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Animal Behaviour 112 (2016) 53e62
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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.
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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). This research
was funded with support from an Animal Behavior Society Student
Research Grant, the Frank McKinney Fund of the Bell Museum of
Natural History, and the University of Minnesota's Department of
Ecology, Evolution, and Behavior. V.K.H. was supported by a National Science Foundation Graduate Research Fellowship under
Grant No. 00039202. Any opinions, findings, conclusions or recommendations presented here are those of the authors and do not
necessarily reflect the views of the National Science Foundation.
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