Superstitious learning of abstract order from random reinforcement

Author:

Jin Yuhao12ORCID,Jensen Greg234ORCID,Gottlieb Jacqueline245,Ferrera Vincent245

Affiliation:

1. Department of Biological Sciences, Columbia University, New York, NY 10027

2. Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027

3. Department of Psychology, Reed College, Portland, OR 97202

4. Department of Neuroscience, Columbia University, New York, NY 10027

5. Kavli Institute for Brain Science, Columbia University, New York, NY 10027

Abstract

Humans and other animals often infer spurious associations among unrelated events. However, such superstitious learning is usually accounted for by conditioned associations, raising the question of whether an animal could develop more complex cognitive structures independent of reinforcement. Here, we tasked monkeys with discovering the serial order of two pictorial sets: a “learnable” set in which the stimuli were implicitly ordered and monkeys were rewarded for choosing the higher-rank stimulus and an “unlearnable” set in which stimuli were unordered and feedback was random regardless of the choice. We replicated prior results that monkeys reliably learned the implicit order of the learnable set. Surprisingly, the monkeys behaved as though some ordering also existed in the unlearnable set, showing consistent choice preference that transferred to novel untrained pairs in this set, even under a preference-discouraging reward schedule that gave rewards more frequently to the stimulus that was selected less often. In simulations, a model-free reinforcement learning algorithm ( Q -learning) displayed a degree of consistent ordering among the unlearnable set but, unlike the monkeys, failed to do so under the preference-discouraging reward schedule. Our results suggest that monkeys infer abstract structures from objectively random events using heuristics that extend beyond stimulus–outcome conditional learning to more cognitive model-based learning mechanisms.

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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