are touched with a sucrose solution as a reward, the bees reflexively extend their proboscis and lick the sucrose; few if any such PERs are typically observed when odors are presented to experimentally naive animals. During PER conditioning, an odor A is presented shortly before a reward (A+; in Pavlovian terminology the CS and US, respectively). After such paired training, increased levels of PER are observed in response to the odor alone. Obviously, without modification this paradigm cannot detect memories of opposite valence after unpaired training (A/+): as spontaneous PER rates are low they remain low after unpaired training. In other words, there is no 'negative' PER that could reveal unpaired-memory. One modification allowing such unpaired-memory to be detected is called retardation of acquisition. In such a two-phase paradigm the bees of two independent experimental groups first receive either paired or unpaired reward-training. In a second training phase, the bees of both groups receive paired training (paired-paired group: A+ training followed by A+ training; unpaired-paired group: A/+ followed by A+). As first reported by Bitterman et al. (1983), during the second training phase the bees in the unpairedpaired group respond less to A than those in the paired-paired group. Given that presentations of odor-alone or of reward-alone during the initial training phase do not have such an effect, this shows that unpaired training establishes an associative memory opposite in valence to paired training in the PER paradigm.

Data from two-odor, differential PER conditioning are consistent with, but are not in themselves conclusive evidence for, unpaired learning. In the course of an extended differential conditioning phase (A+, B, A+, B, A+, B, etc.), levels of PER toward B are initially elevated, arguably because of generalization from the first A+ training trial. As training progresses, however, the response levels to B decrease (Bitterman et al., 1983; Komischke et al., 2002; Boitard et al., 2015; see also Tedjakumala and Giurfa, 2013 for similar results in the aversive domain). This could be due either to a loss of generalized memory (the end-state being no memory for B) or to unpaired learning (the end-state being unpaired-memory for B).

The physiological data regarding the effects of unpaired training in the honeybee are complex. The PE1 neuron, an MBON from the peduncle of the mushroom body, has been shown to decrease its activity to an odor that was previously trained paired with reward (Mauelshagen, 1993; Okada et al., 2007). Regarding an unpaired-trained odor, mild increases or decreases in PE1 activity can be observed depending on trial number and time after odor onset (Mauelshagen, 1993; Okada et al., 2007). Also in other MBONs, in the antennal lobe and in the octopaminergic rewarding VUMmx1 neuron, altered responses to unpaired odors have been observed (Hammer, 1993; Faber et al., 1999; Strube-Bloss et al., 2011). These effects are typically small compared to the effects of reward-paired odors, and in no case have proper baseline levels of activity been determined. Therefore, alternative interpretations of these physiological data, such as non-associative learning or extinction learning, which are well documented in honeybees (Braun and Bicker, 1992; Hammer et al., 1994; Menzel et al., 1999; Eisenhardt and Menzel, 2007; Eisenhardt, 2014), remain tenable.

FIGURE 5 | Paired and unpaired memory in rats. In this previously unpublished experiment replicating a study by Andreatta et al. (2012), independent groups of rats were submitted to paired training of a light stimulus and punishment (left), to unpaired training (middle), or to a truly random procedure (right). Specifically, a light stimulus (red circle) and a mild foot-shock (yellow flash) were presented 15 times. For paired training, the light stimulus immediately preceded the shock (intertrial interval, ITI: 90–150 s); for unpaired training, the light stimulus and the shock were temporally separated from each other by at least 12 s; and in the random procedure, the light stimulus and the shock were randomly presented. One day later (vertical dotted arrow), the effects of the light stimulus on the startle response were measured. Startle probes (noise from a loudspeaker) were presented in the presence or absence of the light stimulus. Plotted are the Startle Difference Scores, i.e., the mean startle magnitude in the presence of the light minus mean startle magnitude in the absence of the light. Sample sizes are displayed below each box plot. For detailed statistical results see Supplementary Table S4. For detailed methods, see the "Materials and Methods" section in the Supplementary Presentation S1. Other details as in Figure 1.

# UNPAIRED-MEMORY IN RODENTS?

In the Section "How to Determine Baseline Odor Preferences in Larval Drosophila" we described a strategy in studying larval Drosophila that provides a baseline against which the associative effects of paired and unpaired training can be assessed - a strategy that prevents associative memories from being behaviorally expressed under baseline conditions. As mentioned, historically it was a different strategy that was applied to determine baseline behavior, namely preventing the formation of associative memories (Rescorla, 1966, 1967, 1968). In the following, we focus on fear conditioning in rodents as one study case for which that strategy has been used.

In laboratory rats or mice, non-reinforcement of a stimulus was often assumed to be mnemonically neutral and was thus used as a control in Pavlovian conditioning. For example, in differential fear conditioning a stimulus such as a tone A (serving as CS) is repeatedly paired with a punishing foot-shock reinforcement (+) (serving as US) (Ciocchi et al., 2010; Lange et al., 2014; Wigestrand et al., 2017). Intended as a control, a stimulus B, which can be a tone of another frequency, is presented in the absence of punishment. In most studies, B is presented before the beginning of the A training period (B, B, B, . . ., A+, A+, A+, etc.) (e.g., Lange et al., 2014). In fewer studies, B is presented during the A training period but unpaired from punishment (A+, B, A+, B, A+, B, etc.) (Wigestrand et al., 2017). Either way, a retention test is carried out, typically a day later. This test involves presenting A and B, in separate trials, and in a novel context. If the animals have learned the predictive relationship between A and shock, they should show freezing behavior upon the presentation of A, i.e., a species-specific defensive behavior consisting of a crouched body position and a cessation of all body movements except breathing. Provided that A and B are sufficiently distinct (Laxmi et al., 2003), freezing is observed upon presenting A but not upon presenting B, and not upon presenting a novel, not previously presented stimulus C. Does this mean that no learning about B has taken place? Not necessarily. This is because freezing is a monovalent measure, just like the PER in honeybees. In a novel context the animals hardly freeze, and thus only increases in freezing caused by negatively valenced memories can be measured. Positively valenced, unpaired memory for B, if it existed, would go unnoticed, since the animals cannot freeze less than not at all. To detect unpaired-learning, therefore, either the retardation-of-acquisition approach discussed above can be used (Pollak et al., 2010), or a bivalent measure is needed that allows positively and negatively valenced memories to be detected by modulations of the same behavioral read-out, with opposite sign. As will be discussed in the following section, up- and down-regulation of moderate levels of contextual freezing or of the startle response can provide such bivalent measures in rodents. In both cases the idea is to induce an affective state that can then either be potentiated by negatively valenced memory or attenuated by positively valenced memory.

#### Bivalent Measures of Valence in Rodents

One suggested approach to measuring positively valenced memory after unpaired training takes advantage of the contextual learning capabilities of rodents (Ostroff et al., 2010; Pollak et al., 2010; Kong et al., 2014). In these experiments, the test takes place in a context in which the animals have previously received foot-shock punishment. Within such a punishment-predicting context, the animals show the freezing behavior described above. To serve as a bivalent measure, it is important that the levels of freezing displayed by the animals should be moderate because this prevents floor or ceiling effects. If in this situation a stimulus A is presented that has itself been unpaired-trained with foot shock, the context-induced freezing is attenuated. By contrast, context-induced freezing is potentiated if A has been paired with shock. One interpretation is that through unpaired training the animals have learned that whenever A is present, punishment will not occur (a.k.a. safety learning). However, as discussed in

Section "How to Determine Baseline Odor Preferences in Larval Drosophila," a firm conclusion would require a proper baseline measure of freezing to disentangle whether the difference in freezing between paired-trained and unpaired-trained conditions results from either one of these two types of training, or from both.

A second approach takes advantage of the startle response, which can be elicited by a sudden, loud noise (a.k.a. the startle probe). This response consists of a short-latency contraction of all body muscles and can be measured by motion-sensitive devices (reviews include Koch, 1999; Fendt and Koch, 2013). To use the startle response as a read-out for a learning experiment, the animals are first trained with pairings of a stimulus A with foot-shock punishment. For the test, the startle probe is delivered either in the presence of A or in its absence. If the animals have learned the predictive relationship between A and punishment, the startle magnitude is higher in the presence than in the absence of A (a.k.a fear-potentiated startle, Davis et al., 1993; Fendt and Fanselow, 1999). This difference, i.e., startle in the presence of A minus startle in the absence of A, is quantified as the Startle Difference Score. Importantly for the present discussion, previously rewarded stimuli exert the opposite effect, i.e., startle is attenuated in their presence (Schmid et al., 1995). Thus, modulations of the startle response can be used as a bivalent measure of memory: positively valenced memories decrease startle, whereas negatively valenced memories increase it. Does this allow unpaired-memory to be revealed? Indeed, when A is presented unpaired from punishment during training, an attenuation of the startle response is observed in the test (Falls and Davis, 1994; Richardson and Fan, 2002). In this case too, one interpretation is that the animals have learned that whenever A is present, punishment will not occur (a.k.a. safety learning). However, a firm conclusion would again require a proper baseline against which to measure startle after unpaired training.

Thus, although both approaches offer bivalent measures of valence, both approaches as such also fall short of providing a proper baseline against which the effects of paired versus unpaired memory can be measured. How can such a baseline be determined?

#### Evidence for Unpaired-Memory in Rats

To determine the baseline response to a stimulus A free of associative effects of either paired or unpaired training, a procedure is needed in which no predictive relationship exists between A and punishment. To this end, Rescorla (1967) introduced the 'truly random' procedure. The idea is that A and punishment occur in a randomized temporal relationship. This means that A and punishment can also, by chance, occur together. If properly implemented, after truly random training A does not predict anything (for a more detailed discussion see Rescorla, 1972; Papini and Bitterman, 1990), whereas after unpaired training A predicts the non-occurrence of punishment (which is therefore often designated 'explicitly unpaired' training). We note that despite this critical difference in experimental outcome, the Methods sections of surprisingly many publications do not specifically state whether an unpaired

fpsyg-09-01494 August 24, 2018 Time: 12:7 # 10

For the evidence for

reinforcement-unpaired

 learning in Drosophila

 larvae mentioned

 in this study, the critical experimental

 parameters

 and site of publication

 of the original studies are presented.

TABLE 2 | Synopsis of parameters varying across experiments on innate odor preference.

For the experiments on the effects of reinforcement presence on innate odor preference in Drosophila larvae mentioned in this study, the critical experimental parameters and site of publication of the original studies are presented.

or a truly random procedure was used. This would be important, however, in order to properly interpret the results from experiments that use these procedures. In the present paper, we use 'unpaired' in the sense of explicitly unpaired throughout.

Using the truly random procedure, startle has turned out to be the same in the presence and in the absence of A, i.e., the Startle Difference Scores are zero (Davis and Astrachan, 1978; Hitchcock and Davis, 1987; Risbrough et al., 2003; Hsu et al., 2012). After unpaired training, by contrast, animals startle less in the presence of A, i.e., the Startle Difference Scores are negative (Falls and Davis, 1994; Richardson and Fan, 2002). However, neither of these studies directly compared the outcome of the two types of training. Indeed, to the best of our knowledge the first study to do so was Andreatta et al. (2012). Their data confirmed that after unpaired training startle is attenuated in the presence of A, whereas following a truly random procedure this is not the case. Critically, the Startle Difference Scores are lower after unpaired training than after the truly random procedure. As these data thus provided the first and, to our knowledge, so far the only direct evidence for unpaired-memory in rats, we here include a hitherto unpublished replication of the experiment in question, with slightly modified parameters (**Figure 5**) (see also the "Materials and Methods" section in the **Supplementary Presentation S1**). Rats were submitted to 15 presentations of a light stimulus A (5 s duration) and foot-shock punishment (0.5 s duration, 0.4 mA) with intertrial-intervals ranging between 90 and 150 s. Different groups of rats underwent one of three training conditions: (1) for one group stimulus A preceded the shock (Paired group); (2) one group received unpaired presentations of A and shock, such that the inter-stimulusinterval was never shorter than 12 s (Unpaired group); and (3) one group underwent the truly random procedure (Random group, i.e., baseline). Confirming Andreatta et al. (2012), startle was potentiated by the presence of A in the Paired group, attenuated in the Unpaired group, and unaffected in the Random group. Critically, the Startle Difference Scores were more negative in the Unpaired than in the Random group.

To summarize, from experiments using startle modulation as a bivalent behavioral read-out and the truly random procedure to determine baseline behavior, we conclude that paired and unpaired training establish oppositely valenced associative memories in rats.

## CONCLUDING DISCUSSION AND OUTLOOK

The evidence presented from larval and adult Drosophila, honeybees, and rats confirms a general principle of classical experimental psychology: that animals learn through both paired and unpaired presentations of a stimulus A with reinforcement, and that the resulting associative memories are opposite in valence. This warns against using the unpairing of A with a reward or punishment as a control for the effects of associative learning. Indeed, unpaired presentations of A and reinforcement are not a safe procedure in controlling for associative learning effects – because such a procedure can in itself establish associative memory for A as a signal that a reward or punishment will not occur.

Importantly, as we show here, this applies not only to 'absolute,' non-differential conditioning, but to differential conditioning as well: when stimulus A is presented paired with reinforcement and, in the same experimental subjects, another stimulus B is presented unpaired from reinforcement, larval Drosophila associatively learn about both stimuli – with opposite valence. Arguably, the behavior after any differential conditioning experiment might thus be a result of either or both of two types of learning process that need to be disentangled in order to fully understand the results.

Despite being established knowledge in classical psychology, the principle of opposite memories through paired and unpaired training is often neglected in neuroscience and genetics. As a consequence, the underlying mechanisms, be it on the circuit and neuronal level or the genetic and molecular level, are largely unknown. We have here presented two behavioral approaches to studying unpaired learning in two different model organisms. These approaches can now be adapted in order to unravel its underlying the mechanisms underlying unpaired learning and memory. Research in insects can play a crucial role in this endeavor. The Drosophila larva in particular has demonstrated its potential for in-depth analyses of the genetic

and neuronal mechanisms of reinforcement learning (Schroll et al., 2006; Michels et al., 2011; Rohwedder et al., 2016; Widmann et al., 2016; Eichler et al., 2017; Saumweber et al., 2018).

A full appreciation of unpaired learning would prompt a re-evaluation of the conclusions drawn from experiments comparing the effects of paired training with unpaired-control conditions, whether in differential or in non-differential 'absolute' conditioning paradigms. Although these approaches are useful to describe the outcome of associative learning in general, they cannot disentangle the effects of paired and unpaired training. If knocking-down a gene or neuronal population is found to reduce memory scores in such a task, it remains uncertain whether this gene or neuronal population is important for paired memory, or unpaired memory, or both. Likewise, if different physiological responses are elicited by a paired-trained and an unpairedtrained stimulus, it remains to be established whether effects of paired training, of unpaired training, or both are responsible for the difference. In any event, future research across species will be required to reveal whether unpaired learning as a behavioral principle is based on common mechanistic principles. If this were found to be the case, such research might help us to understand how our own behavior comes about.

#### ETHICS STATEMENT

All experiments were carried out in accordance with international guidelines for the use of animals in experiments (2010/63/EU). All experiments with rats were approved by the local ethical committee (Landesverwaltungsamt Sachsen-Anhalt, Az. 42502- 2-1309 UniMD).

#### DATA AVAILABILITY

The data for all presented behavioral experiments can be found in the **Supplementary Data Sheet S1**.

#### REFERENCES

#### AUTHOR CONTRIBUTIONS

MS, MF, and BG wrote the manuscript. SS and MS performed and analyzed the experiments displayed in **Figure 4**.

#### FUNDING

This study received institutional support from the Otto-von-Guericke-Universität Magdeburg, the Wissenschaftsgemeinschaft Gottfried Wilhelm Leibniz (WGL), the Leibniz Institute for Neurobiology (LIN), as well as grant support from the Deutsche Forschungsgemeinschaft (DFG) (GE 1091/4-1, to BG, and CRC 779 Neurobiology of motivated behavior, to BG and MF), and the European Commission grant MINIMAL (FP7 – 618045, to BG).

#### ACKNOWLEDGMENTS

Discussions with the members of our groups, as well as A. Khalili, C. König, and A. Yarali, are gratefully acknowledged, as is the continued constructive skepticism toward our conclusions by R. Menzel (Berlin). We appreciate the technical assistance of E. Kahl in performing the experiment displayed in **Figure 5**. We thank R. D. V. Glasgow (Zaragoza, Spain) for language editing.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2018.01494/full#supplementary-material

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Schleyer, Fendt, Schuller and Gerber. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Bimodal Patterning Discrimination in Harnessed Honey Bees

#### Breno E. Mansur, Jean R. V. Rodrigues and Theo Mota\*

Department of Physiology and Biophysics, Federal University of Minas Gerais, Belo Horizonte, Brazil

In natural environments, stimuli and events learned by animals usually occur in a combination of more than one sensory modality. An important problem in experimental psychology has been thus to understand how organisms learn about multimodal compounds and how they discriminate this compounds from their unimodal constituents. Here we tested the ability of honey bees to learn bimodal patterning discriminations in which a visual-olfactory compound (AB) should be differentiated from its visual (A) and olfactory (B) elements. We found that harnessed bees trained in classical conditioning of the proboscis extension reflex (PER) are able to solve bimodal positive and negative patterning (NP) tasks. In positive patterning (PP), bees learned to respond significantly more to a bimodal reinforced compound (AB+) than to nonreinforced presentations of single visual (A−) or olfactory (B−) elements. In NP, bees learned to suppress their responses to a non-reinforced compound (AB−) and increase their responses to reinforced presentations of visual (A+) or olfactory (B+) elements alone. We compared the effect of two different inter-trial intervals (ITI) in our conditioning approaches. Whereas an ITI of 8 min allowed solving both PP and NP, only PP could be solved with a shorter ITI of 3 min. In all successful cases of bimodal PP and NP, bees were still able to discriminate between reinforced and non-reinforced stimuli in memory tests performed one hour after conditioning. The analysis of individual performances in PP and NP revealed that different learning strategies emerged in distinct individuals. Both in PP and NP, high levels of generalization were found between elements and compound at the individual level, suggesting a similar difficulty for bees to solve these bimodal patterning tasks. We discuss our results in light of elemental and configural learning theories that may support the strategies adopted by honey bees to solve bimodal PP or NP discriminations.

Keywords: classical conditioning, bimodal learning, negative patterning, positive patterning, inter-trial interval, insect, honey bee

#### INTRODUCTION

Living in a complex world demands learning and memory of relationships between diverse stimuli in the environment. Animals can learn to associate an originally neutral stimulus (conditioned stimulus, CS) with a meaningful stimulus (unconditioned stimulus, US), an elemental association that constitutes the basis of classical conditioning (Pavlov, 1927). However, natural environments are composed by multimodal stimuli and animals usually associate these compounds with an US, rather than single unimodal elements (Lorenz, 1951). For instance, honey bees Apis mellifera use

#### Edited by:

Lars Chittka, Queen Mary University of London, United Kingdom

#### Reviewed by:

Fei Peng, Southern Medical University, China Mathieu Lihoreau, Centre National de la Recherche Scientifique (CNRS), France Nina Deisig, UMR7618 Institut d'Écologie et des Sciences de l'Environnement de Paris (IEES), France

> \*Correspondence: Theo Mota<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Comparative Psychology, a section of the journal Frontiers in Psychology

Received: 31 March 2018 Accepted: 02 August 2018 Published: 24 August 2018

#### Citation:

Mansur BE, Rodrigues JRV and Mota T (2018) Bimodal Patterning Discrimination in Harnessed Honey Bees. Front. Psychol. 9:1529. doi: 10.3389/fpsyg.2018.01529

their learning capacity to exploit food sources in flowers displaying multimodal signals like colors, shapes and odors (Menzel and Mercer, 2012). Although several studies have shown that honey bees can learn to associate a single odor or color with sucrose reward (Sandoz, 2011; Menzel and Mercer, 2012), little is known about how they combine or filter relevant stimuli of distinct sensory modalities during multimodal learning tasks (Leonard and Masek, 2014).

Multimodal appetitive learning has been mostly studied using operant conditioning of free-flying bees, but results obtained so far gave rise to different conclusions. On the one hand, several studies indicated a synergistic effect between color and odor within a bimodal compound, so that combined colorodor cues led to better learning and memory compared with unimodal cues (Kunze and Gumbert, 2001; Reinhard et al., 2004, 2006; Kulahci et al., 2008). On the other hand, other studies reported inhibitory effects within a color-odor compound, so that odors tend to overshadow colors based on differences in salience (Couvillon and Bitterman, 1982, 1988, 1989; Couvillon et al., 1983; Funayama et al., 1995; Greggers and Mauelshagen, 1997). An important limitation of studies on multimodal learning in free-flying bees could be the reason of these contradictory results: differences in temporal characteristics of the two stimuli. When bees approach a color-odor cued feeder or Y-maze, color may act as a far-distance signal, and odor as a close-up signal. It is thus difficult to interpret bees' performance, given that sequential rather than simultaneous stimulus processing may occur during the approach to the target (Mota et al., 2011). These two scenarios, sequential versus simultaneous stimulus processing, may determine dramatic differences in performance, such as those supporting synergistic versus inhibitory withincompound processing.

Classical conditioning of the proboscis extension reflex (PER) in harnessed bees represents a promising alternative to study bimodal appetitive learning with a precise control of stimuli timing and duration (Gerber and Smith, 1998; Mota et al., 2011; Hussaini and Menzel, 2013). Nevertheless, such experiments are so far rare, probably because of the difficulty of training harnessed bees with visual cues (Avarguès-Weber and Mota, 2016). Whereas PER conditioning has been used for more than 50 years to study olfactory learning and memory in bees (Sandoz, 2011), only in the last two decades successful visual-PER conditioning has been achieved (Avarguès-Weber and Mota, 2016; Vieira et al., 2018). In the present work, we took advantage of this classical conditioning protocol to study bimodal patterning learning in harnessed honey bees.

Solving of patterning discriminations is considered a higherorder form of associative learning, because it involves nonlinearity and intrinsic stimulus ambiguity (Rudy and Sutherland, 1995; Giurfa, 2003). In positive patterning (PP), animals have to differentiate a reinforced compound stimulus AB+ from its nonreinforced single elements A− and B−. In negative patterning (NP), single elements A+ and B+ are reinforced whereas the compound AB− is non-reinforced (Pavlov, 1927). The nonlinearity of these patterning tasks resides in the fact that the contingency of the compound AB cannot be predicted by the simple linear summation of the contingencies of the single elements (A and B). Under these conditions, associative learning also implicates relational dependencies, as the contingencies of a given stimulus (e.g., A) vary as a function of its occurrence alone or in combination with other stimuli (stimulus ambiguity). Therefore, these patterning tasks tend to require configural learning, i.e., the ability to treat the compound stimulus as different from the simple sum of its elements (Giurfa, 2003). Whereas NP could only be solved through configural learning, PP may also be accomplished through elemental learning. According to the elemental summation principle, the associative strength of each single element in a PP task could be subthreshold for the response, but the threshold could be exceeded when both elements are combined in a compound. In NP, however, the sum of the excitatory strengths of the elements in a compound will always be higher than the strength of each single element (Deisig et al., 2001; Pearce and Bouton, 2001).

The honey bee is the only insect model that, as mammals, was shown to have the ability of solving both PP and NP tasks (Devaud et al., 2015). Studies in flies and bumble bees found that these insects can solve PP, but not NP tasks, thus suggesting their inability to accomplish configural learning problems (Young et al., 2011; Sommerlandt et al., 2014). Learning of PP and NP by honey bees has been traditionally studied using olfactory conditioning of the PER (Chandra and Smith, 1998; Deisig et al., 2001, 2002, 2007; Komischke et al., 2003; Devaud et al., 2015). The capacity to solve PP and NP was also demonstrated in free-flying honeybees trained to visual stimuli in an operant framework (Schubert et al., 2002). No study has so far analyzed in a well-controlled way the capacity of insects to solve patterning discriminations using stimuli of distinct sensory modalities, as traditionally performed in rats and rabbits (Whitlow and Wagner, 1972; Bellingham et al., 1985; Kehoe and Graham, 1988). To our knowledge, the only attempt of studying bimodal patterning learning in an insect model was made by Couvillon and Bitterman (1988). Nevertheless, these authors trained freeflying honey bees using visual and olfactory stimuli that presented distinct detection ranges and were thus perceived in a sequential way by bees (Deisig et al., 2001; Mota et al., 2011). Here, we fill this gap by training honey bees to bimodal PP and NP using equivalent duration of all stimuli, as well as simultaneous (not sequential) presentation of visual (A) and olfactory (B) elements in a compound (AB).

Previous studies showed that the temporal separation between stimuli trials (intertrial interval − ITI) clearly affects the learning performance of honey bees in olfactory patterning discrimination. Both in PP and NP tasks, increasing the ITI between conditioned trials led to better differentiation between single olfactory elements and their compound mixture (Deisig et al., 2007). So in the present study, we compare the performance of honeybees in bimodal PP and NP tasks using a shorter or a longer ITI. We also analyze memory retention to each unimodal element and the bimodal compound one hour after conditioning. Furthermore, we evaluate differences in the individual learning and memory performances of bees during these bimodal patterning tasks. Our work represents an important step to uncover the cognitive and neurobiological basis of bimodal patterning discriminations in insects.

#### MATERIALS AND METHODS

fpsyg-09-01529 August 23, 2018 Time: 9:4 # 3

#### Animals

Foragers of honeybee A. mellifera were collected from a feeder containing 30% (v/v) sugar solution 50 meters from six outdoor hives kept in the Ecological Station of the Federal University of Minas Gerais (UFMG, Brazil). All experiments were conducted in the Brazilian spring/summer season (from September to March). Bees were placed in small glass vials, cooled on ice until they ceased their movements and then harnessed in plastic tubes using thin pieces of soft masking tape. The wings were protected by a piece of filter paper. Each bee was fed 1 µl of 30% (v/v) sugar solution after fixation and then kept for one hour in a dark chamber with high humidity.

#### Conditioned and Unconditioned Stimuli

Visual CS (A) consisted of an illuminated 20 × 20 cm screen covered with a chromatic transmission filter (LF124S Dark Green: peak at 535 nm or LF119S Dark Blue: peak at 455 nm; LEE Filters) and tracing paper for light dispersion. A white-LED light source (E27-5W Cool White; Epistar) connected to a linear potentiometer provided illumination with controlled intensity behind the colored screen. Taking into account the spectral sensitivities of the honeybee photoreceptors (Peitsch et al., 1992), the green stimulus excited 0, 15, and 85% of the short- (S), medium- (M), and large-range (L) wavelength photoreceptors, respectively. For the blue stimulus, these values were 2, 68, and 30%, respectively. The large-field colored screen was placed at a distance of 10 cm from the bee right eye, so that it subtended a visual angle of 90◦ . The irradiance of blue or green stimulus was adjusted to 0,4 µW cm−<sup>2</sup> at the level of the bee eye by using a spectrophotometer (USB2000 + UV-VIS-ES, Ocean Optics) radiometrically calibrated using a deuterium/tungsten light source (DH-2000-BAL, 220−1050 nm, Ocean Optics). Absolute irradiance was measured using an optical fiber (QP600-2-UV-VIS, Ocean Optics) coupled to a cosine corrector with Spectralon diffusing material (CC-3-UV-S, Ocean Optics).

Olfactory CS (B) was 2-hexanol or 1-nonanol (Sigma-Aldrich, Brazil). Five microliters of pure odorant were applied onto a 1 cm<sup>2</sup> stripe of filter paper placed into a 30 mL syringe, which allowed frontal odorant delivery to the antennae. An air extractor placed behind the bee prevented odorant accumulation.

The US was 1 µL of 30% (w/w) sugar solution delivered to the bee by means of a micropipette.

The reason we presented a lateral screen stimulating a single eye instead of a frontal one stimulating both eyes was the fact that both the syringe used to deliver the odor and the micropipette used to deliver sugar solution were already presented in a frontal position. When odor, color and reward overlapped during patterning conditioning, the syringe and the micropipette produced large shades on the visual screen. These shades may be used by the bees as conditioned or secondary stimulus. We thus decided to present visual stimulation only to the right eye, because a previous study on visual conditioning of the PER indicates that honey bees learn better in this framework using the right than the left eye (Letzkus et al., 2008). After this work, other authors confirmed that lateral stimulation of the right eye is an efficient method for training harnessed bees to visual stimuli (e.g., Niggebrügge et al., 2009; Vieira et al., 2018).

# Experimental Setup and Conditioning Procedure

All experiments were performed in a dark room illuminated by a low intensity red-light source (peak at 660 nm). During conditioning, the plastic tube holding the bee was tilted to 45◦ and fixed in a platform of 9 cm high (Vieira et al., 2018). In this position, the right eye of the bee was at a distance of 10 cm from the center of the visual stimulation screen. In PP experiments, presentation of visual or olfactory stimulus alone was not rewarded whereas their simultaneous presentation (compound stimulus) was rewarded (A−, B−, and AB+). In NP experiments, individual presentations of the visual or olfactory stimulus were rewarded whereas the compound bimodal stimulus was not (A+, B+, and AB−). Both in PP and NP, training consisted of 10 trials of each stimulus (A, B, and AB), thus totalizing 30 trials presented in a pseudorandom sequence starting with A, B or AB in a balanced way. At most, two trials of a same stimulus followed each other during conditioning.

At the beginning of each rewarded trial the bee was placed in the conditioning setup for 30 s to allow familiarization with the experimental context. Thereafter, CS+ (A, B or AB) was presented for 7 s. Four seconds after the onset of the CS+, the US was delivered to the bee for 3 s. Therefore, the interstimulus interval (ISI) was 4 s and the overlap between CS and US was 3 s. The bee was removed from the setup 23 s after reward, thus completing a total of 60 s per trial. Unrewarded trials followed the same time sequence, but stimulation was not paired with reward. To analyze the effect of inter-trial interval (ITI) on bimodal PP and NP, we trained two independent groups in each of these paradigms with an ITI of 3 and 8 min, respectively. Training with 3 min ITI was performed using green 535 nm as visual stimulus and 2-hexanol as olfactory stimulus (N = 45 bees, both for PP and NP). Training with 8 min ITI was performed using this same pair of stimuli (green 535 nm and 2-hexanol) or an alternative pair consisted of blue 455 nm and 1-nonanol (N = 45 bees/pair of stimuli, both for PP, and NP). One hour after the end of conditioning, all experimental groups were submitted to retention tests consisted of an unrewarded presentation of each stimulus (A, B, or AB) with an ITI equivalent to that used during training (3 min or 8 min ITI). The order of presentation of the three stimuli during retention tests was randomized between subjects in all experimental groups.

The beginning and the end of each trial, as well as the onset and offset of CS and US were signaled by a computer programmed to emit tones of different frequencies for each event. The occurrence of proboscis extension was recorded within the first 4 s of CS presentation (conditioned response), as well as during the US presentation. Animals that did not show PER for more than 3 times during the US presentation (<5%) were excluded from our analysis, as they may present impairment of muscular reflex and/or sucrose responsiveness.

#### Statistical Analysis

fpsyg-09-01529 August 23, 2018 Time: 9:4 # 4

Two-way analysis of variance in generalized linear model (GLM) for repeated measures was used to analyze within (stimulus × trial effect) and between group (group × stimulus × trial effect) performances in PP and NP conditioning. Further Tukey's multiple comparisons were used to analyze differences between: (i) responses to each stimulus; (ii) performances in different trials of conditioning. One-way GLM for repeated measures followed by Tukey's multiple comparisons was used to compare responses to each stimulus in retention tests. The alpha level was set to 0.05 (two tailed) for all analyses. All statistical analyses were conducted using the software IBM SPSS Statistics 21.0.

#### RESULTS

## Bimodal Patterning Discrimination With 3 min ITI

Honeybees trained to discriminate a visual element (A−) and an olfactory element (B−) from its bimodal compound (AB+) in a PP protocol with an ITI of 3 min were successful in learning the task. **Figure 1A** shows the percentage of PER along 10 trials of each stimulus and reveals significant differences between the learning curves of A−, B−, and AB+ (stimulus × trial GLM for repeated measures; stimulus effect: F2,<sup>88</sup> = 19.6, p < 0.001; interaction: F18,<sup>792</sup> = 5.4, p < 0.001). During the first five trials, bees showed equivalent levels of increasing response to the olfactory element (B−) and the compound (AB+) whereas responses to the visual element (A−) remained much lower. After the fifth trial, however, responses to B− started to decrease whereas responses to AB+ kept increasing until the end of conditioning (**Figure 1A**). Although global performances significantly differed between all stimuli (Tukey test; stimulus effect; A− vs. B− and A− vs. AB+: p < 0.001; B− vs. AB+: p < 0.05), no differences were found in responses to A− and B− at the last trial of conditioning (Tukey test; stimulus × trial 10 effect; A− vs. B−: NS). Moreover, bees responded significantly more in the last trial to the compound AB+ than to the elements A− and B− (Tukey test; stimulus × trial 10 effect; A− vs. AB+ and B− vs. AB+: p < 0.001), thus confirming successful PP solving. In retention tests performed one hour after conditioning (**Figure 1A**), bees were again able to discriminate each unimodal element from the bimodal compound (GLM for repeated measures; F2,<sup>88</sup> = 20.2, p < 0.001; Tukey test; A− vs. B−: NS; A− vs. AB+ and B− vs. AB+: p < 0.001).

While honeybees were successful in learning a bimodal PP task with an ITI of 3 min (**Figure 1A**), this was not the case for a NP task with the same ITI (**Figure 1B**). In this patterning approach, bees showed increasing PER to all three stimuli along trials, with equivalent levels of response to the olfactory element B+ and the bimodal compound AB− (stimulus × trial GLM for repeated measures; stimulus effect: F2,<sup>88</sup> = 25.9, p < 0.001; interaction: F18,<sup>792</sup> = 1.8, p < 0.05; Tukey test; stimulus effect; A+ vs. B+ and A+ vs. AB−: p < 0.01; B+ vs. AB−: NS). The comparisons between the responses to each stimulus at the last conditioning trial also show that bees were unable to differentiate B+ from AB− in this NP task (Tukey test; stimulus × trial 10 effect; A+ vs. AB+ and A+ vs. AB− : p < 0.05; B+ vs. AB−: NS). Retention tests performed one hour after conditioning (**Figure 1B**) also confirm an absence of discrimination between the olfactory element and the bimodal compound (GLM for repeated measures; F2,<sup>88</sup> = 11.0, p < 0.001; Tukey test; A+ vs. B+ and A+ vs. AB−: p < 0.05; B+ vs. AB−: NS).

#### Bimodal Patterning Discrimination With 8 min ITI

**Figure 2A** shows the performance of bees trained to a PP task using the same visual (green 535 nm) and olfactory stimuli (2-hexanol) as in **Figure 1A**, but with a longer ITI of 8 min. As previously observed (**Figure 1A**), bees started the task with similar increasing response levels to B− and AB+, but after the fifth trial they begun to discriminate these stimuli (stimulus × trial GLM for repeated measures; stimulus effect: F2,<sup>88</sup> = 10.8, p < 0.001; interaction: F18,<sup>792</sup> = 3.6, p < 0.001; Tukey test; stimulus effect: A− vs. B− and A− vs. AB+: p < 0.001; B− vs. AB+: p < 0.01). Responses at the last conditioning trial significantly differed between each unimodal element and the bimodal compound, but not between the visual and olfactory elements (Tukey test; stimulus × trial 10 effect; A− vs. B−: NS; A− vs. AB+ and B− vs. AB+: p < 0.001). Together with these results, responses of bees during retention tests (**Figure 2A**) confirmed successful bimodal PP solving (GLM for repeated measures; F2,<sup>88</sup> = 20.6, p < 0.001; Tukey test; A− vs. B−: NS; A− vs. AB+ and B− vs. AB+: p < 0.001).

We simultaneously trained another group of bees to the same PP task with 8 min ITI, but using alternative visual (blue 455 nm) and olfactory stimuli (1-nonanol). The performance of bees in this experimental group (**Figure 2B**) was very similar to the one of bees trained using green 535 nm and 2-hexanol as conditioned stimuli (**Figure 2A**). They were able to discriminate each unrewarded unimodal element from the rewarded bimodal compound both during conditioning (stimulus × trial GLM for repeated measures; stimulus effect: F2,<sup>88</sup> = 17.5, p < 0.001; interaction: F18,<sup>792</sup> = 5.5, p < 0.001; Tukey test; stimulus × trial 10 effect; A− vs. B−: NS; A− vs. AB+ and B− vs. AB+: p < 0.001) and retention tests (GLM for repeated measures; F2,<sup>88</sup> = 19.4, p < 0.001; Tukey test; A− vs. B−: NS; A− vs. AB+ and B− vs. AB+: p < 0.001). Since we found no statistical differences between these two experimental groups (**Figures 2A,B**) trained using distinct pairs of stimuli (group × stimulus × trial GLM for repeated measures; group effect: F1,<sup>88</sup> = 1.3, NS), we pooled results from each of them in a single graphic (**Figure 2C**). As expected, all statistical effects in this pooled group (**Figure 2C**) were equivalent to those described for experimental groups presented in **Figures 1A,B** both during conditioning (stimulus x trial interaction: F18,<sup>1584</sup> = 6.7, p < 0.001; Tukey test; stimulus × trial 10 effect; A− vs. B−: NS; A− vs. AB+. and B− vs. AB+: p < 0.001) and retention tests (GLM for repeated measures;

discrimination tasks with an ITI of 3 min. Conditioning consisted of 10 trials of each stimulus presented in a pseudorandom sequence (left). One hour after conditioning, unrewarded retention tests were performed for each stimulus (right). (A) Positive patterning (PP; N = 45). (B) Negative patterning (NP; N = 45). Asterisks indicate significant differences in GLM followed by Tukey test comparing responses to each stimulus in the last conditioning trial. Different lowercase letters (x,y) indicate significant differences in GLM followed by Tukey test comparing responses to each stimulus during retention tests.

F2,<sup>178</sup> = 36.3, p < 0.001; Tukey test; A− vs. B−: NS; A− vs. AB+ and B− vs. AB+: p < 0.001).

**Figure 3** shows the performances of honeybees trained in a bimodal NP task with 8 min ITI using green 535 nm and 2 hexanol (group A; **Figure 3A**) or blue and 1-nonanol (group B; **Figure 3B**) as pair of stimuli. Different from bees trained in a NP task with 3 min ITI (**Figure 1B**), we found that the larger ITI of 8 min allowed honeybees to solve a bimodal NP task. In both experimental groups (**Figures 3A,B**), bees started the task by increasing their responses to all three stimuli, but begun to decrease their levels of response to the unrewarded bimodal compound after the fourth or fifth trial. At the end of conditioning, both groups were able to discriminate each unimodal element from its bimodal compound (stimulus × trial GLM for repeated measures; stimulus effect; group A: F2,<sup>88</sup> = 11.8, p < 0.001; group B: F2,<sup>88</sup> = 16.7, p < 0.001; interaction; group A: F18,<sup>792</sup> = 5.9, p < 0.001; group B: F18,<sup>792</sup> = 6.7, p < 0.001; Tukey test; stimulus × trial 10 effect; both groups: A+ vs. AB−: p < 0.05; B+ vs. AB−: p < 0.001). Different from bees trained to bimodal PP tasks (**Figures 1A**, **2**), levels of response significantly differed at the end of conditioning between the visual and the olfactory elements (**Figures 3A,B**; Tukey test; stimulus × trial 10 effect; both groups: A+ vs. B+: p < 0.05). In retention tests performed one hour after conditioning (**Figures 3A,B**), levels of response were significantly different between all three stimuli (GLM for repeated measures; group A: F2,<sup>88</sup> = 17.1, p < 0.001; group B: F2,<sup>88</sup> = 23.2, p < 0.001; Tukey test; both groups: A+ vs. B+: p < 0.05; A+ vs. AB−: p < 0.05; B+ vs. AB−: p < 0.001).

We found no statistical differences between performances of these two experimental groups shown in **Figures 3A,B**

(group × stimulus × trial GLM for repeated measures; group effect: F1,<sup>88</sup> = 1.1, NS), thus we pooled their results in a single graphic (**Figure 3C**). Statistical effects in this pooled group (**Figure 3C**) were equivalent to those described for experimental groups presented in **Figures 3A,B** both during acquisition (stimulus × trial interaction: F18,<sup>1584</sup> = 11.2, p < 0.001; Tukey test; stimulus × trial 10 effect; A+ vs. B+ and A+ vs. AB−: p < 0.01; B+ vs. AB−: p < 0.001) and retention tests (GLM for repeated measures; F2,<sup>178</sup> = 37.0, p < 0.001; Tukey test; A+ vs. B+ and A+ vs. AB−: p < 0.01; B+ vs. AB−: p < 0.001). In conclusion, while an ITI of 8 min allowed solving both PP (**Figure 2**) and NP (**Figure 3**), only PP could be solved with a shorter ITI of 3 min (**Figure 1**).

# Distinct Learning Categories in Bimodal Patterning Solving

Considering that responses to element and compound stimuli during retention tests reflected very well the level of discrimination reached in bimodal PP (**Figure 2**) or NP (**Figure 3**), we analyzed these responses at the individual level to classify bees into distinct learning categories. In retention tests, bees could respond or not only once to each of the three stimuli (A, B, and AB), thus eight different combinations of response may emerge (000, 111, 100, 010, 001, 011, 110, 101; to A, B, and AB, respectively). Successful learners of a PP task should not respond to the unrewarded A and B elements, and respond to the rewarded compound AB (001). Successful learners of NP should respond to the rewarded elements A and B, and not respond to the unrewarded compound AB (110). We thus asked: what is the proportion of bees presenting successful performances in retention tests after bimodal PP and NP? Which are the other categories of response emerging during these tasks? How do the learning curves of bees in these different categories look like? To answer these questions we analyzed the individual response of 90 bees trained to bimodal PP (**Figure 2C**) or NP (**Figure 3C**) with an ITI of 8 min.

**Figure 4** shows the three major learning categories emerging in bees trained to bimodal PP, classified according to their responses in retention tests. From 90 bees (**Figure 2C**), only 24 (27%) presented exactly correct responses (001) in retention tests (**Figure 4A**). Surprisingly, almost half of the bees (n = 42; 47%) responded equally to all three stimuli (000 or 111), thus presenting a generalist strategy toward unimodal elements and compound stimuli (**Figure 4B**). The third major category of response (n = 21; 23%) emerging during bimodal PP solving consisted of individuals not responding to the unrewarded visual element A, but responding to the unrewarded olfactory element B and the compound stimulus AB (011; **Figure 4C**). Only three bees (3%) could not be classified in one of these three learning categories (**Figure 4D**). Two of them responded only to the olfactory element B (010), whereas one bee responded to A and AB, but not to B (101).

Bees classified as good PP learners (**Figure 4A**) clearly solved the task, but as observed in the overall performance of bees trained to PP (**Figure 2C**), they presented increasing responses to the unrewarded odor (B) at the beginning and around the fifth trial started to suppress these responses. At the end of conditioning, these bees noticeably discriminate between each unrewarded unimodal element and the rewarded bimodal compound (stimulus × trial GLM for repeated measures; interaction: F18,<sup>414</sup> = 9.3, p < 0.001; Tukey test; stimulus × trial 10 effect; A− vs. B−: NS; A− vs. AB+, and B− vs. AB+: p < 0.001). On the other hand, bees presenting generalist responses (**Figure 4B**) were completely unable to discriminate between any of the stimuli during bimodal PP (stimulus × trial GLM for repeated measures; interaction: F18,<sup>738</sup> = 1.4, NS). The last learning category observed in bimodal PP (**Figure 4C**) was composed by bees that discriminate between the visual element A and the other stimuli, but could not differentiate the olfactory element B from the bimodal compound AB (stimulus × trial GLM for repeated measures; interaction: F18,<sup>342</sup> = 2.6, p < 0.001; Tukey test; stimulus × trial 10 effect; A− vs. B− and A− vs. AB+: NS; B− vs. AB+: NS).

All 90 bees trained to bimodal NP (**Figure 3C**) could be classified into one of the following four learning categories according to their responses in retention tests: good NP learners (110; **Figure 5A**); generalists (000 or 111; **Figure 5B**); responding only to B (010; **Figure 5C**); responding to B and AB (011, **Figure 5D**). Bees classified as good NP learners (**Figure 5E**; N = 22; 25%) were clearly able to discriminate between each rewarded unimodal element and the unrewarded bimodal compound (**Figure 5A**; stimulus × trial GLM for repeated measures; interaction: F18,<sup>378</sup> = 7.0, p < 0.001; Tukey test; stimulus × trial 10 effect; A+ vs. B+: NS; A+ vs. AB−, and B+ vs. AB−: p < 0.001). As well as in bimodal PP (**Figure 4B**), a large amount of bees confronted to a bimodal NP task (**Figure 5E**; N = 40, 44%) developed a generalist strategy and were totally unable to discriminate between the three stimuli (**Figure 5B**; stimulus × trial GLM for repeated measures; interaction: F18,<sup>702</sup> = 1.5, NS).

The third most representative learning category observed in bimodal NP (**Figure 5E**; N = 18; 20%) was composed by bees that discriminate between the olfactory element B and the other stimuli, but could not differentiate the visual element A from the bimodal compound AB (**Figure 5C**; stimulus × trial GLM for repeated measures; interaction: F18,<sup>306</sup> = 6.7, p < 0.001; Tukey test; stimulus × trial 10 effect; A+ vs. B+ and B+ vs. AB−: p < 0.001; A+ vs. AB−: NS). Finally, the fourth learning category emerging in bimodal NP (**Figure 5E**; N = 10; 11%) was composed by bees that presented low response levels to the rewarded visual element A, but developed increasing responses to the rewarded element B and the unrewarded compound AB (**Figure 5D**; stimulus × trial GLM for repeated measures; interaction: F18,<sup>162</sup> = 1.9, p < 0.05; Tukey test; stimulus × trial 10 effect; A+ vs. B+ and A+ vs. AB−: p < 0.001; B+ vs. AB−: NS).

# DISCUSSION

Our study shows that honey bees are able to solve bimodal PP and NP in a classical PER conditioning protocol using a colored-light screen as visual stimulus and a pure synthetic odor as olfactory stimulus. The two pairs of visual-olfactory

stimuli used in our experiments (green 535 nm/2-hexanol or blue 455 nm/1-nonanol) induced equivalent levels of bimodal patterning discrimination, both in PP and NP. While an ITI of 8 min allowed solving both PP and NP tasks, only PP could be solved with a shorter ITI of 3 min. This result agrees with previous experiments on olfactory patterning discrimination that found better performances using longer trial-spacing (Deisig et al., 2007). More precisely, these authors found that honeybees trained in olfactory PER conditioning were unable to solve PP or NP with ITIs of 1 min or 3 min, whilst an ITI of 5 min allowed solving only PP. As well as in our bimodal patterning approach, an ITI of 8 min favored solving of both olfactory PP and NP (Deisig et al., 2007). Many reasons can account for the fact that an ITI of 3 min allowed bimodal PP solving in our work, but not olfactory PP solving in that previous study: nature of the stimuli (visual-olfactory vs. only olfactory); number of trials (10 per stimulus vs. 4 per element and 8 for the compound); duration of CS presentation (7 s vs. 6 s); experimental context (dark room and 45% body inclination vs. illuminated room and vertical body position), among others. Altogether, results on trial-spacing effect in patterning solving by bees are in line with an extensive literature showing that animals often present better learning when CS trials are temporally more spaced (Gibbon et al., 1977; Barela, 1999; Sunsay et al., 2004).

Previous studies in olfactory patterning discrimination by bees suggest that a balanced proportion of reinforced and nonreinforced trials (1:1 CS+ /CS− rate) favors discrimination in those tasks (Deisig et al., 2001, 2007). In the new bimodal conditioning approach here developed, we had an important limitation to develop PP and NP with such a contingency balance: reasonable levels of visual learning by harnessed bees are only reached with a large amount of trials (Avarguès-Weber and Mota, 2016). If we performed 10 trials for each element and 20 trials for the bimodal compound (1:1 contingency rate) with an ITI of 8 min, our conditioning protocol together with the memory test would least more than six hours. Considering also the time for capturing and harnessing the bees, as well as the one hour resting period prior to conditioning, it was simply impossible for us to perform such an experiment. We actually tried to perform a protocol using five trials of each element and 10 trials of

the compound with an ITI of 8 min, but the levels of learning obtained for the visual element were very poor and there was no successful discrimination in PP or NP (data not shown).

The fact that only a bimodal PP task could be solved with a shorter ITI of 3 min, but not a NP task is in agreement with several studies observing that PP may be learned using a different strategy than NP (e.g. Rescorla, 1972; Bellingham et al., 1985; Kehoe, 1988; Deisig et al., 2007). Solving of NP tasks in rabbits requires longer CS and ITI duration than PP tasks (Kehoe and Graham, 1988; Kinder and Lachnit, 2002). In humans, longer processing time was found in response to the compound stimulus during NP when compared to PP (Lachnit et al., 2002).

Interestingly, olfactory NP solving in bees requires olfactory input from both the antennae, whereas PP can be solved with unilateral olfactory stimulation of a single antennae (Komischke et al., 2003). Together with our data, these results in different models support the assumption that NP is solved using a learning strategy that requires different resources than the ones employed to solve PP.

It has been suggested that PP solving admits an elemental learning strategy, whereas NP solving can exclusively rely in a configural learning (non-elemental) strategy (Pearce, 1994; Deisig et al., 2001; Giurfa, 2003; Devaud et al., 2015). In the present work, we analyzed the individual performances of 90 honey bees in bimodal PP or NP, and we found that different learning strategies emerged in both these paradigms (see section "Distinct learning categories in bimodal patterning solving"). The first surprising observation from this analysis was the high percentage of bees that presented strong generalization between all stimuli and were thus unable to solve the discrimination both in the PP and the NP approach (47 and 44% of bees, respectively). These results highlight the level of difficulty of these tasks and rule out the possibility that bees solve them using an extreme configural learning strategy (Williams and Braker, 1999). These extreme configural theories are different from Pearce's configural theory (Pearce, 1994), because they predict no generalization between a compound and its elements, since the compound would be treated as a totally new stimulus completely unrelated to its elements (Williams and Braker, 1999; Deisig et al., 2003).

Apart from bees that completely generalized between stimuli in PP or NP, we also found intermediate learning categories that presented generalization between one of the elements and the compound. In the case of PP, generalization between the olfactory element and the compound occurred in 23% of bees. Bees in this learning category seemed to reduce the complexity of the problem by treating it as an elemental differential conditioning task (A− vs. AB/B+). The olfactory element B and the compound AB appeared to be both treated as one rewarded odor. In the case of NP, generalization between one of the elements and the bimodal compound was found not only for the olfactory element B as in the bimodal PP task, but also for the visual element A. Curiously, a representative amount of individuals trained to bimodal NP (20%) generalized between the visual element A and the compound AB, as if they solved the task following a differential conditioning schedule (A/AB− vs. B+). Furthermore, a smaller amount of bees (11%) trained to NP, developed generalized responses between the olfactory stimulus B and the compound AB that were similar to the ones observed in some bees trained to PP (A− vs. AB/B+). In this case, however, bees were twice wrong in their responses, because they were supposed to respond to A+ and not respond to AB− in the NP task. Although different categories of generalization emerged in bimodal PP (to all stimuli; between odor and compound) and NP (to all stimuli; between odor and compound; between color and compound), the equivalent total amount of unsuccessful bees in these tasks (63 and 65%, respectively) suggests a similar difficulty for bees to solve them.

Although most of the bees trained to bimodal PP or NP task presented generalization and were unsuccessful in solving the task, we also identified a category of very efficient learners in both these approaches (27 and 25% of bees, respectively). Our results, therefore, reinforce the notion that averaged learning curves and memory retention scores obtained from a group of animals often hide a more elaborate range of learning dynamics that can only be observed at the individual level (Gallistel et al., 2004). Accordingly, several recent studies on learning and memory by bees emphasize the importance of better analyzing the dynamics of individual performances (e.g., Mota and Giurfa, 2010; Dobrin and Fahrbach, 2012; Pamir et al., 2014; Evans et al., 2017; Vieira et al., 2018). Similar to our results on bimodal patterning, only 30% of 111 bees trained to a multiple olfactory reversal task were able to accurately solve this non-elemental problem (Mota and Giurfa, 2010). An extensive evaluation of individual performances of 3298 bees in different elemental olfactory tasks also revealed that group-averaged learning analysis hid drastic inter-individual differences (Pamir et al., 2014). Altogether, these studies indicate that finding a single associative learning theory to explain the averaged performance of a population is tricky, and may not reflect the real complexity of CS and US representations at the individual level. Effective learners of bimodal PP may use elemental or configural strategies to solve this task, whereas good learners of bimodal NP could only use configural strategies. Finding which of those alternative accounts is actually used by bees remains so far a challenge, even for the more studied unimodal olfactory PP and NP in honey bees (Deisig et al., 2003, 2007; Devaud et al., 2015).

Although individual bees trained to elemental olfactory tasks differ in terms of the number of trials required to develop the first conditioned response (CR), as well as the stability of the CR along trials, high levels of success are usually reached at the end of conditioning (Pamir et al., 2014). Pamir and collaborators found that 54% of the responsive animals trained to elemental olfactory tasks already developed conditioned responses (CR) to the CS+ after a single trial of conditioning. By the third trial about 80% of those animals presented correct responses to the CS+. They also show a high average level of CR stability once the animal starts to respond. The average percentage of nonresponding animals in elemental olfactory tasks was only ∼20%. All in all, this analysis reveals high levels of individual success (∼80%) on the solving of elemental forms of associative learning by harnessed bees (Pamir et al., 2014). On the other hand, only 25 to 30% of harnessed bees were able to solve a non-elemental olfactory task (Mota and Giurfa, 2010) or a bimodal patterning discrimination (present study). These studies indicate that honey bees present much higher rates of success in solving elemental than non-elemental tasks in a classical conditioning framework. Moreover, the equivalent low levels of success obtained in bimodal PP and NP tasks in our study suggests that PP is probably solved by bees using an non-elemental (configural) learning strategy.

Previous studies in non-human mammals suggested that the use of different sensory modalities in patterning tasks may favor the emergence of elemental rather than configural strategies (e.g., Redhead and Pearce, 1995; Brandon et al., 2000; Myers et al., 2001). The relative salience of the elements is also an important feature that can influence the associations acquired in

patterning discriminations, particularly in NP tasks (Delamater, 2012). For instance, when stimuli with different saliences were used in NP tasks, discrimination was first learned between the unrewarded compound and the less salient element, as compared with the more salient element (Redhead and Pearce, 1995; Delamater et al., 1999). Studies on bimodal learning by honey bees have often suggested that odors are more salient cues than colors (e.g. Couvillon and Bitterman, 1989; Funayama et al., 1995; Greggers and Mauelshagen, 1997), especially in harnessed individuals (Gerber and Smith, 1998; Mota et al., 2011). Probably for that reason, learning levels acquired in visual PER conditioning are typically lower than those reported for olfactory PER conditioning (Avarguès-Weber and Mota, 2016), as also observed in the present study. The higher salience of odors over colors in our bimodal patterning approach may be responsible for the strong generalization observed between the olfactory element B and the compound AB, as well as the better levels of discrimination between the visual element A and the compound AB, in certain cases.

A recent study showed that the mushroom bodies (MBs) of the honey bee brain are necessary for solving both olfactory PP and NP (Devaud et al., 2015). Pharmacological inhibition of the MBs disrupted the capacity of bees to solve PP and NP, but not their ability to learn elemental olfactory discriminations. Therefore, apart from the well-known role of the MBs in memory storage and retrieval, theses insect brain structures seem to be implicated in the acquisition of ambiguous olfactory discrimination problems (Devaud et al., 2015). The necessity of the MBs for solving olfactory PP and NP, but not elemental olfactory discriminations, strongly suggests that olfactory PP is solved by bees using a non-elemental rather than an elemental summation strategy (Devaud et al., 2015). Little is known, however, about the role of the MBs on elemental and nonelemental visual or bimodal learning in bees. It might be that the simple necessity of integrating visual and olfactory information for bimodal patterning learning would require the integrative role of the MBs.

The MBs are indeed the main region of the honey bee brain where a convergence between visual and olfactory neural circuits was clearly identified (Mobbs, 1982; Ehmer and Gronenberg, 2002). Considering that a cross-modal interaction between

#### REFERENCES

olfactory and visual cues is necessary to solve bimodal PP and NP, the MBs appear as the most probable structures mediating these discriminations. A recent study in Drosophila found that visual and olfactory associative learning share dopaminergic neural circuits in the MBs, confirming that distinct sensory memories are processed in this common brain center (Vogt et al., 2014). Alternative regions for cross-talk between visual and olfactory circuits in the bee brain have also been suggested in the median, lateral, and posterior protocerebrum (Erber and Menzel, 1977; Maronde, 1991), but the role of these structures in learning and memory remains poorly understood. Future studies should combine the new bimodal PP and NP protocols here presented to pharmacological or neurophysiological techniques, in order to uncover the neural mechanisms underlying these cognitive phenomena.

## AUTHOR CONTRIBUTIONS

TM conceived the study and designed the methodology. BM, JR, and TM performed the experiments and analyzed the data. BM and TM wrote the first draft of the manuscript. All authors contributed to the final version of the manuscript.

# FUNDING

We thank the support of Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq: 457718/2014-5 to TM) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG: APQ-02013-13 to TM). JR received a scholarship from the Program Young Talent of Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/Science without Borders – Brazilian Government).

# ACKNOWLEDGMENTS

We are thankful to Ivan Gastelois and the Ecological Station of the Federal University of Minas Gerais for valuable assistance in beekeeping.

Young, J. M., Wessnitzer, J., Armstrong, J. D., and Webb, B. (2011). Elemental and non-elemental olfactory learning in Drosophila. Neurobiol. Learn. Mem. 96, 339–352. doi: 10.1016/j.nlm.2011.06.009

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Mansur, Rodrigues and Mota. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Does Cognition Have a Role in Plasticity of "Innate Behavior"? A Perspective From Drosophila

#### E. Axel Gorostiza\*

Departamento de Farmacología, Facultad de Ciencias Químicas, Instituto de Farmacología Experimental de Córdoba-CONICET, Universidad Nacional de Córdoba, Córdoba, Argentina

Keywords: innate behavior, insects, behavioral flexibility, Drosophila, stimulus-response, behavioral plasticity

# BEHAVIORAL PLASTICITY AND THE STIMULUS–RESPONSE MODEL

The term innate is commonly used to refer to behaviors inherited and not learned or derived from experience. This definition denies or ignores the inborn components of learning. An animal can only learn if it already has the components required for learning, e.g., the molecular and neuronal substrates. Moreover, all behaviors are, to some extent, susceptible to modification by experience. Hence, no behavior can be strictly learned or innate (Shettleworth, 2010), making this distinction and the terms scientifically inappropriate to some extent (Mameli and Bateson, 2006; Bateson and Mameli, 2007). However, given the absence of a better term, it is still possible to find some behaviors classified as innate behaviors in literature, and defined as "stereotypic patterns of movement inherited from birth that require no prior experience for proper execution" (Kim et al., 2015). Noticeably, the concept of stereotypy was arbitrarily included into the definition of innate behavior. This group includes behaviors as different as escape responses (movements performed by an animal to avoid a possible predator; Card, 2012), taxes (orienting movement of an organism directed in relation to a stimulus; Zupanc, 2010), and courtship. What these behaviors have in common is that they are dominated by innate components and preferences and seem to be stereotypic and automatic responses elicited by a defined stimulus (i.e., reflexes, senso Purves et al., 2004). They are considered sensory-motor routines driven by inborn responses to biologically relevant sensory cues. This is the base of the sensory-response model, wherein the brain only reacts to external stimuli and the behaviors are the responses (Dickinson, 1985). Although there is increasing evidence of an active role of the brain with the external stimuli exerting only a modulatory effect in humans (Raichle, 2010) and invertebrates (Gaudry and Kristan, 2009; Gordus et al., 2015), many innate behaviors in insects are still described using the sensory-response model. This interpretation led to some important aspects of innate behaviors being neglected or misinterpreted. Any behavioral researcher has experienced that these behavioral responses are far from constant among groups or single individuals from the same species, or even the same retested individual (Kain et al., 2012; Buchanan et al., 2015). Researchers work hard to control this behavioral variability by modifying their experiments. Some of these manipulations include only using animals in a certain internal or motivational state. For example, in olfactory appetitive learning in Drosophila, only starved animals are used and the length of the starvation period influences the results (Colomb et al., 2009). Similarly, when using the proboscis extension reflex assay, animals that did not respond to sucrose in a pretraining session or naïve animals that displayed spontaneous proboscis extension to water are discarded (Shiraiwa and Carlson, 2007). However, in our efforts to control the response of animals, we are probably curtailing the repertoire of actions we can observe, thus imposing the response we want to study onto our results. This increases the probability of that specific actions occurring and, importantly, lead us to forget the importance of variability for survival. An

#### Edited by:

Lars Chittka, Queen Mary University of London, United Kingdom

#### Reviewed by:

Klaus Lunau, Heinrich Heine Universität Düsseldorf, Germany Benjamin L. De Bivort, Harvard University, United States

> \*Correspondence: E. Axel Gorostiza<EMAIL_ADDRESS>orcid.org/0000-0002-0185-971X

#### Specialty section:

This article was submitted to Comparative Psychology, a section of the journal Frontiers in Psychology

Received: 01 March 2018 Accepted: 30 July 2018 Published: 31 August 2018

#### Citation:

Gorostiza EA (2018) Does Cognition Have a Role in Plasticity of "Innate Behavior"? A Perspective From Drosophila. Front. Psychol. 9:1502. doi: 10.3389/fpsyg.2018.01502 automatic and rigid response could soon be disadvantageous. What is an appropriate response to a given stimulus when the animal is hungry may be maladaptive when the animal is seeking a mating partner or escaping from a predator, and vice versa. The animal must evaluate its internal state and the external conditions before the most adaptive action is selected. The expected outcome is the driving force that shapes the final action (Heisenberg, 2014, 2015).

The plasticity of innate behaviors is commonly interpreted under the sensory-response model as the existence of a wider repertoire of hard-wired innate routines, each being triggered by a combination of external stimuli and internal drivers. At least two possible scenarios exist under this view. In the first one, every routine has its own neuronal substrate. Any given situation will be considered a new input, activating a specific network that should inhibit all the other routines and behaviors to promote the most adaptive behavioral output. This is the classical view of innate behaviors as hard-wired. In the second scenario, there are fewer neuronal substrates, and these are not dedicated each to a specific routine but rather define the principal features of the behavior. Other modulatory inputs refine the behavioral output of the neuronal network, resulting in a broad spectrum of routines for a behavior. It is possible that both scenarios coexist. The first one could be possible for very simple behaviors with relatively little variation in their inputs. The alternative scenario represents one of the possibilities of how the internal state or the analyses of the internal state vs. external factors modulate a behavior. We do not yet fully understand how internal and external stimuli are integrated, nor how the networks that integrate those factors interact with the networks that trigger behaviors. We also do not know what each type of decision looks like at the neuronal level. It is always a possibility that what we perceive at an observational level as the activation of one of two possible mutually exclusive behaviors shares a lot at the neuronal level with what we describe as a complex decision. It is in this gap in understanding that I believe we could start thinking about whether and how cognitive processes could shape the final action in some innate behaviors.

Drosophila is an excellent model organism for this type of study. Their small size, their great repertoire of behaviors, and the availability of advanced genetic tools (reviewed in Owald et al., 2015; Luo et al., 2018) give us the ability to address these questions at the level of behavior, circuits, and individual cells. Current technology allows us to specifically and reversibly manipulate one or several neurons in living, behaving flies. This makes it possible to dissect the circuits dedicated to behavioral flexibility, decisions, and cognitive processes, and see how different, or not, they are at the neuronal level, and how common they are for different behavioral choices.

Menzel et al. (2007) defined cognition as the "use and handling of knowledge, which allows the animal to decide between different options in reference to the expected outcome of its potential actions" and provided three essential characteristics of cognition as part of the cognitive components of behavior: (1) rich and cross-linked forms of sensory and motor processing; (2) flexibility and experience-dependent plasticity in choice performance; and (3) long-term adaptation of behavioral routines. The goals of this opinion article are to highlight the already known but underestimated complexity of innate behaviors and to explicitly associate these studies with the concept of cognition. Although none of the following examples fall strictly under the definition of cognition, certain aspects of the processes leading to the modulation of these behaviors are similar to the cognitive components of behavior listed above.

In Drosophila, the giant fibers originate in the brain, and project down contralaterally to motor neurons that control the musculature responsible for jump–flight behaviors (reviewed in Allen et al., 2006). A single spike in these neurons is normally sufficient to cause a fly to take-off, resembling a visually-evoked escape response. Consequently, giant fibers were considered command neurons for these behaviors and escape responses as reflexes. However, research conducted over the last 10 years indicates that these responses are more elaborated, extending beyond the Giant Fiber motor outputs (Card, 2012). Drosophila escape behavior contains a sequence of at least three maneuvers (freezing, body leaning or leg posture adjustment, and wing elevation) that end with a jump, but with some degree of independence between each maneuver, allowing the fly to stop the sequence if it chooses to Card and Dickinson (2008a,b) and Card (2012). Each step comprises the addition of new information, resulting in a more variable and carefully shaped final action. This means that even in the small temporal window before a predator reaches the fly, the insect must select from a wide range of evasive maneuvers. Recently, it has been shown that looming stimuli (possibly indicating the approach of an attacker) produce a bimodal distribution in Drosophila escape response—with either short or long take-offs—that can be biased toward short take-offs by increasing stimulus speed (von Reyn et al., 2014). giant fibers are necessary and sufficient for short maneuvers, while long maneuvers require a parallel pathway. Linear integration of angular velocity and angular size from looming stimuli takes place in giant fibers and derives in action selection (von Reyn et al., 2017). Hence, adult Drosophila escape responses involve more neural control elements than a single command neuron, allowing a variety of computational and decision steps to take place before the evasive behavior occurs. Another common defensive strategy is freezing, where the animal remains still, reducing its chances of being noticed. A new study showed that Drosophila flies adopt a freezing strategy in a state-dependent manner (Zacarias et al., 2018). For this study, the authors developed a different setup from the one used in the escape-behavior studies previously mentioned.  Under this condition, flies rarely jumped in response to the stimulus, and most of them froze. Even the flies that initially jumped ended up modifying their defensive strategy during the experiment, since the probability of jumping decreased over the course of the stimulus presentations, and the proportion of flies freezing increased. The decision between fleeing and freezing was modulated by walking speed. If flies were grooming or moving slowly at the time of threat, they were more likely to adopt a freezing strategy. In this study, the authors also started to describe part of the network involved in freezing. Zacarias et al. (2018) perfectly illustrates

how experimental conditions can promote different behavioral outputs, and how the state of the animal shapes the final action. Far from stereotypic and automatic reactions, defensive behaviors appear to be carefully calculated. In the presence of a threat, the animal begins a cost–benefit computation (e.g., to eat or to adopt a defensive strategy). If the threat is near and inescapable, the fly will freeze or flee depending on the action that was performing at the time. If it is an escapable threat, then a visually mediated motor planning will determine the direction of the escape. Next, if the fly decides to jump, at least two types of take-offs could be performed, a short one in which speed is favored over wing stability or a long one that produces a steady flight.

Perhaps even more interesting is how the presence of parasitoid wasps affects oviposition in adult flies, as a mechanism to protect their offspring from a possible future threat. Parasitoid wasps are not dangerous to adult Drosophila, but upon encountering female wasps, female flies adopt different strategies that include choosing food containing toxic levels of alcohol to lay their eggs (promoting the death of wasps' eggs and larvae; Kacsoh et al., 2013), and reducing oviposition rates (Lefevre et al., 2011). These behavioral switches rely on sight to sense wasps. Remarkably, the external conditions are assessed in terms of the danger they represent to their offspring and not to adult flies themselves. Similarly, by choosing food with elevated levels of ethanol, the probability of the fly's offspring being parasitized decreases, and at the same time if parasitization occurs, the fly larvae are more likely to survive (Milan et al., 2012). However, there is no instant benefit for the adults that chooses the substrate. Interestingly, neuropeptide F (NPF) and its receptor NPFR1 are involved in wasp-induced ethanol oviposition preference. NPF and NPFR1 are required for alcohol sensitivity (Wen et al., 2005), but they are also involved in the representation of the internal motivational states of hunger and satiety in the mushroom bodies via dopaminergic neurons that innervate the structure (Krashes et al., 2009). Given the preference-switch between normal food and ethanol-enriched food, and the known role of dopamine (DA) in value-based and goal-directed decision-making (Zhang et al., 2007; Schultz, 2010; Liu et al., 2012; Waddell, 2013), it would be worth investigating whether dopaminergic neurons are also recruited in this case.

Interestingly, flies form a nonassociative long-term memory of the exposure and will lay fewer eggs or choose alcoholenriched food to lay their eggs for 24–48 h after wasp exposure (Kacsoh et al., 2013, 2015). Strikingly, it has been shown that flies visually exposed to wasps can transmit oviposition reduction behavior to naive flies (Kacsoh et al., 2015), an interesting case of social learning (Grüter and Leadbeater, 2014). That is to say, flies that never encounter a wasp can acquire and use the knowledge of others to modify their oviposition behavior. Kacsoh et al. (2015) showed that oviposition reduction behavior of naive flies (students) could last for 24 h after they were separated from wasp-exposed flies (teacher), but they could not teach others. They also demonstrated that learning mutants were unable to teach or be students but showed normal acute oviposition reduction during wasp exposure. Visual cues alone are sufficient for acute reduction in oviposition and memory formation in teachers, and social-learning responses. However, social learning requires teachers to have intact wings for students to learn, suggesting a role for both wings in communication through visual cues (Kacsoh et al., 2015). It is also noteworthy that all these learning processes require the mushroom bodies, structures previously demonstrated to be important for valence and memory-based action selection (Zhang et al., 2007; Aso et al., 2014) and to contain and receive inputs from dopaminergic and octopaminergic neurons (Zhang et al., 2007; Kim et al., 2013; Waddell, 2013; Wu et al., 2013). DA and octopamine (OA) are key modulators of behavior. OA has been implicated in statedependent changes in visual processing (Longden and Krapp, 2009; Suver et al., 2012), experience-dependent modulation of aggression (Bonini, 2000; Stevenson et al., 2005; Hoyer et al., 2008), social decision-making (Certel et al., 2010), and reward (Burke et al., 2012). DA is also known for its roles in reward (Barron et al., 2010; Burke et al., 2012), motivation (Krashes et al., 2009; Zhang et al., 2016) and, as previously mentioned, value-based or goal-directed decision-making (Zhang et al., 2007; Liu et al., 2012; Waddell, 2013; Beeler et al., 2014). Both seem to be involved in mediating certain aspects of value albeit in different modalities or domains (Aso et al., 2010; Burke et al., 2012; Scheiner et al., 2014; Huetteroth et al., 2015).

Curiously, these two biogenic amines differently modulate phototaxis, in what it seems to be a goal-directed or value-based decision-making process. Phototaxis seems to be a special case of photopreference and manipulating the ability of flies to fly can reversibly shift it from approach to avoidance in walking flies (Gorostiza et al., 2016). Photopreference can be influenced by the shape, form, or degree of intactness of the wings, the ability of flies to move them, and the state of sensory organs related to flight. Hence, flies appear to constantly monitor their flying ability, even while walking as these experiments suggest, and adjust their photopreference accordingly. It is worth noting that the neuronal activity of dopaminergic and octopaminergic circuits is indispensable and inducing for the modulation of phototactic behavior, but with opposite effects, suggesting a potential role of DA and OA, and supporting the idea of a value-based decision-making process taking place. In this view, phototaxis is not a response, but an action selected only in rather particular circumstances after a central decision-making stage that negotiates external stimuli as well as internal demands. When flying ability is compromised, the value of the different consequences of moving toward light changes and the dangers become more prominent due to the difficulties to escape; hence, the flies choose to hide until the danger goes away or flying ability is restored. Immediately after emerging from the pupal case, all flies experience a flightless period during the wing expansion phase. In line with the results above, flies go through a phase of reduced phototaxis that extends beyond wing expansion until the stage when its wings render it capable of flying (Chiang, 1963). The alteration in flying ability may promote a shift in the expected outcome (Heisenberg, 2014, 2015), which would eventually drive the selection of an alternative, more adaptive action, as seen in preference suppression assays where air, light, and gravitaxis cues were paired with aversive stimuli (Seugnet et al., 2009; Baggett et al., 2018). In those cases, flies learn that cues that usually indicate an escape route will lead them to negative outcomes (an aversive taste or an aversive temperature). Noticeably, wing

expansion in flies also involves a decision process. After emerging from the pupal case, flies select a suitable perch and expand their wings, but wing expansion can be delayed under adverse environmental conditions, e.g., space restriction (Cottrell, 2009; Peabody et al., 2009). Work in Drosophila uncovered part of the neuronal network involved in the decision to expand the wings, and showed the connection with the decision to perch, which required an assessment of the external factors (Peabody et al., 2009).

These examples serve to demonstrate how innate behaviors can in fact be the outcomes of complex modulatory processes, careful assessment of factors and decisions, and not mere stereotypic and automatic responses. Through these examples, we can see some aspects that resemble cognitive components (Menzel et al., 2007): rich sensory and motor processing (escape response), experience-dependent plasticity in choice performance (oviposition), and flexible and long-term adaptation of behavioral routines (photopreference). In light of this, I argue that the way we frame and refer to these behaviors must change. We should think about them as behaviors dominated by innate components or preferences. This simple paraphrase can change the focus of the innateness from the behavior to some component of it, moving also the preprogrammed conception with it. As mentioned by Menzel et al. (2007), these components or preferences "seem to be essentially useful in guiding the animals' behavior in their first confrontations with the external world." Innate preferences could be extremely relevant in the absence of contradictory cues. Nonetheless, they are certainly not the only things determining the final shape of the behavior. When other factors add complexity to the situation, the innate component becomes diluted and lose strength, leaving only the behavior "without its innateness." Under simple and controlled circumstances (a fly in a tube with a source of light at one end), the behavior looks like

#### REFERENCES

a stereotypic and automatic response (light is turned on, and in most cases the fly approaches the source of light). In this case, the innate component is the only relevant factor for the behavior. However, in complex situations (the flying ability of the fly is compromised), other factors become prominent and the innate component loses relevance or becomes maladaptive. The innate preference becomes another factor to be considered. I propose that in that complex situation, a cognitive process is engaged in the final tuning of the behavior (the fly avoids the light). Hence, cognition could prevent automatic maladaptive responses and also help fine-tune "innate routines," depending on the combination of external stimuli and internal drivers. We should carefully consider the cognitive aspect of any behavior we study, no matter how seemingly dominated by innate components or stereotypic it looks.

#### AUTHOR CONTRIBUTIONS

The author confirms being the sole contributor of this work and approved it for publication.

# FUNDING

This article was supported by an IBRO Return Home Fellowship.

#### ACKNOWLEDGMENTS

Many thanks to Björn Brembs for extensive discussions on this topic. Thanks also to Tomer Czaczkes, Christian Rohrsen, Luciana Pujol-Lereis, and Lia Frenkel for critical reading of the manuscript. I am a member of the National Scientific and Technical Research Council (CONICET) and the National University of Cordoba (UNC). This work was financially supported by CONICET, and an IBRO Return Home Fellowship.

Drosophila using targeted expression of the TRPM8 channel. J. Neurosci. 29, 3343–3353. doi: 10.1523/JNEUROSCI.4241-08.2009

**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Gorostiza. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Bumblebees Express Consistent, but Flexible, Speed-Accuracy Tactics Under Different Levels of Predation Threat

Mu-Yun Wang1,2, Lars Chittka1,3 and Thomas C. Ings1,4 \*

<sup>1</sup> Department of Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom, <sup>2</sup> Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan, <sup>3</sup> Institute for Advanced Study, Berlin, Germany, <sup>4</sup> Department of Biology, Anglia Ruskin University, Cambridge, United Kingdom

#### Edited by:

Patrizia d'Ettorre, Université Paris 13, France

#### Reviewed by:

Nicolas Chaline, Universidade de São Paulo, Brazil Dominic Standage, Queen's University, Canada

> \*Correspondence: Thomas C. Ings<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Comparative Psychology, a section of the journal Frontiers in Psychology

Received: 27 February 2018 Accepted: 13 August 2018 Published: 03 September 2018

#### Citation:

Wang M-Y, Chittka L and Ings TC (2018) Bumblebees Express Consistent, but Flexible, Speed-Accuracy Tactics Under Different Levels of Predation Threat. Front. Psychol. 9:1601. doi: 10.3389/fpsyg.2018.01601 A speed-accuracy trade-off (SAT) in behavioural decisions is known to occur in a wide range of vertebrate and invertebrate taxa. Accurate decisions often take longer for a given condition, while fast decisions can be inaccurate in some tasks. Speed-accuracy tactics are known to vary consistently among individuals, and show a degree of flexibility during colour discrimination tasks in bees. Such individual flexibility in speed-accuracy tactics is likely to be advantageous for animals exposed to fluctuating environments, such as changes in predation threat. We therefore test whether individual speedaccuracy tactics are fixed or flexible under different levels of predation threat in a model invertebrate, the bumblebee Bombus terrestris. The flexibility of speed-accuracy tactics in a foraging context was tested in the laboratory using a "meadow" of artificial flowers harbouring "robotic" crab spider predators. We found that while the ranking of bees along the speed and accuracy continuums was consistent across two levels of predation threat, there was some flexibility in the tactics used by individual bees – most bees became less accurate at colour discrimination when exposed to predation threat when flower types were rewarding. The relationship between decision speed and accuracy was influenced by predator detectability and the risk associated with making incorrect choices during the colour discrimination task. Predator crypsis resulted in a breakdown in the relationship between speed and accuracy, especially when making an incorrect floral choice incurred a distasteful quinine punishment. No single speed-accuracy tactic was found to be optimal in terms of foraging efficiency under either predation threat situation. However, bees that made faster decisions achieved higher nectar collection rates in predator free situations, while accurate bees achieved higher foraging rates under predation threat. Our findings show that while individual bees remain relatively consistent in terms of whether they place greater emphasis on speed or accuracy under predation threat, they can respond flexibly to the additional time costs of detecting predators.

Keywords: animal personality, Bombus terrestris, predation risk, predator crypsis, speed-accuracy trade-offs

# INTRODUCTION

fpsyg-09-01601 August 31, 2018 Time: 19:4 # 2

Choices made by animals frequently involve a trade-off between decision speed and decision accuracy (Wickelgren, 1977; Chittka et al., 2009; Heitz and Schall, 2012), with fast decisions tending to be less accurate than slow decisions for a given task condition. A speed-accuracy trade-off (SAT) has been shown to occur during discrimination tasks across a wide range of taxa including humans (Simen et al., 2009; Bogacz et al., 2010), non-human primates (Heitz and Schall, 2012), birds (Ducatez et al., 2015), fish (Wang et al., 2015), and insects such as bees (Chittka et al., 2003; Burns and Dyer, 2008). The majority of studies on SAT, especially in humans and non-human primates, have used the SAT as a paradigm for exploring behavioural flexibility in decision making and choice behaviour (e.g., Fitts, 1966; Wickelgren, 1977) and its neuronal basis (reviewed by Standage et al., 2014 and also see Heitz and Schall, 2012; Hanks et al., 2014 for nonhuman primate examples). More recently, researchers working on animals, including bees, birds, and fish (Chittka et al., 2003; Ducatez et al., 2015; Wang et al., 2015; but also see Phillips and Rabbit, 1995 for a human example), have also considered SAT from a different perspective, i.e., whether the SAT is a stable trait in which the individual differences are maintained within a population over time.

Response speed has long been an important component describing individually consistent behavioural traits in vertebrates, such as shyness-boldness or neophobia (Van Oers et al., 2005; Toms et al., 2010). These traits, related to response speed, can be heritable (Drent et al., 2003) and different traits can be adaptive depending upon environmental changes (Dingemanse et al., 2004). In invertebrates, this approach has recently been used to consider the relationship between decision speed and accuracy. For example, Chittka et al. (2003) showed that foraging bumblebees express inter-individual variation in speed-accuracy tactics during floral colour discrimination. This variation remained consistent even when the cost of making errors increased, although all bees became slower and more accurate. Similar individual variation in speed-accuracy tactics has also been shown in honeybees (Burns and Dyer, 2008). While these studies indicate that speed-accuracy tactics in invertebrates do vary consistently among individuals, and that there is some flexibility at the level of the individual (Chittka et al., 2003), we still have limited understanding of how they can be adjusted to match changing situations (Chittka et al., 2009), such as increased predation threat.

Levels of inter-individual variability of behavioural phenotypes (O'Steen et al., 2002; Dingemanse et al., 2004) and individual behavioural flexibility (Herborn et al., 2014) are believed to be influenced by environmental fluctuation. We would therefore expect selection to favour flexibility in speed-accuracy tactics in social animals, such as bumblebees, adapted to dynamic environments (reviewed in Klein et al., 2017) where factors such as food availability and predation risk vary temporally and spatially. Bumblebees are social insects where the worker caste collects food (nectar and pollen from flowering plants) for the entire colony (Goulson, 2003). Foraging bees maximise their foraging efficiency by processing visual and olfactory cues to select the flowers of plants that provide the greatest returns (Chittka et al., 1999; Chittka and Raine, 2006). However, the best available options vary considerably through time and space (Von Buttel-Reepen, 1900; Arnold et al., 2009), and foraging bees need to avoid predators such as the crab spider Misumena vatia which hunts on flowers (Morse, 2007). The risk of predation from these predators also varies from patch to patch (Morse, 2007) and, due to their ability to change colour, the detectability of the spider can vary depending upon the colour of the flower it is hunting on (Chittka, 2001).

We therefore use a model invertebrate, the bumblebee Bombus terrestris, and an established predator avoidance learning paradigm (Ings and Chittka, 2008, 2009), to examine the flexibility of individual speed-accuracy tactics in response to changing predation risk. Bees are exposed to a natural scenario where they have to discriminate between two similar flower types to maximise energy intake in an artificial meadow where the risk of predation by model crab spiders is added half way through the experiment. In this design, introduction of predation risk changes the decision task slightly. We therefore do not focus on classical SAT, where participants perform the same task in different motivational conditions (speed or accuracy emphasis), but rather, we focus on inter-individual variation and intraindividual consistency in decision speed and accuracy under changing predation risk. Our main questions are: (1) Do bees maintain consistent speed-accuracy tactics in a floral colour discrimination task when exposed to increased predation risk? (2) Does the optimal speed-accuracy tactic change with predation risk and the difficulty of detecting predators? We hypothesise that the speed-accuracy tactic employed by individual bees will be flexible and that the optimal speed-accuracy tactic will differ depending upon predator crypsis and costs of incorrect choices in flower colour discrimination.

# MATERIALS AND METHODS

#### Study Animals

Bumblebees (B. terrestris dalmatinus, Dalla Torre 1882) from three colonies obtained from a commercial supplier (Syngenta Bioline Bees, Weert, Netherlands) were used in the experiment. Individual bees were marked with numbered tags (Christian Graze KG, Weinstadt-Endersbach, Germany). All colonies were maintained at room temperature (23◦C) and exposed to a 12:12 h light/dark cycle, with the light phase starting at 8 am. All colonies were supplied with ad libitum sucrose solution (50%, v/v) and pollen.

# Experimental Apparatus

Full details of the experimental apparatus are provided in Ings and Chittka (2008) and Ings and Chittka (2009). This experiment was carried out in a wooden flight arena (l = 1 m, w = 0.72 m, and h = 0.73 m) with a UV-transmitting Plexiglas lid. Controlled lighting was provided by two twin lamps [TMS 24 F with HF-B 236 TLD (4.3 kHz) ballasts, Philips, Netherlands], fitted with Activa daylight fluorescent tubes (Osram, Germany), which were suspended above the flight arena. A four by four vertical

array of artificial flowers (7 cm × 7 cm flat cards painted with yellow acrylic colours) was presented on a grey background on the end wall of the arena (**Figure 1A**). Bees entered the arena through an entrance tunnel attached to the opposite wall to the meadow. Each artificial flower (**Figure 1B**) consisted of a small wooden landing platform (40 mm × 60 mm), 10 mm under a small hole through which bees could access rewards (sucrose solution). Syringe pumps (KD Scientific, KD200, Holliston, MA, United States) were used to provide a continuous supply of sucrose solution at the tips of 26G syringe needles (BD Microlance Drogheda, Ireland; 0.45 mm × 13 mm) placed behind the access hole on the flowers.

To simulate predation risk, robotic "spider arms" (custombuilt by Liversidge & Atkinson, Romford, United Kingdom) covered with sponges (**Figures 1A,B**) were set up at the base of the flowers to simulate predation attempts (detailed in Ings and Chittka, 2008, 2009). To provide realistic visual predator cues, a 12 mm wide three dimensional model (made from Gedeo Crystal resin) of a crab spider (M. vatia) was placed just above the feeding hole on the "dangerous flowers" (**Figure 1B**). Full details of the dangerous flowers, including spectral reflectance of the background, spiders and flowers can be found in Ings and Chittka (2008) and Wang et al. (2013).

### Pre-training

To allow bees to become accustomed to the arena and flowers, all bees were given unrestricted access to the flight arena for a minimum of 1 day prior to the beginning of the experiments. No floral signals were placed in the artificial meadow to avoid bees developing any colour bias prior to the experiments. However, all flowers were supplied with a constant flow (1.85 ± 0.3 µl per minute) of 50% (v/v) sucrose. Individual bees that had continued feeding for a minimum of three foraging bouts (i.e., they entered the arena, collected sucrose solution from the artificial flowers and returned to the nest on at least three consecutive occasions) were used in the experiments.

#### Experimental Design

Full details of the experimental procedure are provided in Wang et al. (2013) and summaried in **Figure 2**. During the training phase, the 7 cm × 7 cm floral signals were added to the artificial meadow (**Figure 1A**). Bees were then trained to distinguish between two similar shades of yellow artificial flowers (for details of the colours see Wang et al., 2013) for 200 flower choices - a bee needed to land on the platform of a flower and probe for artificial nectar to be deemed a choice. In two groups (conspicuous spider and cryptic spider), the dark yellow flowers were more rewarding [50% (v/v) sucrose solution] than the light yellow flowers [20% (v/v) sucrose solution]. To encourage flower discrimination in a third group (quinine and cryptic spider – hereafter referred to as just 'quinine'), we replaced the 20% (v/v) sucrose solution with a distasteful 0.12% (w/v) quinine hemisulfate solution that bees are known to rapidly learn to avoid (Chittka et al., 2003). In the testing phase, we introduced spider models, either highly conspicuous (white; conspicuous spider group) or cryptic (same colour as the flowers; cryptic spider and quinine groups) to two of the eight (i.e., 25%) high quality (dark yellow) flowers and

tested the bees for another 200 choices. Three to six foraging bouts were required for bees to make 200 choices, and bees were allowed to complete their final foraging bout and return to the nest (see Wang et al., 2013 for further details). When bees landed on "dangerous" flowers (with a spider model), they were immediately exposed to a simulated predation attempt by being held by the arms of a "robotic crab spider" for 2 s – thus they had no opportunity to collect the sucrose solution. The positions of the flowers were changed in a pseudo-random fashion between each foraging bout (at least three were required to attain 200 choices): positions of different flower types were changed randomly, but the number of high reward flowers in both the top and bottom two rows of the meadow were maintained the same in order to avoid spatial preference bias.

sponge coated pincers for 2 s when a bee lands to feed). It also shows the

syringe tip which dispenses the sucrose or quinine solutions.

#### Data Analysis

The movements and positions of 44 bees from across the three colonies were recorded in real time during the experiment. Of

these, four were excluded from the analyses as they stopped foraging during the experiment (this left 15 bees in each of the conspicuous spider and cryptic spider groups and 10 bees in the quinine group). Three-dimensional coordinates of bee positions were calculated 50 times per second using two video cameras connected to a computer running Trackit 3D software (BIOBSERVE GmbH, Bonn, Germany). We calculated the time bees spent in the investigation zones, which were 7 cm (length) by 9 cm (width) by 9 cm (height) from the holes providing sucrose or quinine solution. Investigation zones were set based on the visual angles of bumblebees where bees were able to detect both flower signals and predators using colour contrast (Spaethe et al., 2001).

To remove learning effects, we calculated the colour discrimination accuracy (proportion of high reward flowers chosen) and decision speed during the final 30 choices (out of 200) made during each phase of the experiment. In the testing phase, only visits to high reward flowers without spiders were scored as correct choices. We used the average time spent inspecting flowers (duration in the investigation zones), rather than average time between choices (e.g., as in Chittka et al., 2003), as our measure of decision speed. Time between flowers is only a proxy of decision time and is influenced by other factors such as flight speed and path length between flowers. Prior to analysis, inspection time was converted to relative decision speed using the following formula: Speed = 1 – [(decision time – minimum decision time)/(maximum – minimum decision time)]. Thus the bee that took the longest (1.31 s per flower) to inspect flowers was scored as 0 and the fastest bee (0.31 s per flower) was scored as 1 (mean ± 1SE decision time = 0.60 ± 0.2 s per flower). All statistical analyses described below were carried out in RStudio 1.1.423 (R Core Team, 2015) running R 3.4.3 (R Core Team, 2017).

# The Relationship Between Speed and Accuracy

Before examining the consistency of floral colour discrimination speed-accuracy tactics across situations (change in predation risk), we used linear correlation analysis (Pearson's product moment correlation) to determine if speed and accuracy were related. Correlation was used as there was no a priori reason to expect decision speed to be dependent upon accuracy or vice versa. Each experimental group was examined separately. Normality tests (Shapiro–Wilk) and visual inspection of the scatterplots were undertaken to check that the assumptions of linear correlation were not violated.

# Consistency of Speed-Accuracy Tactics Under Different Levels of Predation Risk

To test whether floral colour discrimination speed-accuracy tactics are rigid or flexible with changing predation risk, we used a combination of linear regression and paired t-tests. While both speed and accuracy are proportions, inspection of the residuals from fitted models, along with normality tests (Shapiro– Wilk tests) showed that it was not necessary to transform these data or use generalised linear models. Linear regression was used to identify whether speed and accuracy during the testing phase were dependent upon speed and accuracy during the training phase (each group was analysed separately). The associated R 2 -values provide an index of the stability of the rank position of each bee in terms of speed or accuracy, i.e., whether the fastest bee remains the fastest bee under predation risk. The t-tests provide a measure of the consistency of the magnitude of speed and accuracy for individual bees within each group. Thus, if bees in a group maintained consistent speed-accuracy tactics under predation threat they would have high R 2 -values but low t-values. For the regression analysis, residual versus fitted value and quantile–quantile plots were inspected to check that each model met the assumptions of linear regression. The "linearHypothesis" function in the R package "car" version 2.14 (Fox and Weisberg, 2011) was used to test whether slope coefficients differed significantly from 1 (all bees remained equally consistent). The slope coefficient values were used to indicate effect sizes of the regressions and Hedges g was calculated in version 0.7.1 of the "effsize" package (Torchiano, 2017) in R to assess effect sizes for the mean differences in speed and accuracy between training and testing phases.

# Optimal Tactic Under Different Levels of Predation Risk

In a predation free environment, the optimal speed-accuracy tactic should yield the highest nectar collection rates. However, in an environment with a high predation risk, the optimal tactic will involve a trade-off in terms of nectar collection rate and avoiding being killed by predators. However, because all bees had a strong predator avoidance response by the end of training (Wang et al., 2013), there was insufficient variation to allow a meaningful analysis of the influence of speed-accuracy tactics on predation risk. Thus, we focused on nectar collection rates as our measure of optimality in relation to speed-accuracy tactics.

Nectar collection rate (mg sucrose per second) was calculated by dividing the amount of sucrose collected by each individual bee by the total time they spent foraging in the arena. Each of the high reward flowers provided approximately 4 mg of sucrose (4.7 µl of 50% v/v sucrose solution) while each low reward flower provided approximately 0.8 mg of sucrose (4.7 µl of 20% v/v sucrose solution). Therefore, during training there was on average 80% more sugar reward available in the high reward flowers compared to the low reward flowers. During testing, this difference reduced slightly to 73.3% as no sugar could be collected from dangerous flowers. Flowers containing quinine solution provided no sucrose reward, and when bees visited a flower harbouring a crab spider model they were captured before they could collect any sucrose solution.

First, we examined the relationship between decision time and total foraging duration using Pearson's product moment correlation. To meet the assumption of normality, total foraging duration was log (natural) transformed prior to the analysis. The influence of decision speed and accuracy on nectar collection rate was examined using separate general linear models for training and testing phases. Full models (including interactions) with experimental group and both speed and accuracy were initially fitted to determine which variable explained the greatest amount of variation. We included experimental group in the model to test whether the intercepts or slopes differed among the experimental treatments. After fitting the full model (including the interactions between group and speed and accuracy) we used the "Anova" function in the R package "car" to calculate Type III sums of squares to allow us to choose which variables to drop to improve the fit of the models. Model terms were dropped sequentially until we arrived at the minimum adequate model with the lowest Akaike Information Criterion (AIC). Residual versus fitted value plots, quantile–quantile plots and variance inflation factors (VIF) were inspected to check for any violations of model assumptions. We used the same procedure to test whether nectar collection rate during testing was dependent upon nectar foraging rate during training. The "linearHypothesis" function in the R package "car" was used to test whether slope coefficients differed significantly from 1 (all bees remained equally consistent).

# RESULTS

# The Relationship Between Speed and Accuracy

Decision speed (inverse of decision time) and accuracy were generally negatively correlated during both training and testing, i.e., some bees were slow and accurate, while others were fast and error prone. However, the degree of correlation differed between treatment groups (**Figures 3A–C**). In the absence of predation risk, speed and accuracy were significantly correlated in the cryptic spider group (Pearson's r<sup>13</sup> = −0.619, P = 0.013) but not the conspicuous spider group (Pearson's r<sup>13</sup> = −0.399, P = 0.141) or the quinine group (Pearson's r<sup>8</sup> = −0.008, P = 0.982). When the conspicuous spider and cryptic spider groups, which experienced identical conditions during training, were pooled, the overall relationship between speed and accuracy was strongly negatively correlated (Pearson's r<sup>28</sup> = −0.525, P = 0.003). Under predation risk, speed and accuracy were negatively correlated in the conspicuous spider group (Pearson's r<sup>13</sup> = −0.677, P = 0.006) but not the cryptic spider (Pearson's r<sup>13</sup> = −0.457, P = 0.086) or quinine groups (Pearson's r<sup>8</sup> = 0.317, P = 0.373).

# Consistency of Speed-Accuracy Tactics Across Situations

When bees moved from a single visual discrimination task to simultaneous colour discrimination and predator avoidance (testing phase), the individual consistency in both decision speed

and accuracy differed among experimental groups (**Figure 4** and **Table 1**). In the conspicuous spider group, both decision speed and accuracy during testing were dependent upon speed and accuracy during training (**Figure 4A** and **Table 1**), i.e., the rank position of individual bees remained consistent. Although there was a small (2% increase; Hedge's g estimate = −0.131, 95% CI = −0.879 to 0.618), but significant, increase in decision speed within the group (**Table 1**), this change was consistent for all individuals (linear regression: β = 1.065 ± 0.292; contrast against a slope of 1: F<sup>1</sup> = 0.05, P = 0.828). Individual accuracy within the group also fell slightly during testing (6% decrease; **Table 1**; Hedge's g estimate = 0.601, 95% CI = −0.163 to 1.366) and this change was consistent within the group (linear regression: β = 1.481 ± 0.275; contrast against a slope of 1: F<sup>1</sup> = 3.06, P = 0.104). In the cryptic spider group, the rank position of individual bees also remained constant for both speed and accuracy (**Figure 4B** and **Table 1**). Individual decision speed did not change significantly between phases (**Table 1**), but decision accuracy was strongly (by 23.6%) reduced (Hedge's g estimate = 2.421, 95% CI = 1.136 to 3.405) during the testing phase. Furthermore, the reduction in accuracy was greater for the bees which were most accurate in training (**Figure 4B**; linear regression: β = 0.584 ± 0.176; contrast against a slope of 1: F<sup>1</sup> = 5.62, P = 0.034). In contrast to the conspicuous spider and cryptic spider groups, there was no consistency in rank position, or changes in speed or accuracy between training and testing for bees in the quinine group (**Table 1**).

correlation coefficient for each series is indicated with stars (∗P ≤ 0.05, ∗∗P ≤ 0.01, NS for P > 0.05).

#### Optimal Tactic Under Different Levels of Predation Risk

The time taken to visit 200 flowers (natural log) was negatively correlated with relative decision speed during both training (Pearson's r<sup>38</sup> = −0.570, P < 0.001) and testing (Pearson's r<sup>38</sup> = −0.483, P = 0.002) phases. The nectar collection rate (foraging efficiency) during training was dependent upon decision speed (**Figure 5A**; linear regression: R <sup>2</sup> = 0.481, F1,<sup>36</sup> = 11.06, P < 0.001) and differed between treatment groups (**Figure 5A**; linear model: F2,<sup>36</sup> = 9.64, P = 0.003). A bee with 10% higher relative decision speed had a 20% greater nectar collection rate (linear model: β = 0.434 ± 0.140). In contrast, there was no difference in nectar collection rates between groups during testing (linear model: F2,<sup>35</sup> = 1.08, P = 0.351), and nectar collection rates were dependent upon a linear combination of decision accuracy and decision speed (linear regression: R <sup>2</sup> = 0.318, F2,<sup>37</sup> = 8.63, P < 0.001). Decision accuracy explained twice as much variation (25%; β = 0.393 ± 0.102) in nectar collection rate as decision speed (12%; β = 0.266 ± 0.099).

The nectar collection rate under predation risk was dependent upon nectar collection rate during training (linear model: R <sup>2</sup> = 0.337, F1,<sup>38</sup> = 19.27, P < 0.001), irrespective of experimental group (**Figure 6**). The relationship was positive, with the ranking of individual bees being consistent between phases, although the nectar collection rate during testing did not consistently match that during training (**Figure 6**). While most bees had lower nectar collection rates during testing (linear regression: β = 0.513 ± 0.117; contrast against a slope of 1: F<sup>1</sup> = 17.35, P < 0.001) a few bees, especially those in the conspicuous spider group, had higher nectar collection rates during testing.

#### DISCUSSION

We demonstrated that while the inter-individual expression of speed-accuracy tactics remained consistent under increased

spider (A), cryptic spider (B), and quinine (C) groups. Fitted lines represent significant predicted values from linear regression analyses. R <sup>2</sup> and associated P-values are shown in Table 1. To aid interpretation, the dashed grey line represents a hypothetical 1:1 relationship between testing and training phases – deviations from this show a non-uniform change in speed or accuracy along the speed and accuracy continuum.

Significant results (P ≤ 0.05) are shown in bold.

predation threat, they were flexible across changing situations. However, the consistency of the speed-accuracy tactics, and degree of flexibility, were dependent upon predator detectability and the costs of making errors in the colour discrimination task. When predators were easily detected, the relationship between colour discrimination speed and accuracy was consistent when predation threat increased. In contrast, when predator detection was difficult, the relationship between decision speed and accuracy broke down, especially when errors in the floral colour discrimination task were punished with bitter quinine. Although, caution is required interpreting the results of the quinine group due to a smaller sample size and low variation in speed and accuracy among individuals. While we did detect limited flexibility in the speed-accuracy tactics employed by individual bees, there was no evidence to support our hypothesis that bees employ an optimal (in terms of nectar collection rate) speed-accuracy tactic to match the level of predation risk and detectability of the predators. Therefore, we suggest that while perceptual and cognitive constraints in bumblebees may limit the flexibility of speed-accuracy tactics employed by individual bees, a diversity of individually consistent behavioural traits at the colony level may be advantageous in environments with fluctuating predation risk (Muller and Chittka, 2008).

# Consistency of Speed-Accuracy Trade-Off Tactics Across Situations

Even though the ranking of individual speed-accuracy tactics remained consistent with increased predation threat, there were changes in the magnitude of both speed and accuracy that differed among groups. The observed consistency in ranking of bees matches a previous study where the cost of making errors in colour discrimination was increased by use of gustatory punishment (Chittka et al., 2003). In their study, Chittka et al. (2003) found that bees shifted toward the slower-accurate end of the speed-accuracy continuum when errors were punished. In contrast, in our study (which unlike the earlier study, involved predation risk), we found that bees became less accurate, with

values from a significant linear regression model (R <sup>2</sup> = 0.481, F1,<sup>36</sup> = 11.13, P < 0.001) with a common slope but different intercepts for each group (conspicuous spider = black dotted line; cryptic spider = dashed grey line; quinine = solid black line. (B) Individual nectar collection rates during the testing phase are shown against decision accuracy and the black solid line represents predicted values from the linear regression (nectar collection rate ∼decision accuracy + decision speed; R <sup>2</sup> = 0.185, F1,<sup>13</sup> = 8.63, P = 0.006 ) with values for speed set at the group mean value. (C) Nectar collection rates are plotted against decision speed and the black solid line represents predicted values from the same linear regression as in (B) but with values for accuracy set at the group mean value. In all cases, the regression lines are not extrapolated beyond observed values.

little change in decision speed, when they had to solve a difficult colour discrimination task under predation threat. The change in accuracy was only minimal when spiders were conspicuous and was coupled with a very small increase in decision speed. This most likely reflects continued improvement of predator detection after training. In the quinine treatment, which was similar to that used by Chittka et al. (2003), we found no change in either speed or accuracy under predation risk, although interindividual variation was low during both phases. The results from this group did, however, support the findings from Chittka et al. (2003) which showed increased accuracy with the addition of gustatory punishment. These observations lead us to ask two important questions. First, why did predator detectability and the cost of errors affect the relationship between decision speed and accuracy under predation threat? Second, why did bees become less accurate at choosing the most rewarding flowers when under predation threat?

To answer these questions it is worth considering how bees process the visual information relating to predation risk and food rewards. During training, bees needed to discriminate between similar coloured flowers to maximise their foraging returns. Thus, when a bee perceived a flower it should have processed the visual appearance of the flower and matched it with the level of reward it received at similar flowers (Dyer and Chittka, 2004; Dyer et al., 2011). However, when a bee was exposed to the same flowers, but under predation risk, it needed to assess both the risk associated with feeding from a particular flower as well as the difference in reward it may receive (Ings and Chittka, 2008, 2009; Wang et al., 2013; Nityananda and Chittka, 2015). Bees could either (1) scan for predators then process the floral colour, (2) process the floral colour and then scan for predators, (3) simultaneously process floral colour and scan for predators, (4) just scan for predators and visit any safe flower, or, (5) avoid the risky flower type once they have made the association between colour and predation risk.

While bumblebees are believed to use restricted parallellike search (Morawetz and Spaethe, 2012) they still process scenes sequentially using active vision (Nityananda et al., 2014). Furthermore, although discrimination of contrasting colours requires shorter integration times than highly similar colours (Nityananda et al., 2014), bumblebees use a colour independent search image for spiders (Ings et al., 2012), i.e., complex shape recognition, that would also require longer integration times (Nityananda et al., 2014). It is therefore unlikely that they simultaneously process similar floral colours and scan for predators, although they can solve both discrimination tasks concurrently if strongly incentivised (Wang et al., 2013). Bees might avoid the risky flower type when spiders were cryptic and all flowers were rewarding (Ings and Chittka, 2009), but not when the alternative flower type is distasteful. Once bees make the association between predation risk and the highly rewarding flower type, they would only need to discriminate floral colour to avoid predation and reduce overall decision time. While bees exposed to cryptic spiders when both flowers were rewarding did indeed reduce their accuracy, they still visited too many safe high rewarding flowers to reflect avoidance of the risky flower type. It is therefore more likely that reduced accuracy reflects avoidance of spiders first followed by less accurate decisions in the colour discrimination task. This interpretation is supported by the fact that individual bees did not spend more time overall