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This is one of the first studies planned for our long-term research on the role of behavioral flexibility in rapid geographic range expansions. Behavioral flexibility, the ability to adapt behavior to new circumstances, is thought to play an important role in a species’ ability to successfully adapt to new environments and expand its geographic range (e.g., (Lefebvre et al. 1997), (Griffin and Guez 2014), (Chow, Lea, and Leaver 2016), (Sol and Lefebvre 2000), (Sol, Timmermans, and Lefebvre 2002), (Sol et al. 2005), (Sol et al. 2007)). However, behavioral flexibility is rarely directly tested in species in a way that would allow us to determine how it works and how we can make predictions about a species’ ability to adapt their behavior to new environments. We use great-tailed grackles (a bird species) as a model to investigate this question because they have rapidly expanded their range into North America over the past 140 years ((Wehtje 2003), (Peer 2011)). We aim to manipulate grackle behavioral flexibility (color tube reversal learning) to determine whether their flexibility is generalizable across contexts (touch screen reversal learning and multi-access box), whether it is repeatable within individuals and across contexts, and what learning strategies they employ. Results will allow us to understand more about what flexibility is and how it works, and validate whether a touch screen measures the same ability as the color tubes (thus facilitating faster testing that can be conducted in the wild).


This preregistration was written (2017) prior to collecting data. Pilot data on serial reversal learning (using color tubes) in one grackle was collected January through April 2018, which informed the revision of 1) the criterion to pass serial reversal learning, 2) more accurate language for H1 P1 (each subsequent reversal may not be faster than the previous, however their average reversal speed decreases), 3) the removal of shape reversals from H3a and H3b (to reduce the amount of time each bird is tested), and 4) a new passing criterion for touch screen serial reversals in H3b. Part way through data collection on reversal learning (using color tubes) for the first two birds, the criterion for what counts as making a choice was revised (October 2018) and part way through data collection on the first four birds (October 2018; see below for details) the number of trials that birds in the control group receive was revised to make the test battery feasible in the time given.

This preregistration was submitted to PCI Ecology for peer review (July 2018), we received the first round of peer reviews a few days before data collection began (Sep 2018), we revised and resubmitted after data collection had started (Feb 2019) and it passed peer review (Mar 2019) before any of the planned analyses had been conducted. See the peer review history at PCI Ecology.


We may present the different hypotheses in separate papers.


H1: Behavioral flexibility, as measured by reversal learning using colored tubes, is manipulatable.

Prediction 1: Individuals improve their flexibility on a serial reversal learning task using colored tubes by generally requiring fewer trials to reverse a preference as the number of reversals increases (manipulation condition). Their flexibility on this test will have been manipulated relative to control birds who do not undergo serial reversals. Instead, individuals in the control condition will be matched to manipulated birds for experience (they will experience a similar number of trials), but there will be no possibility of a functional tube preference because both tubes will be the same color and both will contain food, therefore either choice will be correct.

P1 alternative 1: If the number of trials to reverse a preference does not correlate with or positively correlates with reversal number, which would account for all potential correlation outcomes, this suggests that some individuals may prefer to rely on information acquired previously (i.e., they are slow to reverse) rather than relying on current cues (e.g., the food is in a new location) (e.g., (Manrique, Völter, and Call 2013); (Griffin and Guez 2014); (Liu et al. 2016), but see (Homberg et al. 2007)).

H2: Manipulating behavioral flexibility (improving reversal learning speed through serial reversals using colored tubers) improves flexibility (rule learning and/or switching) and problem solving in a new context (multi-access box and serial reversals on a touch screen).

P2: Individuals that have improved their flexibility on a serial reversal learning task using colored tubes (requiring fewer trials to reverse a preference as the number of reversals increases) are faster to switch between new methods of solving (latency to solve or attempt to solve a new way of accessing the food [locus]), and learn more new loci (higher total number of solved loci) on a multi-access box flexibility task, and are faster to reverse preferences in a serial reversal task using a touch screen than individuals in the control group where flexibility has not been manipulated. The positive correlation between reversal learning performance using colored tubes and a touch screen (faster birds have fewer trials) and the multi-access box (faster birds have lower latencies) indicates that all three tests measure the same ability even though the multi-access box requires inventing new rules to solve new loci (while potentially learning a rule about switching: “when an option becomes non-functional, try a different option”“) while reversal learning requires switching between two rules (”choose light gray" or “choose dark gray”) or learning the rule to “switch when the previously rewarded option no longer contains a reward.” Serial reversals eliminate the confounds of exploration, inhibition, and persistence in explaining reversal learning speed because, after multiple reversals, what is being measured is the ability to learn one or more rules. If the manipulation works, this indicates that flexibility can be influenced by previous experience and might indicate that any individual has the potential to move into new environments (see relevant hypotheses in preregistrations on genetics (R1) and expansion (H1)).

P2 alternative 1: If the manipulation does not work in that those individuals in the experimental condition do not decrease their reversal speeds more than control individuals, then this experiment will elucidate whether general individual variation in flexibility relates to flexibility in two new contexts (multi-access box and serial reversals on a touch screen) as well as problem solving ability (multi-access box). The prediction is the same in P2, but in this case variation in flexibility is constrained by traits inherent to the individual (some of which will be tested in a separate preregistration), which suggests that certain individuals will be more likely to move into new environments.

P2 alternative 2: If there is no correlation between reversal learning speed (colored tubes) and the latency to solve/attempt a new locus on the multi-access box, this could be because the latency to solve not only measures flexibility but also innovativeness. In this case, an additional analysis will be run with the latency to solve as the response variable, to determine whether the fit of the model (as determined by the lower AIC value) with reversal learning as an explanatory variable is improved if motor diversity (the number of different motor actions used when attempting to solve the multi-access box) is included as an explanatory variable. If the inclusion of motor diversity improves the model fit, then this indicates that the latency to solve a new locus on the multi-access box is influenced by flexibility (reversal learning speed) and innovation (motor diversity).

P2 alternative 3: If there is a negative correlation or no correlation between reversal learning speed on colored tubes and reversal learning speed on the touch screen, then this indicates that it may be difficult for individuals to perceive and/or understand images on the touch screen in contrast with physical objects (colored tubes)(e.g., (O’Hara, Huber, and Gajdon 2015)).