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Chimpanzee problem-solving: contrasting the use of causal and arbitrary cues

Affiliation.

1 Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. [email protected]
PMID: 21647648
DOI: 10.1007/s10071-011-0421-6

Humans are able to benefit from a causally structured problem-solving context rather than arbitrarily structured situations. In order to better understand nonhuman causal cognition, it is therefore important to isolate crucial factors that might differentiate between events that follow a purely spatial and temporal contingency and those that hold a "true" causal relationship. In the first of two experiments, chimpanzee subjects were required to detect a bottle containing juice from five opaque bottles of equal shape and size. In the causal condition, the juice bottle looked identical to the other four bottles, only it was much heavier than the others. In the arbitrary condition, the weight of all five bottles was identical, but the juice bottle was color-marked differently. Since bottle opening was made difficult (and therefore costly), the question was whether subject's manipulative behavior would be random or somehow influenced by the nature of the provided information. Our results show that subjects detected and opened the juice bottle significantly faster when weight was the discriminating feature (causal condition) compared to situations in which the discrimination was necessarily based on a color-cue (arbitrary condition). Experiment 2 ruled out the possibility of a general learning bias toward tactile rather than visual information in chimpanzees. When tested in a simple exchange paradigm that prevented any use of causal information, no predominance of a tactile cue (weight) over a visual cue (color) could be found. Furthermore--and in contrast to the causal condition in Experiment 1--no learning occurred during the course of Experiment 2, neither in the weight nor in the color condition. We therefore conclude that chimpanzees can more easily determine the content of an object based on its causal properties compared to situations in which the only available information is a pure arbitrary regularity. This supports the view that chimpanzees' causal cognition does not rely on mere perceptual information but also on structural abstraction about their physical environment.

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Chimpanzee problem-solving: contrasting the use of causal and arbitrary cues

Original Paper
Published: 07 June 2011
Volume 14 , pages 871–878, ( 2011 )

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Daniel Hanus 1 &
Josep Call 1

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Humans are able to benefit from a causally structured problem-solving context rather than arbitrarily structured situations. In order to better understand nonhuman causal cognition, it is therefore important to isolate crucial factors that might differentiate between events that follow a purely spatial and temporal contingency and those that hold a “true” causal relationship. In the first of two experiments, chimpanzee subjects were required to detect a bottle containing juice from five opaque bottles of equal shape and size. In the causal condition, the juice bottle looked identical to the other four bottles, only it was much heavier than the others. In the arbitrary condition, the weight of all five bottles was identical, but the juice bottle was color-marked differently. Since bottle opening was made difficult (and therefore costly), the question was whether subject’s manipulative behavior would be random or somehow influenced by the nature of the provided information. Our results show that subjects detected and opened the juice bottle significantly faster when weight was the discriminating feature ( causal condition ) compared to situations in which the discrimination was necessarily based on a color-cue ( arbitrary condition ). Experiment 2 ruled out the possibility of a general learning bias toward tactile rather than visual information in chimpanzees. When tested in a simple exchange paradigm that prevented any use of causal information, no predominance of a tactile cue (weight) over a visual cue (color) could be found. Furthermore—and in contrast to the causal condition in Experiment 1—no learning occurred during the course of Experiment 2, neither in the weight nor in the color condition. We therefore conclude that chimpanzees can more easily determine the content of an object based on its causal properties compared to situations in which the only available information is a pure arbitrary regularity. This supports the view that chimpanzees’ causal cognition does not rely on mere perceptual information but also on structural abstraction about their physical environment.

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Chimpanzees use observed temporal directionality to learn novel causal relations

Goal attribution to inanimate moving objects by Japanese macaques ( Macaca fuscata )

The presence of joint predictors generates conjunctive predictions

Only one subject from the original test population had to be excluded because she did not fulfill the pretest criteria.

The colored part covered circa 1/3 of the bottle’s surface.

This exchange experience did not involve any differential reinforcement based on the weight or the color of objects. Instead, subjects were required to return objects (e.g., a pen or a piece of paper) that were “stolen” before regardless of particular physical properties.

All seven subjects easily met the criteria and preferred the juice to the alternative bottle (water or empty) in 100% of the cases.

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Acknowledgments

We are extremely grateful to the management, the trustees and especially the staff of the Ngamba Island Chimpanzee Sanctuary for their help and support. We also appreciate permission from the Ugandan National Council for Science and Technology and the Uganda Wildlife Authority. Special thanks go to Katja Karg for her help with the data collection, to the keepers of the zoo Leipzig, and to Raik Pieszek for his help in constructing the experimental apparatus.

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Hanus, D., Call, J. Chimpanzee problem-solving: contrasting the use of causal and arbitrary cues. Anim Cogn 14 , 871–878 (2011). https://doi.org/10.1007/s10071-011-0421-6

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Received : 06 December 2010

Revised : 11 May 2011

Accepted : 19 May 2011

Published : 07 June 2011

Issue Date : November 2011

DOI : https://doi.org/10.1007/s10071-011-0421-6

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This brief excerpt on Kohler's research is from the book: The Animal Mind by J.L Gould & C. G. Gould Wolfgang Kohler, a psychologist trained at the University of Berlin, was working at a primate research facility maintained by the Prussian Academy of Sciences in the Canary Islands when the First World War broke out. Marooned there, he had at his disposal a large outdoor pen and nine chimpanzees of various ages. The pen, described by Kohler as a playground, was provided with a variety of objects including boxes, poles, and sticks, with which the primates could experiment. Kohler constructed a variety of problems for the chimps, each of which involved obtaining food that was not directly accessible. In the simplest task, food was put on the other side of a barrier. Dogs and cats in previous experiments had faced the barrier in order to reach the food, rather than moving away from the goal to circumvent the barrier. The chimps, however, presented with an apparently analogous situation, set off immediately on the circuitous route to the food. It is important to note that the dogs and cats that had apparently failed this test were not necessarily less intelligent than the chimps. The earlier experiments that psychologists had run on dogs and cats differed from Kohler's experiments on chimps in two important ways. First, the barriers were not familiar to the dogs and cats, and thus there was no opportunity for using latent learning, whereas the chimps were well acquainted with the rooms used in Kohler's tests. Second, whereas the food remained visible in the dog and cat experiments, in the chimp test the food was tossed out the window (after which the window was shut) and fell out of sight. Indeed, when Kohler tried the same test on a dog familiar with the room, the animal (after proving to itself that the window was shut), took the shortest of the possible indirect routes to the unseen food. The ability to select an indirect (or even novel) route to a goal is not restricted to chimps, cats, and dogs. At least some insects routinely perform similar feats. The cognitive processing underlying these abilities will become clearer when we look at navigation by chimps in a later chapter. For now, the point is that the chimpanzees' abilities to plan routes are not as unique as they appeared at the time. Some of the other tests that Kohler is known for are preserved on film. In a typical sequence, a chimp jumps fruitlessly at bananas that have been hung out of reach. Usually, after a period of unsuccessful jumping, the chimp apparently becomes angry or frustrated, walks away in seeming disgust, pauses, then looks at the food in what might be a more reflective way, then at the toys in the enclosure, then back at the food, and then at the toys again. Finally the animal begins to use the toys to get at the food. The details of the chimps' solutions to Kohler's food-gathering puzzle varied. One chimp tried to shinny up a toppling pole it had poised under the bananas; several succeeded by stacking crates underneath, but were hampered by difficulties in getting their centers of gravity right. Another chimp had good luck moving a crate under the bananas and using a pole to knock them down. The theme common to each of these attempts is that, to all appearances, the chimps were solving the problem by a kind of cognitive trial and error, as if they were experimenting in their minds before manipulating the tools. The pattern of these behaviors--failure, pause, looking at the potential tools, and then the attempt--would seem to involve insight and planning, at least on the first occasion. Photos and captions from The Mentality of Apes click on each image to see larger version Chica on the jumping stick Grande on an insecure construction Sulton making a double-stick Konsul, Grande, Sultona and Chica building Grande achieves a four-story structure Read Kohler's Introduction to the Mentality of Apes Kohler's objections to Thorndike's approach to Animal Intelligence Questions about Kohler's conclusions: by P. Schiller's later work looking at the same issue Kenneth Spence's take on this general issue by R. Epstein's work on "insight" in pigeons Back to Main History Page

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Evidence for Emulation in Chimpanzees in Social Settings Using the Floating Peanut Task

* E-mail: [email protected]

Affiliation Department of Developmental and Comparative Psychology, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

Claudio Tennie,
Josep Call,
Michael Tomasello

Published: May 12, 2010
https://doi.org/10.1371/journal.pone.0010544
Reader Comments

It is still unclear which observational learning mechanisms underlie the transmission of difficult problem-solving skills in chimpanzees. In particular, two different mechanisms have been proposed: imitation and emulation. Previous studies have largely failed to control for social factors when these mechanisms were targeted.

In an attempt to resolve the existing discrepancies, we adopted the ‘floating peanut task’, in which subjects need to spit water into a tube until it is sufficiently full for floating peanuts to be grasped. In a previous study only a few chimpanzees were able to invent the necessary solution (and they either did so in their first trials or never). Here we compared success levels in baseline tests with two experimental conditions that followed: 1) A full model condition to test whether social demonstrations would be effective, and 2) A social emulation control condition, in which a human experimenter poured water from a bottle into the tube, to test whether results information alone (present in both experimental conditions) would also induce successes. Crucially, we controlled for social factors in both experimental conditions. Both types of demonstrations significantly increased successful spitting, with no differences between demonstration types. We also found that younger subjects were more likely to succeed than older ones. Our analysis showed that mere order effects could not explain our results.

The full demonstration condition (which potentially offers additional information to observers, in the form of actions), induced no more successes than the emulation condition. Hence, emulation learning could explain the success in both conditions. This finding has broad implications for the interpretation of chimpanzee traditions, for which emulation learning may perhaps suffice.

Citation: Tennie C, Call J, Tomasello M (2010) Evidence for Emulation in Chimpanzees in Social Settings Using the Floating Peanut Task. PLoS ONE 5(5): e10544. https://doi.org/10.1371/journal.pone.0010544

Editor: Pier Francesco Ferrari, Università di Parma, Italy

Received: September 22, 2009; Accepted: April 2, 2010; Published: May 12, 2010

Copyright: © 2010 Tennie et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors have no support or funding to report.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The cumulative nature of human culture appears to be unique within the animal kingdom [1] – [5] . This quality requires a high level of copying fidelity to every stage involved; it has been suggested that cumulative culture requires individuals to rely on imitation learning as this leads to learning not only the products, but also the detailed actions necessary to acquire a certain behaviour (i.e., the process leading to the product [5] ). Academics remain undecided as to whether non-enculturated (i.e., untrained in any way) chimpanzees learn socially in a comparable way to humans, with some arguing that these chimpanzees engage in imitative learning (e.g. [6] – [8] ), and others remaining more sceptical (e.g. [4] , [5] , [9] ). And it is these un-enculturated chimpanzees which represent more closely the state of wild living chimpanzees, since wild-living apes do not have the option of human raising or training (thus, if ecological validity is sought, non-enculturated chimpanzees should be studied). The question of whether or not such chimpanzees can socially learn from others using imitation remains an important debate as it may shed light on what sets human culture apart from other types of cultures in non-human species [4] , [5] , [9] .

Imitation is considered a complex form of social learning that involves copying the demonstrator's bodily actions [10] – [12] . An alternative form of social learning hypothesized to underlie ape traditions is emulation learning ( [13] , see also [14] ). When emulation takes place, the observer “picks up” on changes in the environment that result from the demonstrator's actions, hence the term “results copying” may also be used to describe emulation learning [10] , [11] . These results might be perceived and computed in different ways, ranging from so-called “object-movement re-enactment” [15] to insight learning (for a general overview see [16] ). An emulator ignores the actions of the demonstrator, and focuses primarily (if not solely) on the changes in the environment. As a consequence, if anything is copied following a demonstration, it will be the results, but not the actions involved.

It is, however, possible that observers are not completely blind to actions insofar as these actions can transmit information about the demonstrator's goals. Observers may therefore learn something about the demonstrator's goals based on the observed actions and in combination with what they understand about the observed results, determine how to achieve the same results (or, if the demonstration failed, they may achieve the opposite result instead; see [17] ). The specific details of the actions would however still be lost since focus would be placed on the goals of the demonstrator and not on the actions themselves. Because these goals typically (though not always) revolve around changes in the environment, emulation learning will be required as well. In these cases, the resulting learning type may represent a mixture of goal copying and results copying (named differently by different researchers; e.g., “goal emulation”–with emphasis on the goal copying part [18] , but see also [10] ; “teleological emulation”–which weighs both learning mechanisms equally [19] ).

Empirically, it has proven difficult to separate the effects of actions and results because frequently the two are presented simultaneously (and this is especially problematic if both are somewhat redundant; e.g., a finger pressing a recessed button; see also [20] ). For example, Whiten et al. claimed chimpanzees imitated the actions performed by demonstrators during a so-called two-action foraging task [21] . The apparatus in their study had a block obstructing a food chute, which could be moved to release a piece of food by either lifting or pushing the block. In one experimental group, a chimpanzee demonstrator lifted the block by levering an attached bar with a stick, and in another experimental group, another demonstrator used the same stick to push through a hole at the block itself (two different demonstrations–hence two -action task). The concern with defining this as imitation, is that if a demonstrator pokes a stick into a hole and the accompanying results (possibly together with the goal of inserting a stick) are copied by an observer, then the observer's behaviour will appear as if they had also copied the underlying actions (the “human eye” seems prone to this type of error). Because of the redundant actions, this experimental design does not rule out emulation as the underlying learning force (which is why it would be more precise to call this method the “two-actions/two-results task”). This kind of methodological issue is not unavoidable, as evidenced by studies on birds (e.g. [22] ), dogs [23] and marmosets [24] which overcame these problems by introducing action style components into demonstrations. For great apes, however, most observational learning studies which found copying are unable to distinguish between imitation and emulation learning, because it is unclear exactly what element has been copied (e.g. [21] , [25] – [27] ). To complicate matters, some such studies often add further potential information types, such as local or stimulus enhancement (e.g. [21] , [28] ), which again increase the number of possible underlying learning mechanisms (e.g., the two locations in [21] ). In addition to the typical confound of mixing action with results information (see above), two-action tasks often involve the introduction of relatively trivial differences between groups (e.g., move a lever to the left or right), which can hardly be regarded as a full blown culture even if the respective methods spread (e.g. [12] , [29] ). If such traditions are induced they can at best be described as mere “founder effects” of binary types of information/traditions (compare [5] ), which (even though important in their own rights) may not get at the heart of the question of whether ape traditions have much in common with human culture. Therefore, in an attempt to answer these questions, less trivial tasks should be used (compare also [17] ).

In order to truly investigate whether great apes copy actions spontaneously, a study needs to do one of three things: 1) demonstrate pure actions without any results information at all (“esture copying studies” see [30] ); 2) demonstrate pure results without any action information at all (“host control studies” see [12] , [29] , [31] ) or 3) decouple actions and results (this study). In the latter case, (non-redundant) demonstrated actions Y would lead to the result X (e.g., an approach to a hole walking on one's hands and the insertion of the stick using the foot). Due to them being decoupled from the resulting effect, the peculiar actions Y would later only materialize in the observers if they were indeed copying actions. This would then be a direct test of action copying. The logical counter-variant of such studies–used here–may directly test for emulation learning instead. Here, the demonstrator demonstrates the action Y, but, crucially, the setup is such that observers can only perform an unobserved action Z (because action Y is blocked/unavailable to observers). Again, both actions do lead to the same result (X). Here, if observers produce action Z, then action copying cannot have been responsible–because the observers never have seen action Z being demonstrated. Thus, the observers must have used different types of learning (i.e., emulation learning: reproducing the same result–by necessarily re-inventing an unseen action).

There is only one published study to date that uses the “esture copying”method in non-enculturated chimpanzees (i.e., to demonstrate pure actions without results). In this study, chimpanzees failed to copy a novel action (a “begging gesture” from a conspecific model despite potentially high levels of rewards [30] . Although these findings provided no evidence that chimpanzees copy actions spontaneously in problem solving tasks, subsequent ghost control studies have produced a more ambiguous picture. Three ghost control studies with chimpanzees have now been published, all using conspecific-demonstrator conditions as comparisons (i.e. [12] , [29] , [31] ). In one study, observers showed no evidence of observational learning regardless of the condition [12] . In a different study, chimpanzees learned in the full-demonstration condition, but did not learn in the ghost condition [31] . However, in the third study, there was evidence for observational learning in both conditions (i.e., evidence also for emulation in the ghost condition), but with stronger observational learning in the full demonstration condition [29] . In sum, non-enculturated chimpanzees (henceforth simply chimpanzees) do not seem to copy pure actions without results information [30] –but they also seem to be reluctant to copy pure results (see above). The underlying reason might be that a third factor may be responsible for these discrepant findings, and we believe this factor could be social.

In reviewing the ghost condition literature in chimpanzees, we noticed that these studies systematically differed with respect to social factors, which might explain their conflicting findings. Besides actions and results, there is a third–social-type of information that observers may learn about and copy during demonstrations: goal information [10] , [11] . Goal information describes the state of the world that the demonstrator tries to achieve. Ghost control studies typically lack such goal information (one cannot gather goals from ‘ghosts’). Recent studies suggest that chimpanzees may be able to perceive more about goals than was previously thought [32] , [33] . In the light of such recent findings, it is conceivable that the absence of this type of information may prove detrimental to the observational learning process for chimpanzees, and if that is the case, it is to be expected that chimpanzees' ability to copy will be negatively affected in ghost conditions. In addition to the potential lack of goal information, previous ghost condition studies have also lacked social presence during the demonstration phase. Yet, having a conspecific present during ghost demonstrations may enhance learning by way of social support [34] . Social ghost conditions may act as general motivation enhancers; the only ape study to provide evidence for emulation in a ghost condition found copying only in its social ghost condition (i.e., “enhanced ghost condition” [29] ). Finally, in those cases where a conspecific is present during demonstrations, it may matter whether there is a separation between observer and demonstrator. For example, in Tennie et al., the demonstrator and observer were separated by a glass/mesh during full model demonstrations, which may have led to this study's negative finding [12] . In both Hopper et al. studies, there was no conspecific present in the ghost condition, and no copying was found there [29] , [31] . However, in the Hopper et al. “enhanced” ghost condition (i.e., “ocial”ghost control [29] ) a conspecific was present in the same room as the observer (Lydia Hopper, pers. comm.), and it was here that clear evidence for emulation was found in chimpanzees. The reason that little evidence of emulation (i.e., only in the 1st trial) was found even in this “nhanced”condition might also be simply because in this condition the demonstrators did not directly interact with the apparatus, and thus probably did not transmit any goal information. To summarize, it is conceivable that these three social factors play a role in emulation learning in chimpanzees: 1) goal information, 2) the presence of a conspecific during demonstrations, 3) if a conspecific is present, physical proximity between conspecific and observer.

In the present study, we tried to include all of these social factors in our demonstration phases. We also aimed to avoid the potential pitfalls of studies that employ the “two-action task” procedure, especially the problem of triviality of task (e.g. [21] ). When using less trivial tasks, one can use two different approaches, the first of which would be to use tasks incapable of being solved by individual chimpanzees (i.e., where low-fidelity learning mechanisms alone do not help). These studies are interesting because they can uncover cases of cumulative culture. Currently, only one such study has been conducted, and the four species of great apes tested failed to show evidence of such copying [5] . A second approach would be to use tasks in which most, but not all, chimpanzees fail during baseline trials–an approach chosen for this study. In these experiments one can use the few successful subjects as “natural demonstrators” since they learned the technique during baseline trials and do not need to be trained further (it should be noted that such a practice may introduce a bias against good inventors. However, this is less problematic as long as several experimental conditions are compared with each other. Here each condition then tests apes with comparable performances). The added benefit of this situation is that the demonstrated technique is potentially more representative of a real ape tradition, and hence more ecologically valid (see [5] ; see also discussion ). Here, we tested chimpanzee subjects in such a difficult (but not impossible) problem solving task (“floating peanut task”; see [35] , [36] ). This task consists of a Plexiglas tube mounted vertically to the mesh of a cage, with only the top end open. Shelled peanuts are placed inside the tube resting at the closed bottom. The peanut could not be extracted from the top unless subjects added water to the tube thus causing the peanut to float high enough so that it could then be extracted. Prior to our study, Hanus et al. tested 25 chimpanzees using this task on Ngamba Island, Uganda; as well as 19 chimpanzees at the WKPRC, Leipzig, Germany (total n = 44, [36] ). Overall, in Hanus et al. 's study, only 7 subjects were successful, and all of these invented the solution either in their first or their second trial. Thus, in Hanus et al. 's study, subjects either learned early on in the trials or never at all, despite the fact that all subjects received four to eight trials.

We adapted the Hanus et al. study into a social learning experiment in order to ascertain whether chimpanzees are best described as emulators or imitators. All subjects were first tested in a baseline period, in which no previous information was provided to subjects (partly data from Hanus et al. and partly novel baseline trials established by us). Subjects then entered one of two experimental conditions: the full demonstration condition (providing information about actions, goals and results), or the emulation condition (“ater bottle”, providing only information about results and goals). In the full demonstration condition subjects witnessed a model pouring water from the mouth to the tube in order to get access to the peanut. In the emulation condition, subjects were shown how to solve the task by pouring water from a bottle into the tube. Thus, observers were required to produce the alternative, unobserved action (spitting water into the tube) in order to achieve the demonstrated result (i.e., making the peanut float up to the top with water).

By using these three conditions, we set out to disentangle the contributions of different learning processes potentially involved in the floating peanut task–as a model for behavioural traditions in chimpanzees in the field (e.g. [5] , [37] ). Comparison of the subjects' performances allowed us to do the following: a) measure the probability of innovation in these subjects over trials as a potential general means of solving the problem–and the rate of innovation was determined by baseline performance, b) measure the effects of different demonstration types compared to baseline performance (i.e., whether one or both demonstration types led to more solutions than had occurred during baseline; in other words, whether observational learning could help elicit the behaviour), c) determine the most plausible underlying learning mechanism (imitation or emulation) by comparing the effects of the two demonstration conditions. The underlying logic was that one type of demonstration (the full model; actions, goals and results) would only constitute an advantage if subjects were engaging in action copying (imitation) in order to learn the solutions. However, if no difference between the demonstration conditions could be found the most parsimonious explanation would be that subjects had made use of the same type of information in both conditions (i.e., results information (possibly spurred by goal information)–since this was the only type of information that was present in both experimental conditions).

Ethics statement

All the presented studies were non-invasive and strictly adhered to the legal requirements of the countries in which they were conducted. For Leipzig (Germany), animal husbandry and research complied with the “EAZA Minimum Standards for the Accommodation and Care of Animals in Zoos and Aquaria” and the “WAZA Ethical Guidelines for the Conduct of Research on Animals by Zoos and Aquariums” respectively. For Ngamba Island (Uganda) animal husbandry and research complied with the “PASA Primate Veterinary Healthcare Manual” and the “Chimpanzee Sanctuary & Wildlife Conservation Trust Policy”.

In Leipzig, the apes were housed in semi-natural indoor (overall 533 m 2 chimpanzee group “A”; overall 340 m 2 chimpanzee group “B”) and outdoor (4000 m 2 chimpanzee group “A”; 1400 m 2 chimpanzee group “B”) enclosures with regular feedings, enrichment and water ad lib. Subjects voluntarily participated in the study and were neither food nor water deprived.

In Ngamba, the apes were allowed to roam freely on the 40 ha island during the day and spent the night in seven interconnected sleeping rooms (overall 140 m 2 ) with regular feedings and water ad lib. Subjects voluntarily participated in the study and were neither food nor water deprived.

Thirty-two socially-housed chimpanzees ( Pan troglodytes ) participated in this study. There were eleven males and 21 females, ranging in age between five and 31 years. Twenty-three chimpanzees were housed at the Ngamba Island Chimpanzee Sanctuary ( http://www.ngambaisland.org ), Uganda and ten were housed at the Wolfgang Köhler Primate Research Center in Leipzig Zoo ( http://wkprc.eva.mpg.de ), Germany ( Table 1 ). None of the subjects had ever solved this task either because they had never been tested (n = 3, all in Ngamba) or having been tested in a previous study on non-social problem-solving [36] , they had failed to solve it. Subjects could choose to stop participating at any time and one subject in Ngamba, “Sophie”, was excluded due to this criterion. After participating, subjects were then released back into their home enclosures.

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https://doi.org/10.1371/journal.pone.0010544.t001

Three additional chimpanzees (two from Ngamba: Yoyo, Umutama and one from Leipzig: Frodo) who had learned to solve the task in a previous study were used during the demonstration conditions. All three individuals were dominant over their partners during the demonstration conditions. This was done to insure that the partners would watch but not interfere with the demonstrations-something that fortunately never happened during the study.

A vertically-oriented Plexiglas tube (25 cm long; 5 cm outward diameter and 5 mm thick) closed at the bottom was securely fastened to the caging. One peanut pod (containing two peanuts) was dropped inside the tube so that it rested at its bottom outside of the subject's reach. Prior to testing it was ensured that no tools were available in the cage. A drinker situated within 1 m from the tube (with the spigot at the same height as the tube opening) provided the water source. Such a drinker was installed prior to the test and it was not available outside of the testing situation (such “new drinkers”may protect against functional fixedness potentially attached to “old drinkers” see [36] ).

Subjects received two conditions: one baseline condition and one of the two experimental conditions. Prior to receiving one of the experimental conditions, all subjects had received the baseline condition to assess whether subjects were able to solve the task individually. However, subjects differed both in the number of baseline trials that they received, ranging from 2 to 10 (see Table 1 ) and the source of those trials. In particular, some subjects (included subjects only, see Table 1 ) received all their baseline trials from the Hanus et al. study (n = 12, see [36] ), some only from the current study (n = 12), and some from both studies (n = 7). The reason we conducted our own baseline was to ensure that our baseline and the Hanus et al. ' baseline produced comparable results. We found no differences between the subjects tested with the Hanus et al. baseline and those tested in the current study. Therefore, we pooled all the subjects into the same analysis. The different number of trials was an important feature of our design to be able to assess order effects (see below). Upon completing baseline trials, all subjects except four (due to time constraints) were distributed into two groups matched as closely as possible for age, sex and number of previous trials and received one of the two experimental conditions. Thirteen subjects were placed in the full demonstration condition and 14 subjects were placed in the water bottle condition. Next we describe the baseline and the two experimental conditions.

Baseline (total N = 31).

Subjects were presented with the peanut at the bottom of the tube and allowed to attempt to acquire the peanut. The differences with our own baseline trials and those of Hanus et al. 's study were as follows: we let subjects first observe the general setup from an adjacent room (in order to further control for the demonstration/waiting times of our two experimental conditions). Thus, after having observed E place the nuts in the tube, and prior to each trial, subjects spent five minutes in the cage next to the experimental cage (in full view of the tube). Subjects received a maximum of two trials (both on one day) and were alone during trials (except for E, who was present). In this condition, the only way to solve this task was to invent the solution spontaneously.

Full model condition (N = 13).

This condition was the same as our own baseline (see above) except that prior to their first test trial, subjects witnessed four to six demonstrations of the solution (from the initial water spitting until their partner acquired the peanut), and two further demonstrations before their second trial (see Fig. 1a for the general setup). Subjects received a maximum of two trials, depending on their performance (see below). A conspecific demonstrated the solution (spitting multiple times inside the tube in the process) while the subject stayed in the same cage, which means that she could freely approach and closely observe the demonstrator. Before their first trial, observers were required to have witnessed at least two spits into the tube. If they had seen these two spits within four demonstrations (live coded by E: each time subjects were required to face towards the demonstration, open-eyed and with an unobstructed line of sight), they were given their first trial, if not, they were given two more demonstrations. If observers still had not seen the required two spits, they were excluded from the study (though this situation never arose). In this condition, subjects could invent the solution spontaneously, they could imitate the actions of water spitting (action copying: imitation), or they could only copy the results of the demonstrator's actions.

1) full model condition; 2) water bottle condition. Squares in lower right corners represent drinkers. Chimpanzees on the left: subjects. Chimpanzees on the right: demonstrator or stooge (depending on condition). Please note that, for clarity reasons, most bars of the caging have been omitted from the drawing.

https://doi.org/10.1371/journal.pone.0010544.g001

Water bottle condition (N = 14).

This condition was identical to the full model condition (including the number of solutions witnessed) except for the following differences. A solution-naïve, but dominant conspecific (“stooge” demonstrator) was used as a social partner for the subject. E enacted an alternative solution to water-spitting by pouring into the tube the necessary amount of water from a bottle from outside the cage ( Fig. 1b ). In order to fill the tube to the required level, E poured three ‘glugs’ from the bottle of water. If the observer was constantly watching E, then there was approximately two seconds between each glug. However, if the observer was not watching, then E paused the pouring until the observer was watching, then continued to pour. Once the peanut reached the top of the tube, the ‘stooge’ demonstrator invariably took the peanut, thus being comparable to the outcome of the full demonstration condition. In this condition, the subject was able to witness the results producing the solution (i.e., water added to the tube will raise the water level which will raise the nuts to within reach) but without any actions that the subject could use to solve the task. Subjects in this condition thus had two possible routes to solutions: they could invent the solutions spontaneously, or they could emulate (but not imitate–since they never saw the spitting action).

All trials were terminated after ten minutes unless subjects were still attempting to get the peanut after this period has elapsed. In such case, trials could be extended for a maximum of two additional periods of five minutes so that the maximum length of a trial could be 20 minutes (ten plus five plus five). Trials were also terminated once the subject retrieved the reward. In the event of a success, the subject was not tested again.

Data Scoring and Analysis

All trials were videotaped using a wide-angle camera. E scored live whether or not a subject was successful in retrieving the nuts in a given trial (i.e., general success was our main dependent measure). Additionally, we scored from the videotapes the following drinker- and tube-related behaviours: number of times water was collected from the drinker and number of times the subject spat into the tube. In order to assess inter-observer reliability for these tube- and drinker-related behaviour a different coder (C. Tennie) coded 20% of trials (randomly selected from all experimental trials, as well as from the baseline trials that were performed solely for this study). Inter-observer reliability was very high for both measurements (Pearson's: number of times water was collected: r = 0.972; number of times spat into tube: r = 0.982). To assess inter-observer reliability for successes a naïve coder also coded general successes from videotape for randomly chosen trials (60% of all trials). Reliability was nearly perfect (with only one mismatch in total).

We analyzed our dependent measure, success to get the peanut, using a generalized linear mixed model (GLMM; [38] ) with binomial error structure and logit link function calculated using the package lme4 [39] for R [40] . As fixed effects we included the factor ‘condition’ and the covariate ‘trial number’ into the model. Since the number of successes in the dataset was small, the assumptions of this procedure were likely to have been violated, devaluing the validity of the p-values thus derived. Hence we established correct significances based on a permutation test. For this we randomized the outcomes of trials within subjects and then ran a GLMM for the randomized data. We repeated this procedure 1000 times and each time derived the estimated coefficient of an effect (condition or trial). Finally we estimated the p-value for an effect by determining the proportion of permutations that revealed an absolute coefficient being at least as large as that of the original data.

GLMM offered us two key advantages over other statistical techniques. First, it allowed us to incorporate a “subjects” factor as a random effect in order to control for observations that are replicated [41] . Second, since our baseline always preceded the experimental conditions, this could potentially create an order of administration confound. The inclusion of the covariate ‘trial number’ in the model allowed us to control for this aspect (i.e.: when trial/order effects were tested, then condition was controlled for and vice versa). Thus order/trial effects, if they existed for an experimental condition, would not explain a general effect of condition if it were found.

We used the exact Mann-Whitney-U test to analyze whether there were differences between the two experimental conditions. To do so, we calculated the subjects' success ratios (success divided by total numbers of trials, including baseline trials) and compared the ratios calculated for each demonstration condition. Elsewhere, wherever we used either a Mann-Whitney-U test or a Wilcoxon test we used 1 st trial data only (since not every subject had a 2 nd trial). Obviously, this rule did not apply when we compared behaviour between the two trials.

To compare drinker- and tube-related behaviour between baseline and experimental conditions, we used our baseline data derived from Ngamba subjects only. Since we could not perform a meaningful Wilcoxon test on just the resulting six subjects who were in both the baseline and the experimental conditions, we ran a Mann-Whitney-U test comparing subjects in the baseline condition with others in the experimental conditions. This procedure was straightforward for those subjects who only were in one condition, but for those subjects who had been in both conditions (i.e., baseline condition and experimental condition) we only used data from their baseline condition (we did so since the sample size of the baseline condition was smaller than the sample size of the experimental condition).

Overall, eight subjects were successful across the experimental conditions (five in the full model condition and three in the water bottle condition). Both experimental conditions, when compared to baseline, showed significantly more successes after demonstrations (Full model condition; permutation test: p = 0.002; Water bottle condition; permutation test: p = 0.015). We found no additional effects of trials when comparing baseline with both experimental conditions pooled (Effect of exp. condition; permutation test: p = 0.001; Trial effect; permutation test: p = 0.957), When tested alone, the full model condition, but not the water bottle condition, showed additionally an effect of trial (Full model condition; permutation test: p = 0.034; Water bottle condition; permutation test: p = 1.00). Thus, both types of experimental demonstration resulted in more successes than the baseline condition, which means that demonstrations did indeed have a positive effect and thus offered an advantage over individual innovation. While differing in terms of success in retrieving the peanut, baseline subjects did not differ from experimental subjects in tube- and drinker-related behaviour (exact Mann Whitney U tests: number of water retrievals: U = 53, N BL = 10, N EXP = 12, p = 0.673; number of spits into the tube: U = 39.5, N BL = 10, N EXP = 12, p = 0.148).

There were no significant differences between experimental conditions in the success to retrieve the peanut (exact Mann Whitney U test, U = 78, N full demo = 13, N waterbottle = 14, p = 0.475) or the number of subjects who spat into the tube (Fisher's test; p = 0.706; seven and six subjects in the full model and the water bottle conditions, respectively), Furthermore, subjects in both experimental conditions did not differ in general tube-and drinker-related behaviour (exact Mann Whitney U tests: number of water gatherings U = 62.5, N full demo = 13, N waterbottle = 14, p = 0.170; number of spits into tube: U = 79, N full demo = 13, N waterbottle = 14, p = 0.547).

Next we pooled the data from both experimental conditions to explore what might distinguish successful from unsuccessful subjects. Perhaps successful subjects were more motivated to solve the task. If so it would be expected that successful subjects simply tried longer to solve the task than did unsuccessful subjects. Contrary to this idea, we found that successful subjects had shorter trials than unsuccessful subjects (exact Mann Whitney U test, U = 0, N Success = 6, N NoSuccess = 21, p = <0.001). Unsuccessful subjects became less focused on the task in their second trial as evidenced by the fact that they retrieved water less often in their second trial than in their first trial (Wilcoxon; T + = 113.5; n = 16; p = 0.016). Additionally, we found that successful subjects were younger than unsuccessful subjects (exact Mann Whitney U test: U = 21, N NoSuccess = 21, N Success = 6, p = 0.011; median age (years): successful = 7, unsuccessful = 10).

Finally, we checked whether there might have been a difference between the Ngamba and Leipzig subjects concerning drinker- and tube-related behaviour. We detected no such differences (exact Mann Whitney U tests: number of water gatherings: U = 45, N Ngamba = 18, N Leipzig = 9, p = 0.064; number of spits into tube: U = 54, N Ngamba = 18, N Leipzig = 9, p = 0.139).

In stark contrast to baseline performances, both experimental conditions elicited successes in some observers–with no difference between the two experimental conditions. Thus, demonstrations of three simultaneous information types (i.e., actions, goals and results: full demonstration condition) offered no advantage over demonstrations of two information types (i.e., results and goal information only: water bottle condition). The most parsimonious explanation is that the underlying learning mechanism was emulation learning (results copying; here possibly spurred by goal information) in both experimental conditions–since apparently action information offered no advantage to observers. We thus conclude that unsuccessful chimpanzees can be observationally induced to solve the floating peanut task mainly on their own: when trying to arrive at the observed result, they were able to fill in the (unseen) action information themselves.

While this one study alone cannot rule out (spontaneous) action copying in chimpanzees (though see also [30] ), our results show that emulation is a viable mechanism for acquiring target behaviour under social circumstances–that is, if presented together with goal information. The idea that some form of emulation could account for tradition-building in chimpanzees is an explanation that is consistent with the ape social learning literature in general ( [4] , [5] , [9] , [12] , [42] , [43] but for a different view see [8] ) and with more recent experimental evidence for group-specific traditions forming in monkey species that lack complex imitating abilities [44] . At the moment, the most parsimonious explanation seems to be that copying results and goals (rather than copying of actions) could underlie ape traditions. When social support, spatial separation and goal information are controlled for, chimpanzees showed evidence for copying (of results, i.e., emulation), and with no difference in performance to a full demonstration condition. This finding of copying is in contrast to earlier studies that sought to detect emulation learning in chimpanzees and which presented results information while lacking social controls (i.e. [12] , [29] , [31] ). Importantly, no evidence for copying was found in a study that included these social factors, but which presented no results information at all (“pure” action copying study [30] ). It is also worth noting that chimpanzees often do not follow actions demonstrated to them when these same actions are also available to them (e.g. [5] , [12] )–and instead prefer to act independently from demonstrations, which is further evidence that emulation learning is important for them (see also an example for this in keas: [45] ).

Our results are not due to mere stimulus or local enhancement [46] , [47] to the drinkers in the full model condition. This information was not necessary, since there was no difference between successes (or indeed any drinker- or tube-related behaviour) elicited by both experimental conditions, despite the fact that there was no drinker enhancement in the water bottle condition (the water bottles were instead filled out of the observer's sight). One might argue that observers would have copied even in cases where water was merely present to some degree in the tube (i.e., either a semi-filled tube, or a fully filled tube)–without having seen the filling of the tube (so called “end-state conditions” [17] ). However, we do not think that subjects would have copied in such a stationary condition, for the following reasons: Baseline subjects did not differ from experimental subjects in general drinker- and tube-related behaviour, suggesting that indeed something extra–and crucial for success–has been transmitted by both demonstration types. Also, such semi-end-state conditions (semi-filled tubes at start of trial) were already conducted as part of the problem solving study of Hanus et al. and they found no difference between their fully dry (like in our baseline) and semi-filled condition [36] . In contrast, our dynamic (and social) emulation condition led to successes in subjects who had proven unsuccessful before–which suggests that dynamic physics matter more to chimpanzees than do at least semi-end-states (at least for difficult tasks; for an easier task in chimpanzees with opposite findings see [17] ). Or else it may suggest that goal information needs to be additionally present, since this type of information was missing in Hanus et al. [36] . However, the possibility remains that a special end-state condition–a fully filled tube–would be as effective as our emulation condition. Future studies will be needed to address this possibility.

Based on the literature, we believe the following additional factors were ultimately responsible for our finding that chimpanzees are able to invent unseen actions for solutions that they can potentially invent on their own (as evidenced by some successful subjects in Hanus et al. [36] ). As hypothesized in the introduction, it is likely to be social factors that lead to clearer evidence for emulation rather than (somewhat non-naturalistic) ghost controls. We aimed to provide observers with as much social information as possible, in order to induce their natural tendency to emulate, and it seems that we have succeeded. What we cannot do, however, is determine which of these three social factors was the most relevant (or whether there was an interaction between them). Chimpanzees in social learning experiments might require only one or else several of the following: goal information, social support and/or non-separation of subjects from observers. Should future studies identify goal information as being strictly necessary for chimpanzees to induce emulation then the learning mechanism itself would require renaming (e.g., teleological emulation: [19] ; Else, using the simplified terminology of Call & Carpenter one may speak of “goal and results copying” [10] ).

Due to the general ecological validity of our study (in terms of social factors, goal information, conspecific demonstrators [48] , as well as using a difficult task), and in light of a previous study that failed to detect action copying in chimpanzees when only action copying would have led to success [30] , our finding supports the recent hypothesis that emulation learning via re-invention could, at least in principle, underlie many, most, or all socially learnt behaviours in wild chimpanzees [5] . Once one subject has found the required solution, it will be considerably easier for others who observe her to derive at the (same) solution themselves (as shown in this study). In accordance with this view, there seems to be no behavioural tradition in chimpanzees (or any great ape-) which could not be invented by a single (perhaps specially gifted, or perhaps especially “lucky” or motivated) individual–and then spread by way of emulation learning (possibly helped by enhancement effects). It is apparently unnecessary for actions to be copied during such a process–emulation suffices. It is true that not all observers in our study acquired the target behaviour (i.e. successful behaviour), suggesting that additional factors might be necessary before a behaviour appears on a population-wide scale (e.g. more demonstrations or equal levels of motivation etc.; but see also below for a hypothesis based on age-effects).

By emulation, observers in effect “re-invent” a solution once they have witnessed it–an effect best described as “catalystic”, rather than as “transmissive” (i.e., a domino-like effect). This would mean that great apes like chimpanzees can only learn what they could, in principle, also invent on their own–at least given the right individual circumstances (i.e., enough motivation, access to all necessary material, focus on the right objects, reduced neophobia, social support etc.). The sheer number of these interacting factors ensures that, overall, such inventions (and re-inventions) must be regarded as a probabilistic process, and so, while the appearance of certain behaviours in a given single chimpanzee can still have a low baseline probability, the fact remains that the task could potentially be learned in its entirety without the help of observational learning at all (example of such “atent solutions”include: chimpanzee nutcracking (see one subject in the baseline of a recent study [49] , which may have invented this solution spontaneously); gorilla nettle feeding: [42] , [43] ; chimpanzee leaf swallowing: [50] ; chimpanzee termite fishing: [51] ; capuchin nut-cracking: [52] ). During the spread of the behaviour, the necessary actions then are generated from within each observer anew and independently–and thus actions not copied (and, crucially, they do not need to be copied).

This view has implications for the general limits of ape traditions. If this hypothesis [5] proves correct, then ape traditions consist entirely of “atent solutions” the scope of which is basically determined by the limits of the emulative capacities of the species (in other words: by the underlying problem solving skills–developed via natural selection). Additionally, many other factors likely play a role in the realization of traditions (e.g. motivational differences between populations due to prior food choices). In concert, these factors may lead to the observed “patchy pattern” of traditions across living populations of chimpanzees (e.g., they lead to different “atent solution”mixtures in different populations, which explains the mosaic picture of chimpanzee traditions described by Whiten et al. [37] –for which human like imitative abilities are usually claimed as the underlying reason).

Our findings confirm an earlier observational learning study [53] that described a similar age effect in emulation in chimpanzees–and that also used a difficult, but not impossible, task [compare also 5]. Noting that the age of successful learners (4–6 years) coincided with the “earliest tool-use behaviours in the wild” Tomasello et al. [53] introduced a “critical time period” hypothesis. Thus, the reason why most (or all) chimpanzees in a given wild population show skill in certain tool-“traditions”–in contrast to our and others' [53] more partial findings–might be that, earlier, these chimpanzees were able to learn during their critical time period. If true, this hypothesis would explain why not all subjects in our and the other [53] study became successful after demonstrations. Once subjects have become too old they might cease to be able (or to be motivated) to emulate in such situations.

Acknowledgments

We thank Nathan Pyne Carter, Marietta Dindo, Heinz Gretscher, Daniel Hanus, Alicia Melis, Natacha Mendes, Nadja Miosga, Roger Mundry, Raik Pieszek, Yvonne Rekers and the anonymous referees. We thank Leonard Erlbruch for his drawings. We also thank the Max Planck Society; Leipzig Zoo and CSWCT (Uganda)–as well as all animal keepers. We are very thankful to UNCST (Ugandan National Council for Science and Technology) and UWA (Ugandan Wildlife Authorities) for allowing us to conduct our research at Ngamba Island.

Author Contributions

Conceived and designed the experiments: CT JC MT. Performed the experiments: CT. Analyzed the data: CT. Contributed reagents/materials/analysis tools: CT. Wrote the paper: CT JC.

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Table Of Contents

Köhler’s best known contribution to animal psychology arose from his studies of problem solving in a group of captive chimpanzees . Like other Gestalt psychologists, Köhler was strongly opposed to associationist interpretations of psychological phenomena, and he argued that Thorndike’s analysis of problem solving in terms of associations between stimuli and responses was wholly inadequate. The task he set his chimpanzees was usually one of obtaining a banana that was hanging from the ceiling of their cage or lying out of reach outside the cage. After much fruitless endeavour, the chimpanzees would apparently give up and sit quietly in a corner, but some minutes later they might jump up and solve the problem in an apparently novel manner—for example, by using a bamboo pole to rake in the banana from outside or, if one pole was not long enough, by fitting one pole into another to form a longer rake. Other chimpanzees reached the banana hanging from the ceiling by using a wooden box, or a series of boxes stacked precariously on top of one another, as a makeshift ladder.

Köhler believed that his chimpanzees had shown insight into the nature of the problem and the means necessary to solve it. According to Köhler’s interpretation, the solution depended on a perceptual reorganization of the chimpanzee’s world—seeing a pole as a rake, or a series of boxes as a ladder—rather than on forming any new associations. But subsequent experimental analysis has cast some doubts on Köhler’s claims. The critical observation is that the sorts of solutions that Köhler took as evidence of insight quite clearly depend on relevant prior experience. Chimpanzees will not fit two poles together to form a rake or stack boxes up to form a ladder unless they have had a great deal of prior experience with those objects. This experience may well occur during play, when the young chimpanzee discovers that using a stick can extend the reach of an arm, or that standing on a box can put one within reach of high objects. Thus, what Köhler was studying, without knowing it, was probably the transfer of earlier instrumental conditioning to new situations. As we have already seen, the ability to transfer an old solution to a new stimulus situation is an important one, relevant to a wide range of problem-solving activities. This ability is not at all well understood, but it will not necessarily be greatly illuminated by describing it as insight. Certainly it is not a process unique to the great apes: if the component tasks are sufficiently well-structured, even pigeons can put together two independently learned patterns of behaviour to solve a novel problem.

Combining information from separate sources to reach a new conclusion is one form of reasoning . The paradigm case of reasoning is the solution of syllogisms; for example, when we conclude that Socrates is mortal given the two separate premises that Socrates is a man and that all men are mortal. Employing transitive inference , we can use the premises that Adam is taller than Bertram and that Bertram is taller than Charles to conclude that Adam must be taller than Charles. Reasoning has often been regarded as a uniquely human faculty, one of the few factors, along with the possession of language, that distinguishes us from the rest of the animal kingdom.

But are humans the only animals that can reason? The unsatisfying answer must be that it depends on what is meant by reasoning. In a very general sense, most animals appear perfectly able to arrive at a conclusion based on combining information obtained on two separate occasions. A formal demonstration is provided by an experiment on instrumental conditioning discussed earlier. If rats learn that pressing a lever provides sucrose pellets and later learn that eating sucrose pellets makes them ill, they will subsequently put these two pieces of information together and refrain from pressing the lever. Monkeys and chimpanzees, however, have been trained to solve problems that appear more similar to transitive inference. They are first given discriminative training between pairs of coloured boxes, called, for example, A, B, C, D, E. Confronted with the choice between A and B, they learn that choice of A is rewarded and B is not. When B and C are the alternatives , they learn that B is correct; when C and D are the alternatives, C is correct; and so on. Although choice of A is always rewarded, and that of E never is, the remaining three boxes each are associated equally often with reward and with nonreward. Nonetheless, given a choice between B and D on a test trial, the animals choose B.

Syllogistic and transitive inference are not the only forms of reasoning: humans also reason inductively or by analogy . Indeed, analogical reasoning problems (black is to white as night is to —?) form a staple ingredient of some IQ tests. One chimpanzee, a mature female called Sarah , was tested by David Premack and his colleagues on a series of analogical reasoning tasks. Sarah previously had been extensively trained in solving matching-to-sample discriminations , to the point where she could use two plastic tokens, one meaning same , which she would place between any two objects that were the same, and another meaning different , which she would place between two different objects. For her analogical reasoning tasks, Sarah was shown four objects grouped into two pairs, with each pair symmetrically placed on either side of an empty space. If the relationship between the paired objects on the left was the same as the relationship between those on the right, her task was to place the same token in the space between the two pairs. Thus in one series of geometrical analogies , a simple problem would display a blue circle and a red circle on the left and a blue triangle and a red triangle on the right; the correct answer, of course, was same . But Sarah was equally correct on more complex problems, even when the relationships in question were functional rather than simply perceptual. For example, she correctly answered same when the two objects on the left were a tin can and a can opener and the two on the right a padlock and a key.

Solution of analogies requires one to see that the relationship between one pair of items (whether they are words, diagrams, pictures, or objects) is the same as the relationship between a different pair of items. If simple matching-to-sample requires animals to see that one comparison stimulus is the same as the sample and another is different, solving analogies requires them to match relationships between stimuli. The difficulties encountered in training pigeons to generalize simple matching-to-sample discriminations does not encourage one to believe that they would find analogies very easy.

The ability to speak was regarded by Descartes as the single most important distinction between humans and other animals , and many modern linguists, most notably Noam Chomsky , have agreed that language is a uniquely human characteristic. Once again, of course, there are problems of definition. Animals of many species undoubtedly communicate with one another. Honeybees communicate the direction and distance of a new source of nectar; a male songbird informs rival males of the location of his territory’s boundaries and lets females know of the presence of a territory-owning potential mate; vervet monkeys give different calls to signal to other members of the troop the presence of a snake, a leopard, or a bird of prey . None of these naturally occurring examples of communication, however, contains all of the most salient features of human language. In human language, the relationship between a word and its referent is a purely arbitrary and conventional one, which must be learned by anyone wishing to speak that language; many words, of course, have no obvious referent at all. Moreover, language can be used flexibly and innovatively to talk about situations that have never yet arisen in the speaker’s experience—or indeed, about situations that never could arise. Finally, the same words in a different order may mean something quite different, and the rules of syntax that dictate this change of meaning are general ones applying to an indefinite number of other sequences of words in the language.

During the first half of the 20th century, several psychologists bravely attempted to teach human language to chimpanzees. They were uniformly unsuccessful, and it is now known that the structure of the ape’s vocal tract differs in critical ways from that of a human, thus dooming these attempts to failure. Since then, however, several groups of investigators have employed the idea of teaching a nonvocal language to apes. Some have used a gestural sign language widely used by the deaf to communicate with one another; others have used plastic tokens that stand for words; still others have taught chimpanzees to press symbols on a keyboard. All have had significant success, and several apes have acquired what appears to be a vocabulary of several dozen, and in some cases 100 or 200, “words.”

Washoe , a female chimpanzee trained by Beatrice and Allan Gardner, learned to use well over 150 signs. Some apparently were used as nouns, standing for people and objects in her daily life, such as the names of her trainers, various kinds of food and drink, clothes, dolls, etc. Others she used as requests, such as please, hurry , and more ; and yet others as verbs, such as come, go, tickle , and so on. Sarah , the chimpanzee trained by Premack to use plastic tokens as words, also apparently learned to use tokens for nouns, verbs ( give, take, put ), adjectives ( red, round, large ), and prepositions ( in, under ). But do these signs or tokens really function as words? Does the ape using them, or obeying instructions from a trainer who uses them, really understand their meaning? Or is the ape simply performing various arbitrary instrumental responses in the presence of particular stimuli because she had previously been rewarded for doing so?

There can be little doubt that chimpanzees do have some understanding of what their “words” refer to. Sarah responded appropriately with her token for red if asked the question “What colour of apple?” both when an actual red apple was shown as part of the question and when only the token for an apple (which happened to be a blue triangle) was presented. To Sarah, the blue triangle surely stood for, or was associated with, the red apple. In another study, after two chimpanzees had been taught the meaning of a number of symbols for different kinds of food and different tools, they were able not only to fetch the appropriate but absent object when requested to do so, but they could also sort the symbols into two groups, one for foods and one for tools. In another series of studies, a pygmy chimpanzee named Kanzi demonstrated remarkable linguistic abilities. Unlike other apes, he learned to communicate using keyboard symbols without undergoing long training sessions involving food rewards. Even more impressive, he demonstrated an understanding of spoken English words under rigorous testing conditions in which gestural clues from his trainers were eliminated.

As noted above, human language is more than a large number of unrelated words: in accordance with certain implicitly understood syntactic rules, humans combine words to form sentences that communicate a more or less complex meaning to a listener. Can apes understand or use sentences? Undoubtedly they can put together several gestures or tokens in a row. A chimpanzee named Lana, who was trained to press symbols on a keyboard, could type out “Please machine give Lana drink”; Washoe and other chimpanzees trained in gestural sign language frequently produced strings of gestures such as “You me go out,” “Roger tickle Washoe,” and so on. Skeptical critics, however, have raised doubts about the significance of these strings of signs and symbols. They have pointed out, for example, that when Lana pressed a series of coloured symbols on her keyboard, it was humans who interpreted her actions as the production of a sentence meaning “Please machine give Lana drink.” Might it not be equally reasonable to say that she learned to perform an arbitrary sequence of responses in order to obtain a drink? Pigeons can be trained to press four coloured keys—red, white, yellow, and green—in a particular order to obtain food. Psychologists do not feel any temptation to interpret this behaviour as the production of a sentence. What is it about Lana’s behaviour that requires this richer interpretation?

In the case of apes trained to use sign language, two other doubts have been raised. First, there is some reason to believe that a disappointingly high proportion of the apes’ gestures may be direct imitations of gestures recently executed by their trainers. Second, a sequence of gestures interpreted as a single sentence is often just as readily interpreted as a number of independent gestures, each prompted, in turn, by a gesture from the trainer. Both these conclusions are based on careful examinations of video recordings of interactions between trainers and apes. Whether they will turn out to be generally true remains an open, and heatedly debated, question.

Without any explicit training, apes have nevertheless learned to produce strings of two or three signs in certain preferred orders: “more drink” or “give me,” for example, rather than “drink more” or “me give.” Do the animals understand that a string of signs in one order means something different from the same signs in a different order? The following anecdote is suggestive. A chimpanzee called Lucy was accustomed to instructing her trainer, Roger Fouts, by gesturing “Roger tickle Lucy.” One day, instead of complying with this request, Fouts signed back “No, Lucy tickle Roger.” Although at first nonplussed, after several similar exchanges Lucy eventually did as asked. A simple instance of this sort proves little or nothing, but it may suggest what is needed—namely, that Lucy should understand that changing the order of a set of signs alters their meaning in certain predictable ways. She must generalize the rule that the relationship between the meanings of the signs A-B-C and C-B-A (the same signs in reverse order) is similar to the relationship between the meanings of certain other triplets of signs in her vocabulary when their order is reversed.

The research on language in apes forcefully illustrates a conflict, or tension , that is common to many other areas of research on learning in animals. If the investigators are interested in language and communication, they can attempt to communicate as naturally and informally as possible with their apes. This approach involves treating an ape as a fellow social being, with whom one plays and interacts as far as possible as one would with a human child; it also, almost inevitably, results in a style of research where it is exceptionally difficult to control precisely the cues that the ape may be using and even hard to avoid an overly rich, anthropomorphic interpretation of the ape’s behaviour. If, on the other hand, the researchers are interested in rigorous experimental control and economical interpretation of the processes underlying the ape’s performances, they are likely to set the ape formal problems to solve, with rewards for correct responses and no rewards for errors. But such an approach, however scientific it may seem, must run the risk of missing the point. This is not language; the investigators are not communicating with the ape in the way they would communicate with a child. The very nature of the experimental problems ensures that the ape will not use its language in the way that a child does: to communicate shared interests, to attract a parent’s attention to what the child has seen or is doing, to comment on a matter of concern to both.

There is no resolution to this conflict, for both approaches have their virtues as well as their dangers, and both are therefore necessary. In just the same way, the study of a rat pressing a lever in a Skinner box or of a dog salivating to the ticking of a metronome seems to many critics a sterile and narrow approach to animal learning—one that simply misses the point that, if the ability to learn or profit from experience has evolved by natural selection , it must have done so in particular settings or environments because it paid the learner to learn something. It would be foolish to deny this obvious truism: of course it pays animals to learn. Indeed, it may pay them to learn quite particular things in specific situations, and different groups of animals may be particularly adapted to learning rather different things in similar situations. None of this should be forgotten, and the study of such questions requires the scientist to forsake the laboratory for the real world, where animals live and struggle to survive. But few sciences can afford to miss the opportunity to manipulate and experiment under laboratory conditions where this is possible, and none can afford to forget the benefits of precise observation under controlled conditions.

Chimpanzees’ (Pan troglodytes) problem-solving skills are influenced by housing facility and captive care duration

Chimpanzees’ ( pan troglodytes ) problem-solving skills are influenced by housing facility and captive care duration.

Although a large body of primate cognition research is done in captive institutions, little is known about how much individuals from different facilities vary in their experiences and cognitive skills. Here we present the results of an experimental study investigating how physical cognitive skills vary between chimpanzees in relation to captive settings and their time in captivity. We tested 59 chimpanzees housed at two different captive facilities (a rehabilitation center (sanctuary) and a zoo) in three problem-solving tasks. Our results showed that chimpanzees at the two housing facilities significantly differed in overall task performance. On average, the sanctuary chimpanzees outperformed the chimpanzees housed at the zoo in the detour reaching task and the honey trap task. However, the zoo chimpanzees performed slightly better on average in the learning task. We propose that, for this particular sample, the documented differences result from a combination of factors, such as prior experience with cognitive testing, motivation levels and varying degrees of human exposure. Within the sanctuary sample, we found that chimpanzees who arrived at an earlier age at the sanctuary and had therefore spent a larger percentage of their lives in a captive environment, were better problem-solvers than those that arrived at a later age to the sanctuary. Thus, rehabilitation and time in captivity contributed to improved physical cognitive skills in sanctuary chimpanzees. Our results highlight the importance of studying intraspecific variation and the effect that previous experience and living conditions might have on physical cognitive skills in non-human apes. Accordingly, we should be cautious when extrapolating findings of cognitive studies from one population to the species as a whole.

Introduction

In order to understand the evolution of cognitive abilities we need to disentangle environmental and genetic influences from cognitive phenotypes in non-human animals. To this end, it is important to consider intraspecific variation and to identify what factors correlate with between-individual differences ( Boesch, 2020 ). Previous research has shown that early life experience can influence cognition in several species: enriched captive conditions during early life increase spatial learning abilities in fish ( Salmo salar ) ( Salvanes et al., 2013 ); the early incubation environment of lizard eggs ( Pogona vitticeps ) influences the adult lizards’ socio-cognitive skills assessed through social learning tasks ( Siviter et al., 2017 ); early maternal separation reduces learning ability in male mice ( Mus musculus ) ( Wang, Jiao & Dulawa, 2011 ) and hand-raised parakeets ( Melopsittacus undulatus ) perform better at object permanence than parent-raised ones ( Funk, 1996 ).

In humans ( Homo sapiens ), part of the observed intraspecific variation in cognitive abilities is explained by genetic inheritance ( Deary, Johnson & Houlihan, 2009 ; Nisbett et al., 2012 ; Bates, Lewis & Weiss, 2013 ). Thus, to identify the variation in cognitive abilities explained by environmental factors and early-life experiences in our species, large research efforts have been deployed into studies of identical twins and adopted children. Such study design has provided insight into how, besides genes, the nurturing environment where a child is raised contributes to outcomes such as educational attainment and income later in life ( Sacerdote, 2011 ). Compared to children from high socio-economic-status (SES) families, children as young as six months old from low SES families already show lowered attentiveness influencing a cascade of cognitive skills ( Clearfield & Niman, 2012 ). Furthermore, studies on adopted institutionalized children have shown that children who move to foster families at an earlier stage of development have better chances at cognitive recovery from early social deprivation ( Nelson et al., 2007 ).

Similar to humans, studies on chimpanzees suggest that great ape (henceforth apes) cognition is influenced both by genetics ( Hopkins, Russell & Schaeffer, 2014 ) and the socio-cultural environment where the individuals develop ( Russon, Bard & Parker, 1998 ; Reader & Laland, 2002 ; Russell et al., 2011 ; van Schaik & Burkart, 2011 ). Ideally, we would assess the influence of experience and early life environments on cognitive abilities in the species’ ecological context. However, it is often challenging to conduct controlled, cognitive experiments in the natural environment of apes. Due to this limitation, most cognitive studies are performed in captive, settings where the learning opportunities during experiments can be controlled for.

Captive apes show large individual variation in cognitive performance ( Herrmann & Call, 2012 ) and even within the same facility, chimpanzees have been shown to differ in their tendencies to use social information during problem-solving tasks ( Watson et al., 2018 ). Thus, when measuring cognitive skills in captive apes, we need to take into consideration this variation as well as its potential underlying factors. Sources of intraspecific variation can be different motivational levels among individuals to participate in an experiment; familiarity with the test apparatuses and methodological procedures and differences in housing conditions or routines between facilities. In addition, the apes’ contact with peers, their degree of human contact and their exposure to human artefacts can also influence the apes’ performance in cognitive tasks ( Tomasello, Savage-Rumbaugh & Kruger, 1993 ; Call & Tomasello, 1996 ; Tomasello & Call, 2004 ; Bering, 2004 ; Furlong, Boose & Boysen, 2008 ; Damerius et al., 2017a ; Damerius et al., 2017b ). Lastly, the rearing background where an individual develops is also likely to influence the individual’s cognitive development. For instance, enculturated apes (defined as apes raised by humans in an anthropomorphic environment and attended to as intentional agents exposed to a wide variety of human cultural experiences; Tomasello, Savage-Rumbaugh & Kruger, 1993 ) show enhanced physical cognitive skills compared to conspecifics reared by their own mother or nursery-reared with peers ( Russell et al., 2011 ).

Therefore, research on ape cognition would benefit from a better understanding of the extent of the variation between captive populations, namely whether chimpanzees at different facilities vary in their physical cognitive skills. In addition, studies exploring the role that different factors play in ape performance within a housing facility can further improve our knowledge regarding sources of intraspecific variation. If, for instance, housing conditions or the duration of human care influence apes’ performance in cognitive tasks, research performed at a single location should not be automatically extrapolated to other populations of the same species or to the species as a whole ( Boesch, 2020 ).

In the present study we systematically compared the problem-solving skills of chimpanzees housed at two different facilities (a zoo and a sanctuary) in several cognitive tasks in order to assess possible between-facility differences. As we were also interested in the individual variation within a single facility, we used the sanctuary sample to evaluate if problem-solving skills differed depending on an individual’s age at arrival at the sanctuary and on the cumulative time they had spent in captive care.

Ethical statement

All problem-solving tests were non-invasive and solely behavioral observations were made. All tests complied with the ethical principles set by the UWA, Ugandan Wildlife Authority (UWA/COD/95/06) and the National Council for Science and Technology (UNCST) (reference number NS27ES). The study was also supported by the BIAZA Animal Care Committee (British and Irish association for Zoos and Aquariums) and approved by the Swiss veterinary institution (2960/ 29815). Barbara Gessmann (Leintal zoo, Schweigern, Germany) provided permission to test the chimpanzees at Leintal zoo.

Subjects and facilities

We tested chimpanzees’ problem-solving skills with three tasks targeting different aspects of physical cognition that yielded six different cognitive measurements ( Table 1 ). The total sample size varied for each task from N = 49 to N = 59, as subjects participated on a voluntarily basis in the tasks ( Table 1 ). We collected part of the data ( N = 40) at Ngamba Island chimpanzee sanctuary in Uganda during September and October 2017. For data collection at Ngamba Island, a field permit was granted from the Chimpanzee Sanctuary and Wildlife Conservation Trust. At the time of data collection, with the exception of two chimpanzees born at the institution (not part of our study), the sanctuary housed a large group of orphan and confiscated chimpanzees, most of them rescued from the illegal pet and bushmeat trade. These chimpanzees have gone through rehabilitation due to traumatic experiences including maternal loss, malnutrition, and periods of restricted movement in small cages. They are currently housed and taken care of by the chimpanzee sanctuary and conservation trust in a semi-natural environment. At Ngamba Island the chimpanzees have access to 95 acres of tropical forest but can return to the facilities voluntarily for feeding multiple times each day as well as for overnighting. The chimpanzees at Ngamba are familiar with being individually separated occasionally for health checkups or research purposes. The sanctuary provided an informative record on the background histories of some of the chimpanzees, including their age at arrival at the sanctuary, health condition upon arrival at the sanctuary and what kind of environment they had been in prior to rescue (i.e., found in cage, malnourished, human held as pet/ “entertainment hostage” or brought straight to sanctuary, described in Table S1 ). This set of information enabled us to address (within the Ngamba Island sanctuary) how the age at arrival at the sanctuary and the percentage of lifetime spent at the sanctuary influenced task performance.


Detour reaching	Find solution (Yes/No)	40	19	98%	47%
Detour reaching	Find solution without physical exploration (Yes/No)	40	19	63%	0%
Visible honey trap	Find stick solution (Yes/No)	40	14	95%	62%
Visible honey trap	Innovate rope solution (Yes/No)	40	14	38%	0%
Reversal learning	Learn color-food association (Yes/No)	35	14	77%	100%
Reversal learning	Learn the reverse pattern (Yes/No)	35	14	54%	43%

Our second sample was comprised by the chimpanzees housed in Leintal zoo, Germany, during September and October 2017. With a total group size of 33 individuals, this group of chimpanzees represents the largest zoo housed chimpanzee community in Europe. Except for a few training experiments (observational data, Hrubesch 2007–2008), this chimpanzee community had not been targeted for research projects on cognition before the data was collected for this study. The majority of individuals at Leintal zoo were zoo born and mother-reared, with the exception of six individuals, one with an unknown background and five that had lived with humans up to the age of roughly one year. The rearing information of those chimpanzees that participated in the cognitive tasks is included in Table S1 . The chimpanzees at Leintal zoo live in a large outdoor area comprised of multiple enclosures connected to indoor quarters with attached sleeping rooms. Given that the chimpanzees at Leintal zoo were not used to being separated from their group (except for health check-ups) and because they could participate in our cognitive tasks on a voluntarily basis, our zoo sample varies in size between 14 and 19 individuals depending on the task. Due to the large peer group constellation and scarce experience with cognitive tests (including materials and apparatuses), the enculturation of these chimpanzees may be described as minimal for captivity (with the exception of the five hand-reared individuals).

Cognitive tasks and experimental set up

We tested the chimpanzees in three novel (to them) problem-solving tasks (detour reaching task, visible honey trap task and reversal learning task) generating six different measurements on different aspects of physical cognition ( Table 1 ). The tasks were performed in the above listed order, to ensure that all subjects participating in the study had a similar experience through the testing phase. These standardized problem-solving tasks have been previously used to investigate physical cognition in orangutans ( Forss et al., 2016 ; Damerius et al., 2017a ; Damerius et al., 2017b ). Prior to testing the chimpanzees in the problem-solving tasks, we also assessed the level of human orientation of each of the chimpanzees through a previously established Human Orientation Index, HOI ( Damerius et al., 2017a ). In each task, every subject was tested alone and thus for a shorter period separated from its social group. Throughout the study, all chimpanzees were fed according to their normal daily routines and none of the subjects were food deprived during the experiments.

At Ngamba Island, the chimpanzees were tested in their sleeping quarters after the morning feeding before they went into the forest habitat. At Leintal zoo testing took place in the smaller sleeping rooms mostly before mid-day. All tests were recorded with SONY HD handy cameras.

Each subject was tested once on each task (meaning that each problem-solving task was presented only one time to each subject) with the exception of the reversal learning task, which measures working memory and therefore was presented to each subject on four consecutive days. For each task, the subjects’ response towards the test apparatus was coded from video recordings. There were no demonstrations or pre-training trials.

Detour reaching task

The detour reaching task tests for inhibitory control ( Fig. 1A ). In the task, the subject is allowed access to a large, completely transparent, Plexiglas box (100 cm × 30 cm × 30 cm). The front side of the box has one small round hole within a 50 cm distance of a larger rectangular hole where the subjects’ arm can pass through. Inside the box, a food reward (whole banana) is placed behind the small hole. Each subject is given a maximum of five minutes to solve the task. The variables measured in this task that were later used as dependent variables in the statistical models were (a) whether the chimpanzee solved the task and reached for the banana through the big hole (Yes/No) and (b) if the chimpanzee managed to solve the task without any physical trial and error exploration of the box (Yes/No). Explorative actions were defined as touching the Plexiglas box, poking with a finger through the small or big holes, hitting, lifting, or kicking the box.

Figure 1: Test apparatuses for assessing problem-solving skills.

Visible honey trap task.

The visible honey trap task (henceforth honey trap task, Fig. 1B ) measures the understanding of tool properties of two types of tools: a rigid stick and a bendable rope. The upper part of the apparatus contains a straight channel that fits a 40 cm long stick. This channel was baited with a stick dipped in a little bit of honey. At the lower part of the trap there is a curved channel filled with much more honey to motivate the subjects to attempt this more difficult problem requiring the innovation of a novel tool (in this case a rope fragment laid out on the floor) to dip for honey. As the stick does not fit in the curved channel, the subjects need to dip a bendable piece of rope into the curved channel to extract the honey. The front side of the apparatus is transparent so that the subjects can see the different shapes of the channels as well as the honey. Each subject is provided with two extra sticks and three pieces of rope on the floor in front of the apparatus. Each trial lasts ten minutes during which the individuals have time to solve both tool tasks. As dependent variables for the statistical analyses we measured (a) if a subject solved the stick task by either re-using the inserted stick or inserting one of the provided sticks into the straight channel (Yes/No) and (b) if a subject solved the rope task by using the rope to obtain honey from the curved channel (Yes/No). We defined a subject as successful when he/she inserted 1/3 of the length of the stick in the straight channel or 1/3 of the rope in the bent channel. Given that this test apparatus was presented to the chimpanzees within their enclosure and they could interact with it for a longer time, we also assessed their motivation to interact with the apparatus, measured as time the chimpanzees spent exploring the apparatus before they found the rope solution or until end of task.

Reversal learning task

The reversal learning task measures working memory, inhibition, and flexibility ( Fig. 1C ). In this task the subjects need to first learn the association between the color of six lids that hide a food reward (black or white) and distinguish those lids from the other color that covers empty holes. A subject is considered to have learned the right color association when, out of the first six lids that he/she touches, five are of the correct/baited color. After learning the color-food association, the subjects need to pass a control trial fulfilling the same criterion. Once this control trial was conducted, we switched the color that hides the food rewards and thereafter the subject needed to learn the reverse pattern. Each individual was tested in this task for four continuous days and subjects were tested in three trials every day for four continuous days. However, if a subject learned the color association in the third trial of a day, we ran the additional control trial that day to confirm that the subject had learned the correct color-food association. In this task we measured (a) if the subjects learnt the correct color-food association (dependent variable) and (b) if the subjects learnt the reverse-food color association (dependent variable).

Human Orientation Index

To quantify variation in human habituation we performed an additional test that measured each ape’s Human Orientation Index (HOI) previously established by Damerius et al. (2017a) . The HOI differs from the standard Human Intruder Test (HIT) ( Gottlieb & Capitanio, 2013 ) because it does not incorporate any threatening positions or movements, instead it aims at testing reactions towards a stranger with and without food. We calculated the HOI of each chimpanzee by testing them individually in three subsequent experimental conditions. In the first condition a male human (to be consistent between facilities) who is unfamiliar to the apes, calmly approaches the enclosure and positions himself 1–1.5 m in front of the enclosure and stands still for 30 s with the side of his body towards the enclosure. In the second condition, the man turns around so that his body frontally faces the subject and tries to establish eye contact with the subject for one minute. Because we additionally wanted to quantify the orientation to humans when food is involved, and when it is not, we conducted one additional condition to those implemented by Damerius et al. (2017a) including an unfamiliar human offering food out of reach of the chimpanzee. In the third condition, the man remains frontally facing the ape but instead of keeping eye contact with the subject, he directs his gaze down at the ground and takes out a preferred food item (peanuts) from his pocket and holds the food beside his body in his hand, out of reach of the subject for 30 s. After condition three is completed, the food reward is placed in front of the enclosure and the test subject can collect it. For each condition we coded the proximity of the ape to the man as follows: 0 = the ape positioned itself as far away as possible from the human; 1 = the ape was more than one meter away from the human; 2 = the ape was within one meter from the human; 3 = the ape placed itself as close to the human as possible. We also scored the subject’s initial behavioral reaction in each condition as follows: 0 = a negative reaction, including retreat, stress vocalization, pilo-erection, nervous swinging or turning away from the human; 1 = a neutral reaction, including resting, moving calmly or play behavior; 2 = a positive reaction, if the ape approached the human; 3 = an active positive reaction including begging gestures (either by using lips or hands) or any other proactive behavior aimed at establishing contact with the human as well as attempts to trade objects from the enclosure for food. In addition, for each condition we scored whether or not the ape engaged in any active positive reactions (begging, trying to get the man’s attention) beyond the initial coded behaviors. The sum of the proximity and reaction scores across conditions constitutes the Human Orientation Index (HOI), which can range from 0 to 21.

Statistical analysis

We conducted the statistical analysis in R (version 3.3.3, R Core Team, 2017) and RStudio (version 1.1.383). We z-transformed covariates (age, age at arrival at the sanctuary and percentage of life at sanctuary) to a mean of 0 and standard deviation of 1 before including them in the models to facilitate the interpretation of the coefficient estimates ( Schielzeth, 2010 ). Factors (facility and sex) were manually dummy coded before being introduced in the models. All data and code employed in these analyses can be found in the link https://osf.io/qeydu/ .

Comparisons between facilities in task performance

To investigate if there were differences in the cognitive measurements between facilities, we fitted a Generalized Linear Mixed Model (glmm) with binomial error structure and logit link function ( Lee & Nelder, 1998 ). The response in Model 1 was a binary variable composed of 1s and 0s with each row indicating the success or failure of an individual in each task. We fitted a single model with a response variable combining the outcomes of all tasks tested in order to reduce the number of models fitted and consequently, the risk of false positives (type I error). As fixed effects, Model 1 included facility, sex and age of the individuals at the time of testing (the last two as control predictors). To prevent pseudoreplication due to the participation of the individuals in several (or all) tasks, we also included the random intercept of individual ID. We adopted a maximal random slope structure ( Barr et al., 2013 ) by including the random slopes of sex, facility (both dummy coded) and age within task. As we were interested in the overall differences in task performance between facilities, the variable of interest in Model 1 was the random slope of facility within task. Task was a factor with six levels (each cognitive measurement). We chose to investigate the differences between facilities in performance across tasks as a random slope rather than an interaction in order to make the model independent of the specific tasks employed and generalizable. Model 1 was fitted with the function glmer from the package lme4 ( Bates et al., 2015 ) using the optimizer bobyqa. The fitted model was checked for collinearity using variance inflation factors (VIF function from package car; ( Fox & Weisberg, 2019 ; Fox & Weisberg, 2011 ), overdispersion and overall stability (see Fig. S1 ) without finding any issues. Inference was drawn by comparing the full model with maximal random slope structure with a reduced (null) model lacking the slope of interest (facility within task, Forstmeier & Schielzeth, 2011 ) but containing all other predictors using a likelihood ratio test (test “Chisq” in the R function anova). Following Bolker et al. (2009) , the p value of the full-null model comparison was divided by 2. R squared was calculated with the function r.squaredGLMM.

Due to the experimental set up and duration of the task, the honey trap allowed us to further compare the motivation of the chimpanzees from the two facilities to interact with the test apparatus (measured as the time spent interacting with the honey trap before innovating the rope solution). We fitted a linear model with the function lm (package lme4) in which the response variable was the time the chimpanzees spent interacting with the honey trap and the test predictor was facility (Model 2). We further controlled for the effects of sex and age. As before, inference was drawn comparing the full model including both control and test predictors with a null model lacking the control predictor (facility) using a likelihood ratio test. When potential influential cases were investigated in Model 2 using the function dffits, it was found that the maximum absolute value was 2, suggesting that influential cases were not an issue. R squared was calculated with the function r2 from the package “performance” ( Lüdecke et al., 2020 ).

Potential differences in the degree of human orientation (HOI) between individuals housed at the two facilities were investigated by fitting a model with the function polr (package MASS; Venables & Ripley, 2002 ) with HOI as response variable and facility as test predictor (Model 3). As before, age and sex were included as control predictors. Inference was drawn as described for Model 2. Given the results of Model 3, two post-hoc analyses were conducted in order to investigate if the HOI differences between facilities were due to the scores in non-food related conditions (conditions 1 and 2, Model 3.1) or in the food related condition (condition 3, Model 3.2). These models were identical to Model 3 except for the response variable, which was the combined score obtained in conditions 1 and 2 (Model 3.1) or the score obtained in condition 3 (Model 3.2). R squared was calculated with the function r2 from the package “performance” ( Lüdecke et al., 2020 ).

Analysis of within facility variation: effects of arrival age and lifetime spent in the sanctuary

The honey trap was the task with the largest sample size that generated the most variability in performance amongst the chimpanzees. Therefore, we used the outcomes of this task (binary response with one row per individual and task) within the sanctuary sample to further investigate intraspecific variation on cognitive performance, while controlling for housing differences (as only one facility was tested). We assessed how the probability of success in the honey trap tasks varied based on how old the chimpanzees were when they first arrived at the sanctuary (Model 4) and the percentage of their lives they had spent in the sanctuary (Model 5). Given that these two variables (percentage of life in sanctuary and age at arrival) were highly correlated (Spearman correlation, rho=-0.91, p < 0.001), they had to be included in different models. This decision was taken based on the fact that the inclusion of correlated variables in the same model increases the uncertainty of the estimates derived from the model, increase the probability of type II errors and makes the assessment of the relative importance of individual predictors in the model unreliable ( Freckleton, 2011 ). Models 4 and 5 were glmms with binomial error structure and logit link function ( Lee & Nelder, 1998 ). The response was binary (composed of 0s and 1s) with one row per individual and task variant (solving the task with a rope or a stick). We included as control predictor in both models the age of the individuals when tests were conducted (z-transformed). The test predictor of interest in these models was the interaction between task (factor with two levels) and the age of the individuals when they arrived at the sanctuary (Model 4) or the percentage of life spent in the sanctuary (Model 5). In Models 4 and 5, task only included two levels corresponding to the two variants of the honey trap task (solved with stick or rope). The random intercept of individual ID was included to prevent pseudoreplication, as individuals were tested in both tasks. To avoid convergence problems, these two models were fitted using the function glmmTBM from the package of the same name ( Brooks et al., 2017 ). Collinearity in Models 4 and 5 was checked with the function vif from the package car without finding any issues. Model stability was visually checked creating stability plots ( Figs. S2 and S3 ) with the packages broom.mixed and dotwhisker ( Bolker, 2016 ). Inference was drawn by comparing the full model containing all control and test predictors with a reduced model lacking the interaction of interest by means of a likelihood ratio test (test set to “Chisq” in function anova). R squared was calculated with the function r2 from the package “performance” ( Lüdecke et al., 2020 ).

Furthermore, we checked inter-rater reliability by having a research assistant (MP) independently code the success variable (Yes/No if a subject solved or not solved the task). The second coder re-coded 15% of the trials, which were randomly selected from all video recorded tasks. The two coders reached a Cohen’s Kappa of 0.81 (N events = 41, p = 0.001), which represents a substantial to almost perfect level of agreement ( Landis & Koch, 1977 ).

Although we tried to additionally account for the rearing background of the individuals included in Model 1 (whether they were hand reared by humans or reared by their mothers), this variable was strongly dependent on the facility and very unbalanced. In Ngamba Island Sanctuary, more than 90% of the individuals had been human reared. On the other hand, in Leintal zoo more than 70% of the individuals had been raised by their mothers. Consequently, the inclusion of this variable in the models created strong model instability and it was decided to exclude it from the main analysis. However, we conducted an additional explorative model (Supplementary Model 1) where we investigated the effect that rearing background had on chimpanzee cognitive performance ( Fig. S4 ). This model is identical to Model 1 except for the substitution of facility by rearing background. Model estimates and BLUPS can be found in Tables S2 and S3 respectively. As in the previous models, the stability ( Fig. S5 ), collinearity and overdispersion of the Supplementary Model 1 were investigated without finding any issues.

Differences between facilities in task performance

We found that facility had a significant effect on the success probability of the chimpanzees in the different cognitive tasks (Model 1; likelihood ratio test between full and reduced model: X 2 = 21.37, df = 1, p = 0.001; R 2 full model=0.57). Table 2 includes the estimates of the fixed effects and Table 3 includes the best linear unbiased prediction (BLUPs) for each of the levels of the random slope of facility within task. The BLUPs (which reveal the variation from the mean slope estimate of facility caused by the different tasks in which the chimpanzees participated) show that in all cognitive measurements from the detour and honey trap tasks, proportionally more chimpanzees at Ngamba Island were successful compared to conspecifics at Leintal zoo (see positive BLUP values in Table 3 and Fig. 2 ).

The difference between facilities was larger in the more demanding tasks, such as solving the detour reaching without physical exploration (25/40 successful individuals in Ngamba Island versus 0/19 at Leintal zoo) and solving the honey dipping problem with a rope (15/40 successful individuals at Ngamba Island versus 0/14 at Leintal zoo) ( Table 1 ).

In the reversal learning task, the chimpanzees housed at Leintal zoo had an average success probability higher than the Ngamba Island chimpanzees (negative BLUP values in Table 3 ). This difference was probably driven by the fact that all chimpanzees that participated at Leintal zoo ( N = 14) learned the first color association (first part of task), but only 27/35 of the chimpanzees at Ngamba Island did so ( Table 1 , Table 3 and Fig. 2 ). In the learn-the-reverse part of the task, both facilities had lower proportions of successful chimpanzees compared to the first part of the task (Leinthal zoo: 6/14, Ngamba Island: 10/35, Table 1 ).

	Estimate	SE	df
Intercept	−0.987	1.11
Facility	2.14	1.15	1	0.06
Sex	0.11	0.20	1	0.70
Age	0.23	0.15	1	0.12

Regarding differences in motivation levels (measured as time exploring the honey trap task prior to finding the rope solution) between individuals from the two facilities, we found that Ngamba chimpanzees were significantly more motivated to interact with the test apparatus than chimpanzees housed at Leintal Zoo (Model 2; likelihood ratio test between full and reduced model: X 2 = 120.15, df = 1, p < 0.001; R 2 = 0.26, Fig. 3 , Table 4 ).

	Intercept	Facility
Detour task	1.69	1.24
Detour task without exploration	−1.24	2.23
Honey trap task solved with stick	1.54	0.24
Honey trap task solved with rope	−1.98	1.03
Learn association	1.16	−3.64
Learn reverse association	−1.26	−2.13

Mean probability of success and 95% confidence intervals across tasks of the chimpanzees housed at Leintal Zoo and Ngamba Island Sanctuary.

Figure 2: Mean probability of success and 95% confidence intervals across tasks of the chimpanzees housed at Leintal Zoo and Ngamba Island Sanctuary.

When the degree of human orientation of the chimpanzees at both facilities was compared (HOI), it was found that there were significant differences between the chimpanzees at both facilities, with the chimpanzees housed at Ngamba Island Sanctuary being more human oriented than those housed at Leintal Zoo (Model 3; likelihood ratio test between full and reduced model: X 2 = 5.47, df = 1, p = 0.019; R 2 = 0.14, Fig. 4 , Table 5 ). When these differences were explored further in two post-hoc analyses to determine in which conditions did the chimpanzees from the two facilities differ, no effect of facility was found on the combined score of condition 1 and 2 (no food visible) (Model 3.1: likelihood ratio test between full and reduced model: X 2 = 0.36, df = 1, p = 0.55; R 2 = 0.05, Table 6 ) and a tendency was found on condition 3 (food visible) (Model 3.2: likelihood ratio test between full and reduced model: X 2 = 3.51, df = 1, p = 0.061; R 2 = 0.11, Table 7 ).

Mean (dashed horizontal lines) and median (solid horizontal lines) interaction times of the chimpanzees housed in Leintal zoo and Ngamba Island with the honey trap.

Figure 3: Mean (dashed horizontal lines) and median (solid horizontal lines) interaction times of the chimpanzees housed in Leintal zoo and Ngamba Island with the honey trap.

	Estimate	SE	df
Intercept	5.03	0.70
Facility	3.43	0.78	1	<0.001
Sex	−0.27	0.71	1	0.71
Age	−0.44	0.34	1	0.21

Effects of arrival age and lifetime spent in the sanctuary

By examining the performance in the honey trap task in the facility with the largest sample size (Ngamba Island sanctuary), we found a significant interaction between the age of arrival at the Ngamba Sanctuary and the performance in the honey trap tasks. Performance in the honey trap varied between sub-tasks (solved with a stick or a rope) depending on the age at which the chimpanzees had arrived at Ngamba Island Sanctuary (Model 4; likelihood ratio test between full and reduced model: X 2 = 64.76, df = 3, p = 0.001; R 2 = 0.15, Table 8 ; Fig. 5 ).

Mean (dashed horizontal lines) and median (solid horizontal lines) of the Human Orientation Index (HOI) of the chimpanzees housed in Leintal zoo and Ngamba Island.

Figure 4: Mean (dashed horizontal lines) and median (solid horizontal lines) of the Human Orientation Index (HOI) of the chimpanzees housed in Leintal zoo and Ngamba Island.

	Estimate	SE	df
Facility	1.23	0.54	1	0.02
Sex	0.43	0.49	1	0.38
Age	0.49	0.24	1	0.05

	Estimate	SE	df
Facility	0.31	0.52	1	0.55
Sex	−0.31	0.48	1	0.52
Age	0.39	0.24	1	0.10

	Estimate	SE	df
Facility	1.04	0.56	1	0.06
Sex	0.57	0.52	1	0.28
Age	0.39	0.24	1	0.13

Similarly, we found that there was a significant interaction between the proportion of lifetime spent in the sanctuary and the performance in the honey trap task. Problem-solving skills varied between the two sub-tasks (solved with a stick or a rope) depending on the percentage of the life a chimpanzee had lived at Ngamba Island Sanctuary (Model 5; likelihood ratio test between full and reduced model: X 2 = 65.21, df = 3, p = 0.001; R 2 = 0.13, Table 9 ; Fig. 6 ).

	Estimate	SE	df
Intercept	24.744	8.209
Age	1.078	5.025	1	0.83
Age at arrival at the sanctuary	−5.723	4.643	1	0.217
Honey trap task	−47.517	12.875	1	<0.001
Interaction between percentage of life and honey trap task	−33.985	9.875	2	<0.001

Mean probability of success and SE in the honey trap task with a stick (circles) and rope (triangle) depending on the age of the chimpanzees when they arrived at the sanctuary.

Figure 5: Mean probability of success and SE in the honey trap task with a stick (circles) and rope (triangle) depending on the age of the chimpanzees when they arrived at the sanctuary.

We found overall differences in cognitive performance between two populations of captive chimpanzees housed at a sanctuary and a zoo, with the sanctuary chimpanzees showing an average probability of success across tasks significantly higher than the zoo housed chimpanzees. We observed that in four out of the six cognitive measurements, there was intraspecific variation in problem-solving skills. However, in the reversal learning (in one of which), we did not find observable differences between facilities, even if the zoo chimpanzees had a higher average probability of success than the sanctuary chimpanzees at the first stage of the task (associative learning). In addition, we also found that within the same facility, chimpanzees differed in tool use skills, with those individuals that had arrived to the sanctuary at an earlier age and thereby spent a longer proportion of their lives in captivity being better at flexibly using tools than those individuals who arrived at a later developmental stage and thereby spent a shorter proportion of their lifetime in captivity.

	Estimate	SE	df
Intercept	25.890	9.309
Age	−5.189	5.464	1	0.342
Percentage of life spent in the sanctuary	3.853	7.611	1	0.612
Honey trap task	−51.352	17.535	1	0.003
Interaction between percentage of life and honey trap task	36.704	15.512	2	0.018

Mean probability of success and SE in the honey trap task with a stick (circles) and rope (triangle) depending on the percentage of their lives the chimpanzees had been in the sanctuary.

Figure 6: Mean probability of success and SE in the honey trap task with a stick (circles) and rope (triangle) depending on the percentage of their lives the chimpanzees had been in the sanctuary.

Problem-solving differences between captive facilities.

We found that chimpanzees from the Ngamba Island sanctuary had on average a higher probability of success than their conspecifics housed in a Leintal zoo in the detour reaching and honey trap task ( Table 1 , Fig. 2 ). Similarly, prior research applying the Primate Cognitive Test Battery (PCTB) to sanctuary and zoo chimpanzees as well as bonobos ( Pan paniscus ), found that sanctuary apes showed similar, or improved cognitive skills compared to zoo housed conspecifics ( Wobber & Hare, 2011 ). Previous findings on tool use skills of captive apes suggest that the intensity of human contact can impact tool use understanding in chimpanzees ( Furlong, Boose & Boysen, 2008 ) and increase curiosity in orangutans, which in turn can lead them to improve their tool-use skills ( Damerius et al., 2017a ). Most chimpanzees at Ngamba Island sanctuary are orphans due to poaching, habitat destruction, or maternal death and went through years of rehabilitation. Consequently, these chimpanzees were raised and cared for by humans with extensive and close human exposure. One possible factor contributing to the observed variation in task performance between the two facilities could be the different degree of exposure to humans experienced by the chimpanzees at the zoo and the sanctuary. At Ngamba Island volunteers and tourists, who are visiting and supporting the wildlife sanctuary, can participate in chimpanzee feeding routines under the supervision of staff members. Therefore, chimpanzees at Ngamba sanctuary are often exposed to different unfamiliar humans in relatively close contact. Chimpanzees at Leintal zoo on the other hand, are only fed by their familiar caretakers and thus experience less diversity and frequency of humans closely interacting with them during feeding times. These differences in exposure to unfamiliar humans during feeding times might explain why we found differences between facilities in the degree of human orientation in the third condition of the HOI (where an unfamiliar man offered the subjects food, Model 3.2).

Between-facility differences in task performance could also be influenced by the different rearing backgrounds of the majority of chimpanzees housed at the sanctuary and the zoo. Most of the chimpanzees housed at Leintal zoo had been raised by their own mothers, in a large group of conspecifics, representing a developmental socio-environment closer to that of natural chimpanzee communities than that present in Ngamba sanctuary. Such mother-reared individuals may be less influenced by human exposure (being less human oriented, Fig. 4 ) despite living in a captive setting than individuals that have lost their mother and thereby have been mainly hand-reared by humans. However, it is important to note that in the zoo sample of our study, the four chimpanzees that were hand-raised by humans did not solve the more demanding tasks of detour reaching without trial and error exploration and did not use the rope tool to extract honey. Such cases suggest that, in our sample, housing facility and prior exposure to cognitive testing might have a stronger effect on task performance than rearing background. Unfortunately, given the structure of our data, it was not possible to disentangle the individual contributions of rearing background and facility to task performance. Therefore, we encourage future research to investigate the potential role of rearing background during the first years of life on multiple measures of cognitive performance. Likewise, it would be important to disentangle how various factors, such as group compositions, contact with caretakers and diet routines, at different housing facilities affects cognitive development and other behavioral outcomes ( Novak et al., 2014 ). Other factors that could potentially contribute to the observed variation in task performance between facilities are the different levels of familiarity of the chimpanzees with the presented materials and their overall past experience in cognitive tests. The Ngamba Island chimpanzees were more used to being separated from their social group than the chimpanzees at Leintal zoo, which could explain why these chimpanzees showed higher interaction times with the honey trap test apparatus compared to the Leintal chimpanzees ( Fig. 3 ). This higher motivation to manipulate and explore the apparatus could explain why they were more successful at innovating the use of the rope as a tool in this task. Recent findings from avian cognition have shown that Goffin’s cockatoos ( Cacatua goffiniana ) that were temporarily held captive were less motivated to interact with a problem-solving task than those birds that had been hand-raised and lived longer in captivity ( Rössler et al., 2020 ). Together with our study, such results suggest that captivity and human exposure may influence the motivational level rather than the animals’ cognitive capacity per se.

The apparatus used in the detour reaching task was entirely made of Plexiglas ( Fig. 1A ) and previous studies have shown that orangutans with different rearing experiences vary in their problem-solving skills when presented with human-made materials like Plexiglas ( Damerius et al., 2017b ). The Ngamba Island chimpanzee sanctuary has hosted multiple cognitive research projects, some of which have focused on cognitive performance by presenting the chimpanzees with different types of test apparatuses including transparent materials (e.g., Horner & Whiten, 2005 ; Horner & Whiten, 2007 ; Marshall-Pescini & Whiten, 2008 ; Tennie, Call & Tomasello, 2010 ; Bullinger, Melis & Tomasello, 2014 ). Therefore, the Ngamba chimpanzees had previous experience with Plexiglass-like objects at the time of this study. The Leintal zoo chimpanzees on the other hand, have no previous experience with test apparatuses made from Plexiglas (and hardly any experience at all with other testing materials). Since previous experience with certain human-made materials can influence an individual’s reaction and perception of the test apparatus, the chimpanzees less familiar with these materials may need to explore the box more thoroughly before finding a solution. Accordingly, in our experiments, the differences between chimpanzees at Ngamba Island sanctuary and Leintal zoo were stronger when we examined subjects that solved the detour-reaching task without any prior exploration compared to when they solved the task through physical exploration ( Fig. 2 ).

In the reversal learning task, the chimpanzees at Leintal zoo had higher average success probabilities than the sanctuary chimpanzees. This difference was likely driven by the fact that the zoo chimpanzees did better in the first part of the task (learning color association). The success in the second part of the task (reversal learning) was similar in both facilities. Since reversal learning directly depends on how fast an individual learns the first part of the task, one would have expected the zoo chimpanzees to perform better than the sanctuary chimpanzees, as their likelihood of success in the first step of the task was higher than that of sanctuary chimpanzees. However, overall success in the task was low and only 33% of all tested chimpanzees learned the reverse pattern ( Table 1 ). Even though primates’ success in reversal learning tasks has been associated with increased brain size ( Deaner et al., 2007 ), a comparative study across taxa reported that cleaner fish ( Labroides dimidiatus ) outperformed three primate species (Capuchin monkeys, Cebus apella ; orangutans, Pongo spp ; chimpanzees, Pan troglodytes ) in reversal learning tasks ( Salwiczek et al., 2012 ). The superior reversal learning skills of this fish species compared to the primates are arguably the consequence of the species-specific foraging ecology involving unpredictable social interactions with reef fish. In the same study, capuchins outperformed apes in reversal learning tasks ( Salwiczek et al., 2012 ). Reversal learning tasks require a high inhibitory control and as opening the different lids in our study did not impose any direct cost for the chimpanzees, they may not have been motivated enough to inhibit incorrect responses, which could explain why the tested chimpanzees performed particularly poorly in this task.

Tool use differences within the sanctuary

Due to the large sample size in Ngamba, we were able to examine variation in tool use skills while keeping facility constant. Comparing chimpanzees within the Ngamba Island sanctuary revealed that it was harder for the chimpanzees to innovate the rope solution than using the stick in the honey trap task ( Table 1 ). The higher success rate of the stick solution could be due to the generalized use of sticks for varied foraging activities across chimpanzee populations ( Whiten et al., 1999 ; Koops, Furuichi & Hashimoto, 2015 ) –i.e., to a higher likelihood of using sticks than rope as a latent solution in chimpanzees ( Tennie, Call & Tomasello, 2009 ).

Orphans that had arrived at the sanctuary younger than two years old had the highest probability of innovating the rope solution ( Fig. 5 ). We also found that the proportion of lifetime spent at the sanctuary had a significant influence in the success probability in the honey trap task ( Fig. 6 ). Individuals that had spent over 90% of their lives at the sanctuary were more likely to innovate the rope solution. These results suggest that just like in human foster children ( Nelson et al., 2007 ), the arrival age of an infant chimpanzee at the place of care (here the sanctuary) can have an impact on its cognitive skills. Given that the development of chimpanzee brain structure can be influenced by early rearing ( Bogart et al., 2014 ), varying rearing conditions during sensitive developmental periods in the first years of life (see overview by ( Gerhardt, 2014 ) for human infants) could have influenced the chimpanzees’ physical cognition in the tool use task. Since orphans arriving at the sanctuary before reaching their second year (thus deprived from maternal care) performed better than later arriving orphans, physical cognitive skills (at least in this task) may not be negatively influenced by early maternal separation. Previous studies on apes have shown that social interactions with conspecifics and sufficient healthcare provided by humans can compensate for early maternal loss and reverse some of the negative effects that traumatic events can have on social cognition ( Meder, 1989 ; Martin, 2002 ; Bloomsmith et al., 2006 ; Reimers, Schwarzenberger & Preuschoft, 2007 ; Wobber & Hare, 2011 ).

Our study examined within species variation in problem-solving skills by (a) comparing two groups of chimpanzees from different facilities (sanctuary versus zoo) with identical test paradigms and (b) within the same facility by evaluating the influence of time spent in captive rehabilitation. The present study shows intraspecific variation in physical cognition and an effect of facility in the chimpanzees’ motivation to explore test apparatuses, which in turn could influence cognitive performance. Intraspecific differences between facilities in task success probabilities could also be influenced (among others) by previous experience with testing apparatuses and procedures as well as different levels of exposure to humans. Chimpanzees that arrived at an early age to the sanctuary after losing their mothers and that consequently spent large parts of their lives under human care, were better tool innovators than individuals that arrived later and spent less time in the sanctuary. Given the variation between and within housing facilities that we established empirically, researchers should be careful not to automatically extrapolate from one study population towards others without considering the subjects background histories. As such, our findings stress the importance of considering cross-facility differences in primate cognitive performance and the value of multi-institutional studies that allow for further evaluation of experience and developmental effects ( Many Primates et al., 2019 ).

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Proc Biol Sci
v.279(1749); 2012 Dec 22

How chimpanzees solve collective action problems

We presented small groups of chimpanzees with two collective action situations, in which action was necessary for reward but there was a disincentive for individuals to act owing to the possibility of free-riding on the efforts of others. We found that in simpler scenarios (experiment 1) in which group size was small, there was a positive relationship between rank and action with more dominant individuals volunteering to act more often, particularly when the reward was less dispersed. Social tolerance also seemed to mediate action whereby higher tolerance levels within a group resulted in individuals of lower ranks sometimes acting and appropriating more of the reward. In more complex scenarios, when group size was larger and cooperation was necessary (experiment 2), overcoming the problem was more challenging. There was highly significant variability in the action rates of different individuals as well as between dyads, suggesting success was more greatly influenced by the individual personalities and personal relationships present in the group.

1. Introduction

Collective action problems (CAPs) arise in situations in which effortful action is required, at a cost to the actor(s), and such action results in the distribution or availability of a shared good of which non-actors may also benefit. In collective action scenarios, the optimal strategy for any individual is to free-ride—that is, let others assume the cost of action yet benefit from the rewards. This disincentive to action produces what is known as a CAP [ 1 ].

One of the best-known examples of a CAP in a non-human species involves individual variability in participation in group-territorial conflict among female lions, in which certain females were prone to lead the move towards an intruder, whereas others lagged behind, avoiding the risks of conflict yet benefiting in the renewed safety of their territory [ 2 ]. Other examples of collective action include aggressive extra-group encounters in monkeys and lemurs [ 1 , 3 ] and nested female guarding alliances in dolphins [ 4 ].

There are two outstanding questions important to determining how groups overcome CAPs. First, what mechanisms allow for impasse being averted? Nunn [ 1 ] suggests different classes of factors that may potentially promote action, including: (i) asymmetrical benefits and privileged groups, which would provide extra incentive for certain individuals to act (e.g. Mitani and Watts found a positive correlation between male chimpanzee participation in boundary patrols and mating success, suggesting that participants are individuals with the most to gain [ 5 ]); (ii) private incentives, additional goods only received by contributors and (iii) coercion. Second, in situations where a CAP is successfully overcome, what governs the strategies different individuals adopt, and do patterns in decision-making arise? For instance, it is possible that rank or tolerance between group members may influence an individual's motivation to act.

Among wild chimpanzees, one example of a potential CAP is group hunting of colobus monkeys. All chimpanzees in a group are motivated to obtain nutrient-rich scraps of meat from a monkey carcass. It is important to note the probability of a male obtaining a scrap does increase with the number of hunters [ 6 ], and therefore, there is an incentive for an individual to act; however, two possible types of deterrent are also still present. First, hunting always entails opportunity cost, risk of injury and energy cost. Second, in the largest groups, hunters and non-hunters may be equally likely to get some of the prize [ 6 ], suggesting that at least in large party contexts an individual still has an incentive to free-ride (although this appears to vary by site, see [ 6 – 9 ]). These factors remain reasons an individual might limit participation and suggest that hunts can represent CAPs, especially in large groups. How chimpanzees overcome the disincentives to action and initiate and complete successful hunts remains unclear [ 6 , 9 ].

Gilby et al. [ 6 ] argue that individual variation in hunting motivation is the most important factor predicting the likelihood of a hunt (i.e. solution of the CAP).They identified certain highly motivated males as ‘impact hunters,’ owing to their critical role in triggering hunts. It remains unclear which traits are most important for shaping an impact hunter. Age is unlikely to be a determining factor, as impact hunters seem to retain this role over several years [ 9 ]. Dominance could potentially have an effect; however, it is hard to disentangle dominance from qualities important to hunting ability such as strength and agility [ 9 ]. Skill is most likely essential, and personality appears central [ 9 ].

With few exceptions, experimental approaches exploring chimpanzee cooperative problem-solving have focused on dyadic interactions [ 10 – 13 ], which limits their application to more complex, and perhaps more naturalistic, interactions such as CAPs. Controlled experimental examination of carefully constructed CAP scenarios is necessary to enrich our understanding of the mechanisms underlying the behaviours documented in such contexts in the field.

Different types of CAP reflect particular payoff structures. Our experiments are modelled on the Volunteer's Dilemma, a scenario in which a shared good is produced only if at least one individual volunteers to pay a cost. Each individual wants the good to be produced but prefers someone else volunteers. This preference leads some to behave as free-riders, defined as individuals who receive rewards from the efforts of others without contributing to their production. However, if no one else volunteers, all individuals lose [ 14 – 16 ].

In a first experiment, we took the initial step in looking beyond dyadic interactions to investigate the factors that potentially govern individual strategy in a shared goods game. We designed a scenario as follows: groups of chimpanzees were given access to three indoor testing rooms in which any individual could choose to push a button resulting in juice being dispensed into drinking troughs on the opposite side of the testing area. We manipulated group size (such that subjects were either tested in dyads or triads) and dispersion (such that the juice flowed into either a single trough or three troughs side by side, with higher dispersion making the reward less excludable).

Our second experiment had three primary differences from the first: we made the action cooperative, the reward was more widely dispersed (but kept constant across conditions) and we increased the difference between group sizes. Groups of chimpanzees were tested in a set up that allowed any two individuals to choose to coordinate pulling two ends of a rope, which would result in peanuts being sprayed into an opposing room. Subjects were tested in either triads or sextets, which should intensify the CAP and make it more striking for the subjects.

Because of the rivalrous nature of the reward, and the distance between the action and reward locations, the principle cost for the actor is a reduction in the amount of potential reward. While hunting, wild chimpanzees face more severe costs such as opportunity costs, energy expenditure and risk of injury, and while it is not an option to mimic all of these experimentally, it is important to keep this difference in mind.

We hypothesized that changes to group size and reward dispersion would alter the payoff structure among subjects, resulting in individuals demonstrating different strategies and propensities for action dependent on condition. Specifically, that dominant individuals would be most likely to act across conditions, whereas subordinate individuals would act more when the reward was more greatly dispersed and group size was small.

2. Experiment 1

(a) subjects.

Subjects include 12 chimpanzees housed at the Wolfgang Köhler Primate Research Center at the Leipzig Zoo, Germany. Subjects were seven females (age range: 7–16 years old) and five males (age range: 5–33 years old). Three subjects lived in a social group comprised of six individuals; nine subjects lived in a social group comprised of 17 individuals. See the electronic supplementary material, S1 for more details concerning the subjects' sex, age, relations, grouping and living conditions. Subjects were separated into four groups of three individuals and tested in dyad and triad combinations within these groups. Groups were formed with the guidance of the keepers to ensure sufficient tolerance levels between individuals to prevent undue stress and aggression. A dominance hierarchy for each group was generated in consultation with the keepers.

(b) Apparatus

The apparatus consisted of four principal parts: an action box, a collecting box, reward troughs and the tubes that connected them ( figure 1 ). The action box had an inner rectangular basin into which juice could be poured by the experimenter. The action box was connected to the collecting box (a smaller juice basin attached to an adjacent room) by a short clear tube. Continuous pushing of a button on the action box would cause the level of liquid in the collecting box to rise until it was high enough for juice to flow out of the three long tubes anchored to its top edge, allowing the liquid to become accessible to the subjects as it moved through the tubes and spread out in the reward troughs two rooms away. See the electronic supplementary material, S2 for a more detailed description.

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( a ) Close up of the action box and collecting box; ( b ) bird's eye view of the layout used in experiment 1. The trough used in the 1trough condition is shaded grey.

(c) Procedure and design

(i) individual introduction to the apparatus.

Individuals passed through a series of pre-tests to ensure that they understood how the apparatus functioned and were motivated to push the button for the desired length of time when alone. The pre-tests and test were ordered as follows (see the electronic supplementary material, S3 for more details on each pre-test):

One trough, one room. Subjects were in one room and had access only to the action box and one drinking trough (directly underneath), and learned how to dispense the reward.

One trough, three rooms . Subjects had access to the action box and one trough placed two rooms away and learned to dispense juice and then leave the action box and move to the trough room to retrieve the reward.

Three troughs, three rooms . Subjects had access to the action box and all three troughs placed two rooms away and learned that when they dispensed juice it would spread out in all the troughs.

Half liquid . Same as previous pre-test except that the amount of juice in the action box was reduced by half such that subjects had to learn to push longer before juice was dispensed.

Test . Subjects were separated into four distinct groups of three individuals. We manipulated two variables (number of troughs and group size) resulting in four different conditions: dyad-1trough, dyad-3troughs, triad-1trough and triad-3troughs. Number of troughs was counterbalanced across groups such that two test groups started with three troughs and continued to one trough and two groups received one trough first. Group size followed an ABA design such that subjects underwent six dyad sessions followed by six triad sessions followed by six dyad sessions. Each dyad session consisted of three trials (one for each possible dyad combination, with each individual being tested twice). Each triad session consisted of two trials (again each individual was tested twice).

Subjects began each trial confined to the room with the reward troughs. A trial began when the door of this room was raised, allowing all individuals access to the adjacent room as well as to the room with the action box. Trials lasted 5 min. The experimenter and keeper were absent from the testing area throughout the duration of the trial.

(ii) Coding and analysis

All trials were recorded by three cameras. One camera focused on the action box and collecting box, while the two others were directed at the drinking trough(s). After each trial, the amount of juice that had been released was measured. The following variables were later coded from videotape: (i) latency to first push (latency); (ii) whether the subject pushed the button (push.yn); (iii) the duration of each pushing bout (push_total) (a bout begins when the subject pushes the button and ends when they release the button for more than 3 s); (iv) whether the subject drank from the trough (drink.yn); (v) the duration of each drinking bout (drink_total) (a drinking bout begins when the subject drinks or licks the trough and ends when they stop for 3 or more seconds).

To test whether pushing behaviour (push.yn, pushing duration) and drinking duration were influenced by the number of troughs, dominance rank or the size of the group, we used a generalized linear mixed model (GLMM) [ 17 ]. We included the following predictors as fixed effects: rank, group size (dyad versus triad), number of troughs (one versus three; and all interactions between them up to the three-way interaction), session number and trial number, and as random effects: trial, group_id, subject and triad_id. We did two additional analyses to test whether the amount released or the latency to first push were influenced by the number of troughs or the size of the group, into which we included the following predictors as fixed effects: group size (dyad versus triad), number of troughs (one versus three; and all interactions between them), session and trial and as random effects group_id and triad_id (see the electronic supplementary material, S4). Data: doi:10.5061/dryad.b5c1f .

(d) Results

Owing to the small sample size of four triad groups, all results must be interpreted with caution and we complement these statistical results with more qualitative descriptions of behaviour within groups and overall patterns (see the electronic supplementary material, S8).

In testing whether an individual pushed, we found a three-way interaction effect between rank, group size and number of troughs (GLMM, p = 0.0116, electronic supplementary material, S5). In general, there appears to be a positive relationship between rank and tendency to push, with higher-ranking individuals pushing more. This relationship breaks down in the triad-3troughs condition. Pushing time is more uniformly distributed in the triad condition. Number of troughs appears to have an effect only in the triad condition ( figure 2 ).

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Median (bar) and quartile (line) pushing probability in 1trough and 3troughs conditions as a function of group and rank. The GLMM revealed a three-way interaction between group size, rank, and number of troughs (see text). Rank from highest (1) to lowest (3).

With respect to pushing duration, we found a two-way interaction effect between rank and number of troughs as well as between number of troughs and group size (GLMM, estimated p MCMC = 0.0010 and 0.0094, respectively; electronic supplementary material, S6). Among dyads, there is little difference in pushing duration regardless of number of troughs, while in triads the middle-ranking individual pushes more in the 3troughs than in the 1trough condition.

In testing for the factors affecting drinking duration, we found a two-way interaction effect between rank and number of troughs (GLMM, estimated p MCMC = 0.0001, electronic supplementary material, S7). In the 1-trough condition, drinking duration was positively associated with rank, with the most dominant individual drinking the longest on average. In the 3troughs condition, drinking durations were almost equal.

Finally, none of the factors had a significant influence on the amount of liquid released or on latency.

(e) Discussion

In this Volunteer's Dilemma context, reward dispersion did affect the payoff expectations of individuals of different rank and thus also their willingness to act. In all conditions except for triad-3troughs, dominants tended to face a relatively low cost for action, as they could always be assured a worthwhile portion of the reward while for subordinates the opposite was true. In the triad-3troughs condition, however, both dispersion and rivalry for the reward were maximized and thus dominants were no longer assured a large portion of the juice after acting (their expected payoff decreased), whereas subordinates would be more interested in making sure the reward is produced, as their chance to sneak into the trough area and drink increased. These changes in strategy are reflected in figure 2 . While dispersion and rivalry affected all subjects, what strategies were viable in each particular group was likely due to interactions between dominance and group tolerance levels [ 10 , 18 ]. While each dyad and triad had the same dominance structure (dominant/subordinate in the dyad and dominant/middle/subordinate in the triad), the magnitude of the differences in rank varied between groups. The degree of tolerance between individuals also varied.

Overall, role patterns can be summarized as follows (see the electronic supplementary material, S8 for details about strategies evident in each group as well as the identity of group members): pushers tended to be individuals higher in rank, free-riders tended to be subordinate individuals in groups with high tolerance levels or subordinate individuals with particularly tolerant relationships with the dominant in their group. Dominants who pushed were always assured a share of the reward, which lessened the risk of action. It was also the case that individuals in groups with medium tolerance levels tended to use flexible strategies, switching roles based on reward dispersion. Finally, in dyads with low tolerance if the subordinate was especially fearful of the dominant, they generally ended up obtaining scant reward.

3. Experiment 2

In our second experiment, we sought to exacerbate the dilemma (through larger group size and more dispersed reward).

Subjects were 12 semi-free ranging residents of the Ngamba Island Chimpanzee Sanctuary in Lake Victoria, Uganda ( www.ngambaisland.org ). Subjects were eight males (age range: 8–13 years) and four females (age range: 8–13 years). See the electronic supplementary material, S9 for details concerning the subjects' sex, age, experimental history, grouping and living conditions. All subjects had demonstrated an understanding of a similar cooperative rope mechanism in previous studies [ 10 – 12 , 19 ]. With input from keepers, six different groups of six individuals were formed for the test phase. Each of these groups was further divided into four groups of three individuals to allow for two group-size conditions.

The principal testing area consisted of two opposing rooms connected by an overhead raceway. A large funnel was attached to one room ( figure 3 ). The funnel's circular opening was blocked by a sliding piece of Plexiglas with a hole at the far end. A rope was guided around two wooden dowels attached to the Plexiglas and the ends passed through the cage bars such that when the two ends were pulled in synchrony, the piece would slide, the holes align and the peanuts would be released. A long ramp was attached directly under where the holes of the funnel and Plexiglas piece aligned, allowing the peanuts that were released to roll down across the hallway to a battery-operated feed dispenser attached to the opposing cage. Once peanuts reached the feeder, E would start the motor at a distance by remote. The peanuts (up to 320, depending on how long the ropes were pulled) would then spray into the cage. See the electronic supplementary material, S10 for a more detailed description.

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( a ) Close up of the peanut dispenser; ( b ) bird's eye view of the layout used in experiment 2. Note that the peanuts spray into the room opposite where the rope ends are pulled forcing subjects to climb up to and cross an overhead raceway to transition from action to reward access.

Rope ends were inserted at a distance which allowed the subjects to pull alone. They learned to release peanuts and climb from the action room over to the peanut room to eat them. See the electronic supplementary material, S11 for more details.

(ii) Dyad pre-test

Cooperation trials. All possible dyad combinations within each sextet were tested to assess their willingness to work together to release peanuts. It has already been demonstrated that tolerance levels have a great influence on success in cooperative tasks [ 10 ], and it was important to decipher which pairs would work together in a simpler non-CAP context to potentially better understand their behaviour in the larger group dynamic. See the electronic supplementary material, S11 for more details.

Dominance trials. Cooperation trials were followed by two dominance trials in which an eighth of a watermelon was placed within reach in the corridor. Pairs were allowed to enter the baited room through the door furthest from the reward. The first individual to reach the watermelon and to eat all the most attractive bits (flesh and light green rind) was noted; this individual was considered dominant over their partner. Once all dyad combinations had been tested, the results were used to form a dominance hierarchy of all subjects.

As members of the first two groups of six individuals/sextets were non-overlapping, we ran the dyad combinations through the cooperation and dominance pre-tests and continued with the first two rounds of the test phase before individuals underwent the remainder of the dyad trials in the combinations that would appear in the remaining four sextets (and four rounds) of the test.

(iii) Test phase

To equalize subject experience as much as possible, triads were formed such that each individual appeared in two of the four triads within each group, and particular dyads appeared in no more than one triad within each group. Each individual appeared in three of the six groups. The majority of dyad combinations appeared in two groups (the rest appeared only in one).

Triad condition. Groups of three started in the room where peanuts could potentially be released. In addition, the room adjacent to the peanut room was open to reduce the potential for fighting over the reward. Trials started when the door to the overhead raceway was opened, allowing free access to the rope room. Trials lasted a maximum of 2 min.

Sextet condition. Same as for triad condition, except six individuals were tested together.

Testing order. Group size was counterbalanced such that three groups began with the triad condition, and three groups started with the sextet condition. Subjects experienced three sessions of three trials per condition. Trials were repeated up to three times if the rope was pulled out or on the few occasions an individual managed to pull both ends alone—in this case, the long rope used in the test was replaced on subsequent trials by a rope measuring 345 cm, the shortest length that still allowed for possible success. Each individual trial thus had three possible ‘takes’ (repeated trials). Original trial outcome could be success or 2 min—no repetitions needed and continue to trial 2, or rope out or pulled alone—up to a maximum of three further repetitions available. All six groups experienced two sessions of each condition before adding a third round of each condition.

(d) Coding and analysis

All trials were recorded by four cameras: one focused on one rope end and the funnel, one directed at the second rope end, one aimed at the feed dispenser and the area around it, and one capturing as much of the peanut room as possible. After each successful trial, the amount of peanuts released was measured. The variables coded were: (i) the result of the trial—success (when two individuals pulled the rope, releasing peanuts) versus no success (when 2 min elapsed without any action on the rope or the rope was pulled out); (ii) latency to pull (iii) who pulled; (iv) an approximation of how much each individual ate—could be none (score 0), scrounging from the floor (score 1) or sitting directly in front of the feed dispenser (score 2).

(i) Relationship between pulling and feeding. We analysed whether pulling the rope in a particular trial had an effect on the respective individuals' feeding success in that trial using a GLMM. We also used a GLMM to check whether cost (pulling the rope) and benefit (food eaten) might balance out over the sequence of trials within the same group (calculating the proportion of trials in which an individual pulled as well as the average food reward they got). For each analysis, we fitted a model into which we included whether or not the individual pulled in a given trial (or proportion pulling over the course of trials), the individual's rank, group size, the session and order of conditions as fixed effects and the ID of the group and the subject as random effects (see the electronic supplementary material, S12.1). Note that for the main analysis (all GLMMs), we counted any instance of success whether it occurred on an original trial or a repeated trial.

(ii) Factors influencing action (i.e. pulling) at an individual and dyadic level. To test what determined whether an individual pulled the rope (yes or no) in a successful trial, we used a GLMM. Into this, we included group size, session and order of conditions and all their interactions up to order three as fixed effects. To control for possible effects of rank, we also included this variable. The identity of the specific group, subject and individual trial were included as random effects. Another GLMM was used to test whether a given dyad pulled the rope in a successful trial. We included as fixed effects group size, the rank of the individual, the session and order of conditions. The identity of the specific group, of the dyad, of the lower- and higher-ranking individual and the individual trial were included as random effects (see the electronic supplementary material, S12.2).

(iii) Time to success. We analysed latencies (on a dyadic level) using a GLMM in which we included group size, the rank of the individual, the session and order of conditions as fixed effects, as well as group, dyad and the identities of the higher- and lower-ranked individual as random effects (see the electronic supplementary material, S12.3). Data: doi:10.5061/dryad.b5c1f .

(e) Results

(i) dyad pre-test.

In the dyad pre-test, 88 per cent of dyad combinations had some level of success (45% of pairs succeeded immediately on all four trials with no mistakes while the rest were successful on a repeated trial). In comparison, in the test phase, 41 per cent of dyad combinations pulled successfully (16% pulled in both the triad and sextet condition, 20% pulled only in the triad condition and 5% pulled only in the sextet condition). Of these successful dyads, 60 per cent had a perfect success record in the pre-test, 24 per cent succeeded on a repeated trial in the pre-test and 16 per cent had never succeeded in the pre-test.

(ii) Test phase

We found that subjects succeeded in 34.4 per cent of the triad and 42 per cent of the sextet initial trials. Subjects did not pull the rope during the entire 2 min trial duration in 28 per cent of the triad trials and 22 per cent of the sextet trials. The remaining trials (37.6% and 36% of the triad and sextet trials, respectively) were repeated owing to one individual pulling the rope out and/or succeeding alone. Of these repeated trials, 32 per cent in the triad condition and 56 per cent in the sextet condition resulted in success, and in 40 per cent of the triad trials and 22 per cent of the sextet trials, subjects let 2 min elapse without acting on the rope. In the remaining trials, subjects were unsuccessful owing to one individual pulling the rope out.

(i) Relationship between pulling and feeding. To investigate whether pulling resulted in a cost to the actor, we analysed whether pulling the rope in a particular trial influenced the respective individuals' feeding success. Pulling the rope clearly reduced the amount of food an individual received (GLMM, p < 0.0001). In addition, higher-ranking individuals received more food ( p < 0.0001). The interaction between rank and pulling was not significant ( p = 0.153; electronic supplementary material, S13.1). The second analysis looked at the costs (pulling the rope) and benefits (getting food) over the course of trials. There was a clearly significant interaction, over the course of trials, between pulling rate and rank ( p = 0.005; electronic supplementary material, S13.2). While among individuals with low pulling rates, dominants ate more than subordinates, high- and low-ranking individuals who pulled the rope frequently had similar feeding success. Hence, high-ranking subjects suffered a more striking cost for pulling than lower-ranking individuals. There was a greater reduction in the amount eaten by a dominant puller versus a dominant free-rider than there was in the amount eaten by a subordinate puller versus a subordinate free-rider ( figure 4 ).

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Influence of rank and pulling on feeding success. The surface represents results from the GLMM looking at pulling and feeding over the course of trials. The x -axis represents rank. Dots above the surface are in black, and dots below the surface are open. The area of the dots corresponds to the number of subjects in the respective combination of pulling rate and rank. Pulling rate is represented on the y -axis, with 0 indicating no pulling and 1 indicating pulling. Feeding score is represented on the z -axis: 2 = dispenser, 1 = ground, 0 = none (see methods for more details).

(ii) Factors influencing action. Whether an individual pulled or not was clearly affected by the factors investigated and the interactions between them. More specifically, the three-way interaction between group size, order and session was significant ( p = 0.044; electronic supplementary material, S14), meaning that all three factors impacted the individuals' pulling probabilities. But note that rank did not have a significant impact, in contrast to experiment 1. When individuals received the triad condition first, the individual pulling rate started relatively high and decreased as the study progressed. When individuals received the sextet condition first, the individual pulling rate started low and increased in the triad condition ( figure 5 ). Furthermore, pulling probabilities clearly differed between individuals (likelihood ratio test comparing full model with model not including the random effect of subject but everything else: χ 2 = 88.43, d.f. = 1, p < 0.0001).

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Pulling probability in the triad and sextet conditions as a function of order (triad or sextet treatment first) and session. The GLMM revealed a three-way interaction between group size, order and session (see text). Rank from highest (1) to lowest (3).

We were also interested in investigating whether particular dyads were more prone to pulling. There was a clear effect of dyads ( χ ² = 30.55, d.f. = 1, p < 0.0001), such that certain combinations were more likely to pull than others (see the electronic supplementary material, S15 for non-significant results on the factors influencing dyadic pulling).

(iii) Time to success

Latencies were clearly influenced by the three factors investigated and their interactions. Specifically, the three-way interaction was significant ( p MCMC = 0.0096, electronic supplementary material, S16). Regardless of order of conditions (triad or sextet first), latencies tended to increase in the first condition and decrease in the second condition. Generally, latencies were longer and changes were bigger when the sextet condition was received first.

(f) Discussion

The results demonstrate that the experimental design successfully engaged subjects in a Volunteer's Dilemma scenario, in which there was a distinct cost to acting. More dominant individuals were better able to feed than subordinate individuals on a given trial because if they did not pull, they could guard the position in front of the dispenser, and if they did pull, they were more likely than a subordinate to still successfully feed from the floor. Over the course of trials, dominants had to pay a higher cost for pulling than subordinates most likely because if they pulled they could not occupy the coveted dispenser position and could only feed from the floor.

Pulling rate was clearly affected by group size, order of conditions and session. It seems most likely that the subjects were not able to calculate the complexities of the dilemma offhand but adjusted their pulling rates as they experienced the dilemma reward structure over sessions. It is important to remember that while all group constellations were unique, individuals reappeared in more than one group and thus potentially already had experience in the two group sizes with other individuals.

One result of interest is that in the triad condition, pulling rates appear to be converging on some low but stable value in both the sextet first and triad first conditions. For those groups that experienced the sextet first, pulling rate increases across sessions, likely as a result of the greater potential for reward in the triad versus sextet condition. Conversely, in the groups that experience the triad first, pulling rate declines across triad sessions and remains low in sextet sessions. The first decline may be partly a function of their experiencing the reduced potential for reward relative to the dyad pre-test which came just before.

While it could be argued that pulling rates decrease simply as a result of a conditioning and extinction pattern prompted by receiving less reward, we believe this explanation is unlikely. Extinction from conditioning is typically very slow, and given the small number of trials and the incomplete cessation of reward, it is highly unlikely that extinction would occur in our experimental context. Furthermore, subjects showed flexibility in their decision-making, evident in their being motivated to pull when alone in the first pre-test, then acting or not acting in the dyad pre-test depending on their partner, and overall, displaying the tendency to pull less when in larger groups than in smaller ones regardless of order of condition. One may further note that some subjects who were unwilling to pull in the dyad pre-test did pull in the test phase when the amount of reward was reduced but group context had changed. Previous experiments have shown chimpanzees to be very sensitive to social intricacies in dyadic cooperative contexts [ 10 – 12 ], and it seems likely that their behaviour would be similarly influenced in our tests as well.

In the sextet condition, pulling rates either remain low (in the sextet first groups) or decline to low values (in triad first groups), which suggests resistance to solving the dilemma and potential for breakdown if further sessions were added. The highly significant variability in the pulling rates of different individuals suggests that certain subjects acted as ‘impact pullers,’ analogous to the impact hunters reported by Gilby and co-workers [ 6 , 9 ]. In the experiment, ‘impact pullers’ are highly motivated to pull the rope in any given trial, regardless of group size, order of condition or session number. Gilby & Connor [ 9 ] suggest that personality may be a significant factor determining motivation to initiate and persist in a hunt, and that those individuals most motivated to do so precipitate the collective hunt, most likely by reducing the cost of participation to subsequent hunters. It is possible that personality may account for some of the variance observed in pulling rates between individuals. However, it is still unclear whether these individuals influence success in the same way in an experimental setting as they do in the wild. While it has been suggested that an impact hunter reduces the costs for whoever follows by engendering chaos among the prey and increasing the likelihood that another hunter may succeed, it is as of yet unclear in exactly what ways an impact puller reduces the cost of pulling for another other than by assuring whoever follows that they have a willing partner and therefore, that the action would most likely be successful.

Furthermore, we detected a significant variation in pulling rates between dyads. This suggests that dyadic dynamics, perhaps revolving around tolerance between individuals, are also affecting variation in success rates.

4. General discussion

These experiments presented subjects with a Volunteer's Dilemma in which action was required to release a reward that could potentially be shared by all individuals present. As with any CAP, there were higher rewards associated with free-riding and disincentives to action. In experiment 1, the dilemma was overcome in all conditions, the typical pattern being that a particular higher-ranking individual would consistently choose to push in the majority of trials. The payoff structure appeared to be influenced by the dispersion of the reward, the strength of the hierarchy in each group as well as by the level of tolerance displayed between individuals. These results suggest that in this case, dominance was the primary force mediating the costs of action. This is an example of Nunn's [ 1 ] first class of influential factors: asymmetrical benefits and privileged groups. Because higher-ranked individuals have the security of knowing they will be rewarded for their effort, it is in their interest to produce the goods even when free-riding occurs, whereas subordinates typically pay a very high cost for pushing as they are subsequently unable to gain much access to the reward and thus they tend not to act. The only exception being when dispersion and rivalry are maximized, making it hard for dominants to take control of the reward and easier for subordinates to profit. Similar dominance effects in an experimental CAP have been observed in pigs [ 20 ].

In experiment 2, the dilemma was overcome in most triad trials but there appeared to be a decrease in action over sessions in the sextet condition. In our particular design, because the peanuts were sprayed into the room and thus highly dispersed, all individuals should have some motivation to act. The reward distribution was complicated by the fact that individuals learned to sit directly in front of the dispenser in order to catch the majority of peanuts. Dominants could use their rank to monopolize this favourable position if they remained in the peanut room and did not pull. In triad trials, lower-ranking individuals may be encouraged to pull because they would only have to compete with two other individuals; for the same reason, dominants may be more willing to sacrifice the dispenser strategy. In the sextet condition, the number of competitors also vying for a share of the peanuts more than doubles (going from two to five from the perspective of the individual actor), and thus subordinates have little incentive to pull while dominants would be reluctant to leave the coveted dispenser position. The cooperative aspect of the CAP in the second experiment also hindered success as not only one, but two individuals would have to decide the cost of action was worthwhile. Because of the changes in dispersion, group size and type of action (individual to cooperative), dominance (as an example of privileged groups) is unlikely to have been a strong mechanism leading to overcoming the CAP, particularly in the sextet condition.

Instead, in experiment 2, significant variation in individual motivation to act seemed to be the force propelling success. Impact pullers may have increased chances of peanuts being released in two ways. First, two impact pullers could have been tested in the same group and been willing to collaborate. This may explain in part the variation in pulling rates between dyads. Second, the movement of one impact puller towards a rope end may have acted as a catalyst for another individual deciding to act, in particular if the second individual could have access to the rope end under the raceway, which allowed the fastest return to the peanut room. At present, it is unclear what characteristics mark the personality of an impact puller.

The results of these experiments suggest that chimpanzees are able to overcome a Volunteer's Dilemma scenario, but that they may have a limited set of mechanisms available to them to do so. Asymmetrical benefits accruing from membership in a privileged group (higher-ranking individuals) may be one solution in simple small group scenarios. However, when dominants pay a higher cost for acting or lose opportunities to reap high rewards, this mechanism breaks down. Impact actors may play an important role in maintaining levels of success when general motivation levels are sufficiently high to encourage interest in collaboration, but in larger groups, when expected payoff drops, the influence of these impact individuals weakens. While a couple of subjects in experiment 2 began routinely claiming the dispenser position and proceeding to vocalize and bang on the bars and floor, and one individual was observed reaching towards another who was commonly an actor, these attention getters did not seem to have a direct effect on the success of the trial. No additional long-term mechanism for increasing the likelihood that someone volunteers to produce the shared good, such as increased coordination allowing for turn-taking, emerged. In this study, the principal cost of action was reducing the potential for reward. This is not exactly analogous to chimpanzee collective hunts where hunters increase rather than decrease their potential for possessing the reward or securing larger shares [ 7 ]. The costs of hunting are instead energetic effort and risk of injury. Therefore, in future, it would be interesting to examine CAP scenarios that reflect the payoff structure of hunting contexts more closely.

Acknowledgements

We are very grateful to the animal caretakers at the Wolfgang Köhler Research Center and to L. Ajarova, the trustees and all the staff of Ngamba Island Chimpanzee Sanctuary for their continuous help and support. Many special thanks to the animal caretakers of Ngamba Island. We also thank O. Boniface and R. Pieszek for building the apparatuses. We also appreciate permission from the Ugandan National Council for Science and Technology and the Uganda Wildlife Authority for allowing us to conduct our research in Uganda. We are extremely thankful to Roger Mundry for his invaluable help analysing the data.

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Why Do Apes Make Gestures?

Chimps and other apes have been observed making more than 80 meaningful gestures. Three theories have tried to explain why.

Several chimpanzees interact in close quarters in an area with bare dirt and grass.

By Carl Zimmer

In the 1960s, Jane Goodall started spending weeks at a time in Gombe Stream National Park in Tanzania watching chimpanzees. One of her most important discoveries was that the apes regularly made gestures to one another. Male chimpanzees tipped their heads up as a threat, for example, while mothers motioned to their young to climb on their backs for a ride.

Generations of primatologists have followed up on Dr. Goodall’s work, discovering over 80 meaningful gestures made by not only chimpanzees, but also bonobos, gorillas and orangutans.

Now researchers are using these gestures to peer into the minds of apes. Some even think they offer clues about how our own species evolved full-blown language. “Certainly, gestures played a big role,” said Richard Moore, a philosopher of language at the University of Warwick.

In the 1980s, Michael Tomasello, then a young comparative psychologist, pioneered the first theory about ape gestures based on observations of infant chimpanzees in captivity as they grew into adults.

He noticed that the baby apes made gestures to their mothers and, as they matured, developed new gestures directed at other chimpanzees.

Based on his observations, Dr. Tomasello argued that gestures develop among apes as simple habits. If a baby repeatedly tries to grab food from its mother’s mouth, for example, the mother may eventually start to give it food while the baby is still stretching out its arm. The baby, in turn, may stop bothering with the full action.

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Steve Clarke's Scotland evolution needs to speed up or familiar problem will be waiting for successor to solve

Clarke's reluctance to afford opportunities to fresh Scotland faces isn't just an in-game issue

10:13, 6 SEP 2024

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Evolution takes place over millions of years as species change and adapt to their environments.

If that's the pace of change we're looking at with Scotland , then Steve Clarke could find himself in endangered territory before long. He included some fresh faces in the Nations League squad to face Poland, but in what has been a theme of Clarke's tenure, kept them on the bench whist sticking with what he knows in the starting XI.

That was the main Tartan Army bugbear when the team was announced. Clarke was criticised for learning nothing from a disastrous Euro 2024 campaign, although his radio silence for over two months since we crashed out and the spiky response when there finally was an opportunity to ask about it suggested the national team boss wasn't in the mood for reflection long before the teamsheets went in at Hampden against Poland.

The opening stages therefore surprised no-one. Scotland kept possession for lengthy spells in their own defensive third, lost the ball when they eventually tried to progress through midfield and Poland scored inside ten minutes as a result.

It was 2-0 before half-time and the argument for the same old approach just wasn't there as our most winnable fixture of this Nations League campaign on paper was proving anything but. Improvement did come swiftly after the break as a welcome second goal in Dark Blue from Billy Gilmour reduced the deficit before Scott McTominay levelled.

The former Man Utd man, now in Italy with Gilmour after the pair joined Napoli, has replaced John McGinn as Scotland's totem. He's just about the only guy scoring goals and if he doesn't, there's no-one else stepping up to fill the void.

The subs eventually came with just under 20 minutes to go and they provided a much needed spark. Ben Doak in particular got the Tartan Army interested again, while Ryan Gauld's international bow has been a long time coming.

Doak won't fix all of Scotland's problems, but he does at least bring a freshness and attacking intent. He needs minutes to prove he can do it at international level - as do all the new call ups - and there appears little value in waiting for so long to unleash him.

Clarke's reluctance to utilise other options when results are good is understandable. Less so when we sit on one win in 13 games. It was a case of needs must with Anthony Ralston at the Euros given injuries to both Aaron Hickey and Nathan Patterson. The Celtic man was found out badly against top opposition and didn't cover himself in glory against Poland, although a second half assist was a bright spot.

It's harsh to criticise Ralston - he simply isn't at the level of the guys he's stepping in for. Max Johnston might not be either, but to not at least see how he does in what has become a problem position seems counter productive.

Clarke maybe feels he can't take risks given the wretched run of form. However, he seems pretty confident he's the man to lead the next attempt at qualifying for a World Cup despite the struggles over the past year or so. Whether we make it to USA, Canada and Mexico or not, it will likely be his final campaign.

That is where the next man could have a problem. The lack of opportunity for the next wave of Scotland squad regulars means they'll have to play catch up when needed.

It's not a unique situation either. Craig Brown's departure coincided with the retirement of many Scotland stalwarts, and little transition planning had taken place. Berti Vogts was much maligned for handing out caps like sweeties, but in the German's defence, he had little to go on given his predecessor struck pretty rigidly to the same names.

It's less extreme now, but still an issue that could crop up not too much farther down the line if Clarke remains overly loyal for too long.

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Mark Cuban is Kamala Harris’ on-call billionaire. What’s he after?

In this article:

Investor Mark Cuban says he speaks to Kamala Harris’ campaign “three or four times” a week about fiscal policy, even as he lobbies against her bid to tax the unrealized gains of billionaires like himself. Just don’t call him an adviser.

“I don’t have a title of any sort,” Cuban told Semafor via email. “It’s up to them whether they answer the phone or return my messages.”

Cuban has become perhaps Harris’s loudest supporter in the circuit of billionaires helping power her presidential bid. He’s lauded Harris as a “pro-business” candidate and described her campaign as more responsive than President Joe Biden’s now-defunct reelection operation.

He even revealed this week on CNBC that he pitched himself as a potential Harris pick for the Securities and Exchange Commission, the same agency that unsuccessfully prosecuted him for alleged insider trading more than 10 years ago. Cuban said he sees himself as a real-world voice among those counseling the vice president’s team, but he declined to describe himself as a “counterweight” to more vocal progressives in her camp.

“I’m an entrepreneur, not a politician, not an economist,” Cuban told Semafor, adding that “the challenge they face is they have too many economists that look at problems academically rather than operationally. I just try to give my honest thoughts. Not as a counter balance. Just as a perspective from my experience.”

Cuban has previously talked with the well-funded group No Labels about a third-party presidential run, though he ruled that out last year. Soon after that public show of distaste for the two-party system, however, Cuban stood with Biden even after the June debate that ended up dooming his candidacy.

The Harris campaign did not respond to requests for comment about Cuban’s informal role. Her team continues to add business advisors, however, bringing on Richard Garcia last month as the national director of small business engagement, per his LinkedIn. He was previously chief of staff at the US Hispanic Chamber of Commerce, among other roles.

Cuban acknowledged in his exchanges with Semafor that “people get sick of rich people trying to solve problems.” Still, he delivered a subtle case for his own value to Harris, calling himself a “compassionate capitalist” who believes business can play a constructive role in rebalancing the economy towards the middle class.

He also recently said on X that Harris is the only presidential candidate in “founder mode,” a Silicon Valley term referring to a leader who embraces an activist role in managing a company and eschews stodgy management practices.

Despite Cuban’s avowal that he’s merely seeking to help the Harris camp, some observers say his advice may be partly motivated by self-interest. Cuban has spent years crusading against the SEC, making him an unorthodox potential pick for the Democratic nominee.

Lee Reiners, lecturing fellow at the Duke Financial Economics Center, said Cuban and the Dallas Mavericks – which he sold his majority stake in last year – are still embroiled in a lawsuit for promoting unregistered securities through Voyager, a defunct cryptocurrency company.

“I support any legal effort that will help defeat Trump. If Cuban having a seat [at Harris’ table] helps, then so be it,” Reiners told Semafor. “But I do worry about his potential influence on crypto policy under a Harris administration, as he is not a disinterested observer.”

Cuban has also assailed Harris’ so-called “billionaire tax” proposal, which would require Americans who claim at least $100 million in assets to pay a 25% tax on stocks and bonds that have accrued in value, even if they haven’t sold those assets yet.

“If you tax unrealized gains, you’re going to kill the stock market,” Cuban said on CNBC this week.

Harris recently unveiled a second plank to her economic platform that’s geared towards small businesses, with a $50,000 tax deduction. She also rolled out a proposal to tax capital gains at 28% for Americans earning over $1 million, a lower rate than Biden has sought.

“Her engagement with the [business] community I think is probably deeper than the President’s,” a person close to the Harris campaign told Semafor. “I don’t know if I’d say she’s more pro-business, but has she engaged more with the business community and does that shift her worldview? I think that’s right.”

Eighty-eight corporate leaders endorsed Harris on Friday in a new letter, CNBC reports .
Cuban called for the firing of current SEC Chair Gary Gensler earlier this year, per the Dallas Morning News .

COMMENTS

Competing Interests
Prior to testing the chimpanzees in the problem-solving tasks, we also assessed the level of human orientation of each of the chimpanzees through a previously established Human Orientation Index, HOI (Damerius et al., 2017a). In each task, every subject was tested alone and thus for a shorter period separated from its social group.
Chimpanzee Problem-Solving: A Test for Comprehension
Abstract. An adult chimpanzee was shown videotaped scenes of a human actor struggling with one of eight problems and was then shown two photographs, one of which depicted an action or an object (or both) that could constitute a solution to the problem. On seven of the eight problems, the animal consistently chose the correct photograph.
The origins of cognitive flexibility in chimpanzees
Several studies have found that chimpanzees are fairly conservative in problem‐solving tasks: once they have learned one solution, they may not readily change their response—even if it is less optimal than a new solution (Hrubesch et al., 2009; Marshall‐Pescini & Whiten, 2008; Van Leeuwen & Call, 2017). Enhanced flexibility in humans may ...
Chimpanzees' ( Pan troglodytes) problem-solving skills are influenced
We tested 59 chimpanzees housed at two different captive facilities (a rehabilitation center (sanctuary) and a zoo) in three problem-solving tasks. Our results showed that chimpanzees at the two housing facilities significantly differed in overall task performance. On average, the sanctuary chimpanzees outperformed the chimpanzees housed at the ...
PDF Chimpanzees' (Pantroglodytes) problem- solving skills are ...
We tested the chimpanzees in three novel (to them) problem-solving tasks (detour reaching task, visible honey trap task and reversal learning task) generating six different measurements on different aspects of physical cognition (Table 1). The tasks were performed in the above listed order, to ensure that all subjects participating in the study
Chimpanzee problem-solving: contrasting the use of causal and arbitrary
Humans are able to benefit from a causally structured problem-solving context rather than arbitrarily structured situations. In order to better understand nonhuman causal cognition, it is therefore important to isolate crucial factors that might differentiate between events that follow a purely spatial and temporal contingency and those that hold a "true" causal relationship.
Chimpanzee problem-solving: contrasting the use of causal ...
Humans are able to benefit from a causally structured problem-solving context rather than arbitrarily structured situations. In order to better understand nonhuman causal cognition, it is therefore important to isolate crucial factors that might differentiate between events that follow a purely spatial and temporal contingency and those that hold a "true" causal relationship. In the first ...
(PDF) Chimpanzees' (Pan troglodytes) problem- solving skills are
Test apparatuses for assessing problem-solving skills. (A) Detour reaching task, (B) visible honey trap and (C) reversal learning task. Full-size DOI: 10.7717/peerj.10263/fig-1
(PDF) Chimpanzee problem-solving: Contrasting the use of causal and
experiments, chimpanzee subject s were required to detect a. bottle containing juice from Wve opaque bottles of equal. shape and size. In the causal condition, the juice bottle. looked identical ...
Insight learning: Chimpanzee Problem Solving
Insight learning. Experiment much like the one's conducted by Wolfgang Köhler (Mentality of the Apes). The apes experience insight (aha experience or aha-erl...
Chimpanzee problem-solving: Contrasting the use of causal and arbitrary
Humans are able to benefit from a causally structured problem-solving context rather than arbitrarily structured situations. In order to better understand nonhuman causal cognition, it is therefore important to isolate crucial factors that might differentiate between events that follow a purely spatial and temporal contingency and those that hold a "true" causal relationship. In the first ...
Kohler's Work on Insight Learning
Another chimp had good luck moving a crate under the bananas and using a pole to knock them down. The theme common to each of these attempts is that, to all appearances, the chimps were solving the problem by a kind of cognitive trial and error, as if they were experimenting in their minds before manipulating the tools.
Evidence for Emulation in Chimpanzees in Social Settings Using the
Background It is still unclear which observational learning mechanisms underlie the transmission of difficult problem-solving skills in chimpanzees. In particular, two different mechanisms have been proposed: imitation and emulation. Previous studies have largely failed to control for social factors when these mechanisms were targeted. Methods In an attempt to resolve the existing ...
Animal learning
Animal learning - Insight, Reasoning, Behavior: Köhler's best known contribution to animal psychology arose from his studies of problem solving in a group of captive chimpanzees. Like other Gestalt psychologists, Köhler was strongly opposed to associationist interpretations of psychological phenomena, and he argued that Thorndike's analysis of problem solving in terms of associations ...
Chimpanzees' (Pan troglodytes) problem-solving skills are ...
We tested chimpanzees' problem-solving skills with three tasks targeting different aspects of physical cognition that yielded six different cognitive measurements . The total sample size varied for each task from N = 49 to N = 59, as subjects participated on a voluntarily basis in the tasks .
How chimpanzees solve collective action problems
How chimpanzees overcome the disincentives to action and initiate and complete successful hunts remains unclear [6,9]. Gilby et al. [ 6 ] argue that individual variation in hunting motivation is the most important factor predicting the likelihood of a hunt (i.e. solution of the CAP).They identified certain highly motivated males as 'impact ...
Chimpanzees shown spontaneously 'taking turns' to solve number puzzle
In this exercise, the numbers 1 to 8 were split between two screens, with pairs of chimpanzees required to take turns to ensure the numbers were picked in the right order. For example, one ...
Chimpanzee Problem Solving by Cooperation
A brief, interesting clip from National Geographic's 'Ape Genius' documentary, demonstrating problem solving skills in chimpanzees, by requesting cooperation...
Chimpanzee problem-solving: A test for comprehension.
An adult chimpanzee was shown videotaped scenes of a human actor struggling with 1 of 8 problems and was then shown 2 photographs, 1 of which depicted an action or an object (or both) that could constitute a solution to the problem. On 7 of the 8 problems, S consistently chose the correct photograph. This test of problem-solving comprehension permits the animal's knowledge about problem ...
Chimpanzee Problem-Solving: A Test for Comprehension
Title: Chimpanzee Problem-Solving: A Test for Comprehension Created Date: 20160730040911Z
Problem Solving by Chimpanzees.
The experiments required the chimpanzees to maneuver a box into such a position that food could be reached that was pinned on a wall, sometimes the use of a stick by the animal was also observed in an ingenious mixture of methods to reach the desired reward. ... Problem Solving by Chimpanzees. In W. Dennis (Ed.), Readings in general psychology ...
What chimps can teach us about problem solving
06:05 - Source: CNN. Stories worth watching 16 videos. What chimps can teach us about problem solving. 06:05. Bride's sister springs into action when snake interrupts wedding party. 01:33. Hear ...
Why Do Apes Make Gestures?
An ape can reach out an arm, for example, as a way to ask for food, like a piece of a freshly caught monkey. "I have the monkey, and you're sitting very close to me, looking very closely at ...
Steve Clarke's Scotland evolution needs to speed up or familiar problem
Scotland's Scott McTominay celebrates. Clarke's reluctance to utilise other options when results are good is understandable. Less so when we sit on one win in 13 games.
Mark Cuban is Kamala Harris' on-call billionaire. What's he after?
Cuban acknowledged in his exchanges with Semafor that "people get sick of rich people trying to solve problems." Still, he delivered a subtle case for his own value to Harris, calling himself a "compassionate capitalist" who believes business can play a constructive role in rebalancing the economy towards the middle class.

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Evidence for Emulation in Chimpanzees in Social Settings Using the Floating Peanut Task

Introduction

Ethics statement

Baseline (total N = 31).

Full model condition (N = 13).

Water bottle condition (N = 14).

Data Scoring and Analysis

Acknowledgments

Author Contributions

Insight and reasoning

Chimpanzees’ (Pan troglodytes) problem-solving skills are influenced by housing facility and captive care duration

Introduction

Ethical statement

Subjects and facilities

Cognitive tasks and experimental set up

Detour reaching task

Figure 1: Test apparatuses for assessing problem-solving skills.

Reversal learning task

Human Orientation Index

Statistical analysis

Comparisons between facilities in task performance

Analysis of within facility variation: effects of arrival age and lifetime spent in the sanctuary

Differences between facilities in task performance

Figure 2: Mean probability of success and 95% confidence intervals across tasks of the chimpanzees housed at Leintal Zoo and Ngamba Island Sanctuary.

Figure 3: Mean (dashed horizontal lines) and median (solid horizontal lines) interaction times of the chimpanzees housed in Leintal zoo and Ngamba Island with the honey trap.

Effects of arrival age and lifetime spent in the sanctuary

Figure 4: Mean (dashed horizontal lines) and median (solid horizontal lines) of the Human Orientation Index (HOI) of the chimpanzees housed in Leintal zoo and Ngamba Island.

Figure 5: Mean probability of success and SE in the honey trap task with a stick (circles) and rope (triangle) depending on the age of the chimpanzees when they arrived at the sanctuary.

Figure 6: Mean probability of success and SE in the honey trap task with a stick (circles) and rope (triangle) depending on the percentage of their lives the chimpanzees had been in the sanctuary.

Tool use differences within the sanctuary

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How chimpanzees solve collective action problems

1. Introduction

2. Experiment 1

(b) Apparatus

(c) Procedure and design

(ii) Coding and analysis

(d) Results

(e) Discussion

3. Experiment 2

(ii) Dyad pre-test

(iii) Test phase

(d) Coding and analysis

(e) Results

(ii) Test phase

(iii) Time to success

(f) Discussion

4. General discussion

Acknowledgements

Why Do Apes Make Gestures?

Steve Clarke's Scotland evolution needs to speed up or familiar problem will be waiting for successor to solve

Mark Cuban is Kamala Harris’ on-call billionaire. What’s he after?

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