Curiosity as filling, compressing, and reconfiguring knowledge networks

Abstract

Theoretical constructs, such as the information gap theory and compression progress theory, seek to explain how humans practice curiosity. According to the former, curiosity is the drive to acquire information missing from our understanding of the world. According to the latter, curiosity is the drive to construct parsimonious mental world models. To complement the densification and simplification processes inherent to these frameworks, we propose the conformational change theory, wherein we posit that curiosity builds mental models with marked conceptual flexibility. We formalize curiosity as a knowledge-network-building process to investigate each theoretical account for individuals and collectives. In knowledge networks, gaps can be identified as topological cavities, compression progress can be quantified using network compressibility, and flexibility can be measured as the number of conformational degrees of freedom. We find that curiosity fills gaps and constructs increasingly compressible and flexible knowledge networks. Across individuals and collectives, we determine the contexts in which each account is explanatory, clarifying their complementary and distinct contributions. Our findings offer a novel networks-based perspective that harmonizes with (and compels an expansion of) the traditional taxonomy of curiosity.

Expanding horizons in reinforcement learning for curious exploration and creative planning

“…Humans can search over more complex structures in knowledge representations (Yoo et al ., 2023). That knowledge may be more modular and compressible, allowing for the grouped representation of a more diverse chain of actions (Lai & Gershman, 2021; Momennejad, 2020; Patankar et al ., 2023; Schapiro, Rogers, Cordova, Turk-Browne, & Botvinick, 2013; Stachenfeld, Botvinick, & Gershman, 2017). The ability to use more complex knowledge structures may involve a spatial-like ability to navigate those structures (Rmus, Ritz, Hunter, Bornstein, & Shenhav, 2022), as well as a metacognitive ability to balance knowledge uncertainty with deeper planning (Schulz & Bonawitz, 2007; Wade & Kidd, 2019; Nussenbaum et al , 2023).…”