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Affiliations: 1) University of California Santa Barbara, USA, 2) University of California Los Angeles, USA, 3) Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, 4) Arizona State University, Tempe, AZ USA. *Corresponding author:


This is the post-study manuscript of the preregistration that was pre-study peer reviewed and received an In Principle Recommendation on 26 Mar 2019 by:

Aurélie Coulon (2019) Can context changes improve behavioral flexibility? Towards a better understanding of species adaptability to environmental changes. Peer Community in Ecology, 100019. 10.24072/pci.ecology.100019. Reviewers: Maxime Dahirel and Andrea Griffin

Preregistration: html, pdf, rmd

Post-study manuscript (submitted to PCI Ecology for post-study peer review on 3 Jan 2022): html, preprint pdf at EcoEvoRxiv, rmd


Research into animal cognitive abilities is increasing quickly and often uses methods where behavioral performance on a task is assumed to represent variation in the underlying cognitive trait. However, because these methods rely on behavioral responses as a proxy for cognitive ability, it is important to validate that the task structure does, in fact, target the cognitive trait of interest rather than non-target cognitive, personality, or motivational traits (construct validity). One way to validate that task structure elicits performance based on the target cognitive trait is to assess the temporal and contextual repeatability of performance. In other words, individual performance is likely to represent an inherent trait when it is consistent across time and across similar or different tasks that theoretically test the same trait. Here, we assessed the temporal and contextual repeatability of the cognitive trait behavioral flexibility in great-tailed grackles. For temporal repeatability, we quantified the number of trials to form a color preference after each of multiple color reversals on a serial reversal learning task. For contextual repeatability, we then compared performance on this task to the latency to switch solutions on two different multi-access boxes. We found that the number of trials to form a preference in reversal learning was repeatable across serial reversals and the latency to switch a preference was repeatable across reversal learning and the multi-access box contexts. This supports the idea that reversal learning and solution switching on multi-access boxes similarly reflect the inherent trait of behavioral flexibility.


To clarify factors that influenced the evolution of human cognition, mechanisms relating cognition to ecological and evolutionary dynamics, or to facilitate more humane treatment of captive individuals, it is important to increase our understanding of the cognitive abilities of non-human animals (Shettleworth, 2010). In the last 50 years, comparative psychologists and behavioral ecologists have led a surge in studies innovating methods for measuring cognitive traits in animals. Consequently, evidence now exists that various species possess cognitive abilities in both the physical (e.g. object permanence: Salwiczek et al., 2009; causal understanding: Taylor et al., 2012) and social domains (e.g. social learning: Hoppitt et al., 2012; transitive inference: MacLean et al., 2008).

While many cognitive abilities have been tested, and various methods used, it is rare for one study to use multiple methods to test for a given cognitive ability. Because nearly all methods use behavioral performance as a proxy for cognitive ability, it is possible that non-target cognitive, personality, or motivational traits could be affecting performance on the task (Morand-Ferron et al., 2016). For example, the success of pheasants on multiple similar and different problem-solving tasks was related to individual variation in persistence and motivation, rather than problem solving ability (Horik & Madden, 2016). Additionally, performance on cognitive tasks can be affected by different learning styles, where individuals consistently vary in their perception of the salience of stimuli, the impact of a reward (or non-reward) on future behavior, or the propensity to sample alternative stimuli (Rowe & Healy, 2014). Without comparing individual differences in performance within and across tasks, it is impossible to determine whether some aspect of performance on a single task is reflective of the target inherent cognitive trait, which would indicate that the task has construct validity (Völter et al., 2018). We use the term “inherent trait” to indicate a trait that is intrinsic to the individual, such as from genetic or developmental effects (Réale et al., 2007). Some plasticity can still be present but the baseline trait value and the amount of plasticity in the trait consistently varies among individuals (Sih, 2013). One way to evaluate the validity of the task structure for measuring the target trait is to quantify the temporal and contextual repeatability of performance (Carter et al., 2013).

Behavioral flexibility, the ability to change behavior when circumstances change, is a general cognitive ability that likely affects interactions with both the social and physical environment (Bond et al., 2007). Behavioral flexibility could be measured using a variety of methods (Mikhalevich et al., 2017), but the most popular method is reversal learning (Bond et al., 2007) where behavioral flexibility is quantified as the speed that individuals are able to switch a learned preference. However, to our knowledge, no studies have assessed the validity of this task by comparing performance of individuals over time and across different tasks that are predicted to require flexible behavior.

In the wild, this ability to change behavior when circumstances change is expected to result in individuals and species that adapt quickly to novelty by showing a high rate of foraging innovations. For example, cross-taxon correlational studies found that species that were “behaviorally flexible”, in that there were many documented foraging innovations, were also more likely to become invasive when introduced to novel habitats (Sol et al., 2002). The ability to innovate solutions to novel problems can also be more directly quantified using a multi-access or puzzle box task, where the subject must use new behavior patterns to solve the task to get food. While it is generally assumed that foraging innovation rate corresponds to the cognitive ability behavioral flexibility (Sol et al., 2002), few studies compare innovation performance and solution switching (a measure of flexibility) on a multi-access box task to performance on a behavioral flexibility task like reversal learning.

We tested two hypotheses about the validity of the reversal learning method as a measure of behavioral flexibility in the great-tailed grackle (Quiscalus mexicanus; hereafter “grackle”). First, we determined whether performance on a reversal learning task represents an inherent trait by assessing the repeatability of performance across serial reversals (temporal repeatability). While our previous research found that behavioral flexibility does affect innovation ability on a multi-access box (C. Logan et al., 2022), here we tested the contextual repeatability of flexibility by comparing performance on the reversal learning task to the latency of solution switching on two different multi-access boxes (Fig. 1). We chose solution switching because it requires similar attention to changing reward contingencies, thus serving as a measure of flexibility, but in a different context (e.g. the food is always visible, there is no color association learning required). In other words, in both reversal learning and solution switching individuals learned a preferred way to obtain food, but then contingencies changed such that food can no longer be obtained with this learned preference and the grackle must be able to switch to a new method. As a human-associated species, the grackle is an ideal subject for this study because they adapt quickly in response to human-induced rapid environmental change (Summers et al., 2022; Wehtje, 2003) and the genus Quiscalus has a high rate of foraging innovations in the wild (Grabrucker & Grabrucker, 2010; Lefebvre & Sol, 2008). Therefore, as their environment may select for flexible and innovative behavior, we believe that these tasks are ecologically relevant and will elicit individual variation in performance.