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Humans often appear to desire information for its own sake, but it is presently unclear what drives this desire. The important role that resolving uncertainty plays in stimulating information seeking has suggested a tight coupling between the intrinsic motivation to gather information and performance gains, construed as a drive for long-term learning. Using an asteroid-avoidance game that allows us to study learning and information seeking at an experimental time-scale, we show that the incentive for information-seeking can be separated from a long-term learning outcome, with information-seeking best predicted by per-trial outcome uncertainty. Specifically, participants were more willing to take time penalties to receive feedback on trials with increasing uncertainty in the outcome of their choices. We found strong group and individual level support for a linear relationship between feedback request rate and information gain as determined by per-trial outcome uncertainty. This information better reflects filling in the gaps of the episodic record of choice outcomes than long-term skill acquisition or assessment. Our results suggest that this easy to compute quantity can drive information-seeking, potentially allowing simple organisms to intelligently gather information for a diverse episodic record of the environment without having to anticipate the impact on future performance.
Pubmed ID: 31375718
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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