Towards Modeling the Variability of Human Attention

By: Kuno Kim, Megumi Sano, Julian De Freitas, Daniel Yamins and Nick Haber

Format:Electronic
| Language:English

Abstract

Children exhibit extraordinary exploratory behaviors hypothesized to contribute to the building of models of their world. Harnessing this capacity in artificial systems promises not only more flexible technology but also cognitive models of the developmental processes we seek to mimic. Yet not all children learn the same way, and for instance children with autism exhibit characteristically different exploratory strategies early in life. What if we could, by developing artificial systems that learn through exploration, model not only typically development, but all its variations? In this work, we present a preliminary analysis of curiosity-driven agents in social environments that establishes links between early behavior and later acuity, with implications for the future of both diagnostics and personalized learning.

Keywords

Exploratory Learning Behaviors; Modeling; Artificial Intelligence; AI and Machine Learning

Citation

Kim, Kuno, Megumi Sano, Julian De Freitas, Daniel Yamins, and Nick Haber. "Towards Modeling the Variability of Human Attention." In Bridging AI and Cognitive Science (BAICS) Workshop. 8th International Conference on Learning Representations (ICLR), April 26, 2020.