Asymmetric reinforcement learning facilitates human inference of transitive relations

Simon Ciranka, Juan Linde-Domingo, Ivan Padezhki, Clara Wicharz, Charley M. Wu and Bernhard Spitzer ()
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Simon Ciranka: Max Planck Institute for Human Development
Juan Linde-Domingo: Max Planck Institute for Human Development
Ivan Padezhki: Max Planck Institute for Human Development
Clara Wicharz: Max Planck Institute for Human Development
Charley M. Wu: Max Planck Institute for Human Development
Bernhard Spitzer: Max Planck Institute for Human Development

Nature Human Behaviour, 2022, vol. 6, issue 4, 555-564

Abstract: Abstract Humans and other animals are capable of inferring never-experienced relations (for example, A > C) from other relational observations (for example, A > B and B > C). The processes behind such transitive inference are subject to intense research. Here we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across four experiments (n = 145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values that is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgements.

Date: 2022
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DOI: 10.1038/s41562-021-01263-w

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