Year: 2025
Type: preprint
Abstract: A bstract Curiosity, defined as a transient motivational state for information, explains many non-greedy decisions in humans. Novel, surprising and uncertain situations are thought to elicit curiosity, leading to various models for information-seeking behaviors. We present a unified comparison of curiosity models based on experimental results and previous models from the decision-making and reinforcement learning literature. We provide and explain at least one mathematical formula for each curiosity category, implement them, and compare them on the same uncertain and changing task. This comparison illustrates how motivation and exploration may evolve differently based on what elicits curiosity. This work promises to increase the understanding of curiosity models and provide a synthesized toolbox for future models of information-seeking behaviors in animals or artificial agents.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4416424512