Abstract
Cognitive
bias has become a popular way to access non-human animal mood, though
inconsistent results have been found. In humans, mood and personality
interact to determine cognitive bias, but to date, this has not been
investigated in non-human animals. Here, we demonstrate for the first
time, to the best of our knowledge, in a non-human animal, the domestic
pig (Sus scrofa domesticus), that mood and personality
interact, impacting on judgement. Pigs with a more proactive personality
were more likely to respond optimistically to unrewarded ambiguous
probes (spatially positioned between locations that were previously
rewarded and unrewarded) independent of their housing (or enrichment)
conditions. However, optimism/pessimism of reactive pigs in this task
was affected by their housing conditions, which are likely to have
influenced their mood state. Reactive pigs in the less enriched
environment were more pessimistic and those in the more enriched
environment, more optimistic. These results suggest that judgement in
non-human animals is similar to humans, incorporating aspects of stable
personality traits and more transient mood states.
1. Introduction
Information
processing in humans is known to be pessimistically biased by a
negative mood, with a greater expectation of a worse outcome when
confronted with ambiguous stimuli [1–4]. By analogy, biases in judgement or cognitive biases have become a popular way to access non-human animal moods [5–8].
Animals in a more positive mood state show ‘optimistic’ biases,
characterized by responding to ambiguous stimuli as though they
predicted a positive outcome. Conversely, animals in a negative mood
show ‘pessimistic’ biases, responding to ambiguous stimuli as if
anticipating a negative outcome. If such processes in human and
non-human animals operate similarly, then mood is predicted to interact
with personality to determine cognitive bias.
Here, we
test the hypothesis that mood and personality interact to influence
cognitive bias in the domestic pig. The pigs were housed in one of two
environments known to influence their mood [9].
Mood, defined as ‘relatively enduring affective states that arise when
negative or positive experience in one context or time period alters the
individual's threshold for responding to potentially negative or
positive events in subsequent contexts or time periods' [10, p. R712], can be affected by the environment [11],
with better environments assumed to induce better moods. In contrast,
personality is defined as a set of consistent individual differences in
behaviour across contexts and time [12]. In pigs, personality is frequently measured, using the coping styles approach [13,14].
Proactivity, at one end of the coping style spectrum, is characterized
by more active behavioural responses and less flexible behaviour [15].
Conversely, reactivity indicates more flexible but more passive
behaviour. Proactivity/reactivity has been linked to extraversion and
neuroticism personality traits in humans. A tendency towards optimism in
humans is linked with extraversion, and pessimism with neuroticism [16]
and thus may also influence judgement in other animals. We predicted
that proactive pigs would respond optimistically in the cognitive bias
task, regardless of their housing conditions (and inferred mood state),
but reactive pigs would be affected by their housing conditions (and
inferred mood state).
2. Material and methods
(a) Animal housing and husbandry
Weaned at four weeks, 36 pigs (commercial crossbreed PIC337 (large white × landrace), n = 24 males, n
= 12 females) were assigned (pseudo-randomly controlling for sex,
weight and dam) to either a high- or low-level enriched environment in
two groups of 18, replicated three times. Six pigs from each environment
and replicate were selected for training. Both environments had solid
floors, a slatted area and wooden blocks on chains as enrichment. More
enriched environments had deep straw and a larger space allowance (more
enriched: 0.62 m2 pig−1; less enriched: 0.41 m2 pig−1).
Pigs received an ad libitum conventional diet, with artificial lighting
12 h daily and natural light through windows. Ventilation and
temperature were automatically controlled (28°C decreasing 0.5°C daily
to 19°C). Personality testing occurred at six and eight weeks of age;
cognitive bias training and testing was completed by 7–10 weeks of age.
(b) Cognitive bias testing
Pigs were habituated to feeding from a bowl in a test arena (figure 1).
After habituation, a false-bottom bowl was used, containing three
sugar-coated chocolate sweets and coffee beans to minimize use of
olfactory cues. Pigs were trained to associate bowl location with a
positive or negative outcome; the positive (P) location in one corner of
the arena contained a reward of three sweets, and in the opposite
corner, the negative (N) location contained three coffee beans. The
location of P/N was pseudo-randomly allocated and counterbalanced over
the environmental treatments for each individual. Training progressed
from presentations of P, N, P to 5P and 4N in random order, with just
one bowl present in each trial. The criteria for learning the task were
80% ‘success’, defined by approaching location P within 30 s and not
approaching location N within 30 s. All pigs reached criterion except
for nine who failed to habituate (total n = 27, 17 male and 10
female; 12 better environment and 15 worse environment). Two tests were
conducted per pig, with bowls in three intermediate probe locations
(near positive, NP; middle, M; near negative, NN). These were presented
in a pseudo-randomized order once per test with the proviso that the M
probe was always the first ambiguous probe presented, between ‘recap’
presentations at P and N locations, resulting in nine trials per test
(e.g. P, N, M, N, P, NN, P, N, NP). Ambiguous probes were unrewarded but
locations P and N contained either sweets or coffee beans, as in
training. Pigs were given 30 s to approach the probe after which they
were returned to the start box for the next trial. Time to approach the
probe was recorded from the point when all four feet were outside the
start box.
Figure 1.
Cognitive
bias training and testing arena. N, negative unrewarded location; NN,
near negative probe location; M, middle probe location; NP, near
positive probe location; P, positive rewarded location.
(c) Personality testing
For
a social isolation (SI) test, pigs were placed individually in a pen (l
× w × h: 2.2 × 1.7 × 1.2 m) away from the home environment, where they
remained for 3 min without disturbance. After SI test 1, each pig
received a 5-min habituation period in the novel object (NO) arena.
On
the days following SI tests 1 and 2, pigs participated in an NO test.
They were released from a start box (l × w × h: 1 × 1×1.2 m) through a
sliding wooden door after 1 min into the arena (l × w × h: 3.6 × 2×1.2
m). A large white bucket and an orange traffic cone were NOs, presented
in a pseudo-randomized (across tests, with only one being presented in
each test) and counterbalanced (across environments) order. Pigs were
given 2 min to enter the arena from the start box. After entering the
arena, the door to the start box was closed and an NO was lowered into
the middle of the arena on a rope until it was 10 cm from the ground.
Once the object was in its final position, the NO test started and
lasted for 5 min.
Both test areas and start boxes had
plywood walls and concrete floors, which were cleaned between tests and
deep cleaned between testing pigs from different pens. Pigs were tested
in a randomized order between pens. Within pens, pigs were tested
sequentially to minimize disruption to the rest of the pen. Video
cameras filmed the tests from above, and duration of standing,
exploring, locomotion and line crossing (measure of activity) in both
tests was subsequently recorded. In the NO test, latency to contact the
object and duration of contact with the NO were also recorded.
(d) Statistical analysis
To
assess personality, repeatability of the behaviours measured in the SI
and NO tests was tested using the intraclass correlation coefficient.
Proactivity–reactivity (P–R) scores were then calculated from the
repeatable behaviours (SI tests: duration standing and exploring; and NO
tests: duration of standing, exploring and latency to approach the NO).
The P–R scores were calculated as the mean of the z-scores of repeatable behavioural measures ([14]
has full details of this), with Cronbach's alpha used to measure
internal consistency. Only data from the first cognitive bias test were
used owing to a decreased latency to approach NP and increased latency
to approach NN in test 2 relative to test 1, which was considered
evidence of learning such that the probe stimuli may no longer be
ambiguous. To standardize for differences in speed of running between
individuals, a standardized time to run was created: 
where T is time to run, on the ith probe trial or to P. 
indicates mean time per individual to reach location P and Tmax
is maximum time per individual to reach location P. A standardized
score of 0 indicates the pig is treating the probe like location N; a
score of 1 indicates it is treating the probe like location P. Scores of
above 1 are possible if Ti is faster than 
.
where T is time to run, on the ith probe trial or to P. 
indicates mean time per individual to reach location P and Tmax
is maximum time per individual to reach location P. A standardized
score of 0 indicates the pig is treating the probe like location N; a
score of 1 indicates it is treating the probe like location P. Scores of
above 1 are possible if Ti is faster than 
.
A
linear mixed-effects model with restricted maximum-likelihood was used
to analyse log ‘time to run’ as the outcome variable (using lme in nlme
package [17]).
Individual differences were accounted for as models were weighted by
speed of approach to location P, pig and pen identity were included as
random effects, P–R scores were covariate and the fixed effects were:
treatment (environment), sex and probe location. Fixed effects were
dropped if they did not influence model fit, assessed using ANOVA. Sex
was dropped from the final model for this reason.
Three post hoc
mixed-effects models were fitted, one for each probe location.
Personality (P–R rank) and environment were included as fixed effects,
pen was a random effect and models were weighted by time to approach
location P. All analyses were conducted using R [18]. Full analysis, with data and R script are available in electronic supplementary material, S1.
3. Results and discussion
We
profiled personality in all 36 pigs. High internal reliability
(Cronbach's alpha= 0.858) of the repeatable behaviours allowed them to
be combined to create the P–R scores for each individual. Lower scores
on the P–R index indicate more reactive pigs and higher scores more
proactive.
In humans, information processing biases are dependent on both current mood state and personality [16,17].
Here, we find an analogous effect on cognitive bias in pigs. The speed
of approach to the probe locations was significantly affected by an
interaction between the location of the probe, personality (rank on P–R
scale) and housing environment (which is likely to have affected mood;
LMM weighted by individual approach to location P and with pig ID and
pen as random effects: t42.7 =−2.92, p =
0.005). Separate analyses on the interaction term revealed that there
was no difference between the environments in pigs' speed of approach to
the ‘near positive’ probe (LMM weighted by approach to location P and
pen as a random effect: t21.8 = 1.37, p = 0.183), or effect of personality (t20.6 = 0.97, p = 0.345, figure 2a). To the ‘near negative’ and ‘middle’ probes, there was an interactive effect of environment and personality (near negative: t18 = 2.38, p = 0.028; middle: t23.6 = 2.40, p
= 0.025); pigs in the more enriched environment were more optimistic if
they were more reactive. However, pigs in the less enriched environment
became more pessimistic to the near negative probe if they had a more
reactive personality (figure 2b,c).
Figure 2.
(a–c)
Latency to approach (standardized per individual) unrewarded probes
(spatially positioned between locations that were previously rewarded
and unrewarded) in a cognitive bias test in more proactive and reactive
pigs. Higher standardized time ran scores indicate greater optimism.
More proactive personalities were more likely to respond optimistically
to unrewarded ambiguous probes. Reactive pigs' optimism/pessimism was
affected by their housing conditions. (Online version in colour.)
Because
proactive pigs behaved differently from reactive pigs, these findings
could explain some of the inconsistent results between animal cognitive
bias tests [19].
Accounting for personality differences between individuals may reduce
some of this otherwise unexplained variation, making cognitive bias test
outcomes more reliable and robust.
Proactive pigs were
less flexible in their response to probes. This fits with existing
knowledge about the low flexibility in proactive animals [13].
The reactive pigs were more influenced by their housing environment.
Those living in a worse environment were more pessimistic and those in a
better environment were more optimistic. Importantly, this finding
demonstrates that humans are not unique in combining longer-term
personality biases with shorter-term mood biases in judging stimuli [20]. Optimistic and pessimistic responses can both be adaptive depending on the environment [10,21],
allowing appropriate responses to reward or threat signals,
respectively. The presence of autocorrelated variation in the occurrence
of environmental events or in an individual's own state makes
fine-scale tuning of responses to cues through the mood system
advantageous in comparison with a fixed threshold response system [10].
Therefore, personality and mood jointly influencing an individual's
behaviour allows longer-term consistency with shorter-term flexibility
for responses to dynamic conditions.
Ethics
Ethical approval was granted from University of Lincoln's College of Science Ethics Committee (COSREC62, 8/9/15).
Data accessibility
Data, R code and analysis output are available in the electronic supplementary material, S1–S3.
Authors' contributions
L.M.C.
and L.A. designed the study; M.F. and K.G. managed data collection;
L.A. conducted analyses; L.A., M.F. and L.M.C. drafted the manuscript;
all authors contributed substantially to revision of the manuscript,
gave final approval and agree to be held accountable for this
publication.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by the BBSRC (BB/K002554/2).
Acknowledgements
We are grateful to AFBI Hillsborough staff for field assistance and animal care.
Footnotes
- Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.3552537.
- Received May 12, 2016.
- Accepted October 20, 2016.
- © 2016 The Authors.
Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
