Relational learning in honeybees (Apis mellifera): Oddity and nonoddity discrimination

Behavioural Processes 115 (2015) 81–93

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Relational learning in honeybees (Apis mellifera): Oddity and nonoddity discrimination Nicole M. Muszynski a,b,∗ , P.A. Couvillon a,b a b

¯ Department of Psychology, University of Hawai‘i at Manoa, 2530 Dole Street, Sakamaki C400, Honolulu, HI 96822, USA Pacific Biosciences Research Center, Békésy Laboratory of Neurobiology, 1993 East–West Road, Honolulu, HI 96822, USA

a r t i c l e

i n f o

Article history: Received 14 June 2014 Received in revised form 24 February 2015 Accepted 1 March 2015 Available online 3 March 2015 Keywords: Comparative cognition Relational learning Honeybee Oddity discrimination Concept learning

a b s t r a c t Honeybee learning is surprisingly similar to vertebrate learning and one implication is that the basic associative learning principles are also similar. This research extends the work to more complex cognitive phenomena. Forager bees were trained individually to visit a laboratory window for sucrose. On each training trial for all experiments, bees found three stimuli, two identical and one different. In Experiments 1 and 2, stimuli were three-dimensional two-color patterns, and in Experiments 3 and 4, stimuli were two-color patterns displayed on a computer monitor. Training was trial-unique, that is, a different triad of stimuli was presented on each trial. In Experiments 1 and 3, choice of odd was rewarded and choice of nonodd was punished. In Experiments 2 and 4, choice of nonodd was rewarded and choice of odd was punished. On every trial, the initial choice was recorded and correction permitted. Honeybees learned to choose the odd stimulus in Experiments 1 and 3 and the nonodd stimuli in Experiments 2 and 4. The results provide compelling evidence of oddity and nonoddity learning, often interpreted as relational learning in vertebrates. Whether the mechanism of such learning in honeybees is similar to that of vertebrate species remains to be determined. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Honeybees have shown an extensive array of associative learning phenomena in experiments that are analogous to experiments conducted with vertebrate species (Bitterman, 1988, 1996). The two basic techniques developed for studying learning in honeybees include the proboscis-extension procedure for work with restrained subjects and the free-flying procedure for work with unrestrained foragers (Von Frisch, 1967). The range of experiments includes Pavlovian and instrumental as well as appetitive and aversive paradigms. Such similarities in general learning phenomena are remarkable given that vertebrates and invertebrates probably shared a common ancestor some half a billion years ago. Furthermore, the brain structure of honeybees is very different from that of vertebrates. The similarities might make sense, however, if the biological mechanisms of learning occur at the cellular level (Clarac and Pearlstein, 2007; Kandel and Hawkins, 1992). Albeit, conver-

∗ Corresponding author at: Pacific Biosciences Research Center, Békésy Laboratory of Neurobiology, 1993 East–West Road, Honolulu, HI 96822, USA. Tel.: +1 808 956 6991; fax: +1 808 956 6984. E-mail addresses: [email protected] (N.M. Muszynski), [email protected] (P.A. Couvillon). http://dx.doi.org/10.1016/j.beproc.2015.03.001 0376-6357/© 2015 Elsevier B.V. All rights reserved.

gent evolution is perhaps more likely, that is, “different phenomena may be produced by the same processes, and what appear to be identical phenomena may be produced by different processes” (Bitterman, 1975). Given the large number of similarities between the associative learning of honeybees and vertebrates, it seems reasonable now to turn to an exploration of cognitive phenomena that may not be easily explained with associative principles. Of particular interest is whether honeybees are capable of solving discrimination problems which may not lend themselves to a solution based on absolute stimulus properties, but which require learning about the relationships among sets of stimuli. The strategy is the same as that used for the study of basic associative phenomena, that is, to explore the capacity of honeybees for such learning in analogs of the procedures typically used to study relational learning in vertebrates. These include same/different discrimination, matching-to-sample and nonmatching-to-sample, oddity-from-sample, and oddity discrimination. It is important first to determine the extent to which honeybees perform like vertebrates and then to determine whether the similarities, if any, reflect similar mechanisms. Work on these problems has been done mostly with primates and birds. Successful performance has been interpreted in the vertebrate literature in a variety of ways including, but not limited to, relational discrimination, stimulus relationship learning, concept

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learning, relational concept learning, abstract concept learning, higher-order relational learning, and generalized concept learning (cf., Cook and Wasserman, 2012). These terms are often used interchangeably, and clear-cut, widely-accepted definitions are lacking in both the historical and contemporary literature (Bromer, 1940; Wasserman and Bhatt, 1992; Zentall et al., 2002). The more descriptive term, relational learning, is used here. As noted, most of the work has been done with vertebrate species, and there is very little work in invertebrates. In the experiments reported here, honeybees were trained in oddity and nonoddity discrimination problems, the first such attempt with honeybees. There is only one oddity discrimination study with an invertebrate, and that is with octopus by Boal (1991) who failed to find evidence for relational learning. Although there have been no explicit oddity experiments with bees, there are a few studies of relational learning of other types (e.g., same/different discrimination, categorization, matching-to-sample, and nonmatching-to-sample) with bees. While these studies are well-conceived, they have methodological issues, and the results of each may be more readily interpreted with basic associative learning principles. It is interesting to note that the same ambiguity of terminology found in the vertebrate literature can be found also in the invertebrate literature. Giurfa et al. (1996) conducted a categorization experiment to determine if honeybees could perceive symmetry, and they raised the question of whether honeybees can respond in terms of a symmetry concept. Bees were trained with a succession of stimulus triads. Half of the bees were rewarded for choosing the one symmetrical stimulus and nonrewarded for choosing either of the two unique asymmetrical stimuli; the other half of the bees were rewarded for choosing the one asymmetrical stimulus and nonrewarded for choosing either of the two unique symmetrical stimuli. Choice was defined as hovering above an individual stimulus for a minimum period of time. Unrewarded tests with different sets of stimuli were periodically interspersed among the training trials. The authors reported the relative frequency, duration, and intensity (a function of hovering time and distance from the stimulus) of the bees’ approaches to the correct stimulus for each test. While no data were presented for the training trials, the bees’ performance on the repeated transfer tests showed improvement, suggesting that the bees had learned to respond on the basis of symmetry or asymmetry. However, the results are difficult to interpret for several reasons. Unlike analogous vertebrate choice experiments, there is no discrete measure of correct choice. It is important to note that the bees only performed above chance in the later transfer tests, and it is not clear whether or not the test stimuli were repeated in the successive tests. Furthermore, in none of the tests was the first choice performance reported. Typically, evidence of transfer of learning to novel stimuli is provided by above chance performance on the first trial before any additional learning can occur during the test (Thomas and Noble, 1988). It is possible that instead of solving the problem on the basis of symmetry and asymmetry, the bees, after extensive training, had learned to discriminate common features of symmetrical and asymmetrical stimuli, a result more readily explained with associative learning principles. Giurfa et al. (2001) conducted a series of matching-to-sample and nonmatching-to-sample experiments with honeybees in a Y-maze to assess same/different learning. In one series of experiments, bees were rewarded for choosing the stimulus that matched the sample. In another series of experiments, bees were rewarded for choosing the stimulus that did not match the sample. A series of unrewarded transfer tests with novel stimuli was interspersed among the training trials. The bees showed good performance in both training and transfer tests, results that the authors suggest is evidence of same/different concept learning. The repeated transfer tests, however, were with the same two “novel” stimuli and initial transfer performance was not reported. Furthermore, the number

of choices during transfer tests was summed for all subjects, possibly giving disproportionate weight to the performance of high responders. Avarguès-Weber et al. (2011) conducted two experiments to assess whether honeybees could learn to conceptualize above and below relationships. The bees were trained to choose between two stimulus cards, one with a stimulus pattern displayed above the referent (e.g., a line) and the other with a stimulus pattern displayed below the referent. An unrewarded transfer test with novel stimuli in the same configuration followed the training. Although the bees showed positive transfer, they may not have learned a relationship of above/below, but instead may have learned to use the referent as a landmark for a spatial discrimination problem (Chittka and Jenson, 2011). Avarguès-Weber et al. (2012) conducted a series of three experiments to assess whether honeybees could learn two abstract concepts at the same time, same/different and either left/right or above/below. The bees were trained to choose between two stimulus cards; for one group the cards contained two different achromatic patterns and for the other group the cards contained two differently colored discs. The bees in both groups had to choose between a stimulus pattern displayed in a left/right configuration and a stimulus pattern displayed in an above/below configuration. The training in each of the three experiments was much the same, and in each experiment, unrewarded transfer tests were interspersed among training trials. In the transfer tests of the first experiment, bees trained with colors were presented with achromatic patterns and bees trained with achromatic patterns were presented with colors; both groups showed good transfer. While it is possible that the bees had learned about the above/below and left/right relationships, it is also possible the bees had learned in training to choose on the basis of spatial configurations. For example, a bee rewarded for choosing two different colors always in the vertical configuration (above/below) might be expected to choose two different novel stimuli in the vertical configuration simply on the basis of the absolute properties of the spatial configurations used in training. In the transfer tests of the second experiment, the bees were presented with two stimulus cards one of which contained a vertical or a horizontal bar instead of the two colors or the two achromatic patterns used in training. For example, a bee rewarded for choosing two different colors always in the vertical configuration (above/below) might be expected to choose two different novel colors in the vertical configuration (even if slightly offset) rather than a novel solid vertical bar simply on the basis of the absolute properties of the vertical configuration of the two colors rewarded in training. In the transfer tests of the third experiment, the bees were presented with two stimulus cards one of which contained a pair of identical stimuli instead of the two colors or two achromatic patterns used in training. For example, a bee that had been rewarded in training for choosing two different colors always in the vertical configuration (above/below) might be expected to choose two different novel colors in the vertical configuration rather than two identical novel colors in the vertical configuration simply on the basis of generalization from the training with two different colors. If that is the case, there is no need to assume that the bees used a same/different concept. In a recent experiment, Avarguès-Weber et al. (2014) conducted an experiment in which honeybees were presented with colored shapes that differed in size. One group of bees was rewarded for choice of the larger shape and the other group was rewarded for choice of the smaller shape. There were fifteen different combinations of size and shape repeated across training trials. The bees in both groups showed good training performance, and the results of transfer tests suggest that the bees had learned to choose on the basis of size. The authors point out some methodological concerns including the consistent reward or nonreward of the ends of the size

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continuum they used and the effect of viewing distance on the bees’ choices. Furthermore, they note that an associative explanation is not ruled out by their results. Brown and Sayde (2013) trained bumblebees in a simultaneous same/different problem with colored or patterned stimuli displayed on a computer monitor. The bees were required to choose one reinforcement chamber (left) if the two projected stimuli were the same and to choose the other reinforcement chamber (right) if the two stimuli were different. If the choice was correct, the bees were rewarded with sucrose in the reinforcement chamber. In a series of transfer tests with a new stimulus set, the bees showed positive transfer. Although the results are intriguing, they are difficult to interpret, as noted by the authors, because of methodological issues. For example, the colony was the unit of analysis, individual bees were not identified, and multiple bees were trained at the same time. Despite these methodological concerns, the results of these studies on honeybees and bumblebees, taken together, hint at the possibility that invertebrates may solve relational discrimination problems. If such learning is not unique to vertebrates, then demonstrations in invertebrates may provide insight into its evolutionary development. Our particular interest here is oddity learning, one of the earliest paradigms for the study of relational learning in nonhuman animals. Robinson (1933) may have been the first to study oddity learning. Using a single macaque monkey, she tested whether the animal was capable of responding based on the relationship among stimuli. Presented with one odd colored object and two identical nonodd colored objects simultaneously, the monkey was rewarded with food for choice of the odd object. There were two stimulus configurations; on half the trials, the stimuli were presented as A+ B− B−, and on the other half, the stimuli were presented as B+ A− A−, with position (left, middle, right) balanced across trials for both configurations. The subject did solve the problem, albeit, after more than 400 training trials, suggesting the monkey had learned, in the words of Robinson, “the abstraction of oddity.” An associative explanation, however, is possible since there were only six arrangements of stimuli (ABB, BAA, ABA, BAB, AAB, BBA), and, with such extensive training, the animal may simply have learned the consequences for response to each of the combinations. Since Robinson’s original experiment, there have been several procedural variations of the oddity problem. These variations can be categorized into five types: traditional oddity, multiple-stimulus-set oddity, oddity-from-sample (also known as nonmatching-to-sample), Weigl/dimension abstracted oddity, and odd-item search. The first two types, traditional oddity and multiple-stimulus-set oddity, are most relevant to the honeybee work presented here. The archetypal traditional oddity problem is characterized by having a limited stimulus set consisting of only two trial types (ABB and BAA). Interestingly, little work has been conducted using the traditional oddity problem, most likely due to the concern that subjects may simply learn the correct response to each of the stimulus configurations. Twenty years after Robinson’s first experiment, Pastore (1954) conducted a traditional oddity experiment but with nine stimuli instead of three. Three canaries were presented with one odd object and eight identical nonodd objects simultaneously. Choice of the odd object (jumping to a perch in front of the object) was rewarded with food. The subjects received two trial types (ABBBBBBBB and BAAAAAAAA) in alternation across trials. All three canaries showed better than chance performance after extensive training. Although the subjects had success learning the problem, the author characterizes the traditional oddity problem as a series of reversals and prefers a perceptual interpretation to an abstract interpretation but provides no specifics of the perceptual mechanism.

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Zentall et al. (1980) conducted a series of traditional oddity experiments with pigeons in which the number of incorrect alternatives varied from 2 to 24. The pigeons were presented with differently colored keylights and were rewarded with food for choosing the odd color whose position was balanced or varied over trials. The pigeons’ performance was facilitated by the increase in the number of incorrect alternatives, which the authors suggest may be due to a perceptual relationship, such as the Gestalt figureground principle or, in contemporary terms, a “pop-out” effect (Blough, 2001). Clearly, it is difficult to interpret the results of traditional oddity experiments, a situation that perhaps facilitated the development of a new type of oddity problem. This type can be characterized by the use of either multiple stimulus sets, trial-unique stimuli, multiple oddity problems, transfer tests with novel stimuli, or some combination of these, which all might simply be called multiplestimulus-set oddity to distinguish them from traditional oddity. The animals used in such experiments included, octopus, rats, pigeons, cats, a sea lion, goats, chimpanzees, raccoons, ravens, gulls, old world monkeys, new world monkeys, and lemurs. The earliest experiments with multiple-stimulus-set oddity problems were conducted with monkeys using the recently developed Wisconsin General Test Apparatus (WGTA: Harlow and Bromer, 1938). Monkeys were trained to choose the odd object from a group of three, three-dimensional objects in order to obtain reward, and then they were successful in a series of transfer problems, each with different stimuli (Bromer, 1940; Meyer and Harlow, 1949, 1955). Levine and Harlow (1959) found that oddity learning was faster when monkeys were trained with multiple problems simultaneously, each with different stimuli, than when trained in oddity problems with a limited set of stimuli. Subsequent studies with monkeys used this multiple problem procedure to explore the effects of variations in training parameters and stimulus sets on oddity learning (Draper, 1967; Davis et al., 1967; Noble and Thomas, 1970; Shaffer, 1967; Strong, 1965; Strong and Hedges, 1966). The first multiple-stimulus-set oddity experiment with a species other than monkeys was conducted by Wodinsky and Bitterman (1953). Rats were trained in the jumping stand developed by Lashley (1938) to choose the odd stimulus of three stimulus cards to obtain reward. The rats mastered the original oddity problem and successfully transferred to new problems. The results of later studies, however, suggest that this finding is not robust. In a study by Koronakos and Arnold (1957), rats were trained in a multiple-choice runway apparatus developed by Fields (1953) to choose the odd stimulus of five stimulus cards to obtain reward. The rats had difficulty in transfer problems. In more recent work (Bailey and Thomas, 1998; Thomas and Noble, 1988), rats were presented with a tray of three objects and were rewarded for choosing the odd object based on either visual or odor cues. Again, the rats had difficulty in transfer problems. As was the case with rats, studies with cats produced mixed results. Cats trained in the WGTA to choose the odd stimulus of three stimulus objects to obtain reward successfully transferred only with specialized training (Warren, 1960; Boyd and Warren, 1957). In a broader comparative study using the WGTA, both cats and raccoons failed to solve the original oddity problem and were not given transfer tests (Strong and Hedges, 1966), but chimpanzees showed successful oddity transfer. Surprisingly, there has been very little work on oddity learning in chimpanzees, perhaps because of early indication that chimpanzees may have a small innate tendency to choose novel stimuli (Nissen and McCulloch, 1937). In a formal study to assess that tendency, Davenport and Menzel (1960) presented chimpanzees with unrewarded multiple-stimulus-set oddity problems. The chimpanzees showed a tendency to choose the odd object from a tray of objects with a probability greater than expected by chance. Nonetheless,

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a later study showed that chimpanzees trained in an instrumental chamber with images projected on response keys were able to successfully solve a series of oddity problems with a probability of correct choice too high to be explained by the small innate tendency to choose novel stimuli (Devine and Ivens, 1969). Similarly, monkeys (Bromer, 1940), rats (Forwood et al., 2007), and gray jays (Waite, 2008) also have shown a small initial preference for the odd stimulus. More recently, work on oddity learning has been conducted with a wide variety of vertebrate species including, ravens, gulls, goats, and a sea lion. Ravens and gulls trained to peck the odd object out of six three-dimensional objects arranged horizontally on a tray were successful in transfer tests (Benjamini, 1983). Goats trained to choose the odd stimulus of four stimuli projected in equal sectors on a computer monitor learned a simplified traditional oddity problem, but only a single goat was able to successfully transfer to novel stimulus oddity problems (Roitberg and Franz, 2004). A sea lion was first trained to press the odd stimulus card of three stimulus cards in a series of simplified traditional oddity problems and then was successful in transfer problems with novel stimuli (Hille et al., 2006). In summary, multiple-stimulus-set oddity experiments have been conducted with a variety of vertebrate species, most of which have shown reasonably good performance. In contrast, there has been almost no work on oddity learning in an invertebrate species. There is, however, one oddity study with octopuses. In a series of experiments, wild caught octopuses were trained inside laboratory tanks to grab one of three mollusk shells to obtain frozen squid. Octopuses presented with a series of simplified traditional oddity problems were successful. However, octopuses presented with different stimuli on every trial were not successful (Boal, 1991). Clearly, more research needs to be conducted in order to explore whether invertebrates are capable of oddity learning. The aim of this work is to determine whether honeybees are able to solve oddity problems under conditions that do not lend themselves to an associative interpretation. Therefore, the multiple-stimulus-set procedure was used in the experiments reported here. It is clear from the review of the vertebrate studies that some species have an innate tendency to choose on the basis of novelty which would facilitate oddity learning. However, there is no information to suggest that honeybees have a spontaneous preference either for novelty or for familiarity, and so it seemed reasonable to proceed with both oddity and nonoddity problems. In all of the experiments, honeybees were trained with two-color patterns which were three-dimensional stimuli for Experiments 1 and 2 and two-dimensional computer-generated stimuli for Experiments 3 and 4. In Experiments 1 and 3, the bees were rewarded for choosing the odd stimulus from a set of three, and in Experiments 2 and 4, the bees were rewarded for choosing either of the two nonodd stimuli from a set of three. Successful performance on both oddity and nonoddity problems should provide strong evidence for relational learning in honeybees.

2. Experiment 1: Oddity Success in solving traditional oddity problems, which typically use only two different stimuli in training, may or may not suggest relational learning because alternative associative explanations are possible. On the other hand, success in solving multiple-stimulusset oddity problems, which typically use multiple stimuli in training or transfer tests, may provide more convincing evidence for relational learning. In one variation of the multiple-stimulus-set oddity problems, the stimulus sets are different for every training trial. Historically these problems are referred to as 1-trial problems but

Fig. 1. The wooden enclosure used in Experiments 1 and 2.

in the contemporary literature they are referred to as trial-unique problems. The aim of this experiment was to investigate oddity learning in honeybees using a trial-unique procedure. Free-flying honeybees were trained to visit a laboratory window and on each visit to the window were presented with a novel set of three stimuli. Two of the stimuli were identical and one was different from the other two. In order to receive a sucrose reward, the bees had to choose the odd stimulus. Oddity learning would be indicated by better than chance choice performance, which given the trial-unique procedure, would suggest relational learning in honeybees. 3. Method 3.1. Subjects The subjects were 12 forager honeybees (Apis mellifera) never used in prior experiments. They were captured at feeders containing 10–20% sucrose solution which were located near the hives in the back of the Békésy Laboratory at the University of Hawai‘i ¯ at Manoa. Each subject was trained individually in a single daily session lasting from one to several hours. 3.2. Apparatus The training situation, as shown in Fig. 1, was a resined wooden enclosure, 61 cm wide, 61 cm high, and 61 cm deep, recessed in the exterior wall of a laboratory window. 3.3. Stimuli The stimuli used were composed of two colors arranged in a “pinwheel” pattern of six equally segmented triangles. The colored triangles were made of vinyl plastic with a matte-finish and were glued to the top of a Petri dish (5.5 cm in diameter) using silicon sealant. Each stimulus had two of the following colors in alternating sequence: blue, green, orange, and yellow. The combination of two different alternating colors allowed for six unique two-color pat-

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terns. All four colors have been used in many previous experiments from this laboratory and are highly discriminable and equally preferred. However, the “pin-wheel” arrangement of two colors in compound has not been used in previous experiments. In order to ensure that each two-color pattern was discriminable from each of the others, honeybees were run in a preliminary series of simultaneous discrimination problems. The results indicated that all of the two-color patterns could be discriminated from each other.

3.4. Pretraining Each subject in the experiment was collected in a matchbox at the feeder and brought to the laboratory for pretraining. In order to ensure exposure to all of the colors to be used in training, the pretraining stimulus was constructed of four equally divided segments of the four colors (blue, green, yellow, orange). This stimulus was placed in the middle of the floor of the wooden enclosure and contained a 100-␮l drop of 50% sucrose. The bee was placed at the drop on the surface of the pretraining stimulus, and as the bee began to drink, it was marked on the thorax with colored enamel for identification purposes. The bee drank until replete and then flew to the hive to unload the sucrose. If the bee returned to the enclosure on its own (usually in three to five minutes), it again found the pretraining stimulus with a drop of 50% sucrose. The bee then landed and drank the sucrose until replete and returned to the hive to unload. In the event that a marked bee did not return to the enclosure, it was recaptured at the feeder and re-placed on the pretraining stimulus. If the marked bee still did not return on its own, another bee was selected from the feeder. Pretraining ended after a bee had returned on its own to the pretraining stimulus.

3.5. Training On each of the fifteen training trials there were three stimuli, two identical nonodd and one odd. Using the six two-color patterns, it was possible to create fifteen unique stimulus triads. An example of a training sequence is illustrated in Fig. 2. In the first trial of that sequence, there were two yellow–blue stimulus compounds and one yellow–orange stimulus compound. The three compounds were presented simultaneously and positioned horizontally in the center of the floor of the wooden enclosure, 2.5 cm apart edge-toedge. A 100-␮l drop of 50% sucrose solution was placed on the top of the odd stimulus and served as reward. A 100-␮l drop of 10% stevia solution was placed on the top of the nonodd stimuli and served as punishment. (The results of the previous experiments from this laboratory indicate that stevia is highly aversive to honeybees and cannot be discriminated from sucrose except by taste.) The odd stimulus occurred five times in each position (left, middle, right) in a quasi-random sequence over the 15 training trials. There were four different trial sequences of the stimulus triads, and each sequence was used for three subjects. The sequences were constructed so that successive trials did not share any identical stimuli, ensuring that the same two-color pattern was not odd on two trials in a row. Each two-color pattern could serve as the odd stimulus two or three times over the training trials but never more than once in any position. After pretraining, the bee returned from the hive to the enclosure for the first training trial. If the bee landed first on the odd stimulus and drank the sucrose, a correct choice was recorded. The bee then drank the sucrose until full and flew back to the hive, unloaded the sucrose, and returned to the enclosure for another trial. If the bee landed first on a nonodd stimulus, whether or not the bee tasted the stevia solution, an incorrect choice or error was recorded. A correction procedure was used here so if the bee chose

Fig. 2. The pretraining stimulus and an example of a trial-unique sequence used in Experiments 1 and 2. Note that the stimuli consisted only of solid colors. There were no patterns on the stimuli. The patterns shown here serve to distinguish the stimuli when viewed in grayscale or black and white instead of in color. The vertical bar denotes orange, the black dot denotes green, the solid black denotes blue, and solid gray denotes yellow.

Fig. 3. The results of the oddity training in Experiment 1.

incorrectly, it was allowed to choose again until it landed on the odd stimulus, drank the sucrose, and returned to the hive. 4. Results The performance of the bees is plotted in Fig. 3. The left panel shows the proportion of bees that chose correctly on each training trial. For analysis of the data, the proportion of correct choice in all 15 trials was computed for each bee. The overall mean proportion

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of correct choice was .49, which is significantly greater than the chance value of .33, t(11) = 4.55, p = .001 with a standard error of the mean (SEM) of .03. In the right panel, the data are plotted as the mean proportion of correct choice in three-trial blocks in order to reduce the trial-by-trial fluctuations apparent in the left panel. The blocked curve shows more clearly that the tendency of the bees to choose correctly increased over training. As reported above, the overall proportion of correct choice was indeed greater than that expected by chance. Given the trial-by-trial fluctuations and variability in performance across subjects, it did not seem reasonable to expect a significant acquisition curve even with the trials blocked. Nonetheless, it did seem reasonable to assess terminal performance. The last six trials were used for this purpose because the rewarded position (left, middle, and right) was balanced in the last six trials, as was the number of presentations of each of the two-color patterns. The mean proportion of correct choice for the last six trials (pooling the last two blocks shown in the right panel of Fig. 3) was computed for each bee. For only one bee was the proportion less than chance. The mean proportion of correct choice for all bees for the last six trials was .51, which again is significantly greater than the chance value of .33, t(11) = 2.71, p = .020, SEM = .07. These results suggest that honeybees can learn an oddity problem with unique stimulus triads on every trial. In fact, their performance in a single problem with only 15 trials is similar to that of vertebrate species trained in multiple-stimulus-set problems with considerably more trials in each problem (cf., Aust and Steurer (2013): pigeons; Koronakos and Arnold (1957): rats; Roitberg and Franz (2004): goats; Strong and Hedges (1966): cats, raccoons). Interestingly, even though monkeys achieve a higher level of performance in multiple-stimulus-set problems, it is not perfect, and questions have been raised about the sources of error and variability in their performance. As Moon and Harlow (1955) note, stimulus and position preferences as well as reward-following from trial to trial may all produce variability in performance. As they also note, such preferences may not interfere with the learning of oddity problems, but may simply interfere with the overall performance in oddity problems. The fact that bees and many other vertebrate species reach a level of correct choice that is significantly greater than chance but is considerably less than 100% may be due to the error produced by preferences and response strategies. It seemed reasonable, therefore, to do an error analysis of the bees’ choices. 4.1. Stimulus preference In discrimination problems where the rewarded alternative is not predictable from trial to trial, it is not uncommon for subjects to choose on one trial the stimulus that was rewarded on the previous trial, that is, stimulus reward-following. As noted above, the trial-unique sequences used in this experiment ensured that no two-color pattern was presented in two consecutive trials. Therefore, it was not possible for the bees to adopt a stimulus reward-following strategy in their choice of a two-color pattern. However, bees may still show stimulus preferences, that is, a tendency to choose one or more of the six two-color patterns more than the others. If bees have no stimulus preference, they should have equal frequencies of choice of each of the two-color patterns. A chi-square test showed a significant deviation from equal frequencies, 2 (5) = 15.67, p = .0078, which is due to a preference for the blue-green compound by four of the 12 bees. Such a preference may have increased the variability of performance. 4.2. Position reward-following The other stimulus to which the bees may be responding is position (left, middle, and right). The sequences were constructed such

that it was rare for the same position to be rewarded in two consecutive trials. Nonetheless, it was possible for subjects to choose on one trial the position that was rewarded on the previous trial. For example, after reward on a two-color pattern in the left position, the bee might on the next trial be more likely to choose the two-color pattern in the left position rather than a compound in the middle or the right position. The chance probability of choosing any of the three positions is .33. Position reward-following was analyzed to determine whether the bees had a tendency to deviate from chance. The mean proportion of position reward-following was .22 which was significantly less than chance, t(11) = −4.74, p = .001, SEM = .02. This result suggests that the bees had a tendency to not follow a previously rewarded position. Instead of choosing the same position, they chose a different position, which is not surprising because a persistent choice of a position is rarely rewarded (cf., Fig. 2). Furthermore, to the extent that the bees were learning to choose on the basis of oddity, switching positions is almost always rewarded. It is unlikely that position-switching contributed much to the variability of performance.

4.3. Position preference Position preference, the tendency to choose one position (left, middle, right) more than another position, also was analyzed. If bees do not have a position preference, it is expected that they will have equal frequencies of initial position choices across all training trials. A chi-square test for equal frequencies showed no significant position preference, 2 (2) = 2.50, p = .286, suggesting that the bees have no systematic tendency to choose any one of the three positions. Therefore, it is unlikely that the variability in the performance is due to position preferences.

5. Discussion In summary, the results of this experiment provide the first evidence of oddity learning in honeybees. Their performance cannot be accounted for in terms of systematic stimulus or position preferences nor in terms of systematic response strategies such as reward-following. The bees’ oddity learning also cannot be explained by associative learning, such as conditional discrimination, since on every trial there were unique stimulus triads. In theory, the bees may have an inherent bias for oddity, although there is no suggestion of such a bias in the performance shown in Fig. 3, that is, the initial choices hover around chance. Therefore, the bees should perform equally well in an experiment in which choice of the odd stimulus is punished and choice of either of the two nonodd stimuli is rewarded. Such a problem has been referred to in the past as “nonoddity” (Thomas and Crosby, 1977) and has been conducted in conjunction with oddity. Monkeys solved both oddity and nonoddity problems when trained in the WGTA to choose the odd object from three stimulus objects presented on a white tray to obtain reward and to choose a nonodd object from the three stimulus objects presented on a black tray (Thomas and Crosby, 1977; Thomas and Kerr, 1976). Typically, vertebrate species show a preference for oddity. Rats spontaneously chose the odd stimulus in a three-stimulus unrewarded preference test (Forwood et al., 2007), as did chimpanzees (Davenport and Menzel, 1960). Gray jays spontaneously chose the odd stimulus in a four-stimulus test with all stimuli rewarded (Waite, 2008). Wilson et al. (1985a,b) found that both pigeons and jays learn an oddity problem (nonmatching-to-sample) more quickly than a nonoddity problem (matching-to-sample). However, honeybees trained in both matching- and nonmatching-to-sample problems provide no indication of a preference for oddity (Shishimi,

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2013). The solution of a nonoddity problem would provide additional evidence of relational learning in honeybees. 6. Experiment 2: Nonoddity The aim of this experiment was to determine whether honeybees could learn to choose on the basis of nonoddity. The same trial-unique procedure used in the oddity problem of Experiment 1 was used here except that the sucrose reward was provided on both of the nonodd stimuli and the stevia solution was provided on the odd stimulus. Since the results of the previous oddity experiment provided no indication that honeybees have a spontaneous preference for oddity, the expectation was that the bees could also solve a nonoddity problem.

Fig. 4. The results of the nonoddity training in Experiment 2.

8. Results 7. Method 7.1. Subjects The subjects were 12 honeybees (Apis mellifera) never used in prior experiments. They were captured at feeders containing 10–20% sucrose solution which were located near the hives in back of the laboratory. Each subject was trained individually in a single daily session lasting from one to several hours. 7.2. Apparatus and Stimuli The same apparatus and stimuli used in Experiment 1 were used here. 7.3. Pretraining The pretraining procedure was the same as that used in Experiment 1. 7.4. Training The same trial-unique training procedure that was used in Experiment 1 was used again here. On each of the 15 training trials there were three stimuli, two identical nonodd and one odd. An example of a training sequence is illustrated in Fig. 2. In the first trial of that sequence, there were two yellow-blue and one yellow–orange stimulus compounds. A 100-␮l drop of 50% sucrose solution was placed on the top of the two identical nonodd stimuli and served as reward. A 100-␮l drop of 10% stevia solution was placed on the top of the odd stimulus and served as punishment. There were three different trial sequences and each sequence was used for four subjects. As was the case in Experiment 1, the sequences were constructed so that successive trials did not share any identical stimuli, that is, neither the odd nor the nonodd twocolor patterns were presented on two trials in a row. A nonodd stimulus occurred ten times in each position (left, middle, right) in quasi-random sequence over the 15 training trials. Each two-color pattern could serve as a nonodd stimulus two or three times over the training trials. A correct initial choice was recorded if the bee landed first on either of the two nonodd stimuli and tasted the sucrose on that stimulus. An incorrect initial choice or error was recorded if the bee landed first on the odd stimulus, whether or not the bee tasted the stevia solution on that stimulus. A correction procedure was used, so if the bee chose incorrectly it was allowed to choose again until it settled on one of the nonodd stimuli, drank the sucrose, and returned to the hive.

The performance of the bees is plotted in Fig. 4. The left panel shows the proportion of bees that chose correctly on each training trial. For analysis of the data, the proportion of correct choice in all 15 trials was computed for each bee. The overall mean proportion of correct choice was .69 which is not significantly greater than the chance value of .66, t(11) = .81, p = .438, SEM = .03. As was done for Experiment 1, the data also are plotted as the mean proportion of correct choice in three-trial blocks which is shown in the right panel of Fig. 4. It is clear that the likelihood of a correct choice increased over trials and reached a level of about .80 by the end of training. In the early trials, the bees showed a small tendency to choose the odd stimulus even though it was not rewarded, but that tendency disappeared as the bees learned to choose a nonodd stimulus. Given the early tendency to choose incorrectly, it did not seem reasonable to expect a significant acquisition curve even with the trials blocked. Nonetheless, it did seem reasonable to assess terminal performance. The mean proportion of correct choice for the last six trials (pooling the last two blocks shown in the right panel of Fig. 4) was computed for each bee and for only two bees was the proportion less than chance. The mean proportion of correct choice for all bees for the last six trials was .79 which is significantly greater than the chance value of .66, t(11) = 2.54, p = .028, SEM = .05. These results suggest that honeybees can learn a nonoddity problem with unique stimulus triads on every trial. As discussed in the context of the oddity results for Experiment 1, stimulus and position preferences, as well as reward-following from trial to trial, may not interfere with the learning of oddity (or nonoddity) problems but may produce variability in performance (Moon and Harlow, 1955). To better understand the bees’ performance in these problems, it is reasonable to do an error analysis of the bees’ choices. 8.1. Stimulus preference In discrimination problems where the rewarded alternative is not predictable, it is common for subjects to adopt a stimulus reward-following strategy. The trial-unique sequences used in this experiment did not permit stimulus reward-following because no two-color pattern was presented in two consecutive trials. The bees may still show stimulus preferences, that is, a tendency to choose one or more of the six two-color patterns more than the others. If bees have no preference, they should have equal frequencies of initial stimulus choice across all training trials. In fact, the bees had no preferences for any of the two-color patterns, 2 (5) = 6.33, p = .275. 8.2. Position reward-following It is also possible that the bees showed position (left, middle, right) reward-following, that is, a tendency on any given trial to

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follow the position rewarded on the preceding trial. The mean proportion of position reward-following was .32 which was not significantly different than the chance value of .33, t(11) = −.34, p = .737, SEM = .03. In other words, there was no indication of position reward-following.

8.3. Position preference Position preference, the tendency to choose one position more than another position, also was analyzed. If bees do not have a position preference, they should have equal frequencies of initial position choices across all training trials. A chi-square test for equal frequencies did not show a significant position preference, 2 (2) = 1.2, p = .548. 9. Discussion In summary, the results of this experiment provide the first evidence of nonoddity learning in honeybees. Their performance cannot be accounted for in terms of systematic stimulus or position preferences nor in terms of systematic response strategies. These results, along with those of Experiment 1 in which the bees solved an oddity problem also with trial-unique stimuli, present compelling evidence of relational learning in the honeybee. A reasonable strategy is to further explore the capacities of honeybees to learn about stimulus relationships in more complex oddity problems and in other kinds of same-different problems. The training procedure used here with the two-color patterns on Petri dishes is not ideal for using more than three stimuli which must be manipulated by hand, and the colors available in the vinyl plastics used to create the compounds are limited. Increased flexibility for future work with honeybees might be achieved by using computer-generated stimuli as has been done in studies with pigeons (see Washburn et al., 1990). In fact, there are a few studies of matching and nonmatching-to-sample with bees that have used computer-generated stimuli (Brown et al., 1998; Brown and Sayde, 2013; Couvillon et al., 2003). The plan here, therefore, was to attempt to replicate the oddity and nonddity results for Experiments 1 and 2 with computer-generated stimuli. Successful replication would indicate the robustness of the oddity and nonoddity results.

10. Experiment 3: Oddity with digital stimuli The aim of this experiment was essentially to repeat the oddity study of Experiment 1 with computer-generated stimuli. The digital stimuli were constructed to approximate the color patterns, shape, and size of the compound stimuli used in both Experiments 1 and 2. The same trial-unique procedure was used again here, and, in each trial, bees were presented with two nonodd stimuli and one odd stimulus. Choice of the odd stimulus was rewarded and better than chance performance would provide additional evidence for oddity learning in honeybees.

11. Method 11.1. Subjects The subjects were 12 honeybees (Apis mellifera) never used in prior experiments. They were captured at feeders containing 10–20% sucrose solution which were located near the hives in back of the laboratory. Each subject was trained individually in a single daily session lasting from one to several hours.

Fig. 5. The wooden enclosure used in Experiments 3 and 4. A computer monitor was mounted, display side up, in the floor of the enclosure.

11.2. Apparatus The main apparatus used for training was the wooden enclosure, shown in Fig. 5, that was 61 cm wide, 61 cm high, 61 cm deep, and recessed in the exterior wall of a laboratory window. A flat screen 15-inch computer monitor with a glass surface was mounted in the floor of the apparatus. The surrounding floor area was covered by a piece of plywood (painted gray) with an opening cut out to expose the monitor screen such that the display surface was recessed by 1.5 cm below the gray floor covering. The visible surface of the monitor was 21 cm by 29 cm, with the longer dimension parallel to the window opening. The enclosure was open to the outside and the inside was fitted with two sliding Plexiglas panels. The transparent panels allowed the experimenter access to the enclosure and permitted observation of the bee during training trials. They also served to prevent unwanted entrance of the bee into the laboratory. 11.3. Stimuli The shape, size, and colors of the two-color pattern stimuli used in Experiment 1 were approximated with a series of slides generated in Microsoft PowerPoint. Like the physical stimuli in the previous two experiments, the digital stimuli were circles approximately 5.5 cm in diameter composed of two colors arranged in a “pinwheel” pattern of six equally segmented triangles. Each stimulus had two of the following colors in alternating sequence: purple, green, white, and yellow. Note that all of the colors had been used in previous discrimination experiments conducted in this laboratory with computer-generated images; the colors are discriminable from each other. The colors were created in Microsoft Paint by specifying values for yellow, white, green, and purple. The values are as follows: yellow (red = 255, green = 255, blue = 0; hue = 40, saturation = 240, luminosity = 120), white (red = 255, green = 255, blue = 255; hue = 160, saturation = 0, luminosity = 240), green (red = 0, green = 153, blue = 0; hue = 80, saturation = 240, luminosity = 60), purple (red = 153, green = 0, blue = 153; hue = 200, saturation = 240, luminosity = 72). To facil-

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Fig. 6. The top picture is a PowerPoint slide of the pretraining stimulus that was used for all of the subjects in Experiments 3 and 4. The bottom picture is a PowerPoint slide of one of the 15 stimulus sets used in the training for Experiments 3 and 4. In this stimulus set, the two nonodd stimulus patterns are purple–yellow and the odd pattern is white–yellow.

itate the bees’ detection of the stimuli, the background of the Microsoft PowerPoint slides was black to maximize contrast with the colored stimuli. 11.4. Pretraining The pretraining procedure was the same as in Experiments 1 and 2. Each subject was collected in a matchbox at the feeder and brought into the laboratory. Each bee was exposed to a stimulus compound of the four colors (yellow, white, green, and purple) to be used in training, as shown in the upper panel of Fig. 6. The stimulus was projected in the middle of the visible display surface of the computer monitor and a 100-␮l drop of 50% sucrose was placed in the center of the stimulus, directly on the monitor. The bee was placed at the drop on the stimulus, began to drink, and was marked on its thorax with colored enamel for identification. The bee drank until replete and then flew to the hive to unload the sucrose. If the bee returned to the enclosure, it again found the pretraining stimulus with a 100-␮l drop of 50% sucrose and pretraining ended.

taneously and positioned horizontally in the center of the visible computer screen, about 2 cm apart edge-to-edge. A 100-␮l drop of 50% sucrose was placed directly on top of the odd stimulus and served as a reward. A 100-␮l drop of 15% salt solution was placed on top of each of the nonodd stimuli and served as a punishment. (Previous experiments with stimuli projected on computer monitors with honeybees in this laboratory used salt solution for punishment of error instead of the stevia solution used in Experiments 1 and 2. Both stevia and salt solutions are highly aversive to honeybees. The reason for the substitution was a concern that the bees might be able to discriminate between sucrose and stevia drops on the computer monitor based on very slight differences in the light refracting patterns of the two solutions. There are no discriminable differences in the light refracting patterns between sucrose and salt as evidenced by previous control experiments, in which honeybees failed to discriminate sucrose and salt solutions on the computer monitor.) As in Experiments 1 and 2, the odd stimulus occurred five times in each position (left, middle, and right) in a quasi-random sequence over 15 training trials. There were three different trial sequences of the 15 stimulus triads, and each sequence was used for four subjects. Sequences were constructed so that successive trials did not share any identical stimuli and each compound was rewarded two or three times over the training trials but never more than once in any position. After pretraining, the bee returned from the hive to the enclosure for the first training trial. If the bee landed first on the odd stimulus and drank the sucrose, a correct choice was recorded as in Experiments 1 and 2 with three-dimensional stimuli. An error, however, was recorded differently. With three-dimensional stimuli, if the bee landed first on a nonodd stimulus, whether or not the bee tasted the aversive solution, an incorrect choice or error was recorded. With the digital stimuli, the bees tended to land first on the black background of the stimulus display and then walk toward one of the digital stimuli. Therefore, an error was recorded only if the bee actually tasted the salt solution on either of the nonodd stimuli. In other words, if the bee walked across a nonodd stimulus without tasting the drop, no error was recorded. Again, a correction procedure was used here so if the bee chose incorrectly, it was allowed to choose again until it landed or walked on the odd stimulus and drank the sucrose. 12. Results The performance of the bees is plotted in Fig. 7. The left panel shows the proportion of bees that chose correctly on each training trial. For the analysis of the data, the proportion correct choice in all 15 trials was computed for each bee. The overall mean proportion correct choice was .55, which is significantly greater than the chance value of .33, t(11) = 5.96, p < .001, SEM = .04. In the right

11.5. Training The training procedure approximated that used in Experiments 1 and 2. On each of the fifteen training trials there were three stimuli, two identical and one odd. There were six digital stimuli, which allowed for fifteen unique stimulus triads as in the sequence shown in Fig. 2 for Experiments 1 and 2. Note that while the threedimensional stimuli used in Experiments 1 and 2 were composed of the colors blue, yellow, orange, and green, the digital stimuli used here were purple, yellow, white, and green. Purple and white were substituted for the blue and orange respectively in the trial sequences, such as, the sample sequence shown in Fig. 2. An example of a stimulus triad on the computer monitor is shown in the lower panel of Fig. 6. The stimuli were presented simul-

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Fig. 7. The results of the oddity training with digital stimuli in Experiment 3.

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panel, the data are plotted as the mean proportion correct choice in three-trial blocks. The curve shows that the tendency of the bees to choose correctly increased across training trials. The mean proportion of correct choice for the last six trials (pooling the last two blocks shown in the right panel) was computed for each bee. For only one bee was the proportion less than chance. The mean proportion of correct choice for all bees for the last six trials was .62 which is significantly greater than the chance value of .33, t(11) = 5.32, p < .001, SEM = .05. The results replicate those of Experiment 1 and provide additional evidence for oddity learning with trial-unique stimuli. Again, as in the above experiments, the bees’ choices were further analyzed to determine if there were stimulus preferences, position preferences, or position reward-following, all of which could produce variability in performance.

15. Method

12.1. Stimulus preference

15.3. Pretraining

The bees’ tendency to choose any one of the six two-color patterns more than another was analyzed. If there is no stimulus preference, there should be equal frequencies of initial stimulus choice across all training trials. A chi-square test for equal frequencies did not show a significant preference, 2 (5) = 4.07, p = .539. 12.2. Position reward-following The bees’ tendency on any given trial to follow the position rewarded on the preceding trial was analyzed. The mean proportion of position reward-following was .329 which was not significantly different than the chance value of .33, t(11) = −1.02, p = .331, SEM = .04. The bees did not chose a position based on immediate prior reward of that position. 12.3. Position preference The bees’ tendency to choose one position (left, middle, right) more than another position was analyzed. If there is no position preference, there should be equal frequencies of initial position choices across all training trials. A chi-square test for equal frequencies, however, showed a significant position preference, 2 (2) = 9.65, p = .005 which is due primarily to a preference for the left position by 4 of the 12 bees. Such a preference may have increased the variability of performance. 13. Discussion In conclusion, the results of this experiment provide evidence of oddity learning in honeybees, here with digital stimuli. These results together with those for Experiment 1 with threedimensional stimuli provide strong evidence for oddity learning in honeybees. The results for Experiment 2 with three-dimensional stimuli indicated that bees also can learn a nonoddity problem. In the next experiment, bees were trained again in a nonoddity problem but now with the digital stimuli.

15.1. Subjects The subjects were 12 honeybees (Apis mellifera) never used in prior experiments. They were captured at feeders containing 10–20% sucrose solution which were located near the hives in back of the laboratory. Each subject was trained individually in a single daily session lasting from one to several hours. 15.2. Apparatus and Stimuli The same apparatus and stimuli used in Experiment 3 were used here.

Pretraining was the same as in Experiment 3. 15.4. Training Training was the same as in Experiment 3 except that on all 15 unique trials a 100-␮l drop of 50% sucrose solution was placed on both nonodd stimuli and a 100-␮l drop of 15% salt solution was placed on the one odd stimulus. 16. Results The performance of the bees is plotted in Fig. 8. The left panel shows the proportion of bees that chose correctly on each training trial. For analysis of the data, the proportion of correct choice in all 15 trials was computed for each bee. The overall mean proportion correct choice was .75, which is significantly greater than the chance value of .66, t(11) = 2.94, p = .014, SEM = .03. Close inspection of the data revealed no obvious reason for the deviant point which appears simply to be chance variation. In the right panel, the data are plotted as the mean proportion of correct choice in three-trial blocks. It is clear that correct choice increases gradually over training. The mean proportion of correct choice for the last six trials (pooling the last two blocks shown in the right panel) was computed for each bee. For only one bee was the proportion less than chance. The mean proportion of correct choice for all bees for the last six trials was .79 which is significantly greater than the chance value of .66, t(11) = 2.86, p = .016, SEM = .05. These results with digital stimuli, like those of Experiment 2 with threedimensional stimuli, show that honeybees can learn a nonoddity problem. Again, as in the above experiments, the bees’ choices were further analyzed to look for stimulus preferences, position preferences, and position reward-following, all of which could account for variability in performance.

14. Experiment 4: Nonoddity with digital stimuli The aim of this experiment was to repeat the nonoddity study of Experiment 2 with computer-generated stimuli. The same trialunique procedure was used again here, and, in each trial, bees were presented with two nonodd stimuli and one odd stimulus. Choice of either nonodd stimulus was rewarded and better than chance performance would provide additional evidence that honeybees can learn nonoddity problems.

Fig. 8. The results of the nonoddity training with digital stimuli in Experiment 4.

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16.1. Stimulus preference The bees’ tendency to choose any of the six two-color patterns more than another was analyzed. If there is no stimulus preference, there should be equal frequencies of initial stimulus choice across all training trials. A chi-square test for equal frequencies did not show a significant preference, 2 (5) = 3.27, p = .658. 16.2. Position reward-following The bees’ tendency to follow the position rewarded on the preceding trial was analyzed. The mean proportion of position reward-following was .29 which was not significantly different than the chance value of .33, t(11) = −1.31, p = .218, SEM = .03. 16.3. Position preference The bees’ tendency to choose one position (left, middle, right) more than another position was analyzed. If there is no position preference, there should be equal frequencies of initial position choices across all training trials. A chi-square test for equal frequencies did not show a significant position preference, 2 (2) = 1.6, p = .449, suggesting that the bees do not have a systematic tendency to choose any one of the three positions. 17. Discussion In conclusion, the results of this experiment provide additional evidence of nonoddity learning in honeybees. It is worth noting that the likelihood of choosing correctly by chance (.66) is very high in nonoddity experiments. Even in simple choice discrimination problems honeybees rarely choose correctly 100% of the time, often achieving about .90 proportion of correct choice (cf., Couvillon and Bitterman, 1987). It is noteworthy that the bees in this experiment performed significantly better than chance (.75) even with the possibility of a ceiling effect. That honeybees show both oddity and nonoddity learning with trial-unique stimuli is a compelling case for relational learning. 18. General discussion This work began as an initial step in an exploration with honeybees of some of the cognitive learning phenomena that have been demonstrated in vertebrate species. Of particular interest were problems that may require relational solutions, such as, matchingand nonmatching-to-sample, oddity, and same/different discriminations which have been most extensively studied in pigeons and primates. In some variations of these problems, it is not clear that the results can be interpreted with associative principles. As discussed in the introduction, oddity problems seemed a reasonable starting point for honeybees, as it had been for vertebrates, with a wide variety of vertebrate species represented in the oddity literature (Bailey and Thomas, 1998). The approach to the study of associative learning in honeybees has been to explore basic learning phenomena using analogs of vertebrate learning experiments. Here again, the approach was to explore how honeybees perform in oddity problems that are analogous to those used with vertebrates. In this series of experiments, honeybees were trained in both oddity and nonoddity problems. The procedure used in all of the experiments was trial-unique, that is, the stimulus triads were different on every trial. The trial-unique procedure rules out the possibility of learning to respond to specific stimuli (Wright et al., 1988). The honeybees solved both oddity and nonoddity problems in the two different training situations, with three-dimensional and computer-generated stimuli. These results cannot be explained

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by associative learning (such as, conditional discrimination) or unlearned preference for novelty. The honeybees’ successful performance is noteworthy because vertebrate performance in oddity problems is highly variable, sometimes successful and sometimes not (for example, see Boyd and Warren, 1957; Warren, 1960; Strong and Hedges, 1966). In that context, it is difficult to compare the honeybee results with those of vertebrates. A comparison of methodological differences between the honeybee experiments and the vertebrate experiments may be a useful beginning. These differences include trial spacing, reward quality, the use of punishment, and contiguity of stimulus and consequences. The honeybee experiments used spaced trials while typically vertebrate experiments use massed trials. The free-flying procedure dictates the intertrial interval which is determined by the time (3–5 min) required for the bee to leave the experimental window, deposit the sucrose at the hive, and return to the window for another trial. In oddity studies with vertebrates, training is usually conducted over multiple sessions with multiple trials per session, often with short intertrial intervals. (It is worth noting that in general learning is fast in honeybees, perhaps reflecting their relatively short lifespan. In reality, it is not possible to conduct experiments with honeybees that extend over multiple days as has been done with the vertebrates.) The honeybee experiments also used a highly-desirable reward for correct choice and punishment for incorrect choice. In vertebrate experiments, subjects are rewarded for correct choice but are not explicitly punished for incorrect choice which may be followed by a “time-out.” In addition, the honeybee experiments had a correction procedure which is not consistently the case in the vertebrate experiments. In the honeybee experiments the reward and the punishment were presented directly on the stimuli while in most vertebrate experiments, the reward is presented at a location away from the stimuli. These methodological differences may explain some of the variability in the vertebrate performance as well as the successful honeybee performance in oddity problems. Spaced trials may minimize trial-to-trial stimulus interference. Punishment may facilitate learning by increasing the cost of incorrect choice and by increasing attention to the choice alternatives. The stimulus-reward contiguity and the stimulus-punishment contiguity effectively decrease the delay between choice and its consequences. This methodological comparison is useful in that it highlights variables that may contribute to successful solution of relational discriminations. The real question, however, is how do honeybees, or any other species, solve problems like the oddity problem? While such learning in vertebrate species is often described as concept learning or abstract learning, as noted in the introduction, these terms have a variety of definitions. Such ambiguity complicates comparison across species; for example, the solution of oddity problems in primates may be attributed to abstract-reasoning and in pigeons to concept learning when, in fact, the empirical results are the same. Furthermore, the terms are descriptive rather than explanatory. The oddity solution likely requires the subject to look at all of the stimuli present and to respond to some relational property. That property might be same/different or something like “more of” and “less of” (cf., Aust and Steurer, 2013 for “one-ness” and “three-ness”). It is clear that whatever is controlling responding is independent of the specific stimuli when multiple-stimulus-sets are used as was the case here with the trial-unique procedure. In order to begin an analysis of how honeybees may solve oddity (and nonoddity) problems, the next step is to explore their performance in other oddity problems. For example, increasing the number of nonodd stimuli might improve performance by facilitating a perceptual “pop-out” effect (Blough, 2001). In addition, it will be important to explore the generality of the current findings with a much more diverse set of stimuli. While the stimuli used here were presented in unique triads on every trial, there was

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some reuse of the actual stimuli. If there is some feature of the stimuli that might have influenced responding, it is neither obvious nor intuitive. Nonetheless, if honeybees can discriminate oddity with a variety of stimulus sets which are entirely novel on every trial, it is unlikely that some common feature could explain the results. The digital display, as used in Experiments 3 and 4, increases the flexibility for producing a variety of computer-generated stimuli. Future work on oddity then might include iconic displays, black and white patterns, and photographs. It will be important also to explore the behavior of honeybees in other kinds of relational problems. Recent work in this laboratory suggests that honeybees can solve both simultaneous and delayed matching- and nonmatching-to-sample problems with a trial-unique procedure (Shishimi, 2013). It will be instructive to look at the performance of honeybees in analogs of the vertebrate simultaneous same/different discrimination (Blaisdell and Cook, 2005; Cook and Wasserman, 2012). Exploration of a wide range of relational learning problems in honeybees should maximize the value of comparison of their performance with that of vertebrates. The pattern of results may provide critical insight for answering questions about shared mechanisms. For instance, if honeybees are able to solve only some of the vertebrate relational learning problems but not others, then the implication is that the mechanisms may not be shared. On the other hand, if the honeybee results are similar to those for vertebrates in a wide variety of relational problems, then the implication is that the mechanisms may also be the same (Bitterman, 1975; Macphail and Barlow, 1985). Honeybees have shown a remarkable array of vertebrate associative learning phenomena (Bitterman, 1996), and the results of these oddity/nonoddity experiments suggest similarity in more complex learning phenomena as well. While further analysis is clearly required, not only with honeybees but with other invertebrates, these results do hint at the possibility that the ability to perceive and respond to relations among stimuli is a general characteristic of learning. Acknowledgements We would like to thank Dr. Gentaro Shishimi for lively discourse and Dr. Alex Doumas and Dr. Scott Sinnett for their critical and insightful comments. In addition, we would like to thank the anonymous reviewers for their constructive comments on the original paper submission. This research was supported by NSF grant IOS-0845116 and a graduate assistantship from the Department of ¯ Psychology at the University of Hawai‘i at Manoa. We are grateful also for the technical assistance provided by the Pacific Biosciences Research Center’s shop and computer facilities. References Aust, U., Steurer, M.M., 2013. Learning of an oddity rule by pigeons in a four-choice touch screen procedure. Anim. Cognit. 16, 321–341. Avarguès-Weber, A., d’Amaro, D., Metzler, M., Dyer, A.G., 2014. Conceptualization of relative size by honeybees. Front. Behav. Neurosci. 8, 1–8. Avarguès-Weber, A., Dyer, A.G., Combe, M., Giurfa, M., 2012. Simultaneous mastering of two abstract concepts by the miniature brain of bees. Proc. Natl. Acad. Sci. 109, 7481–7486. Avarguès-Weber, A., Dyer, A.G., Giurfa, M., 2011. Conceptualization of above and below relationships by an insect. Proc. R. Soc. B: Biol. Sci. 278, 898–905. Bailey, A.M., Thomas, R.K., 1998. An investigation of oddity concept learning by rats. Psychol. Rec. 48, 333–344. Benjamini, L., 1983. Studies in the learning abilities of brown-necked ravens and herring gulls: I. Oddity learning. Behaviour 84, 173–194. Bitterman, M.E., 1975. The comparative analysis of learning. Science 188, 699–709. Bitterman, M.E., 1988. Vertebrate–invertebrate comparisons. In: Jerison, H., Jerison, I. (Eds.), Intelligence and Evolutionary Biology. Springer, Berlin, pp. 251–276. Bitterman, M.E., 1996. Comparative analysis of learning in honeybees. Anim. Learn. Behav. 24, 123–141. Blaisdell, A.P., Cook, R.G., 2005. Two-item same-different concept learning in pigeons. Learn. Behav. 33, 67–77.

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