AUTHOR=Suomala Jyrki , Kauttonen Janne 

TITLE=Human’s Intuitive Mental Models as a Source of Realistic Artificial Intelligence and Engineering

JOURNAL=Frontiers in Psychology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.873289

DOI=10.3389/fpsyg.2022.873289

ISSN=1664-1078

ABSTRACT=Despite the success of Artificial Intelligence (AI), we’re still far away from AI that model the world as humans do. This article concentrates for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, humans follow Bayesian inference to combines intuitive models and new information to make decisions.  We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision-making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, progress in data analysis techniques in AI have allowed us to decipher how human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing current research from this perspective. At the end of the paper, we outline basic aspects for human-like AI, and we discuss, how science can benefit from AI. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. Both the development of AI and understanding of human behavior go hand in hand.