Open… access code data peer review

 

Affiliations: 1) Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, 2) University of California Los Angeles, USA, 3) University of California Santa Barbara, USA, 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, underwent 3 rounds of revisions, R2 submitted Mar 2023, R3 submitted Apr 2023): preprint pdf at EcoEvoRxiv, html, rmd

ABSTRACT

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. However, flexibility is rarely directly tested in a way that would allow us to determine how flexibility works to predict a species’ ability to adapt their behavior to new environments. We use great-tailed grackles (Quiscalus mexicanus; a bird species) as a model to investigate this question because they have recently rapidly expanded their range into North America. We attempted to manipulate grackle flexibility using shaded (light and dark gray) tube reversal learning to determine whether flexibility is generalizable across contexts (multi-access box), and what learning strategies grackles employ. We found that flexibility was manipulable: birds in the manipulated group took fewer trials to pass criterion with increasing reversal number, and they reversed a shade preference in fewer trials by the end of their serial reversals compared to control birds who had only one reversal. Birds that passed their last reversal faster were also more flexible (faster to switch between loci) and innovative (solved more loci) on a multi-access box. All grackles in the manipulated reversal learning group used one learning strategy (epsilon-decreasing) in all reversals, and none used a particular exploration or exploitation strategy earlier or later in their serial reversals. Understanding how flexibility causally relates to other traits will allow researchers to develop robust theory about what flexibility is and when to invoke it as a primary driver in a given context, such as a rapid geographic range expansion.

INTRODUCTION

Behavioral flexibility, the ability to adapt behavior to new circumstances through packaging information and making it available to other cognitive processes (see Mikhalevich et al., 2017 for the theoretical background on this definition), 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; Sol et al., 2002, 2005, 2007; Sol & Lefebvre, 2000). The behavioral flexibility (hereafter referred to as flexibility) of individuals is considered an important trait that facilitates the capacity for learning, which is then associated with problem solving ability (applying what one has learned about the world to then attempt to access a resource that is not readily accessible) (see review in Lea et al., 2020). It is hypothesized that, through flexibility, individuals can increase the diversity of their behaviors either via asocial learning (innovativeness) or social learning, leading to the establishment of the population in a new area (Wright et al., 2010).

It is predicted that flexibility should positively relate with innovativeness, the ability to create a new behavior or use an existing behavior in a new situation (Griffin & Guez, 2014). However, these predictions are based on species-level data and proxies for flexibility and for innovation (e.g., brain size, number of anecdotal reports of “novel” foods consumed) when examining such relationships (see Logan et al., 2018). Flexibility is rarely directly tested in species that are rapidly expanding their geographic ranges in a way that would allow us to determine how flexibility works and predict a species’ ability to adapt their behavior to new areas. Those investigations that examine the relationship between flexibility and innovation or problem solving in species that are expanding their range show mixed results, with these variables correlating positively (e.g., grey squirrels: Chow et al., 2016), negatively (e.g., Indian mynas: Griffin et al., 2013), or not at all (e.g., stick tool use and string pulling in great-tailed grackles: Logan, 2016). Problem solving in these contexts involves experimental assays that do not necessarily require innovativeness to solve (e.g., the ability to solve tasks using pre-trained behaviors: Griffin & Guez, 2014). However, none of these experiments manipulated flexibility.

Here, we take the first step to improving our understanding of whether and how flexibility relates to innovativeness by starting with one population and performing a manipulative experiment on one of the variables to determine whether there is an associated change in the other. Once this association is known, future research can then investigate whether flexibility and innovativeness are involved in a range expansion. Manipulative experiments go beyond correlations to infer a cause and effect relationship between the manipulated variable and the variable(s) measured after the manipulation (Hernán & Robins, 2006; McElreath, 2020). A manipulative experiment combined with the random assignment of subjects to a condition (manipulated group or control group), eliminates many confounds associated with internal and external variation (for example, season, motivation, sex, and so on). Such manipulative experiments in behavioral ecology have primarily been conducted in laboratory settings because of the increased feasibility, however such experiments are now also being conducted in wild settings (e.g., Aplin et al., 2015).

We focused our study on one population of great-tailed grackles (Quiscalus mexicanus, hereafter grackles), a bird species that is flexible (Logan, 2016). While they are originally from Central America, grackles have rapidly expanded their geographic range across the US since 1880 (Summers et al., 2023; Wehtje, 2003). We attempted to manipulate grackle flexibility using serial reversals of a shade (light or dark gray) preference to determine whether their flexibility is generalizable across additional experimental contexts (touchscreen reversal learning and multi-access box solution switching), whether improving flexibility also improves innovativeness (number of loci solved on a multi-access box), and what learning strategies grackles employ (Figure 1).

Reversal learning is a common way of measuring flexibility that has been used for many decades across many species, therefore lending itself well to comparative analyses and generalizations (see review in Lea et al., 2020). In this test, an individual learns to prefer the rewarded option, which differs from the non-rewarded option in shade/color, shape, space, or another discriminable feature. Once this initial preference is formed, the previously non-rewarded option becomes the rewarded option and vice versa, and the preference is reversed. Individuals who are faster to reverse their preference are considered more flexible - better able to change their behavior when the circumstances change. Serial reversal learning involves continuing to reverse the preference back and forth to determine whether individuals learn a “win-stay, lose-shift” rule that, when the reward no longer follows the expected option, they should switch to preferring the other option (Spence, 1936; J. Warren, 1965; J. M. Warren, 1965). Once this rule is learned, it can then be applied to new contexts and result in improved performance over individuals who have not learned this rule (J. M. Warren, 1965). We randomly assigned individuals to a manipulated or control condition and used serial reversals (for the manipulated group) to attempt to manipulate flexibility and determine whether the manipulated individuals were then more flexible and more innovative in other contexts.

If grackle flexibility is manipulable using serial reversals, this would provide us with a useful tool for investigating the relationship between flexibility and any number of other variables implicated in geographic range expansions. It would provide researchers with a way to examine the direct links between, for example, flexibility and exploration, to determine whether they are connected and in which direction, which could provide insights into how populations establish in a new location if cross-population manipulations were conducted. If the flexibility manipulation is not successful, this could indicate either that we did not manipulate the right aspect of flexibility (e.g., perhaps training them to solve a variety of different types of tasks quickly would be more effective) or that grackle flexibility is not a trait that is trainable.

Figure 1. A visual illustration of Hypothesis 1 (A), Hypothesis 2 (B), and Hypothesis 4 (C). Longer black arrows indicate slower reversal times, the two yellow circles represent experience with the two yellow tubes that both contained food for the control group.

PREREGISTERED HYPOTHESES

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

  • Prediction 1: Individuals improve their flexibility on a serial reversal learning task using shaded tubes by generally requiring fewer trials to reverse a preference as the number of reversals increases (manipulation condition). Their flexibility on this test is manipulated relative to control birds who do not undergo serial reversals. Instead, individuals in the control condition are matched to manipulated birds for experience (they experience a similar number of trials), but there is no possibility of a functional tube preference because both tubes are the same shade (yellow) and both contain food, therefore either choice is 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) (Griffin & Guez, 2014; Liu et al., 2016; e.g., Manrique et al., 2013; but see Homberg et al., 2007).

H2: Manipulating behavioral flexibility (improving reversal learning speed through serial reversals using shaded tubes) improves flexibility (rule learning and/or switching) and innovativeness in a new context (two distinct multi-access boxes and serial reversals on a touchscreen).

  • P2: Individuals that have improved their flexibility on a serial reversal learning task using shaded 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 multi-access box flexibility tasks, and are faster to reverse preferences in a serial reversal task using a touchscreen than individuals in the control group where flexibility has not been manipulated. The positive correlation between reversal learning performance using shaded tubes and a touchscreen (faster birds have fewer trials) and the multi-access boxes (faster birds have lower latencies) indicates that all three tests measure the same ability even though the multi-access boxes require 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 elucidates whether general individual variation in flexibility relates to flexibility in new contexts (two distinct multi-access boxes and serial reversals on a touchscreen) as well as innovativeness (multi-access boxes). The prediction is the same as in P2, but in this case variation in flexibility is constrained by traits inherent to the individual (some of which are tested in McCune KB et al., 2019), 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 (shaded tubes) and the latency to solve/attempt a new locus on the multi-access boxes, this could be because the latency to solve not only measures flexibility but also innovativeness. In this case, an additional analysis is 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 (see Diquelou et al., 2015; Griffin et al., 2016). 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 shaded tubes and reversal learning speed on the touchscreen, then this indicates that it may be difficult for individuals to perceive and/or understand images on the touchscreen in contrast with physical objects (shaded tubes) (e.g., O’Hara et al., 2015).

H3: Behavioral flexibility within a context is repeatable within individuals.


This hypothesis from the original preregistration is now being treated in a separate manuscript (K. McCune et al., 2022).

H4: Individuals should converge on an epsilon-first learning strategy (learn the correct choice after one trial) as they progress through serial reversals.

  • P4: Individuals prefer a mixture of learning strategies in the first serial reversals (an epsilon-decreasing strategy where individuals explore both options extensively before learning to prefer the rewarded option, and an epsilon-first strategy where the correct choice is consistently made after the first trial), and then move toward the epsilon-first learning strategy. The epsilon-first strategy works better later in the serial reversals where the reward is all or nothing because individuals have learned the environment is changing in predictable ways (Bergstrom & Lachmann, 2004): only one option is consistently rewarded, and if the reward isn’t in the previously rewarded option, it must be in the other option.

  • P4 alternative 1: Individuals continue to prefer a mixture of learning strategies, and/or they do not converge on the more functional epsilon-first learning strategy, regardless of how many reversals they participate in. This pattern could suggest that the grackles do not attend to functional meta-strategies, that is, they do not learn the overarching rule (once food is found in the non-preferred tube, one must switch to preferring that tube shade), but rather they learn each preference change as if it was new.

METHODS

This study is based on a preregistration that received in principle acceptance at PCI Ecology (PDF version), which included a description of the analyses we initially planned to perform. In the following, we first outline the rationale for any changes from the preregistered methods before describing the methods that were used to derive the results presented here.

Changes after pilot data were collected and before the actual data collection began

  1. We initially (in 2017) set the serial reversal passing criterion as the following. During the data collection period, the number of trials required to reverse a preference will be documented per bird, and reversals will continue until the first batch of birds tested reaches an asymptote (i.e., there are negligible further decreases in the number of trials required to reverse a preference). The number of reversals to reach the asymptote will be the number of reversals that subsequent birds experience. Due to delays in setting up the field site, we were only able to test two grackles in early 2018 (January through April) and, due to randomization, only one (Fajita) was in the experimental condition that involved undergoing the flexibility manipulation (Empanada was in the control condition). While Fajita’s reversal speeds generally improved with increasing serial reversals, she never reached an asymptote (which we defined as passing three consecutive reversals in the same number of trials), even after 38 reversals. These 38 reversals took 2.5 months, which is an impractical amount of time if birds are to participate in the rest of the test battery (multi-access box, detour, causal cognition, go no-go, reversal on a touchscreen) after undergoing the reversal manipulation (we were initially permitted to keep them in aviaries for up to three months per bird, which we extended to 6 months per bird in Dec 2018). Because our objective in this experiment was to manipulate an individual’s flexibility, we decided to revise our serial reversal passing criterion to something more species relevant based on Fajita’s serial reversal performance and the performance of seven grackles in Santa Barbara who underwent only one reversal in 2014 and 2015 (Logan, 2016). The revised serial reversal passing criterion was: passing two reversals in a row at or under 50 trials. 50 trials is fewer trials than any of the nine grackles required to pass their first reversal (range 70-130), therefore it should reflect an improvement in flexibility.

Changes at the beginning of data collection

  1. Reversal learning shaded tube choice criterion. At the beginning of the second bird’s initial discrimination in the reversal learning shaded tube experiment (October 2018), we revised the criterion for what counts as a choice from A) the bird’s head needs to pass an invisible line on the table that ran perpendicular to the the tube opening to B) the bird needs to bend its body or head down to look in the tube (see B demonstrated in Figure 3). Criterion A resulted in birds making more choices than the number of learning opportunities they were exposed to (because they could not see whether there was food in the tube unless they bent their head down to look in the tube) and appeared to result in slower learning. It is important that one choice equals one learning opportunity, therefore we revised the choice criterion to the latter. Anecdotally, this choice matters because the first three birds in the experiment (Tomatillo, Chalupa, and Queso) learned faster than the pilot birds (Empanada and Fajita) in their initial discriminations and first reversals. Thus, it was an important change to make at the beginning of the experiment (after testing the two pilot birds and before collecting any data that were included in analyses).

Figure 3. Tzanatl preciosa bending down to look into the dark gray tube.

  1. Criterion to pass the control condition: Before collecting experimental data, we set the number of trials experienced by the birds in the control group as 1100 because this is how many trials it would have taken the pilot bird in the manipulated group, Fajita, to pass serial reversals 2-17 according to our revised serial reversal passing criterion. However, after 25 and 17 days (after Tomatillo and Queso’s first reversals, respectively) of testing the first two individuals in the control group, it became apparent that 1100 trials is impractical given the time constraints for how long we were permitted to keep each bird temporarily in captivity and would prevent birds from completing the test battery before their release. Additionally, after revising the choice criterion, it was going to be likely that birds in the manipulated group would require fewer than 1100 trials to meet the serial reversal passing criterion. Therefore, reducing the number of trials the control birds experience would result in a better match of experience with birds in the manipulated group. On 2 November 2018 we set the number of trials control birds experience after their first (and only) reversal to the number of trials it requires the first bird in the manipulated group to pass (the first bird had not passed yet, therefore we did not yet know what this number was). After more individuals in the manipulated group passed, we updated this number to the average number of trials to pass. This applied to all birds in the control condition, except Mofongo. Mofongo (control condition) was a slow participator and would not have finished his test battery by the time it got too hot to keep birds in the aviaries if we used the current average number of trials (420). Instead, we matched him with the fastest bird in the manipulated group (Habanero=290 trials) to make it more likely that Mofongo could get through the rest of the test battery in time.

Changes in the middle of data collection

  1. 10 April 2019, we discontinued the reversal learning experiment on the touchscreen because it appeared to measure something other than what we intended to test and it required a huge time investment for each bird (which consequently reduced the number of other tests they were available to participate in). This is not necessarily surprising because this was the first time touchscreen tests have been conducted in this species, and also the first time (to our knowledge) this particular reversal experiment has been conducted on a touchscreen with birds. We based this decision on data from four grackles (2 in the flexibility manipulation group and 2 in the flexibility control group; 3 males and 1 female). All four of these individuals showed highly inconsistent learning curves and required hundreds more trials to form each preference when compared to the performance of these individuals on the shaded tube reversal experiment. It appeared that there was a confounding variable with the touchscreen such that they were extremely slow to learn a preference as indicated by passing our criterion of 17 correct trials out of the most recent 20. We did not include the data from this experiment when conducting the cross-test comparisons in the Analysis Plan section of the preregistration. Instead, in Supplementary Material 4, we provided summary results for this experiment and, in the Discussion, qualitatively compared it with performance on the shaded tube reversal test to explain what might have confounded the touchscreen experiment.

  2. 16 April 2019, because we discontinued the touchscreen reversal learning experiment, we added an additional but distinct multi-access box task, which allowed us to continue to measure flexibility across three different experiments. There are two main differences between the first multi-access box, which is made of plastic, and the new multi-access box, which is made of wood. First, the wooden multi-access box is a natural log in which we carved out 4 compartments. As a result, the apparatus and solving options are more comparable to what grackles experience in the wild, though each compartment is covered by a transparent plastic door that requires different behaviors to open. Furthermore, there is only one food item available in the plastic multi-access box and the bird could use any of 4 loci to reach it. In contrast, the wooden multi-access box has a piece of food in each of the 4 separate compartments.

Updates and changes post data collection, pre-data analysis

  1. We completed our simulation to explore the lower boundary of a minimum sample size and determined that our sample size for the Arizona study site is above the minimum (see details and code in Supplementary Material 1; 17 April 2020).

  2. Please see our Alternative Analyses section in the preregistration where we stated that we would learn and implement Bayesian models, which resulted in our changing the analysis for P2 and that we are replacing this analysis with the new models in the Ability to detect actual effects section (Supplementary Material 1; 14 May 2020). We also describe in SM1 that we realized that Condition (manipulated or control) does not need to be a variable in our models because our analyses in P1 demonstrate that the manipulation causally changed reversal speeds, which is the key assumption in P2.

  3. We originally planned on testing only adults to have a better understanding of what the species is capable of, assuming the abilities we are testing are at their optimal levels in adulthood, and so we could increase our statistical power by eliminating the need to include age as an independent variable in the models. Because the grackles in Arizona were extremely difficult to catch, we ended up testing two juveniles: Taco and Chilaquile. We did not conduct the full test battery with Taco or put him in the flexibility manipulation or control groups (he received 1 reversal and then moved on to the next test) because he was the first juvenile and we wanted to see whether his performance was different from adult performances. His performances were similar to the adults, therefore we decided to put Chilaquile in the full test battery. Chilaquile’s performances were also similar to the adults, therefore we decided not to add age as an independent variable in the models to avoid reducing our statistical power.

  4. We removed experimenter as a random effect from all analyses because the interobserver reliability scores were so high, indicating there was no difference between experimenters, therefore we could keep our models simpler by leaving this variable out.

  5. P2 alternative 2: We used the average latency rather than the number of trials to attempt a new locus because this would make the model comparable with the model in P2. Using the number of trials was an artifact from a previous version and we had missed updating this. We omitted the number of trials to solve a new locus as described in the deviation from the plan in P2 above. We used a GLM rather than a GLMM because there was only one data point per bird (note that there would have been only one data point per bird in the preregistration as well, but we didn’t realize this until after in principle acceptance).

  6. P4 (Aug 2021): The grackles were tested in 10-trial blocks and not 20-trial blocks as in Federspiel et al. (2017), which would mean that if there were <20 trials in the last block of a reversal, they would be omitted from the analysis. Therefore, we changed the block size to 10 trials and adjusted the sampling blocks to 2-9 correct choices, and the acquisition blocks to 9-10 correct choices using significance levels in the binomial test as did Federspiel et al. (2017).

Changes post data collection, mid-data analysis

  1. P2 (April 2020): We realized that the average latency to solve a new locus after solving a different locus is confounded with the total number of loci solved because the measure of innovation is included in the definition. Therefore, we removed average latency to solve a locus from analyses so that we are only examining pure measures of flexibility (average latency to attempt to solve) and innovation (total number of loci solved).

  2. P2: Removed aviary batch (random variable) from the original model for P2 (Table SM3: Model 1). Batch ended up confounding the analysis because control and manipulated individuals, while randomly assigned to these conditions, ended up in particular batches as a result of their willingness to participate in tests offered during their time in the aviary (Table SM3: Model 3). Several grackles never passed habituation or training such that their first experiment could begin, therefore we replaced these grackles in the aviaries with others who were willing to participate. This means that batch did not indicate a particular temporal period. Therefore, we removed batch from the models (post data collection, mid-data analysis).

  3. P2: When making the bespoke Bayesian models, we realized that we had previously misinterpreted which variable should be the response variable in this analysis. We originally set the number of trials to reverse as the response variable, however we should have instead set the number of loci solved as the response variable and then planned to conduct a second model with the latency to attempt a new locus as the response variable and number of trials as the explanatory variable. This is because a) we manipulated the number of trials to reverse, therefore it must be the explanatory variable (Hernán & Robins, 2006); and b) they should be split into two models, one each for average latency and number of loci solved, because of a and because these are two very different relationships that should be considered in their own models. We also realized that Condition (manipulated or control) does not need to be a variable in any of our models because our analyses in P1 demonstrate that the manipulation causally changed reversal speeds, which is the key assumption in P2.

Changes post data collection, post-data analysis

  1. We present the results from different hypotheses in separate articles: this one, K. McCune et al. (2022), and Lukas et al. (2022).

Sample


Grackles were caught in the wild in Tempe, Arizona, USA for individual identification (colored leg bands in unique combinations). Some individuals (34: 13 in the control group (they receive 1 reversal; only 11 completed the experiment) and 10 in the flexibility manipulation (they receive multiple reversals; only 8 completed the experiment), and 11 who did not participate enough to enter the experiments) were brought temporarily into aviaries for testing, and then released back to the wild.

Data collection stopping rule

 
We stopped testing birds after we completed two full aviary seasons because the sample size was above the minimum suggested boundary of 15 (to detect a medium effect size) based on model simulations (see Supplementary Material 1).

Summary of testing protocols (Figure 2)

  • Reversal learning with shaded tubes: One light gray and one dark gray tube were placed such that the openings were not visible (shades were pseudorandomized for side). One shade always contained a food reward. The individual had the opportunity to choose to look inside one tube per trial. Once the individual chose correctly on 17 out of the most recent 20 trials, they were considered to have a shade preference, and then the food was always placed in the previously non-rewarded shade and the same passing criterion was used to determine their reversal learning performance. Individuals were randomly placed in the manipulated condition (serial reversals until they passed two consecutive reversals in 50 trials or less) or the control condition (receive only one reversal and then a similar number of total trials to the manipulated individuals, but with two yellow tubes, both of which always had food).

  • Plastic multi-access box: This was a puzzlebox made of plexiglas and plastic, which contained one piece of food on a post in the center of the box. The box was placed in the aviary for up to 15 minutes per trial. Each plexiglas wall had one option (locus) for retrieving the food, but each option required a different method for obtaining the food. The individual had the opportunity to attempt (touch, but not obtain the food) or solve a locus. Once a locus was used successfully three times to get the food, it was considered solved and rendered non-functional in subsequent trials. The experiment ended when an individual solved all four loci or if they did not interact with or successfully solve a locus in three consecutive trials.

  • Wooden multi-access box: This was a puzzlebox carved from a log to have four loci containing a food item. Each locus required a different motor action to solve. Three loci were covered with a plastic door on a hinge and one locus was a drawer that must be pulled out. Trials lasted for up to 15 minutes. The passing criterion and experiment ending criteria were the same as for the plastic multi-access box.

  • Reversal learning of shapes on a touchscreen: This is the same experimental design as with the shaded tubes, except it was carried out on a touchscreen computer where the individual was presented with two white symbols that differed in shape (pentagon or diamond). Touching the screen over the rewarded shape resulted in food dropping from a food hopper into a dish accessible to the grackle, while touching the screen over the non-rewarded shape resulted in no food and a longer inter-trial interval.