Bridging Species Differences in Rule Switching: How Humans and Monkeys Solve the Same Wisconsin Card Sorting Task

Applying learned rules to solve new problems and adapting responses after rule change are fundamental aspects of intelligence. How we accomplish these tasks has long intrigued researchers, but the experiments conducted to answer this often focus on a single species. This makes it challenging to identify general principles of cognition and build frameworks for neural implementation and algorithms that may inspire the development of artificial intelligence. Only recently have studies begun to examine behavioral strategies across species within a shared decision-making paradigm, adopting rigorous computational modeling for direct comparisons. Such cross-species comparisons can reveal both unique and common cognitive functions and the underlying neurobiological basis. But despite the emergence of cross-species comparisons, advances in our understanding of rule learning and flexible decision-making have been limited, however, by the use of simplistic stimuli, with only one feature varying from trial to trial. This contrasts the complexity of real-life decision-making, where one must consider multiple features to determine the best option. This limitation can be addressed using the Wisconsin Card Sorting Test (WCST), which requires inference of multiple rules that govern reward outcome. This test has been used extensively to examine flexible rule-based cognition in primates (Kuwabara et al., 2014; Mansouri et al., 2020). In the canonical WCST, subjects sort cards into different piles based on one of several visual features, such as shape, color, or number, of graphical elements on the card. Importantly, the sorting rule is not explicitly revealed. Instead, after assigning a card to a given pile, subjects are told whether the card was correctly or incorrectly sorted. Subjects must learn the sorting rule based on this feedback. Rule contingency changes without warning after several correct matches, so subjects should continue to use the outcome feedback to infer what … Correspondence should be addressed to Ningyu Zhang at zhangny{at}ion.ac.cn.