Developmental changes in exploration resemble stochastic optimization

Anna P. Giron, Simon Ciranka, Eric Schulz, Wouter Bos, Azzurra Ruggeri, Björn Meder and Charley M. Wu ()
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Anna P. Giron: Human and Machine Cognition Lab, University of Tübingen
Simon Ciranka: Center for Adaptive Rationality, Max Planck Institute for Human Development
Eric Schulz: MPRG Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics
Wouter Bos: University of Amsterdam
Azzurra Ruggeri: MPRG iSearch, Max Planck Institute for Human Development
Björn Meder: MPRG iSearch, Max Planck Institute for Human Development
Charley M. Wu: Human and Machine Cognition Lab, University of Tübingen

Nature Human Behaviour, 2023, vol. 7, issue 11, 1955-1967

Abstract: Abstract Human development is often described as a ‘cooling off’ process, analogous to stochastic optimization algorithms that implement a gradual reduction in randomness over time. Yet there is ambiguity in how to interpret this analogy, due to a lack of concrete empirical comparisons. Using data from n = 281 participants ages 5 to 55, we show that cooling off does not only apply to the single dimension of randomness. Rather, human development resembles an optimization process of multiple learning parameters, for example, reward generalization, uncertainty-directed exploration and random temperature. Rapid changes in parameters occur during childhood, but these changes plateau and converge to efficient values in adulthood. We show that while the developmental trajectory of human parameters is strikingly similar to several stochastic optimization algorithms, there are important differences in convergence. None of the optimization algorithms tested were able to discover reliably better regions of the strategy space than adult participants on this task.

Date: 2023
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DOI: 10.1038/s41562-023-01662-1

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