There is a large gap between current AI systems and human intelligence. We study intelligence with a focus on metacognitive activities while following the recent development of deep learning, and eventually aim at their concrete implementation through the three approaches shown below.
Metacognition and Curiosity for Efficient Exploration
Many tasks have sparse rewards in the world. Random exploration for sparse rewards, however, is computationally expensive. A human agent explores the world by seeking sparse rewards efficiently. We focus on metacognition and curiosity as the crucial factors for such efficient explorative strategies. We proposed experience sampling methods to approach metacognition [1,2]. We demonstrated our experience sampling methods to collect introspective experience effiently. We also investigate neural mechanisms of curiosity for such an efficient explorative algorithm and its relation to metacognition.
 Niikawa, T.*, Miyahara, K., Hamada, H.T., Nishida, S. (2020) A new experimental phenomenological method to explore the subjective features of psychological phenomena: its application to binocular rivalry. Neuroscience of Consciousness, 2020 (1), niaa018. https://doi.org/10.1093/nc/niaa018
 Miyahara, K.*, Niikawa T., Hamada, H.T., Nishida, S. (2020) Developing a Short-term Phenomenological Training Program: A Report of Methodological Lessons. New Ideas in Psychology. 58. https://doi.org/10.1016/j.newideapsych.2020.100780