Suomala Jyrki, Kauttonen Janne
NeuroLab, Laurea University of Applied Sciences, Vantaa, Finland.
Competences, RDI and Digitalization, Haaga-Helia University of Applied Sciences, Helsinki, Finland.
Front Psychol. 2022 May 30;13:873289. doi: 10.3389/fpsyg.2022.873289. eCollection 2022.
Despite the success of artificial intelligence (AI), we are still far away from AI that model the world as humans do. This study focuses 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, we follow Bayesian inference to combine intuitive models and new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the 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, a progress in data analysis techniques in AI has allowed us to decipher how the 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 the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on 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 development of AI and understanding of human behavior go hand in hand.
尽管人工智能取得了成功，但我们距离能够像人类一样对世界进行建模的人工智能仍相距甚远。本研究聚焦于从直观心理模型的角度解释人类行为。我们描述了行为在生物系统中是如何产生的，以及对这个生物系统的更好理解如何能够推动类人人工智能的发展。人类可以从物理、社会和文化情境中构建直观模型。此外，我们遵循贝叶斯推理来结合直观模型和新信息以做出决策。我们应该为新的人工智能构建类似的直观模型和贝叶斯算法。我们认为，贝叶斯意义上的概率计算对由观察和先验经验形成的对象组合的语义属性敏感。当这些对象概率的出现可信时，我们将这种大脑过程称为计算意义，它更接近贝叶斯理想情况。人类大脑是如何形成世界模型并将这些模型应用于其行为中的呢？我们从三个角度概述答案。首先，直观模型支持个体在当前情境中以有意义的方式使用信息。其次，神经经济学提出大脑中的估值网络在人类决策中起着至关重要的作用。它结合了心理学、经济学和神经科学方法来揭示决策制定的生物学机制。然后，大脑是一个过度参数化的建模器官，在复杂世界中产生最优行为。最后，人工智能数据分析技术的进步使我们能够解读人类大脑在复杂情境中如何评估不同选项。通过将大数据集与机器学习模型相结合，有可能从复杂的神经数据中获得比以往更多的见解。我们从这个角度回顾当前研究来描述这些解决方案。在本研究中，我们概述了类人人工智能的基本方面，并讨论了科学如何能从人工智能中受益。我们对人类大脑机制理解得越好，就越能将这种理解应用于构建新的人工智能。人工智能的发展和对人类行为的理解是相辅相成的。
