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Animal Behaviour 112 (2016) 53e62 Contents lists available at ScienceDirect Animal Behaviour journal homepage: www.elsevier.com/locate/anbehav Blue jays, Cyanocitta cristata, devalue social information in uncertain environments V. K. Heinen*, D. W. Stephens 1 Department of Ecology, Evolution, and Behavior, University of Minnesota, St Paul, MN, U.S.A. a r t i c l e i n f o Article history: Received 23 June 2015 Initial acceptance 10 September 2015 Final acceptance 13 October 2015 Available online MS. number: A15-00540 Keywords: blue jay Cyanocitta cristata environmental uncertainty signal reliability social information social learning Animals are selective about when to learn by observing others. Models predict that social information becomes less reliable in uncertain environments, and therefore animals should reduce their use of social information in these environments; however, these parameters are often difficult to manipulate and control. We investigated how information reliability and environmental uncertainty affect the use of both social and nonsocial signals. Captive blue jays, Cyanocitta cristata, were given a choice between two perches, one of which was rewarded. Jays could see either a social signal (a conspecific) or a nonsocial signal (a light) that provided some information about the rewarded perch. The nonsocial signal was yoked to the bird that generated the social signal, ensuring the two signals were of identical reliability. We manipulated signal reliability (i.e. the probability that the signal correctly indicated the rewarded perch) and environmental certainty (i.e. the probability that a given perch was rewarded). Qualitatively, jays used both social and nonsocial signals more often when the signals were reliable, and used them less often when environments were predictable. However, jays used social signals less than equally reliable nonsocial signals when environments were unpredictable. Our results suggest that signal reliability and environmental predictability interact to determine signal use, but they do not affect social and nonsocial signals in the same way. © 2015 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. Social animals have the opportunity to acquire information by monitoring others' interactions with their environment. Using social information avoids the time and energy costs of independent learning, but it carries its own set of costs. The benefits of social information are frequency dependent: if too many individuals copy each other and few interact directly with the environment, the fitness of social learners is reduced (Barnard & Sibly, 1981; Bikhchandani, Hirshleifer, & Welch, 1998; Vickery, Giraldeau, Templeton, Kramer, & Chapman, 1991). Animals must therefore use social information selectively and spend some time independently sampling the environment (Galef, 1995; Giraldeau, Valone, & Templeton, 2002; Rogers, 1988), and indeed we see that animals do not always use available social information (Dukas & Simpson, 2009; Webster & Hart, 2006). Exactly how animals chose when to use social information, however, remains an open question. A prevailing view is that natural selection has favoured ‘social * Correspondence: V. K. Heinen, Department of Ecology, Evolution, and Behavior, University of Minnesota, 100 Ecology Building, 1987 Upper Buford Circle, St Paul, MN 55108, U.S.A. E-mail address: [email protected] (V. K. Heinen). 1 E-mail address: [email protected] (D. W. Stephens). learning strategies’, decision-making frameworks used to determine when individuals will use information generated by others, and when they will use nonsocial information (van Bergen, Coolen, & Laland, 2004; Kendal, Coolen, & Laland, 2004; Rieucau & Giraldeau, 2011; Rogers, 1988). One proposed strategy is that individuals should use social information when environments are relatively predictable, and ignore it when environments are less predictable. The basic insight is intuitive: when the most beneficial behaviour changes quickly across space or time or is otherwise difficult to predict, the probability that others will be performing outdated or suboptimal behaviours increases (Laland, 2004). When social demonstrators are likely to have outdated information, the benefits of social learning are reduced and independent learning should prevail. This prediction has been supported through extensive modelling (Aoki & Feldman, 1987; Aoki, Wakano, & Feldman, 2005; Boyd & Richerson, 1988; Laland, 2004). Empirical studies are few, but those that exist also show reduced social learning in complex or unpredictable environments, although the type of environmental complexity tested varies (Galef & Whiskin, 2004; Rendell, Boyd, et al., 2011, Rendell, Fogarty, et al., 2011; Toelch et al., 2009; Wilkinson & Boughman, 1999). http://dx.doi.org/10.1016/j.anbehav.2015.11.015 0003-3472/© 2015 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. V. K. Heinen, D. W. Stephens / Animal Behaviour 112 (2016) 53e62 However, the same pattern is predicted by information economics, a theory agnostic to the sociality of information. Reliability is a critical determinant of information use; individuals use reliable signals or cues and ignore unreliable ones (Dall, Giraldeau, Olsson, McNamara, & Stephens, 2005; Maynard Smith & Harper, 2003; McLinn & Stephens, 2010). If social information becomes less reliable in unpredictable environments, as modellers have predicted, then that change in reliability alone could be enough for the information to be ignored. To determine whether an individual is treating social information differently than nonsocial information, or whether it is simply attending to the reliability of information regardless of its source, we first need a way to predict how reliability should affect information use. The ‘flag model’, a simple but important model proposed in McLinn and Stephens (2006) offers such a prediction. Picture an animal that exists in an environment with two possible conditions, and with two behaviours available to it. When the environment is in Condition A, Behaviour A is rewarded; when the environment is in Condition B, Behaviour B is rewarded. A mismatch between the state and behaviour results in no reward. The animal cannot directly determine the state of the environment. What behaviour should the animal perform? Let p be the probability that the environment is in Condition A. If p ¼ 0.5, then there is an equal chance of being in Condition A or Condition B. If p ¼ 0.8, there is an 80% chance of being in Condition A; without further information, the subject should perform Behaviour A. We can call p ‘environmental certainty’. The higher p is, the more certain the animal is of the condition of the environment: p ¼ 0.5 represents the least certain environment possible, where the condition is essentially a coin flip, while p ¼ 1.0 represents the most certain environment, which is always in Condition A and never in Condition B. We consider p to be analogous, although not identical, to the terms ‘predictability’, ‘complexity’ or ‘variability’ that are often used in models of learning. Now let there be some kind of experience, such as an available signal, that is correlated with the condition of the environment: say, a light that is usually red in Condition A and blue in Condition B. If the animal attends to this experience, it could use the signal to guide its behaviour, performing Behaviour A when it sees red and Behaviour B when it sees blue. We can represent signal reliability with q. If q ¼ 1.0, the signal)condition correlation is perfect. If q is lower, the signal will be mismatched or ‘incorrect’ some of the time. If q ¼ 0.5, the lowest possible reliability, there is no correlation between the signal and the condition. Should the animal pay attention to the signal? Not always. In a completely certain environment (p ¼ 1.0), the animal can adopt an ‘environment averaging’ strategy of always performing Behaviour A. Any time p > q, in fact, the environment averaging strategy gives a higher payoff than attending to the signal, which can result in the initially counterintuitive behaviour of ignoring a very reliable signal. Conversely, if q > p, the animal should adopt a ‘signal following’ strategy and do whatever the signal indicates. Using this strategy sometimes means following a somewhat unreliable signal; consider a situation where p ¼ 0.5 and q ¼ 0.6. A signal that is only 60% reliable sounds poor, but following it is certainly better than the alternative. Experimentally, these predictions have been qualitatively upheld (McLinn & Stephens, 2006, 2010). Empirical research on social learning has measured or manipulated the reliability of social information (Rafacz & Templeton, 2003; van Bergen et al., 2004), or the environment (Galef & Whiskin, 2004, 2008), but rarely both at once. However, from the model above, we see that a measure of one parameter is not enough. Without concurrent measurements of both the reliability of the social information and the certainty of the environment, we cannot determine whether an animal is using or ignoring a social signal based on a social-learning specific rule, or based on general information economics. In addition, if there are nonsocial sources of information that the animal could use, measuring its reliability could provide further important information about social learning. Are animals picking the most reliable information, or do they preferentially choose social or nonsocial information, regardless of its reliability, in some situations? Our experiment deals with these issues in two ways. First, by testing animals in artificial foraging environments, we can precisely control the environmental certainty and the reliability of any available social information. Second, we presented a nonsocial signal yoked to the behaviour of a social signaller. The yoking technique creates social and nonsocial signals that are identical in reliability and timing, allowing us to compare the signals directly. If subjects are assessing all information by reliability alone, patterns of signal following should be identical for social and nonsocial signals. However, if they are predisposed to ignore social information in an uncertain environment, we would see less use of social signals than similarly reliable nonsocial signals in uncertain environments. To test these predictions, we presented captive blue jays, Cyancocitta cristata, with social and nonsocial signals of varying reliability, in environments of varying certainty. We tested three signal reliabilities and three environments, for both social and nonsocial signals, for a total of 18 treatments (Fig. 1). We examined the response patterns to determine when subjects were attending to information and when they were adopting an environmenttracking strategy, and we compared these responses between the social and nonsocial contexts. Experimental treatments 1 Follow signal Signal reliability, q 54 0.8 Ignore signal Choose most common option 0.6 0.6 0.8 Environmental certainty, p 1 Figure 1. A plot of signal reliability and environmental certainty, based on the flag model presented in McLinn and Stephens (2006), with the ‘follow signal’ and ‘ignore signal’ strategy regions highlighted. Signal following is beneficial when q > p. Both strategies are equivalent along the 1:1 line. We tested nine combinations of signal reliability and environmental certainly (white circles), with both social and nonsocial signals, for a total of 18 experimental treatments. V. K. Heinen, D. W. Stephens / Animal Behaviour 112 (2016) 53e62 METHODS Subjects Our subjects were 12 adult blue jays of unknown sex and mixed experimental history (band numbers 7, 11, 71, 92, 93, 95, 207, 208, 336, 350, 361 and 380), randomly selected from our colony. Birds were captured as nestlings under appropriate state and federal permits and then hand-reared. We maintained the birds in accordance with the University of Minnesota Institutional Animal Care and Use Committee guidelines (protocol no. 1408-31752A). Birds (a) were grouped into ‘trios’ of one demonstrator and two observers at random. Birds were housed in Skinner boxes (Fig. 2). Subjects stayed in their boxes for 23 h per day; the extra hour was used to sanitize the boxes and perform daily health and weight checks. Subjects were maintained on a 12:12 h light:dark cycle and given water ad libitum. The experiment consisted of a closed economy such that each individual earned all its daily food by performing experimental trials, with the exception that if an individual earned less than 7 g/ day in the experiment, we supplemented its food up to 7 g. In addition, if any individual dropped below 85% of its starting weight, (b) Social observer’s view Rear light Food cup 55 ‘Go light’ Signal (bird) Rear perch Demonstrator’s box ‘Wait light’ Front perch Water dish Nonsocial observer’s view ‘Go light’ Signal (light) Front perch Front perch Transparent partition Social observer’s box ‘Wait light’ Front perch Signal lights Nonsocial observer’s box Figure 2. Diagram of the three-chamber testing apparatus. (a) Overhead view of the testing apparatus showing the demonstrator's box (upper left), the social observer's box (bottom left) and the nonsocial observer's box (bottom right). Each chamber was a metal box (41 53 46 cm high) that held one bird. Each box was fitted with a pellet dispenser (not shown), a food cup for holding dispensed pellets and three perches fitted with microswitches that detected the presence or absence of a bird. A sheet metal partition between the two front perches ensured that the bird could not switch perches once a choice was made. The demonstrator's box and social observer's boxes were adjacent and separated by transparent partitions. (b) The front of the box, as seen by a social observer and a nonsocial observer. The social observer could see the demonstrator above one of the two front perches. The nonsocial observer's box had signal lights in front of the two front perches, which were illuminated to match the demonstrator's position. 56 V. K. Heinen, D. W. Stephens / Animal Behaviour 112 (2016) 53e62 we supplemented its food until its weight rose above that threshold. Testing Apparatus Each Skinner box consisted of one perch in the rear of the box, where subjects waited to begin a trial, and two perches in the front of the box. An observer's ‘objective’ was to select the front perch that would result in a food reward. The demonstrator and social observer were housed in adjacent boxes, with transparent windows above the front perches. When the demonstrator hopped to one of its front perches, it became visible to the social observer. This served as a positional signal for the social observer. The nonsocial observer could not see the demonstrator; instead, a light was positioned above each of the nonsocial observer's front perches. These lights were yoked to the behaviour of the demonstrator, so that when the demonstrator took its position at one of the front perches, the light illuminated above the corresponding perch in the nonsocial observer's box (Fig. 2b). The two observers experienced signals with identical timing and position, and only differed in whether or not the signals came from a conspecific. the nonsocial observer's box. The wait lights in the observers' boxes then extinguished, a green ‘go light’ illuminated above both front perches, and both observers were free to respond by hopping to one of their two front perches. Once an observer responded, the go lights extinguished and a food reward was delivered in the rear of the box, if the subject had made a choice that led to a reward. Food delivery was accompanied by a flashing light, which subjects had previously been conditioned to. The demonstrator was rewarded after every trial, but not until after the social observer had been rewarded. After both observers completed their responses, the rewarded side and signal for the next trial were determined and the ITI restarted. Trials were aborted if any bird did not make a response within 7 min, any bird left its rear perch while its wait lights were illuminated, or the demonstrator selected the perch not indicated by its light cue. Aborts were indicated by all lights extinguishing in every box. After an abort, the trial restarted after the ITI had elapsed. In the event that the number of aborts exceeded 35 after 4 h of testing, indicating that the birds were not successfully completing trials, we removed the birds from testing for the day and excluded that day's data from the analysis. Free and Forced Trials Treatments and Pretraining We tested subjects at three levels of p (0.6, 0.8, 1.0) and three levels of q (0.6, 0.8, 1.0) in a factorial design. In addition, each of these nine combinations was tested in both a social and a nonsocial condition, for a total of 18 treatments. Each observer experienced all treatments, which required that the two demonstrators in each trio periodically switched between roles as the social and nonsocial observer. Treatment order was randomized for each trio but was necessarily linked between pairs of observers. In all treatments, one perch was always rewarded more often than the other. Whether the most commonly rewarded side was the right perch or left perch was randomized for each treatment and each trio, to avoid the possibility of long-term perch bias. Before the experiment began, we trained the observers to follow both the social and nonsocial cue at p ¼ 0.5 and q ¼ 1.0 (a perfectly reliable cue in a completely unpredictable environment) until they could follow both cues with >90% accuracy for 200 trials. One observer in each trio was randomly assigned to train on the social signal first, while its partner was trained on the nonsocial signal first. Trial Walkthrough At the beginning of each trial, the computer randomly determined the condition of the environment (left side rewarded or right side rewarded) and whether or not the signal would correctly indicate that side, using the day's values of p and q. After an intertrial interval (ITI) of 120 s, the start of a trial was indicated by a flashing light at the rear of the demonstrator's box. Once the demonstrator hopped to the rear perch, red ‘wait lights’ illuminated, indicating that the demonstrator should wait on the rear perch. At the same time, flashing lights illuminated at the rear of the social observer and nonsocial observer's boxes. When each observer took its position at the rear of the box, similar red wait lights illuminated. Once all three subjects were in position, the wait lights in the demonstrator's box extinguished, and a green ‘go’ light, visible only to the demonstrator, illuminated above one of the demonstrator's two front perches. When the demonstrator hopped to the indicated perch, becoming visible to the social observer through the window, an orange light illuminated behind the window on the same side in Trials took place in ‘blocks’ of 40, consisting of four forced trials followed by 36 free trials. Forced trials required the observers to respond in a predetermined way. Forced trials were indicated by only one green ‘go light’ illuminating in the observers' boxes, indicating the perch that observers needed to choose. The trial did not complete until the observer selected this perch; if the observer moved to the nonrequested perch, the green light and rear light remained on, as opposed to the extinguishing of both lights (and possible food delivery) that would signal the completion of a trial. The observer could then move back to the rear perch and try again. There were eight types of forced trials to account for all possible combinations of condition, signal and observer choice, ensuring that the observers were familiar with the consequences of all potential actions (Table 1). These eight trials were presented in random order over each set of two blocks. In free trials, observers could respond without restrictions. At the beginning of each free trial, the computer determined which of the two perches would be rewarded (the ‘correct’ perch), and whether or not the demonstrator bird and light cue would indicate the correct perch or the incorrect perch, based on the current parameters of p and q, respectively. Observers were rewarded only if they chose the correct perch. For each free trial we recorded whether the observer chose the perch indicated by the signal or not, whether it chose the most frequently rewarded or the least frequently rewarded perch, and whether the choice was the ‘correct’ (rewarded) perch. We also recorded how long the observer took to select a perch. Each treatment lasted for 600 free trials. The initial 400 trials gave the subjects time to learn the treatment parameters, while the final 200 trials were used for analysis. Dependent Measures and Analysis We used R (R Core Team, 2015) and lme4 (Bates, Maechler, Bolker, & Walker, 2015) to create binomial linear mixed-effects models of the birds' behaviour. Visualization was conducted using ggplot2 (Wickham, 2009). For all models we used signal reliability, environmental certainty and signal type (social or nonsocial), as well as their interactions, as fixed effects. Observer identity and pair identity were included as random effects, with observer nested within pair. We collected data from 12 observers, grouped into six pairs. Demonstrators are not included in this analysis because their V. K. Heinen, D. W. Stephens / Animal Behaviour 112 (2016) 53e62 57 Table 1 The eight possible combinations of environmental condition, signal state and observer choice Environment condition Side indicated by signal Side chosen by observer Observer rewarded? Left rewarded Left rewarded Left rewarded Left rewarded Right rewarded Right rewarded Right rewarded Right rewarded Left Left Right Right Left Left Right Right Left Right Left Right Left Right Left Right Yes No Yes No No Yes No Yes Jays were forced to experience all of these combinations at least once every 80 trials, to ensure they were familiar with all possible actions and their outcomes. behaviour was not variable; they merely served as a cue for the observers. Bird 93 and its partner, Bird 336, were not able to complete three of the 18 treatments. In addition, the group containing Bird 7 and Bird 207 did not complete one treatment due to experimenter error. All other birds completed all 18 treatments. We recorded how often observers picked the perch that was indicated by the signal (the presence of a bird or a light), but this does not directly measure how often observers use or attend to the signal. For example, in the p ¼ 1.0, q ¼ 0.8 treatment, the left perch could be the correct choice 100% of the time, but the demonstrator bird would indicate the left perch 80% of the time. An observer that chooses the left perch in every trial would still match the signal 80% of the time, even if the observer completely ignored the signal. Figure 3c illustrates this problem well. We developed an analysis that attempts to separate signal use from other strategies used to determine perch choice. We assume there are two primary strategies: ‘signal use’, or choosing the perch indicated by the signal, and ‘environment averaging’, or choosing the perch that is best, on average. We include the option of ‘guessing’, or picking a perch at random, to allow for the possibility of error. To separate these strategies, we examined how birds behaved in the subset of trials when the signal and the perch that was best on average conflicted, a situation where the different strategies should result in different behaviours. Environmental certainty (p) Certainty = 0.6 Certainty = 0.8 (a) (b) Certainty = 1 (c) 1 Frequency of signal matching 0.9 0.8 0.7 0.6 0.5 0.6 0.8 1 0.6 0.8 1 Signal reliability (q) 0.6 0.8 1 Figure 3. Observed levels of signal matching for nonsocial signals (solid black line) and social signals (dashed grey line) across three levels of signal reliability and three levels of environmental certainty (a, b, c). Error bars represent adjusted standard errors as described in Cousineau (2005). 58 V. K. Heinen, D. W. Stephens / Animal Behaviour 112 (2016) 53e62 Specifically, we examined the probability of two behaviours: the bird choosing the most frequently rewarded perch (PMF) when the signal indicated the least frequently (SLF) rewarded perch (P(PMFSLF)), a behaviour that would be consistent with an environment averager, and the bird choosing the most frequently rewarded perch when the signal was also indicating that side (P(PMFSMF)), a behaviour consistent with both strategies. We assumed that a guessing bird would choose a perch at random, and therefore had a 50% chance of choosing the most frequently rewarded perch in both of these situations. Some simple arithmetic allows us to calculate the proportion of time that each bird uses the three strategies: P(E) þ P(G)/2 ¼ P(PMFSLF) P(S) þ P(E) þ P(G)/2 ¼ P(PMFSMF) P(S) þ P(E) þ P(G) ¼ 1 where P(E) is environment averaging, P(S) is signal use and P(G) is guessing. RESULTS Training All observers successfully completed the pretraining and learned to follow both the social and nonsocial signal with 90% accuracy or better. To determine whether one type of signal was easier to learn, we examined the number of trials completed between the birds' first experience with the signal and achieving the training criterion. Birds tended to require more trials to learn the social signal (mean ± SE ¼ 706.81 ± 62.01 trials) than they did to learn the nonsocial signal (mean ± SE ¼ 595.36 ± 79.05 trials), but this difference was not significant (paired t test: t11 ¼ 1.781, P ¼ 0.103). Response Latency The response latency, defined as the time in seconds between the presentation of the signal and the observer's choice of a perch, was not significantly different between the social (mean ± SE ¼ 1.34 ± 0.17 s) and nonsocial (mean ± SE ¼ 1.53 ± 0.14 s) treatments (paired t test: t103 ¼ 0.910, P ¼ 0.365). Signal Matching Figure 3 shows the relative frequency with which observers picked the perch on the same side as the demonstrator bird or signal light, referred to here as ‘matching’ the signal, in each of our 18 treatments. Signal matching can be considered a crude measurement of signal use, although it does not fully capture this behaviour (see Dependent Measures and Analyses above). We constructed a binomial linear mixed-effects model with the proportion of signal matching as the dependent measure. A likelihood ratio test between this model and a model without signal type (social or nonsocial) as a fixed effect revealed no effect of social environment (c29 ¼ 12.118, P ¼ 0.2067). However, the full data set does not clearly discriminate between birds that attended to the signal and birds that were simply choosing the most frequently rewarded perch. Examining the subset of trials where the least commonly rewarded perch was the ‘correct’ choice allows for a clearer discrimination between these strategies. We expect birds that attend to the signal to choose the least commonly rewarded perch whenever the signal indicates it, while birds that ignore the signal should never choose the least commonly rewarded perch. A likelihood ratio test showed that signal type significantly affected signal matching (c26 ¼ 20.421, P ¼ 0.0023) for this subset of trials. We conducted further model testing using a type II Wald chi-square test, revealing a significant interaction between signal type and environmental certainty (c22 ¼ 7.0776, P ¼ 0.0290), and between signal reliability and environmental certainty (c22 ¼ 20.2998, P ¼ 0.000039). For both social and nonsocial signals, signal matching increased with increasing signal reliability and decreased with increasing environmental certainty, as predicted by our model. However, in the less certain p ¼ 0.6 and p ¼ 0.8 environments, birds matched the nonsocial signal more than they matched the social signal, with the gap widening as the signal increased in reliability. In the most certain p ¼ 1.0 environment, behaviour was identical for birds that saw social and nonsocial signals, probably because the optimal behaviour in these treatments was to ignore the signal and instead pick the single rewarded perch. Strategies Used Figure 4 shows our estimates of three learning strategies: signal use, environment averaging and guessing, as determined using the technique described in Methods. As before, we constructed linear mixed-effects models for each strategy. Using likelihood ratio tests, we found that signal type significantly affected signal use (c21 ¼ 4.7379, P ¼ 0.0295) and guessing (c21 ¼ 6.7183, P ¼ 0.0095), but not environment averaging (c21 ¼ 0.9635, P ¼ 0.326). The primary pattern was that, again, birds followed the nonsocial signal more than they followed the social signal in the less certain p ¼ 0.6 and p ¼ 0.8 environments, with the difference becoming more pronounced as the signal became more reliable. However, the actions that social observers took instead of signal following differed between the p ¼ 0.6 and the p ¼ 0.8 environments. In the least certain environment (p ¼ 0.6), birds in the social and nonsocial signal treatments tracked the environment identically. But birds seemed to ignore the social signal in favour of picking a side randomly; some birds guessed as much as 25% of the time, even when the social signal was perfectly reliable. Very little guessing was seen when the signal was nonsocial. In the intermediately certain environment (p ¼ 0.8), birds did not guess in either signal type treatment. Behaviour was identical for both signal types in the p ¼ 1.0 environment, where environment tracking was used almost exclusively. No data are shown for the p ¼ 1.0, q ¼ 1.0 treatment, since environment averaging and signal following produced identical behaviours in this treatment and were therefore indistinguishable. In summary, we visually compared the use of signal following and environment tracking strategies to the predictions made by the flag model, by estimating the boundary between signal use and environment averaging strategies (Fig. 5). We assigned each bird a ‘signal use score’ for each treatment based on its signal use, P(S), and environmental averaging, P(E), behaviours. Signal use score was calculated as P(S)/((P(S) þ P(E)), so that a signal use score of 1 indicated that a bird only attended to signals, and a signal use score of 0 indicated that the bird exclusively attended to the environment. For each of the three environments (p ¼ 0.6, 0.8 or 1.0), we interpolated the signal reliability value corresponding to a signal use score of 0.5, the point where birds used signals and tracked the environment interchangeably. We then used least squares regression to connect these points across the three environments. Qualitatively, for both experimental conditions, the diagonal line shifted upward along the Y axis compared to the model predictions. This indicates a bias towards environment tracking over signal use, a V. K. Heinen, D. W. Stephens / Animal Behaviour 112 (2016) 53e62 59 Environmental certainty (p) Certainty = 0.8 Certainty = 0.6 1 (a) Certainty = 1 (b) (c) Strategy 0.75 Frequency of strategy use Signal use Environment averaging Guessing 0.5 Signal type Nonsocial Social 0.25 0 0.6 0.8 1 0.6 0.8 1 0.6 0.8 1 Signal reliability (q) Figure 4. Relative frequency with which blue jays responded to social cues and nonsocial cues by picking the perch indicated by the signal (‘signal use’), tracking the environment by choosing the most frequently rewarded perch (‘environment averaging’), or picking a perch at random (‘guessing’) across three levels of signal reliability and three levels of environmental certainty (a, b, c). Error bars represent adjusted standard errors as described in Cousineau (2005). result consistent with previous studies (McLinn & Stephens, 2006). This bias appeared stronger when the signal was social. DISCUSSION 1 Signal reliability (q) Follow signal 0.9 0.8 Track environment 0.7 0.6 0.6 0.7 0.9 0.8 Environmental certainty (p) 1 Figure 5. Lines showing the border between ‘environment averaging’ and ‘signal following’ predicted by our model (solid line) and estimated for nonsocial signals (dotted line) and social signals (dashed line). This study had two goals: to examine the effects of information reliability and environmental certainty in social information use, and to directly compare the use of social and nonsocial signals. For both social and nonsocial signals, jays' signal use was sensitive to both environmental uncertainty and signal reliability, qualitatively agreeing with the model outlined in the Introduction. As predicted, jays followed both signal types the most when the signal was the most reliable (q ¼ 1.0) and the environment was the least certain (p ¼ 0.6). As environmental certainty increased, or as signal reliability decreased, jays followed the signal less and tracked the environment more. Where signal reliability and environmental certainty were equally informative (p ¼ q), jays favoured environment tracking over signal following. These results align with those of previous experiments (McLinn & Stephens, 2006) and suggest that the jays chose the strategy (environment tracking or signal following) that gave the best results on average, but they showed a bias towards environment tracking. Importantly, signal reliability and environmental certainty are seldom considered together in social-learning models (Laland & Kendal, 2003; Rendell, Fogarty, et al., 2011), but both affected social information use here. 60 V. K. Heinen, D. W. Stephens / Animal Behaviour 112 (2016) 53e62 While the overall patterns of social and nonsocial signal use were similar, there were some striking differences. Most noticeably, social signals were used less than identically reliable nonsocial signals in the two less certain environments (p ¼ 0.6 or 0.8)), although signals were used in both environments. When signals were unreliable (q ¼ 0.6) or the environment was completely certain (p ¼ 1.0), signal following behaviour appeared identical, and low, for both types of signal. These low rates of signal use resulted in an overall reduction in energy intake for birds in the social signal treatments. These results suggest that, under the conditions of this experiment, blue jays are biased against using social signals in uncertain environments. Social and asocial learning were affected by some of the same signal properties, but they were not identical. Results in Context Social learning is a widespread phenomenon, and an enormous body of literature has documented instances of social learning in a variety of taxa. It is well accepted that social learning is frequency dependent; there must be some nonsocial learners in a population for the social learners to copy (Barnard & Sibly, 1981; Bikhchandani et al., 1998). There has also been research into the differences between social information and other nonsocial sources: social information is easy to acquire but prone to becoming outdated (Kameda & Nakanishi, 2002), resulting in the spread of maladaptive or suboptimal behaviour (Giraldeau et al., 2002). This has resulted in investigation into ‘social learning rules’ governing when an animal should learn socially and when it should use personal information (Laland, 2004). Social information studies have been largely isolated from other learning research, perhaps due to an implicit assumption that the mechanisms of social and asocial learning are different (Templeton, Kamil, & Balda, 1999). Recent work has challenged those assumptions, demonstrating that social learning occurs in nonsocial animals (Wilkinson, Kuenstner, Mueller, & Huber, 2010) and utilizes the same types of stimuluseresponse associations as asocial learning (Heyes, 2012). Statistical decision theory provides another way to compare the mechanisms of social and asocial learning. Animals should use information that reduces their uncertainty about the environment, regardless of its source, and ignore information that provides no such advantage (Dall et al., 2005). This means it is critical to understand the animal's prior uncertainty about the best action, and how accurately a piece of information reflects that best action, before we can predict whether the animal should attend to it. These variables can influence animal learning both across evolutionary time (Dunlap & Stephens, 2009) and across an individual's lifetime (McLinn & Stephens, 2006). To predict when social learning is valuable, we must determine when it reduces the user's uncertainty about the best action more than other available sources of information do (Dall et al., 2005). Without this information, it is impossible to know whether animals are attending to, or neglecting, social information based on a unique social information rule, or based on more general mechanisms. This problem is especially noticeable in models of social learning in uncertain environments, where uncertainty is usually created through spatial or temporal heterogeneity. The less certain the environment, the more likely it is that demonstrators and observers will have experienced different conditions, making social information less reliable and leading to the proposed rule ‘copy when environments are stable’ (Laland & Kendal, 2003). But the same outcome can be predicted by an assessment of signal reliability relative to environmental certainty, a learning rule that treats social and nonsocial information identically. In our experiments, certainty and reliability influenced social information use, but did not fully explain it, suggesting that a social learning rule may be combined with assessments of signal reliability and environmental certainty, producing an intermediate strategy. To our knowledge, few other experiments have directly compared a social and nonsocial signal. Rafacaz and Templeton's (2003) findings are remarkably similar to ours. They examined the behaviour of foraging starlings presented with a reliable or unreliable social cue (a foraging conspecific) and a reliable or unreliable nonsocial cue (the colour of the food cup). Starlings successfully learned to use both cues but showed a bias against the social information: when the two cues were of identical reliability, starlings consistently followed the colour cues and ignored conspecifics. In contrast, Takahashi, Masuda, and Yamashita (2014) found that striped jacks, Pseudocaranx dentex, learned about feeding locations using social information (the presence of a feeding conspecific) but ignored nonsocial information (aeration indicating the location of a food source) unless it was presented simultaneously with the conspecific. However, this experiment used a questionable nonsocial signal: no fish ever learned to use the aeration cue alone, suggesting this signal may be difficult for them to detect. Tests comparing the use of a social signal and an environmenttracking type strategy, with no alternative nonsocial signal, are more common. Results from these types of tests, like ours, align with the predictions of statistical decision theory: use whatever information is most reliable (Dall et al., 2005). Sticklebacks (van Bergen et al., 2004), bats (Jones, Ryan, Flores, & Page, 2013), bumblebees (Leadbeater & Chittka, 2009) and rats (Galef & Whiskin, 2008) learn socially when their environment is less certain than the available social information is reliable, or when they do not have much experience with the environment. Other studies have also found reduced use of social information in uncertain environments. Galef and Whiskin (2004) found that rats from an environment where feeding times changed randomly showed less social learning than rats from a stable environment, suggesting that rats are biased against social information in uncertain environments. Similarly, humans (Toelch et al., 2009) used social information to complete a maze more often when environmental certainty was low than when it was high. Like our jays, humans ignored social information in the low-certainty conditions even when that information was very reliable. Limitations and Further Directions Our results suggest that jays have a bias against social information, as they used it less than they used asocial information in four treatments (q ¼ 0.8 or 1.0, p ¼ 0.6 or 0.8). This result is important because many empirical tests of social learning do not provide a direct nonsocial source of information, or do not measure the reliability of nonsocial alternatives. However, we must be careful about generalizing these results. The foraging environment presented was artificial, with only two behavioural alternatives, and the social interaction between the demonstrator and observer was similarly limited. We did see that jays were able to learn and use demonstrator position as a cue. However, this positional signal lacks many elements of natural social information. The observer cannot see the demonstrator acquire or consume food, which has been shown to be an important component of social learning in birds (Avery, 1996; Lefebvre & Bouchard, 2008) and rats (Galef & Wigmore, 1983). Without visible food, this artificial social cue may be less salient than the social cues encountered by jays in natural situations. The social cue in this experiment also lacked other potential advantages of social information. The ease of learning the social cue and nonsocial cue seemed roughly equivalent in this preparation, as there was no significant difference in the number of training trials V. K. Heinen, D. W. Stephens / Animal Behaviour 112 (2016) 53e62 required for learning and no difference in latency to respond to either stimulus and all birds successfully learned both types of cues. This may not be true in nature; for example, when learning a complicated behaviour, social learning can be much faster than trial-and-error learning (Zohar & Terkel, 1991), or social stimuli may be more salient and easily detected than nonsocial stimuli (Heyes, 2012). There was also no cost to making an error in our experiment beyond reduced food intake, but social learning may provide an advantage over independent learning when costs of failure are high, such as when learning predator avoidance behaviours (Griffin, 2004). Social information in natural settings can also be associated with benefits such as group membership and predator defence (Coolen, Ward, Hart, & Laland, 2005), which were not present in our experiment. A potential disadvantage of social learning, competition for food, was also absent. We intentionally excluded these factors in order to examine the role of reliability in social learning more precisely, but these factors are important determinants of real-world behaviour that, in future work, need to be added to the baseline established here. It is possible that our results are due to some general tendency of blue jays to avoid other blue jays, perhaps because of perceived competition, rather than some more abstract ‘social-ness’ of the signal. We believe this is not the case for two reasons. First, in the q ¼ 1.0 treatments, jays in the social treatment picked the same perch as their social demonstrator exactly the same amount as jays in the nonsocial treatment picked the perch indicated by the light. In fact, in the q ¼ 1.0, p ¼ 1.0 treatment, jays picked the same perch as their social demonstrator in almost every trial. If jays were predisposed to avoid each other, we would expect to see a reduction in signal matching across all treatments, instead of only a subset of treatments, as we observed. Second, in a previous study (Polnaszek & Stephens, 2014), jays successfully learned to use a social signal identical to the one presented in our experiment, and when that social signal was reliable, they followed it in up to 98% of trials. It seems difficult to account for these results if jays have a general tendency to avoid each other. Nevertheless, we propose to address this issue through a follow-up experiment where jays will be trained to avoid the side indicated by the signal; for example, a bird on the left could indicate that the reward is on the right, so signal followers should rarely approach the demonstrator bird. If jays are truly biased against social information, the results from these experiments should be comparable. Our results partially confirm the predictions of many social learning models (Laland & Kendal, 2003), which state that the value of attending to social information depends on the environment. One prediction is that social learning should be used less than nonsocial learning in uncertain environments. This differs from the predictions of the flag model, which predicts that animals should attend to any informative signal in uncertain environments. In the present study, the jays' behaviour matched this prediction in the q ¼ 0.6 and q ¼ 0.8 treatments, where signal following behaviour was higher for asocial signals than for social signals. A second prediction of social learning models as well as the flag model is that no learning, social or asocial, is adaptive in an extremely certain environment. Our q ¼ 1.0 treatment supports this prediction, where jays in both social and nonsocial treatments relied on an environment-tracking strategy and did not attend to any signals. However, a third prediction of social learning models is that, at some intermediate level of uncertainty, social learning should be used more than asocial learning. We did not find any support for this prediction. It is possible that our treatments were too coarse-grained to capture this behaviour and that the ‘sweet spot’ for social learning is somewhere between q ¼ 0.8 and q ¼ 1.0. If true, then we would expect to see social signal following 61 match or exceed nonsocial signal following in that environment. Further experiments should investigate the range of intermediately certain environments in more detail, to allow us to more thoroughly assess the potential rules or mechanisms governing social signal use. Finally, the nature of the uncertainty in the environment is another important consideration. This experiment examined an absolute, probabilistic type of uncertainty. However, environments may also vary temporally, spatially, or both. Each type of environment suggests a different kind of nonsocial learning strategy; in a temporally fluctuating environment, periodic sampling becomes more important (Stephens, 1987) because environment averaging may not work. Much of the social learning modelling literature focuses on temporally changing environments (Boyd & Richerson, 1988; Feldman, Aoki, & Kumm, 1996), but comparisons of multiple kinds of change sometimes yield contradictory results (Aoki et al., 2005). Direct comparisons between social and nonsocial cue use in these environments would be informative. The use of social information has been proposed to be a first step in the evolution of signalling and culture (Danchin, Giraldeau, Valone, & Wagner, 2004); understanding and predicting when information should be socially transmitted is critical to extrapolating to these long-term processes. The techniques developed in this experiment can be extended to directly manipulate and measure social information in a variety of contexts and species, and allow for a better comparison of empirical and theoretical results. Acknowledgments We thank T. L. Rubi, T. J. Polnaszak, T. J. Bergman, two anonymous referees and the University of Minnesota behaviour group for helpful feedback on the project and manuscript. We also thank the numerous undergraduate students who helped with bird care and data collection. The Statistical Consulting Center at the University of Minnesota, and in particular Aaron Rendahl, helped with the analysis of this experiment. We conducted this research with the approval of the Institutional Animal Care and Use Committee, University of Minnesota (protocol no. 1408-31752A). 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