Probing behaviors correlated with behavioral flexibility

Jeremy Van Cleve based on reviews by 2 anonymous reviewers

A recommendation of:
Kelsey McCune, Carolyn Rowney, Luisa Bergeron, Corina Logan. Does manipulating behavioral flexibility affect exploration, but not boldness, persistence, or motor diversity? (2019), gitHub Repository at OSF, GCA5V, v1.3 of the 28th February 2019 peer-reviewed and recommended by Peer Community in Ecology. 10.17605/OSF.IO/GCA5V
Submitted: 27 September 2018, Recommended: 26 March 2019
Cite this recommendation as:
Jeremy Van Cleve (2019) Probing behaviors correlated with behavioral flexibility. Peer Community in Ecology, 100020. 10.24072/pci.ecology.100020

Behavioral plasticity, which is a subset of phenotypic plasticity, is an important component of foraging, defense against predators, mating, and many other behaviors. More specifically, behavioral flexibility, in this study, captures how quickly individuals adapt to new circumstances. In cases where individuals disperse to new environments, which often occurs in range expansions, behavioral flexibility is likely crucial to the chance that individuals can establish in these environments. Thus, it is important to understand how best to measure behavioral flexibility and how measures of such flexibility might vary across individuals and behavioral contexts and with other measures of learning and problem solving.
In this preregistration, Logan and colleagues propose to use a long-term study of the great-tailed grackle to measure how much they can manipulate behavioral flexibility in a reversal learning task, how much behavioral flexibility in one task predicts flexibility in another task and in problem solving a new task, and how robust these patterns are within individuals and across tasks. Logan and colleagues lay out their hypotheses and predictions for each experiment in a clear and concise manner. They also are very clear about the details of their study system, such as how they determined the number of trials they use in their learning reversal experiments, and how those details have influenced their experimental design. Further, given that the preregistration uses RMarkdown and is stored on GitHub (as are other studies in the larger project), their statistical code and its history of modification are easily available. This is a crucial component of making research more reproducible, which is a recent emphasis in behavioral sciences more broadly.
Reviewers of this preregistration found the study of substantial merit. The authors have responded to the reviewers' comments and their revisions have made the preregistration much clearer and cogent. I am happy to recommend this preregistration.

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