Week 8 – How dynamic environments shape cognitive flexibility

This early draft was authored by Sarah M. Pope.

Flexible problem solving has long been revered as a hallmark of human ingenuity. Yet, humans’ predilection for ‘sticking to what you know’ is evident in a wide range of behaviors and biases. That said, our global expansion and the sheer diversity of contemporaneous human cultures is a testament to our ability to flexibly adapt existing techniques or seek alternatives. The question is not ‘are humans flexible or inflexible problem-solvers?’ but rather ‘when do the benefits of being flexible exceed the costs?’ Here I propose the constrained flexibility framework for understanding how environmental variability, predictability, and harshness work in tandem to shape flexible versus inflexible strategy-use. I also distinguish between strategy switching that occurs as a result of failure and strategy switching that includes searching for and adopting a better alternative, here referred to as responsive and exploratory flexibility, respectively. Efforts to understand human cognitive flexibility have almost exclusively focused on how well we switch when we have to, rather than when, and under what contexts, we choose to. Understanding the conditions in which changing strategies is most beneficial may help explain how our species strikes the balance between maintaining working strategies and finding or creating better ones.

Introduction

I recently heard a Congolese fable describing how the antelope, a weak but wily animal, became the king of the jungle. It had been decreed that whichever animal was strong enough to shoot an arrow through the trunk of the largest tree would win the throne. The elephant and the leopard, the strongest of the animals, practiced and practiced. But on the day of the competition their arrows barely pierced its bark. They laughed when the antelope stepped up to take his turn. However, knowing that he would never be strong enough, the antelope had come up with a different strategy. In the days before the event, the antelope convinced his friend, the bee, to drill a hole through the tree in a place that only he would know. On the day of the competition, the antelope took aim and shot the arrow through the hole, thereby winning the throne (told by Francy Ntamboudila, 2020). The leopard and the elephant failed because they relied solely on their might, whereas the antelope’s flexible thinking showed his true strength.

Flexible behavior is an adaptive response to dynamic environments. As challenges arise or opportunities shift, strategies must be updated in order to meet changing demands. The umbrella term behavioral (or phenotypic) plasticity refers to an individual’s ability to produce different behaviors in response to environmental changes (Fox & Weber, 2002; Snell-Rood, 2013). This chapter will focus on the cognitive mechanisms underlying flexible behavior, or cognitive flexibility. Cognitive flexibility is the ability to adaptively select between known solutions and innovated or acquired novel solutions in response to relevant environmental changes (Laureiro-Martínez & Brusoni, 2018; Pope, 2018; Ueltzhöffer et al., 2015). Thus, cognitive flexibility is defined as the – contextually mediated – optimal balance between repeating or returning to a familiar strategy and searching for an alternative, with suboptimal strategy-selection occurring as a result of either inflexibility, an inability to switch from an active strategy to a better one, or overflexibility, an inability to maintain the best strategy.

Environments fluctuate predictably, with daily or seasonal cycles, and unpredictably, in response to stochastic events like fires or floods. An animal’s lived environment is also a product of its habitat, which may span multiple, even micro-, climates, and its ranging patterns, which might regularly extend into novel territory. Flexible behavior is especially important for long-lived, mobile species, like humans, who must cope with changing environments throughout their lifetimes (Dingemanse & Wolf, 2010; Sol et al., 2016; van Schaik, 2013; Vicente & Wang, 1998a). Indeed, humans exhibit an unprecedented degree of behavioral diversity (Fogarty et al., 2015; Hill et al., 2009) which allows us – for better or for worse – to inhabit and modify over half of earth’s landmass (Henrich & McElreath, 2003; Vitousek et al., 1997), suggesting that, at least as a species, humans are quite capable of updating their existing strategies to meet environmental needs. However, on an individual level, flexibility is not always evident.

Like the elephant and the leopard, humans are often blinded by familiar strategies or known solutions. In a classic example, children and adults learned to solve a set of arithmetic ‘water jar’ problems using a somewhat tedious, four-step solution. After they solved six examples in this way, they were given four problems that could be solved either by the learned solution or by a more efficient, one-step alternative (Luchins, 1942). Luchins tested thousands of American adults and children, using various manipulations, and consistently found that a large majority of participants continued to use their learned strategy despite the presence of a better alternative (Luchins, 1942; Luchins & Luchins, 1950). Termed ‘cognitive set,’[1] the propensity for familiar strategies to occlude – even more efficient – alternatives, has been demonstrated across a wide variety of contexts including strategic reasoning (Bilalić et al., 2008), design and engineering (Chrysikou & Weisberg, 2005; Jansson & Smith, 1991), mathematics (Crooks & McNeil, 2009; Lemaire & Leclère, 2014; Wertheimer, 1945), sequential problem solving (Jacobson & Hopper, 2019a; Pope et al., 2015, 2019, 2020; Watzek et al., 2019), insight problem solving (Hanus et al., 2011; Öllinger et al., 2008), and functional fixedness paradigms (Adamson, 1952; Duncker & Lees, 1945; German & Barrett, 2005). We are faced with a contradiction, on one hand humans are profoundly adaptive, the inventors and modifiers behind a technological revolution, but on the other hand, we are dismally conservative, either unwilling or unable to move beyond the tried and true. How can humans be simultaneously flexible and inflexible? Here, I suggest that the answer lies in a different question, rather ‘when do the benefits of being flexible exceed the costs?’

In this chapter, I begin by discussing the relative risks and benefits of repeating a current strategy (staying), switching to a known alternative (switching), or searching for a novel alternative (searching), highlighting that the optimal balance between the three is highly dependent on exogenous demands. Next, building off of existing theories of human cognitive evolution, I propose a framework for how environmental variability, predictability, and harshness might work in concert with one another to shape cognitive flexibility.

Costs and benefits of staying, switching, and searching

Why change strategies?

Changing strategies is beneficial when an alternative strategy is better than the current strategy. Under forced-switchconditions, when the current strategy no longer works, a shift in goal or approach is clearly adaptive as the value of the current strategy has become zero. Switching or searching that occurs under forced-switch conditions will be henceforth referred to as responsive flexibility. Strategies can become temporarily or even permanently ineffective for a number of reasons. Capabilities, goals, and opportunities shift throughout development and in response to exogenous changes in the environment like predation or other mortality risks, seasonal or spatial fluctuations in weather and resource availability, as well as downstream effects of climatic variability or anthropogenic disturbance. When a current strategy stops working, adaptive behavior might entail the discovery or innovationof a new technique, or the application of another known strategy to a new context. Under forced-switchconditions, inflexibility always results in failure. The majority of human cognitive flexibility research makes use of forced-switch metrics, in the form of intra- and extra-dimensional set-shifting (Doebel & Zelazo, 2015; Zelazo, 2004) or task-switching paradigms (Meiran, 2010; Monsell, 2003; Sakai, 2008). Responsive flexibility, or rather inflexibility, is then measured by the extent to which actors perseverate with their current strategy after it stops working. Changing strategies under forced-switch conditions is considered an integral part of executive functioning (Doebel & Zelazo, 2015; Miyake et al., 2000).

The other context in which flexible behavior can be beneficial is when a current strategy is less efficient than an alternative. Under optional-switch conditions, rather than avoiding failure, changing strategies can reduce inefficiency but not always. Switching or searching that occurs under optional-switch conditions will be henceforth referred to as exploratory flexibility. Exploratory flexibility offers an important mechanism for understanding human ingenuity and cumulative culture, which necessarily builds upon existing techniques. However, within psychology, flexibility under optional-switch conditions is rarely measured. Existing metrics include the Water Jar (described above) and other learned-sequence tasks (Luchins, 1942; Luchins & Luchins, 1950; Pope et al., 2020; Watzek et al., 2019) along with some task shifting (Ardiale & Lemaire, 2012; Arrington & Logan, 2004; Lemaire & Leclère, 2014), token exchange (Hopper et al., 2011; Van Leeuwen et al., 2013; van Leeuwen & Call, 2017), and extractive foraging tasks (Davis et al., 2019; Jacobson & Hopper, 2019b; Price et al., 2009). However, the balance between exploiting useful strategies and exploring alternatives, has been extensively studied within foraging (Charnov, 1976; Cohen et al., 2007; Stephens & Krebs, 1986) and decision making research (Acuna & Schrater, 2007; Fischhoff & Broomell, 2020; Gittins & Jones, 1979; Payne et al., 1994; Peterson & Verstynen, 2019), wherein it is measured using a range of reinforcement learning and sequential decision-making paradigms, like patch-leaving and multi-armed bandit tasks (see Averbeck, 2015 for a review). In optional switch conditions, the benefit of selecting a better strategy is pitted against the computation and search effort required to compare alternatives, combined with the opportunity costs incurred by learning or switching delays.

Why repeat a strategy?

Failure to switch strategies occurs in many contexts, and is not always maladaptive.  In forced-switch conditions, perseveration occurs when a previously successful strategy continues to be evoked, despite evidence that it no longer works (Floresco, 2011; Schillemans, 2011). Perseveration can be adaptive if the failure is transient, or the causal mechanisms are unclear (i.e. the failure might have been a fluke). In optional-switch conditions, where multiple strategies are available, changing strategies is only valuable if it leads to adopting a better one. In other words, it would be maladaptive to switch if the chosen alternative was not more efficient than the current strategy – or if the efficiency benefit of the chosen alternative failed to exceed the time invested in finding, selecting, and honing it. Another benefit of inflexibility is that it can help maintain or refine useful strategies, reallocating the effort that might be spent searching towards skill practice and eventually specialization (Dingemanse & Wolf, 2013). Additionally, learning and maintaining a new strategy is neurologically expensive (Laughlin & Sejnowski, 2003). It seems likely that repeating a strategy is an adaptive default-approach, but one that should be deviated from when the benefits of finding an alternative exceed the costs (Duckworth, 2010).

Dealing with ambiguity

When all possible strategies are known, optimal behavior is simply a matter of switching to or maintaining the best one. However, we do not occupy nor have we evolved within unambiguous environments. Under conditions of uncertainty, the relative risks and rewards of potential strategies are often unclear. To make an optimal decision, one could search for all possible strategies and test their relative risks and payouts, but the time and effort spent gathering these data may quickly outweigh the benefit of selecting the better alternative, at least in the short-term. Heuristics, such as ‘always choose the second-cheapest wine’ (McFadden et al., 1999), are individuals’ set of guiding principles which support decision-making under uncertainty, often by systematically ignoring some of the available information (Gigerenzer et al., 2011). The computational requirements of perfect decision-making are immense and heuristics can be useful cognitive shortcuts, but they lead to predictable biases and errors (Kahneman et al., 1982; Tversky & Kahneman, 1974), especially when cognitive capacity is limited (Cash-Padgett & Hayden, 2019). Behavior guided by heuristics is not often optimal, but on average, it should reach an acceptable level of efficiency, at least within the environment in which it was formed (Fawcett et al., 2013).When the relative risks and benefits of all possible strategies is unknown or unclear, the decision to repeat a strategy, switch to a known alternative, or search for a new one is likely guided by heuristics, which are, ideally, tuned to optimize decision-making in that environment (Todd & Gigerenzer, 2007; Vicente & Wang, 1998b). To understand cognitive flexibility we must first consider how exogenous pressures, like variability, predictability, and harshness might serve to shape our biases towards staying, switching, and searching.

The constrained flexibility framework

When considering how environmental forces shape cognitive flexibility, we can make use of existing hypotheses which posit that adaptive cognition is heavily influenced by environmental variability, the extent to which the environment changes over time or space, predictability, the temporal regularity of changes or the degree to which they are correlated, and harshness, the level of consequence elicited by strategy failure (see Box 1). These hypotheses offer important perspectives; however, they often either conflate variability, predictability and harshness, or consider their effects in isolation.

Here, I propose that recognizing how variability, predictability, and harshness work in concert with one another to regulate cognitive flexibility is integral to predicting when strategies should be repeated or changed, and if change is required, how the relative costs and benefits of responsive vs exploratory flexibility are balanced. Recall that responsive flexibility consists of switching or searching as a result of strategy-failure and exploratory flexibility is switching or search that occurs despite already having a working strategy. Exploratory flexibility is risky but here I argue that under certain circumstances, the adaptive benefit of potentially finding or innovating a better solution is enough to offset the costs.

The constrained flexibility framework unifies existing hypotheses regarding the impacts of environmental variability, predictability, and harshness, on behavioral flexibility, taking into account their unique and combined effects, in order to predict the circumstances under which exploratory flexibility might contribute to enhanced cognitive flexibility. Specifically, it suggests that exploratory flexibility is suppressed in harsh, stable or predictably variable environments, but may be a valuable tool in unpredictable environments, so long as harshness is low.

Specifically, in stable environments, the usefulness of a strategy does not change over time and so responsive flexibility is not needed. Once an optimal strategy is found, there is little benefit derived from maintaining, using, or seeking alternatives. Thus, under stable conditions, exploratory flexibility is maladaptive – and should be suppressed – so long as a working strategy can be maintained. In variable environments, the usefulness of a strategy can either change regularly, as in predictably variable environments or irregularly, as in unpredictably variable environments. In variable environments, regardless of harshness, responsive flexibility is critical for handling situations where no working strategy is known (like in novel environments) or when a previously effective strategy stops working. However, the conditions which predict exploratory flexibility are less clear. In predictably variable environments, exploratory flexibility may only be useful initially, during a strategy accumulation stage.  In this way, a range of strategies might be acquired and then, if memory capacity is high enough, switched between as needed. Extended explorative behavior is not useful in predictably variable environments because even though optimal strategies rotate over time, the set of optimal strategies is unchanged. Thus, cognitive resources should be devoted to maintaining and honing these working strategies rather than searching for alternatives. In unpredictably variable environments, previous strategies may or may not work well, or even at all. Here increased exploratory flexibility might serve to minimize delays in updating to new optimal strategies or in some cases, result in the discovery or innovation of a novel, more efficient technique. Importantly, both predictably and unpredictably variable environments can exist partly or wholly in states of harshness, which places a penalty on exploratory flexibility commensurate with the severity of the consequences of failure. Responsive flexibility is unaffected by harshness, because it only occurs when a previous strategy stops working and thus no amount of harshness will outweigh the benefit of switching or searching for an alternative. The following sections will describe existing hypotheses and evidence supporting the constrained flexibility framework. See Figures 1-2 for a graphic representation of these dynamics.

When to stay, switch, or search

“In an uncertain and changing environment, where values of all potential options are unknown and/or the values of these options change over time, one must adapt by flexibly alternating between exploration and exploitation in order to maintain efficient performance over time and to keep track of the state of the environment.” – (Addicott et al., 2017, p 1932)

Predictable versus unpredictable environments

Both predictable and unpredictable variation are common in the natural world. Predictable variation can be seen in the many cycles which govern our climate on daily, monthly, and annual bases. Seasonal fluctuations, especially, might be drastic and require a range of behaviors throughout the year. However, seasonal transitions occur smoothly and predictably. Riotte-Lambert & Matthiopoulos (2020) suggest that when environmental predictability is high, learning and memory are advantageous, because they allow existing strategies to be recalled and activated when needed, reducing the need for strategy search and its accompanying costs (also see: Colwell, 1974; Milton, 1981). Indeed, in several decision-making tasks, where participants must choose to either exploit a current resource or explore alternatives, more exploratory behavior occurs in response to unpredictable or stochastic, compared to stable, reward structures (Behrens et al., 2007; Navarro et al., 2016; Speekenbrink & Konstantinidis, 2015).

Even within mostly predictable environments, some volatility is inevitable. It seems likely that humans’ suboptimal behavior in optional-switch paradigms, where familiar strategies occlude better alternatives, might be the result of reduced exploratory flexibility that occurs when the environment is (in these cases, inaccurately) considered to be fairly stable (Bilalić et al., 2008; Luchins, 1942). In these studies, participants under-searched their environments and this seems to occur in humans’ everyday lives as well. Following two days of public transportation strikes, an estimated 5% of London commuters, who had been forced to switch from their normal routes by station closures, did not return to their previous routes; suggesting that the need to search for an alternative strategy resulted in their finding a better one (Larcom et al., 2017).

Inaccurate stability judgements may also help explain cross-cultural differences in cognitive set. Pope et al. (2019) found that Namibian Himba participants were ~4 times more likely to find and use a more efficient ‘shortcut’ strategy than Americans on a touch screen sequential problem-solving task. It is possible that unfamiliarity with computer games led Himba participants to consider the game less predictable than their American counterparts. However, another intriguing possibility is that if decision-making heuristics are somewhat domain-general, unpredictability in other areas of Himba participants’ lives might have result in a lowered threshold for exploratory search. This possibility aligns with other research which found that younger children exhibited more exploratory flexibility than adolescents and adults (German & Defeyter, 2000; A Gopnik et al., 2015; Pope et al., 2015). Indeed, young children may be more prone to exploratory flexibility as they are in a constant state of strategy acquisition, wherein predictive decision-making, based on reasoning and executive functioning, may still be out of reach (Alison Gopnik, 2020; Ionescu, 2017).

Harsh environments

Thus, exploratory flexibility may be a valuable tool for handling environmental changes, especially those that are unpredictable, but it comes with a price. Searching for a novel strategy can take time, and may result in failure. If a viable alternative is found, there may be a learning delay before it can be enacted, or attempts to learn it could fail. Finally, the net benefit of using the alternative strategy might be lower than the previous strategy, based on the time invested in finding, learning, and using it.

In harsh environments, time is limited and the consequences of failure are high. It seems likely therefore that harshness places severe constraints on exploratory flexibility.  For example, in a modified water-jar paradigm, Beilock & Decaro (2007) found that participants with higher working memory were more likely to use the suboptimal four-step solution under conditions of high stress. Similarly, Wilson et al. (2017) found that humans decrease their information seeking behavior when time horizons (the maximum number of choices or task duration) are short. Additionally, Averbeck (2015) reviews a number of patch-leaving and multi-armed bandit tasks demonstrating that search is less beneficial when time horizons are short and the consequences of failure are high. Thus, exploratory flexibility should only occur in harsh environments when it is absolutely necessary – when no other options exist.

An analysis of commercial fishing vessel positional records showed that in normal conditions, more exploration did not increase fishing performance. However, during a major disturbance, which resulted in the closure of the most-used fishing grounds, boats that had explored during normal conditions outperformed their non-exploratory peers, and were more likely to continue fishing despite the disturbance (O’Farrell et al., 2019). It follows that strategy accumulation may be most beneficial when an individual spends some, but not all of its time, in harsh environments. Addicott et al. (2017) suggested that increased exploration and strategy accrual during periods of moderation, when the benefit of a possible alternative exceed the search costs, might result in useful back-up strategies that could later be deployed during periods of harshness. They even expanded this idea to account for risky behavior during human adolescence, noting that it likely provides valuable information that can be utilized later in adulthood (Mata et al., 2013). For example, hunter-gatherer toolkits are significantly predicted by proxies for risk of resource failure, suggesting that the accumulation of more tools serves to buffer harshness (Collard et al., 2005).

Thus, in alignment with the ‘necessity is the mother of invention’ hypothesis, constrained flexibility predicts that when no other options are available exploratory flexibility should kick in, even in harsh environments. However, consistent with the spare time hypothesis, it notes that exploration during periods of relative calm can also provide fitness benefits, even at later times. Exploratory flexibility is a powerful, but costly tool. Next, we look at ways in which the price of exploration can be mitigated.

Mitigating costs of exploration

Directed versus random exploration

I’m considering a section on directed vs random exploration here. 

Socially Acquired Information

In humans, the most prevalent means of reducing exploration costs is by copying successful strategies from other individuals. The Cultural Brain hypothesis suggests that larger brains are selected for their ability to acquire adaptive information via both asocial and social learning (Muthukrishna, 2015). Evidence from simulations suggest that the combination of asocial and social information can be very adaptive (Acerbi & Parisi, 2006; Muthukrishna et al., 2018). Especially in technologically advanced settings, where one cannot easily devise the underlying mechanism, social learning is an important tool (Froese & Leavens, 2014). 

Interestingly, humans may be over-reliant on socially acquired strategies, as is evidenced by our proclivity for over-imitation (Hoehl et al., 2019; Horner & Whiten, 2005). Importantly, within the constrained flexibility framework, access to useful socially-acquired information would act to suppress exploration because the costs and benefits of individual exploration are often unknown, and therefore eclipsed by the low cost and known benefit of a socially acquired strategy. Indeed, Bonawitz et al. (2011) found that children who were taught how to play with a novel tool were far less exploratory than naïve peers. However, this does not seem to be maladaptive overall; novel contexts always require strategy search, but if useful social information can be efficiently acquired, then search cost can be almost entirely avoided.

[Considerations regarding reliance on social information, especially in the face of harshness, to be expanded here. Also to discuss the inherent social value, in terms of conformity and cultural cohesion, that arise from using socially-acquired strategies.]

Concluding remarks

In reality, environments exist along continua – from stable to flexible, predictable to unpredictable, mild to harsh. Humans occupy a range of ecological and cultural environments simultaneously and the concepts of variability, predictability, and harshness depend on the domain and time scale being considered. Furthermore, our exposure to environmental pressures is confounded by our behaviors, such as movement patterns or our ability to buffer variability (e.g. niche construction; see Chevin & Hoffmann, 2017 for discussion). By differentiating between strategy changes that occur in response to failure, or responsive flexibility, versus those that occur proactively, or exploratory flexibility, the constrained flexibility framework provides a theoretical basis for understanding how our species balances the need to maintain strategies that work with the benefit of finding or creating better ones.  [To be expanded and tied back into the introduction more succinctly.]

Terminological question for the other authors

In this chapter I have used a common definition of cognitive flexibility, which describes it as the optimal balance between repeating or returning to a familiar strategy versus trying a novel alternative. However, this definition becomes problematic when one simplifies it into: the optimal balance between being inflexible (continuing to use a strategy) and being flexible (switching strategies). Indeed, one could be “cognitively flexible” while acting “inflexibly” if the strategy that is being repeated is the best option. I would like to ask if the other authors are aware of alternative terms that are already in use or, if not, request feedback on the use of ‘adaptive cognition’ as a replacement for ‘cognitive flexibility.’

References

Acerbi, A., & Parisi, D. (2006). Cultural transmission between and within generations. Journal of Artificial Societies and Social Simulation, 9(1), Article 1.

Acuna, D., & Schrater, P. (2007). Bayesian Modeling of Human Sequential Decision-Making on the Multi-Armed Bandit Problem. Bernoulli, 2065–2070.

Adamson, R. E. (1952). Functional fixedness as related to problem solving: A repetition of three experiments. Journal of Experimental Psychology, 44(4), 288–291.

Addicott, M. A., Pearson, J. M., Sweitzer, M. M., Barack, D. L., & Platt, M. L. (2017). A Primer on Foraging and the Explore/Exploit Trade-Off for Psychiatry Research. Neuropsychopharmacology, 42(10), 1931–1939. https://doi.org/10.1038/npp.2017.108

Allman, J., McLaughlin, T., & Hakeem, A. (1993). Brain weight and life-span in primate species. Proceedings of the National Academy of Sciences, 90(1), 118–122. https://doi.org/10.1073/pnas.90.1.118

Ardiale, E., & Lemaire, P. (2012). Within-item strategy switching: An age of comparative study in adults. Psychology and Aging, 27(4), 1138–1151. https://doi.org/10.1037/a0027772

Arrington, C. M., & Logan, G. D. (2004). The cost of  voluntary task switch. Psychological Science, 15(9), 610–615.

Averbeck, B. B. (2015). Theory of Choice in Bandit, Information Sampling and Foraging Tasks. PLoS Computational Biology, 11(3), 1–28. https://doi.org/10.1371/journal.pcbi.1004164

Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 1214–1221. https://doi.org/10.1038/nn1954

Beilock, S. L., & Decaro, M. S. (2007). From poor performance to success under stress: Working memory, strategy selection, and mathematical problem solving under pressure. J Exp Psychol Learn Mem Cogn, 33(6), 983–998. https://doi.org/10.1037/0278-7393.33.6.983

Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M., & Holekamp, K. E. (2016). Brain size predicts problem-solving ability in mammalian carnivores. Proceedings of the National Academy of Sciences, 113(9), 2532–2537. https://doi.org/10.1073/pnas.1505913113

Bilalić, M., McLeod, P., & Gobet, F. (2008). Why good thoughts block better ones: The mechanism of the pernicious Einstellung (set) effect. Cognition, 108(3), 652–661.

Bonawitz, E., Shafto, P., Gweon, H., Goodman, N. D., Spelke, E., & Schulz, L. (2011). The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery. Cognition, 120(3), 322–330. https://doi.org/10.1016/j.cognition.2010.10.001

Brosnan, S. F., & Hopper, L. M. (2014). Psychological limits on animal innovation. Animal Behaviour, 92(March), 325–332. https://doi.org/10.1016/j.anbehav.2014.02.026

Cash-Padgett, T., & Hayden, B. (2019). Overstaying in patchy foraging can be explained by behavioral variability [Preprint]. Animal Behavior and Cognition. https://doi.org/10.1101/868596

Charnov, E. L. (1976). Optimal foraging, the marginal value theorem. Theoretical Population Biology, 9(2), 129–136. https://doi.org/10.1016/0040-5809(76)90040-X

Chevin, L.-M., & Hoffmann, A. A. (2017). Evolution of phenotypic plasticity in extreme environments. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1723), 20160138. https://doi.org/10.1098/rstb.2016.0138

Chrysikou, E. G., & Weisberg, R. W. (2005). Following the wrong footsteps: Fixation effects of pictorial examples in a design problem-solving task. J Exp Psychol Learn Mem Cogn, 31(5), 1134–1148. https://doi.org/10.1037/0278-7393.31.5.1134

Cohen, J. D., McClure, S. M., & Yu, A. J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 933–942. https://doi.org/10.1098/rstb.2007.2098

Collard, M., Kemery, M., & Banks, S. (2005). Causes of Toolkit Variation Among Hunter-Gatherers: A Test of Four Competing Hypotheses. 19.

Colwell, R. K. (1974). Predictability, Constancy, and Contingency of Periodic Phenomena. Ecology, 55(5), 1148–1153. https://doi.org/10.2307/1940366

Crooks, N. M., & McNeil, N. M. (2009). Increased practice with ‘set’ problems hinders performance on the water jar task. Preceedings of the 31st Annual Conference of the Cognitive Science Society, Cognitive, 643–648.

Dart, R. A., & Salmons, A. (1925). Australopithecus africanus: The man-ape of South Africa. A Century of Nature: Twenty-One Discoveries That Changed Science and the World, 10–20.

Davis, S. J., Schapiro, S. J., Lambeth, S. P., Wood, L. A., & Whiten, A. (2019). Behavioral conservatism is linked to complexity of behavior in chimpanzees (<em>Pan troglodytes</em>): Implications for cognition and cumulative culture. Journal of Comparative Psychology, 133(1), 20. https://doi.org/10.1037/com0000123

Deaner, R. O., Barton, R. A., & Van Schaik, C. (2003). Primate brains and life histories: Renewing the connection. Primates Life Histories and Socioecology, 233–265.

deMenocal, P. B. (1995). Plio-pleistocene African climate. Science, 270(5233), 53–59.

Dingemanse, N. J., & Wolf, M. (2010). Recent models for adaptive personality differences: A review. Philos Trans R Soc Lond B Biol Sci, 365(1560), 3947–3958. https://doi.org/10.1098/rstb.2010.0221

Dingemanse, N. J., & Wolf, M. (2013). Between-individual differences in behavioural plasticity within populations: Causes and consequences. Animal Behaviour, 85(5), 1031–1039. https://doi.org/10.1016/j.anbehav.2012.12.032

Doebel, S., & Zelazo, P. D. (2015). A meta-analysis of the Dimensional Change Card Sort: Implications for developmental theories and the measurement of executive function in children. Dev Rev, 38, 241–268. https://doi.org/10.1016/j.dr.2015.09.001

Duckworth, R. A. (2010). Evolution of Personality: Developmental Constraints on Behavioral Flexibility. The Auk, 127(4), 752–758. https://doi.org/10.1525/auk.2010.127.4.752

Duncker, K., & Lees, L. S. (1945). On problem-solving. Psychological Monographs, 58(5), i–113. https://doi.org/10.1037/h0093599

Fawcett, T. W., Hamblin, S., & Giraldeau, L.-A. (2013). Exposing the behavioral gambit: The evolution of learning and decision rules. Behavioral Ecology, 24(1), 2–11. https://doi.org/10.1093/beheco/ars085

Fischhoff, B., & Broomell, S. B. (2020). Judgment and Decision Making. Annual Review of Psychology, 71(1), 331–355. https://doi.org/10.1146/annurev-psych-010419-050747

Floresco, S. B. (2011). Neural circuits underlying behavioral flexibility: Multiple brain regions work together to adapt behavior to a changing environment. Psychological Science Agenda, 25(4), 1–8.

Fogarty, L., Creanza, N., & Feldman, M. W. (2015). Cultural Evolutionary Perspectives on Creativity and Human Innovation. Trends in Ecology & Evolution, 30(12), 736–754. https://doi.org/10.1016/j.tree.2015.10.004

Fox, C. R., & Weber, M. (2002). Ambiguity aversion, comparative ignorance, and decision context. Organizational Behavior and Human Decision Processes, 88(1), 476–498. https://doi.org/10.1006/obhd.2001.2990

Froese, T., & Leavens, D. A. (2014). The direct perception hypothesis: Perceiving the intention of another’s action hinders its precise imitation. Front Psychol, 5, 65–65. https://doi.org/10.3389/fpsyg.2014.00065

German, T. P., & Barrett, H. C. (2005). Functional Fixedness in a Technologically Sparse Culture. American Psychological Society, 16(1), 1–4.

German, T. P., & Defeyter, M. A. (2000). Immunity to functional fixedness in young children. Psychonomic Bulletin & Review, 7(4), 707–712.

Gigerenzer, G., Hertwig, R., & Pachur, T. (Eds.). (2011). Heuristics: The foundations of adaptive behavior. Oxford University Press.

Gittins, J. C., & Jones, D. M. (1979). A dynamic allocation index for the discounted multiarmed bandit problem. Biometrika, 66(3), 561–565. https://doi.org/10.1093/biomet/66.3.561

Gopnik, A, Griffiths, T. L., & Lucas, C. G. (2015). When Younger Learners Can Be Better (or at Least More Open-Minded) Than Older Ones. Current Directions in Psychological Science, 24(2), 87–92. https://doi.org/10.1177/0963721414556653

Gopnik, Alison. (2020). Childhood as a solution to explore–exploit tensions. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1803), 20190502. https://doi.org/10.1098/rstb.2019.0502

Grove, M. (2011). Speciation, diversity, and Mode 1 technologies: The impact of variability selection. Journal of Human Evolution, 61(3), 306–319. https://doi.org/10.1016/j.jhevol.2011.04.005

Hanus, D., Mendes, N., Tennie, C., & Call, J. (2011). Comparing the performances of apes (Gorilla gorilla, Pan troglodytes, Pongo pygmaeus) and human children (Homo sapiens) in the floating peanut task. PLoS One, 6(6), e19555–e19555. https://doi.org/10.1371/journal.pone.0019555

Henrich, J., & McElreath, R. (2003). The evolution of cultural evolution. Evolutionary Anthropology: Issues, News, and Reviews, 12(3), 123–135. https://doi.org/10.1002/evan.10110

Hill, K., Barton, M., & Hurtado, A. M. (2009). The emergence of human uniqueness: Characters underlying behavioral modernity. Evolutionary Anthropology: Issues, News, and Reviews, 18(5), 187–200. https://doi.org/10.1002/evan.20224

Hoehl, S., Keupp, S., Schleihauf, H., McGuigan, N., Buttelmann, D., & Whiten, A. (2019). ‘Over-imitation’: A review and appraisal of a decade of research. Developmental Review, 51, 90–108. https://doi.org/10.1016/j.dr.2018.12.002

Hopper, L. M., Schapiro, S. J., Lambeth, S. P., & Brosnan, S. F. (2011). Chimpanzees’ socially maintained food preferences indicate both conservatism and conformity. Anim Behav, 81(6), 1195–1202. https://doi.org/10.1016/j.anbehav.2011.03.002

Horner, V., & Whiten, A. (2005). Causal knowledge and imitation/emulation switching in chimpanzees (Pan troglodytes) and children (Homo sapiens). Anim Cogn, 8(3), 164–181. https://doi.org/10.1007/s10071-004-0239-6

Hrubesch, C., Preuschoft, S., & van Schaik, C. (2009). Skill mastery inhibits adoption of observed alternative solutions among chimpanzees (Pan troglodytes). Anim Cogn, 12(2), 209–216. https://doi.org/10.1007/s10071-008-0183-y

Ionescu, T. (2017). The variability-stability-flexibility pattern: A possible key to understanding the flexibility of the human mind. Review of General Psychology, 21(2), 123–131. https://doi.org/10.1037/gpr0000110

Jacobson, S. L., & Hopper, L. M. (2019a). Hardly habitual: Chimpanzees and gorillas show flexibility in their motor responses when presented with a causally-clear task. PeerJ, 7, e6195–e6195. https://doi.org/10.7717/peerj.6195

Jacobson, S. L., & Hopper, L. M. (2019b). Hardly habitual: Chimpanzees and gorillas show flexibility in their motor responses when presented with a causally-clear task. PeerJ, 7, e6195–e6195. https://doi.org/10.7717/peerj.6195

Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design Studies, 12(1), 3–11.

Kahneman, D., Slovic, S. P., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge university press.

Kummer, H., & Goodall, J. (1985). Conditions of innovative behavior in primates. Philos Trans R Soc Lond, 308, 203–214.

Laland, K., Matthews, B., & Feldman, M. W. (2016). An introduction to niche construction theory. Evolutionary Ecology, 30, 191–202. https://doi.org/10.1007/s10682-016-9821-z

Laland, K. N., & Reader, S. M. (1999). Foraging innovation in the guppy. Animal Behaviour, 57, 331–340.

Larcom, S., Rauch, F., & Willems, T. (2017). The benefits of forced experimentation: Striking evidence from the London underground network. The Quarterly Journal of Economics, 132(4), 2019–2055.

Laughlin, S. B., & Sejnowski, T. J. (2003). Communication in Neuronal Networks. Science, 301(5641), 1870–1874. https://doi.org/10.1126/science.1089662

Laureiro-Martínez, D., & Brusoni, S. (2018). Cognitive flexibility and adaptive decision-making: Evidence from a laboratory study of expert decision makers. Strategic Management Journal, 39(4), 1031–1058. https://doi.org/10.1002/smj.2774

Lefebvre, L., Reader, S. M., & Sol, D. (2004). Brains, Innovations and Evolution in Birds and Primates. Brain, Behavior and Evolution, 63(4), 233–246. https://doi.org/10.1159/000076784

Lemaire, P., & Leclère, M. (2014). Strategy repetition in young and older adults: A study in arithmetic. Developmental Psychology, 50(2), 460–468. https://doi.org/10.1037/a0033527

Luchins, A. S. (1942). Mechanization of problem solving: The effect of Einstellung. Psychological Monographs, 54(6), 1–95.

Luchins, A. S., & Luchins, E. H. (1950). New experimental attempts at preventing mechanization in problem solving. The Journal of General Psychology, 42, 279–297.

Mata, R., Wilke, A., & Czienskowski, U. (2013). Foraging across the life span: Is there a reduction in exploration with aging? Frontiers in Neuroscience, 7. https://doi.org/10.3389/fnins.2013.00053

McFadden, D., Machina, M. J., & Baron, J. (1999). Rationality for economists? In Elicitation of preferences (pp. 73–110). Springer.

Meiran, N. (2010). Task Switching: Mechanisms Underlying Rigid vs. Flexible Self-Control. In R. Hassin, K. Ochsner, & Y. Trope (Eds.), Self Control in Society, Mind, and Brain (pp. 202–220). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195391381.003.0011

Milton, K. (1981). Distribution Patterns of Tropical Plant Foods as an Evolutionary Stimulus to Primate Mental Development. American Anthropologist, 83(3), 534–548. https://doi.org/10.1525/aa.1981.83.3.02a00020

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cognitive Psychology, 41(1), 49–100. https://doi.org/10.1006/cogp.1999.0734

Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7(3), 134–140. https://doi.org/10.1016/S1364-6613(03)00028-7

Muthukrishna, M. (2015). The Cultural Brain Hypothesis and the transmission and evolution of culture.

Muthukrishna, M., Doebeli, M., Chudek, M., & Henrich, J. (2018). The Cultural Brain Hypothesis: How culture drives brain expansion, sociality, and life history. PLoS Computational Biology, 14(11). https://doi.org/10.1371/journal.pcbi.1006504

Navarro, D. J., Newell, B. R., & Schulze, C. (2016). Learning and choosing in an uncertain world: An investigation of the explore–exploit dilemma in static and dynamic environments. Cognitive Psychology, 85, 43–77. https://doi.org/10.1016/j.cogpsych.2016.01.001

Ntamboudila, F. K. (2020, March 24). The King of the Jungle.

Odling-Smee, F. J. (1988). Niche-constructing phenotypes.

O’Farrell, S., Sanchirico, J. N., Spiegel, O., Depalle, M., Haynie, A. C., Murawski, S. A., Perruso, L., & Strelcheck, A. (2019). Disturbance modifies payoffs in the explore-exploit trade-off. Nature Communications, 10(1), 3363. https://doi.org/10.1038/s41467-019-11106-y

Öllinger, M., Jones, G., & Knoblich, G. (2008). Investigating the effect of mental set on insight problem solving. Experimental Psychology, 55(4), 269–282. https://doi.org/10.1027/1618-3169.55.4.269

Payne, J. W., Bettman, J. R., & Johnson, E. J. (1994). The Adaptive Decision Maker. The Journal of the Operational Research Society, 45(7), 850–850. https://doi.org/10.2307/2584298

Peterson, E., & Verstynen, T. (2019). A way around the exploration-exploitation dilemma. https://doi.org/10.32470/ccn.2019.1365-0

Pope, S. M. (2018). Differences in Cognitive Flexibility Within the Primate Lineage and Across Human Cultures: When Learned Strategies Block Better Alternatives.

Pope, S. M., Fagot, J., Meguerditchian, A., Washburn, D. A., & Hopkins, W. D. (2019). Enhanced Cognitive Flexibility in the Seminomadic Himba. Journal of Cross-Cultural Psychology, 50(1), 47–62. https://doi.org/10.1177/0022022118806581

Pope, S. M., Fagot, J., Meguerditchian, A., Watzek, J., Lew-Levy, S., Autrey, M. M., & Hopkins, W. D. (2020). Optional-switch cognitive flexibility in primates: Chimpanzees’ (Pan troglodytes) intermediate susceptibility to cognitive set. Journal of Comparative Psychology, 134(1), 98–109. https://doi.org/10.1037/com0000194

Pope, S. M., Meguerditchian, A., Hopkins, W. D., & Fagot, J. (2015). Baboons (Papio papio), but not humans, break cognitive set in a visuomotor task. Anim Cogn, 18(6), 1339–1346. https://doi.org/10.1007/s10071-015-0904-y

Potts, R. (1996). Evolution and Climate Variability. Science, 273(5277), 922–923. https://doi.org/10.1126/science.273.5277.922

Potts, R. (2012). Environmental and behavioral evidence pertaining to the evolution of early Homo. Current Anthropology, 53(SUPPL. 6). https://doi.org/10.1086/667704

Potts, R., & Faith, J. T. (2015). Alternating high and low climate variability: The context of natural selection and speciation in Plio-Pleistocene hominin evolution. Journal of Human Evolution, 87, 5–20. https://doi.org/10.1016/j.jhevol.2015.06.014

Price, E. E., Lambeth, S. P., Schapiro, S. J., & Whiten, A. (2009). A potent effect of observational learning on chimpanzee tool construction. Proc Biol Sci, 276(1671), 3377–3383. https://doi.org/10.1098/rspb.2009.0640

Reader, S M, & Laland, K. N. (2001). Primate innovation: Sex, age and social rank differences. International Journal of Primatology, 22(5), 787–805.

Reader, Simon M., & Laland, K. N. (2002). Social intelligence, innovation, and enhanced brain size in primates. Proceedings of the National Academy of Sciences of the United States of America, 99(7), 4436–4441. https://doi.org/10.1073/pnas.062041299

Reader, Simon M., & MacDonald, K. (2003). Environmental Variability and Primate Behavioural Flexibility. In Simon M. Reader & K. N. Laland (Eds.), Animal Innovation (pp. 83–116). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198526223.003.0004

Riotte-Lambert, L., & Matthiopoulos, J. (2020). Environmental Predictability as a Cause and Consequence of Animal Movement. Trends in Ecology & Evolution, 35(2), 163–174. https://doi.org/10.1016/j.tree.2019.09.009

Sakai, K. (2008). Task set and prefrontal cortex. Annu Rev Neurosci, 31, 219–245. https://doi.org/10.1146/annurev.neuro.31.060407.125642

Schillemans, V. (2011). The Perseveration Effect in Individuals’ Strategy Choices.

Snell-Rood, E. C. (2013). An overview of the evolutionary causes and consequences of behavioural plasticity. Animal Behaviour, 85(5), 1004–1011.

Sol, D., Duncan, R. P., Blackburn, T. M., Cassey, P., & Lefebvre, L. (2005). Big brains, enhanced cognition, and response of birds to novel environments. Proceedings of the National Academy of Sciences, 102(15), 5460–5465. https://doi.org/10.1073/pnas.0408145102

Sol, D., Sayol, F., Ducatez, S., & Lefebvre, L. (2016). The life-history basis of behavioural innovations. Philos Trans R Soc Lond B Biol Sci, 371(1690). https://doi.org/10.1098/rstb.2015.0187

Speekenbrink, M., & Konstantinidis, E. (2015). Uncertainty and exploration in a restless bandit problem. Topics in Cognitive Science, 7(2), 351–367. https://doi.org/10.1111/tops.12145

Stephens, D. W., & Krebs, J. R. (1986). Foraging theory (Vol. 1). Princeton University Press.

Todd, P. M., & Gigerenzer, G. (2007). Environments That Make Us Smart: Ecological Rationality. Current Directions in Psychological Science, 16(3), 167–171. https://doi.org/10.1111/j.1467-8721.2007.00497.x

Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, New Series, 185(4157,), 1124–1131.

Ueltzhöffer, K., Armbruster-Genç, D. J. N., & Fiebach, C. J. (2015). Stochastic Dynamics Underlying Cognitive Stability and Flexibility. PLOS Computational Biology, 11(6), e1004331. https://doi.org/10.1371/journal.pcbi.1004331

van de Pol, M., Jenouvrier, S., Cornelissen, J. H. C., & Visser, M. E. (2017). Behavioural, ecological and evolutionary responses to extreme climatic events: Challenges and directions. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1723), 20160134. https://doi.org/10.1098/rstb.2016.0134

van Leeuwen, E. J. C., & Call, J. (2017). Conservatism and “copy-if-better” in chimpanzees (Pan troglodytes). Animal Cognition, 20(3), 575–579. https://doi.org/10.1007/s10071-016-1061-7

Van Leeuwen, E. J. C., Cronin, K. A., Schütte, S., Call, J., & Haun, D. B. M. (2013). Chimpanzees (Pan troglodytes) flexibly adjust their behaviour in order to maximize payoffs, not to conform to majorities. PLoS ONE, 8(11), 1–10. https://doi.org/10.1371/journal.pone.0080945

van Schaik, C. P. (2013). The costs and benefits of flexibility as an expression of behavioural plasticity: A primate perspective. Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1618), 20120339. https://doi.org/10.1098/rstb.2012.0339

Vicente, K. J., & Wang, J. H. (1998a). An ecological theory of expertise effects in memory recall. Psychological Review, 105(1), 33–57. https://doi.org/10.1037/0033-295X.105.1.33

Vicente, K. J., & Wang, J. H. (1998b). An ecological theory of expertise effects in memory recall. Psychological Review, 105(1), 33–57. https://doi.org/10.1037/0033-295X.105.1.33

Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo, J. M. (1997). Human Domination of Earth’s Ecosystems. 277, 7.

Vrba, E. S. (Ed.). (1995). Paleoclimate and evolution, with emphasis on human origins. Yale University Press.

Watzek, J., Pope, S. M., & Brosnan, S. F. (2019). Capuchin and rhesus monkeys but not humans show cognitive flexibility in an optional-switch task. Scientific Reports, 9(1), 13195–13195. https://doi.org/10.1038/s41598-019-49658-0

Wertheimer, M. (1945). Productive Thinking. Harper.

Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A., & Cohen, J. D. (2017). Humans use directed and random exploration to solve the explore–exploit dilemma. Journal of Experimental Psychology: General, 143(6), 2074. https://doi.org/10.1037/a0038199

Wright, T. F., Eberhard, J. R., Hobson, E. A., Avery, M. L., & Russello, M. A. (2010). Behavioral flexibility and species invasions: The adaptive flexibility hypothesis. Ethology Ecology & Evolution, 22(4), 393–404. https://doi.org/10.1080/03949370.2010.505580

Wyles, J. S., Kunkel, J. G., & Wilson, A. C. (1983). Birds, behavior, and anatomical evolution. Proceedings of the National Academy of Sciences, 80(14), 4394–4397. https://doi.org/10.1073/pnas.80.14.4394

Zelazo, P. D. (2004). The development of conscious control in childhood. Trends in Cognitive Sciences, 8(1), 12–17. https://doi.org/10.1016/j.tics.2003.11.001

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[1] Luchins (1942) referred to cognitive set as ‘einstellung.’ Another common term is conservatism, which is the continued use of a strategy without consideration for alternatives (Brosnan & Hopper, 2014; Davis et al., 2019; Hrubesch et al., 2009).