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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 a revision of 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 we submitted the first version of the post-study manuscript to PCI Ecology for post-study peer review on 3 Jan 2022; we revised it per reviewer comments and this piece was split from the other, distinct components of the preregistrations and resubmitted on 15 Aug 2022; additional reviewer feedback is now incorporated and we resubmit this revised version to PCI Ecology: revised 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). Although it can be difficult, or impossible, to definitively assign performance to one cognitive trait, one way to validate that task structure is more likely to elicit 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 performance on tasks intended to test the cognitive trait behavioral flexibility in great-tailed grackles (Quiscalus mexicanus). 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 the serial color reversal task to the latency to switch among solutions on each of two different multi-access boxes. We found that the number of trials to form a preference in reversal learning was repeatable across serial color reversals and the latency to switch a preference was repeatable across color reversal learning and the multi-access box contexts. This supports the idea that the reversal learning task structure elicits performance reflective of an inherent trait, and that reversal learning and solution switching on multi-access boxes similarly reflect the inherent trait of behavioral flexibility.


Behavioral flexibility, repeatability, construct validity, animal cognition


Research on the cognitive abilities of non-human animals is important for several reasons. By understanding animal cognitive abilities, we can clarify factors that influenced the evolution of human cognition, the mechanisms that relate cognition to ecological and evolutionary dynamics, or we can use the knowledge to facilitate more humane treatment of captive individuals (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. As a result, we have come to understand cognition as the process of acquiring information, followed by storage, retrieval, and use of that information for guiding behavior (Shettleworth, 2010). 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).

Cognitive traits are not directly observable and nearly all methods to quantify cognition use behavioral performance as a proxy for cognitive ability. Consequently, it is important to evaluate the validity of the chosen methods for quantifying a cognitive trait. To better understand whether performance on a type of task is likely to reflect a target cognitive trait (i.e., that the method has construct validity), researchers can test for repeatability in individual performance within and across tasks (Völter et al., 2018). However, while many cognitive abilities have been tested, and various methods used, it is rare for one study to repeatedly test individuals with the same method or use multiple methods to test for a given cognitive ability. This could be problematic because cognitive traits are not directly observable, so nearly all methods use behavioral performance as a proxy for cognitive ability. Using only one method to measure a cognitive trait could be problematic because it is hard to discern whether non-target cognitive, personality, or motivational factors may be the cause of variation in 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 can vary in their perception of the salience of stimuli within a task, the impact of a reward (or non-reward) on future behavior, or the propensity to sample alternative stimuli (Rowe & Healy, 2014). By assessing the temporal and contextual repeatability of performance, researchers can quantify the proportion of variation in performance that is attributable to consistent individual differences likely to reflect the level of the cognitive trait relative to other ephemeral factors that affect individual performance (Cauchoix et al., 2018).

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). Although by definition behavioral flexibility incorporates plasticity in behavior through learning, there is also evidence that the ability to change behavior could be an inherent trait that varies among individuals and species. For example, the pinyon jay - a highly social species of corvid - made fewer errors in a serial reversal learning task than the more asocial Clark’s nutcracker or Woodhouse’s scrub-jay, but all three species exhibited similar learning curves over successive reversals (Bond et al., 2007). This indicates that the three species differed in the level of the inherent ability, but were similar in the plasticity of performance through learning. 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 construct 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 different behavioral flexibility task like reversal learning.

We tested two hypotheses about the construct 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). Secondly, we determined whether the inherent trait measured by color reversal learning is likely to represent behavioral flexibility by assessing the cross-contextual repeatability of performance on this task with another task also thought to measure flexibility. Our previous research found that behavioral flexibility does affect innovation ability on a multi-access box (C. Logan et al., 2022), so here our second hypothesis tested whether individuals show contextual repeatability of flexibility by comparing performance on the color 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 color 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 the rapid range expansion suggests that they adapted 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.