanation is Not

Comprehended? Does Cognitive Psychology Reach Its Explanatory Limit? Journal of

Mind & Behavior, 43(3), 255–268.

Red’ko, V. G. (n.d.). The natural way to Artificial Intelligence. Artificial General

Intelligence, 327–351. https://doi.org/10.1007/978-3-540-68677-4_10

Smith, E. E. (1985). Cognitive psychology. Artificial Intelligence, 25(3), 247–253.

https://doi.org/10.1016/0004-3702(85)90073-6

Thagard, P. (2007). Theory and experiment in Cognitive Science. Artificial

Intelligence, 171(18), 1104–1106. https://doi.org/10.1016/j.artint.2007.10.006

Voss, P. (n.d.). Essentials of general intelligence: The direct path to artificial general

intelligence. Artificial General Intelligence, 131–157. https://doi.org/10.1007/978-3-

-68677-4_4

Zhang, X., Wang, R., Sharma, A., & Deverajan, G. G. (2021). Artificial intelligence in

cognitive psychology—Influence of literature based on artificial intelligence on

children's mental disorders. Aggression and Violent Behavior, 101590.

Zhao J, Wu M, Zhou L, Wang X and Jia J (2022) Cognitive psychology-based artificial

intelligence review. Front. Neurosci. 16:1024316. doi: 10.3389/fnins.2022.1024316

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Publicado

2023-03-10

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Artigos