Repeatability of performance within and across contexts measuring behavioral flexibility

Abstract

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.

Implementing a rapid geographic range expansion - the role of behavior changes

“…If an individual did not approach within 20 cm, it was given a latency of 2701 sec (45 min plus 1 sec). In a previous experiment (McCune et al, 2019b), we validated that grackles did not perceive the novel environment as threatening (i.e., it was not a measure of boldness). Experimental protocol (see Supplementary Material 5 for more details).…”

“…However, the Tempe grackles responded differently to the novel environment and novel object, therefore they did not perceive the stimuli as the same. From the Tempe grackle data, we found that responses were only repeatable for the novel environment test (McCune et al, 2019b). Therefore, we conducted this assay (and not the novel object assay) with the Woodland grackles and compared the two populations on this one assay.…”

“…We investigated whether certain behaviors had higher averages and variances in the edge population relative to the older population. Specifically, we investigated behavioral flexibility, measured as reversal learning of food-filled colored tube preferences (Logan, 2016a;Logan et al, 2023a); innovativeness, measured as the number of loci they solve to access food from a puzzle box (Auersperg et al, 2011;Logan et al, 2023a); exploration, measured as the latency in seconds to approach a novel environment in the absence of nearby food (Mettke-Hofmann et al, 2009;McCune et al, 2019b); and persistence, measured as the proportion of trials they participated in during the flexibility and innovativeness experiments (Figure 2). While it is possible for individuals in the wild to learn asocially and socially about new foods or foraging techniques to assess whether the risks are low enough to encourage exploration behavior, we focused on measuring these four behaviors in an asocial context to allow us to obtain the individual's actual cognitive performance (i.e., in the absence of dominant individuals who might hinder subordinates from participating).…”

Implementing a rapid geographic range expansion - the role of behavior changes

“…Consequently, we use a likelihood ratio test to compare a mixed model to a model without the ID random effect, and the function rpt from the package: rptR (Stoffel et al, 2017) to estimate the variance in the dependent variable attributable to consistent differences among individuals across the two tests. We previously found that this method produces the same repeatability results as the MCMCglmm method using a Gaussian distribution (K. McCune et al, 2022).…”

Behavioral flexibility is manipulatable and it improves flexibility and problem solving in a new context: post-hoc analyses of the components of behavioral flexibility

“…The change in the rate of deviation during the manipulation might indicate that individuals learned about the serial nature of the reversal experiment, that they should deviate from their previous attractions as soon as the reward changes. While there were individual differences in learning and deviation rates(McCune et al, 2022), all individuals appeared to change depending on their experiences. The manipulation pushed individuals to levels that were already observed in some individuals at the beginning of the experiment, suggesting that individuals might also change their behavioral flexibility in response to their experiences in their natural habitats.…”