In this study, we assessed the ability of three primate species to break a cognitive set bias in order to use a short cut. We found that capuchin and rhesus monkeys successfully used the shortcut at high rates, soon after it first became available. In doing so, they join the ranks of baboons and chimpanzees in outperforming humans, who tend to stick with the less efficient but familiar learned strategy (i.e., they show a cognitive set bias). Furthermore, using the shortcut was indeed more beneficial, as it both increased accuracy and decreased response times in all species.

We had two rationales for this study. First, we tested the extent to which the previously reported advantage of non-human primates over humans on the LS-DS task extended to New World monkeys and another species of Old World monkey. Second, we tested the hypothesis that lower working memory requirements would increase use of the learned strategy. For this study, we used the modified EZ LS-DS task, in which the Square 1 and Square 2 stimuli were presented at the same time and remained on the screen throughout the trial, rather than appearing briefly and then vanishing, as in the original LS-DS task. Therefore, participants did not need to track stimuli locations nor hold them in working memory in order to succeed. In support of this point, capuchins and rhesus in this study required only a fraction of the training trials that the baboons and chimpanzees needed in the original LS-DS task to learn the rule. However, decreasing the working memory load required by the learned strategy did not make the monkeys more likely to use it when the shortcut was also available.

Although our monkeys did not use the full learned strategy in PROBE trials, we found that 25–30% of monkeys used the switch strategy, in which they began with the learned strategy by selecting the first square, but then took the shortcut (instead of continuing the sequence by selecting the second square). Use of this intermediate strategy suggests a shift toward more habitual rule use, perhaps enabled by the decreased working memory load. Baboons in the original LS-DS very rarely used this intermediate strategy, whereas chimpanzees did so frequently31. Indeed, the reported working memory capacity in baboons32 has been lower than that reported for chimpanzees37 (but see ref.52), though a direct comparison is lacking. We hypothesize that both species would increasingly use the switch or learned strategy when working memory requirements are alleviated (e.g., when testing them on the EZ LS-DS). However, higher working memory availability alone cannot explain the rigid inflexibility that humans show in this optional-switch task.

Another possibility is that differences in primates’ initial rule encoding affect their susceptibility to cognitive set. Although the rule in our task was much easier to learn for the monkeys than it was in the original task, they still required substantially more training than the humans, who typically picked up the rule in just a few trials. Humans’ ability to encode the rule verbally may help them learn and use the strategy much more quickly than other primates can. However, such verbally encoded rules may be more firmly rooted and therefore less likely to be replaced by alternative strategies. Further, it is thought that more cognitive effort is required to switch to and from firmly encoded rules11. In line with this interpretation, we found that humans, but neither of the two monkey species, exhibited switch costs in this study. They made more mistakes when using the learned strategy after just having used the shortcut.

Capuchin and rhesus monkeys, on the other hand, needed more training to meet criterion for the learned strategy and made more mistakes when using it in BASE trials. In other words, the rule was not as easy to learn or use for the monkeys, suggesting that their initial rule encoding may have been weaker. This may have allowed them to adopt the more efficient alternative strategy more readily. Indeed, 70% of the monkeys (but only a single human) used the shortcut on the very first trial it became available. In this study, we had set the criterion for training at 80% accuracy in two trial blocks. It is possible that extended training with the rule would lead to more habitual rule use and a decreased ability to break cognitive set (as it does in humans18). Future studies should investigate this possibility, although we note that we found no effect of training duration (number of trials until criterion was reached) on shortcut use.

Interestingly, humans started using the shortcut at higher rates than in previous studies, but only as testing progressed. To our knowledge, this is the first study to demonstrate this effect. This result is incompatible with the idea that greater working memory availability makes the shortcut less beneficial because 1) humans used it more rather than less in the EZ LS-DS and 2) working memory requirements did not change over time. Instead, we suggest that this result highlights that cognitive flexibility is a balancing act between exploitation and exploration. On the one hand, if solution strategies are so entrenched that new information is ignored, they can lead us to make inefficient decisions and miss opportunities. On the other hand, if strategies are too susceptible to new input and easily replaced, we may get distracted by irrelevant or maladaptive information.

Our results therefore fit nicely into the variability-stability-flexibility pattern of cognitive flexibility53. According to this framework, initial strategy selection follows a variable pattern as a result of trial-and-error learning (e.g., the training phase in the present study), but is then replaced by a stable response strategy (e.g., the learned strategy was acquired and is being used consistently). Finally, people may enter a flexible state in which they can seek and adopt alternative strategies that better meet current demands. Thus far, the framework has focused on developmental trajectories of cognitive flexibility. For example, Gopnik and colleagues54 found that younger children outperformed adolescents and adults on a non-social task because they were more likely to try different strategies (variability) than older participants, who preferred a familiar solution (stability). This perhaps counterintuitive developmental result nicely parallels our cross-species findings that other primate species consistently outperform humans on the Standard and EZ LS-DS task.

Transition into the flexible state can result from better executive functioning (e.g., due to development or individual differences) or it can be induced externally (e.g., by a prompt to try something new25 or by a change in mindset24). In our study, the humans who started to use the shortcut more over time became more flexible without such external prompts, possibly due to increased exposure to and familiarity with the task. We observed this change in strategy use within the same individuals and over the course of a single test session. It is reasonable to assume that these individuals’ working memory capacities stayed essentially constant during that time, and we know that the working memory requirements of the task remained the same. This result can therefore not be attributed to differences in working memory load or individual differences in executive function in general. To our knowledge, this is one of the first studies to provide within-participant evidence for the variability-stability-flexibility pattern.

One possibility is that the relative duration of these three stages varies across development, species, or cultures. Different environments may favour either more stable or more flexible problem-solving strategies. For example, unpredictable environments and limited resource availability (e.g., due to a reliance on ephemeral fruit patches or certain foraging techniques, such as those requiring tool use) may require a willingness to seek out and try alternative strategies, i.e., increased cognitive flexibility. Future research could address the potential effect of a risky environment in several ways through cross-cultural research (e.g., populations with different foraging and farming practices), comparative research (e.g., species with different feeding or social ecologies), or even within the same population (e.g., by making the rewards for different strategies probabilistic).

Good decision-making requires that we recognize when the familiar strategies that we have been exploiting may no longer be the most efficient and when we should instead explore whether other strategies may be more beneficial. In this study, use of the shortcut was both faster and boosted the monkeys’ accuracy (compared to BASE trials, in which they could only use the learned strategy), perhaps creating an incentive to adopt the shortcut early and consistently. In contrast, humans already performed at ceiling with the learned strategy (i.e., there was less of a benefit to using the shortcut) and their accuracy dropped when they did switch strategies (i.e., there was a higher cost to using the shortcut). However, over time, our human participants did begin to explore and use the alternative strategy more. Extended use of the same learned strategy may have made the time savings of the shortcut more attractive (as reduced response times can add up, and the students in our sample were motivated to work quickly) or may encourage tendencies to explore other options in general, perhaps due to boredom.

Consider the following example. Calculating the mean of five numbers by hand is fairly simple, and you can do so many times in a row without problem. Eventually, however, this would get old, and you might look for alternatives and discover the mean function in a statistics program, which allows you to do the calculation more efficiently. On the other hand, calculating an ANOVA by hand is more difficult to learn, more effortful to do correctly, and more prone to errors. In this case, you might try a different strategy as soon as it becomes available in case it is easier. We believe this nicely illustrates the situation for the humans and monkeys in this study, respectively.

Our findings suggest that, contrary to our initial hypothesis, differences in rule encoding and in the relative costs and benefits of the available strategies better explain the observed results than differences in working memory among species. However, we believe there is room for working memory to explain some of the variability in cognitive flexibility within species (e.g., ref.38). Future comparative research should expose the same individuals to different conditions that vary in their working memory requirements. We would expect low working memory load to favour use of the learned strategy and increased load to favour shortcut use (e.g., in the LS-DS task, this could be achieved by presenting more squares and requiring longer sequences to be remembered). Another promising avenue for comparative research would be to assess optional-switch cognitive flexibility and individual differences in executive functioning, such as working memory, at the same time.

In humans, of course, executive functioning typically increases with age. However, it can be difficult to tease apart its effect on cognitive flexibility in developmental studies because other factors such as knowledge and experience with formal schooling also increase with age. In Western cultures, for example, standardized testing and formal schooling may encourage rote repetition and search for a single correct solution29, which could stifle flexible problem-solving from an early age. However, in different studies using the original LS-DS task with the same participant pool, half the sample continued to use the learned strategy even when told explicitly “Don’t be afraid to try something new,”25 and about 30% did so even after watching a video demonstrating the shortcut55. Thus, to some extent, Westerners might stick to the learned strategy because it is what they believe they “should” do, but that is only part of the story.

One advantage of the nonverbal LS-DS task is that it facilitates research across many different populations (e.g., different species, different developmental stages, different cultures). We encourage future research in this area to systematically explore how the degree of formal education and teaching style may affect flexible strategy use independent of individual differences in executive functioning. For example, in a sample of Zoo Atlanta visitors, children (7–10 years) were at least four times more likely to use the shortcut than adults, but still more than half of them continued to use the learned strategy30. Similarly, in a cross-cultural study with the seminomadic Himba of Namibia, 60–70% of participants failed to adopt the shortcut25. They did use it more than Western undergraduates, suggesting that humans’ susceptibility to cognitive set is not universal. However, in no human sample to date have participants used the shortcut nearly as much, as early, or as consistently as any of the non-human species.

Taken together, our results suggest that a lower working memory load may facilitate initial habitual strategy use to some extent (reflected in the monkeys’ use of the switch strategy). However, working memory availability alone does not explain humans’ initial inflexibility, nor does it explain why humans increasingly used the shortcut over time. We suggest that differences in how firmly the learned strategy may have been encoded better explains the observed inter-species variation in susceptibility to cognitive set. Further, it will be important to consider differences in the relative costs and benefits of exploiting a familiar strategy versus exploring alternative strategies, and how they may change over time or different contexts. In doing so, we can move from assessing cognitive flexibility as merely absent or present (by asking yes or no) toward establishing which conditions favour more flexible or more inflexible decision-making (by asking when and how). Ultimately, this lets us take advantage of more efficient alternatives and will help us make better decisions.