Invited Talk 2: Understanding information demand at different levels of complexity

In the 1950s, Daniel Berlyne wrote extensively about the importance of curiosity – our intrinsic desire to know. To understand curiosity, Berlyne argued, we must explain why humans exert so much effort to obtain knowledge, and how they decide which questions to explore, given that exploration is difficult and its long-term benefits are impossible to ascertain. I propose that these questions, although relatively neglected in neuroscience research, are key to understanding cognition and complex decision making of the type that humans routinely engage in and autonomous agents only aspire to. I will describe our investigations of these questions in two types of paradigms. In one paradigm, agents are placed in contexts with different levels of uncertainty and reward probability and can sample information about the eventual outcome. We find that, in humans and monkeys, information sampling is partially sensitive to uncertainty but is also biased by Pavlovian tendencies, which push agents to engage with signals predicting positive outcomes and avoid those predicting negative outcomes in ways that interfere with a reduction of uncertainty. In a second paradigm, agents are given several tasks of different difficulty and can freely organize their exploration in order to learn. In these contexts, uncertainty-based heuristics become ineffective, and optimal strategies are instead based on learning progress – the ability to first engage with and later reduce uncertainty. I will show evidence that humans are motivated to select difficult tasks consistent with learning maximization, but they guide their task selection according to success rates rather than learning progress per se, which risks trapping them in tasks with too high levels of difficulty (e.g., random unlearnable tasks). Together, the results show that information demand has consistent features that can be quantitatively measured at various levels of complexity, and a research agenda exploring these features will greatly expand our understanding of complex decision strategies.