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It has been suggested that during instrumental learning, animals are likely to react emotionally to the reinforcer. They may in addition react emotionally to their own achievements. These reactions are of interest with regard to the animals’ capacity for self-awareness. Therefore, we devised a yoked control experiment involving the acquisition of an operant task. We aimed to identify the emotional reactions of young cattle to their own learning and to separate these from reactions to a food reward. Twelve Holstein–Friesian heifers aged 7–12 months were divided into two groups. Heifers in the experimental group were conditioned over a 14-day period to press a panel in order to open a gate for access to a food reward. For heifers in the control group, the gate opened after a delay equal to their matched partner’s latency to open it. To allow for observation of the heifers’ movements during locomotion after the gate had opened, there was a 15m distance in the form of a race from the gate to the food trough. The heart rate of the heifers, and their behaviour when moving along the race towards the food reward were measured. When experimental heifers made clear improvements in learning, they were more likely than on other occasions to have higher heart rates and tended to move more vigorously along the race in comparison with their controls. This experiment found some, albeit inconclusive, indication that cattle may react emotionally to their own learning improvement.
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Hagen, K. and Broom, D.M. 2004 Emotional reactions to learning in cattle. Appl. Anim.
Behav. Sci. 85, 203-213.
Post-publication copy
It'has'been'suggested 'that'd urin g'ins trum e nta l'learn ing ,'anim als 'are'lik ely'to're act'e m otion ally'
to'the'reinforcer.'They'may'in'addition 'r ea c t'emotionally'to'their'ow n 'a ch ie ve ments.'These'reaction s'
to'open'a'gate'for'access'to'a'food'reward.'For'he ifers'in'the'contro l'group ,'the'gate'op ene d'after'
race'from'the'gate'to'the'food'trough.'The'hea rt'rate'of'the'heifers,'and'their'beha viou r'wh en'm oving'
improvem e n ts'in 'le ar n in g,'th e y'were'more'likely'th an 'o n 'ot he r 'oc ca sio n s'to 'h av e 'h igh e r'h e ar t'r ate s'a n d'
tended'to'move'more'vigoro usly 'along 'the'race'in'com p arison 'w ith'their'contro ls.'This'exp erim ent'
found'some ,'alb eit 'inc on clu s ive ,'ind ic at ion 'th a t'ca tt le'may'react'emotiona lly 'to 'th e ir'o w n 'le a rn in g'
improvem e n t.'
Emotional responses to stress exposure are greater when the stressful stimuli are not
controllable, than when the subject can learn to control them (Drugan et al., 1997). In the'
204 K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213
absence of fear or pain, is it possible that the ability to control something might in itself be
rewarding? Dogs that are trained to assist people with severe disabilities in everyday tasks,
have been noticed to perform at high levels of excitement, reliability and versatility when
they have learned to experience task solving as intrinsically rewarding (N. Bondarenko,
personal communication). During a previously conducted learning experiment with cattle
(Hagen and Broom, 2003).
Thus, animals might not only get excited about, for instance, the expectation of a reward,
butalso aboutrealising thatthey themselvesto some extent control the deliveryof a reward.
In other words, if they develop an understanding of a causal relationship in which they are
the agents, this might be exciting to them. If this were the case, their emotional reactions in
a situation where they learned a causal relationship should be different from their reactions
in a situation where they just learned to expect something. The differencemight occur either
specifically during the process of understanding, or it might be retained after a task has been
acquired. To investigate the occurrences of such differences, we designed a yoked control
learning experiment.
2. Methods
2.1. Animals and apparatus
Twelve Holstein–Friesian yearling heifers were kept together in a 0.5 ha paddock
from about 3 months prior to the experiment. In addition to grazing, they were fed
a total of 80kg of hay and 1–2kg each per day of concentrates (rolled barley and
The heifers were assigned to six matched pairs on the basis of weight, age, and sire (when
known). One heifer from each pair was randomly assigned to the experimental or control
group. One of the heifers became difficult to handle, resulting in the exclusion of both her
and the heifer matched to her from the experiment, reducing the sample size to five pairs.
The experimental heifer had to learn a task, whereas her matched control was yoked, that
is, the control heifers obtained the same conditions as their respective matched partners,
irrespective of what they themselves did.
An experimental apparatus was built within the paddock and consisted of a start area
and a race, partly covered with tarpaulin to visually isolate the heifers from the experi-
menter and from the other heifers (Fig. 1). The start area had an entrance gate, through
which the heifers were let in, and an exit gate, which was opened to let them go down
the race. A small panel, mounted on a wooden plate in the start area, 50cm away from
the exit gate and at a height of 1m, was used as an operant for experimental heifers.
The race to which the exit gate gave access was 15m long, and at the end of it there
was a food trough into which the reward was delivered. A control panel for the experi-
menter was located just outside the entrance to the start area. It had two light emission
diodes (LEDs) that signalled when an operant was manipulated, or when latencies had
passed. In addition, it had a switch with which the onset and offset of a trial could be
K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213 205
Fig. 1. Map of apparatus: 1, corner of grazing area; 2, holding area; 3, experimental and feeding area; 4, place for
putting heart rate equipment on; 5, entrance gate to 6, start area; 7, operant; 8, exit gate; 9, race; 10, food trough;
11, building with computer and video equipment. Cameras, c1–c8.
2.2. Structure of the experiment and learning procedure
One session was carried out per day on consecutive days. All heifers had one trial per
session. Trials were carried out with an experimental animal first, followed by its control.
At the start of each trial, a heifer was taken out of the holding pen, led to the experimental
area, and fed some concentrates while being fitted with the heart rate measurement device.
She was then led intothe start area of the maze and the entrance gate was closed behind her.
If the heifer was in the experimental group, an LED on the control panel, visible only to
the experimenter, would light up as soon as the heifer had pressed the panel once. The exit
gate would then immediately be opened, allowing access to the race and the food reward.
A computer recorded the time at which the panel was pressed. If the panel was not pressed
within 3 min, some concentrate was fed to the heifer near the panel and the gate was opened.
For control heifers, the computer recorded their panel presses, but gave the light signal for
the experimenter to open the door independently of presses, matched to the latency of the
experimental heifer previously recorded.
Once the gate had opened, the heifer was free to go down the race to the trough and eat
the food. Having eaten, the heifer could exit the race or move back into it. However, it was
not possible for the heifer to get into the start area. Three minutes after she had finished
eating, the heifer was rejoined with the group outside the experimental area.
2.3. Data collection and analysis
Latencies from when a heifer’s head was inside the start area until she made the exit
gate open were categorised into four groups with equal counts. The latency categorisation
and the proportion of behaviour that the heifer in the start area directed towards the operant
were used to assign a performance index (Table 1) to the experimental heifers for each trial.
206 K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213
Table 1
Scheme used to assign a performance index
Panel pressed? Latency before
gate opens
Proportion of time
directed towards operant
index value
No 0
Yes >60s <half 2
>half 3
21–60s <half 3
<half 4
9–20s 5
<9s 6
For further analysis, the difference between the performance index of an animal on the trial
in question and its index on the day before was calculated. This change was categorised
into a binary variable where 0 denotes no change from previous day or lower performance
(difference values from 5 to 1) and 1 denotes that performance is clearly better than on
the day before (difference values >1). This binary performance change index was used for
further analysis and is referred to as ‘learning index’. Notes were also kept on behaviours
such as repeated butting of the operant panel after the exit gate had opened. In addition,
whether the heifers chose to go back into the race after they had eaten their reward, and
whether they tried to get back into the start area or tried to reach the panel was recorded.
The heifers’ behaviour from the time that they entered the experimental area and had the
heart rate monitor fitted until they were led out, was recorded on video with eight cameras
(Fig.1).The time taken to go from theexitgatetothe food trough and behavioursduring this
locomotion (Table 2) were coded from the video records by two observers. Before coding,
all video clips were edited out of context and compiledon new tapes in random order. Inter-
andintra-observeragreementwasensuredby randomly interspersedre-observationofclips.
For further analysis, an index for the gait was derived from a combination of the ‘main gait’
and ‘other gait’ classification as outlined in Table 3.
Polar Sport Testers storing 5s interval averages (Polar Electro Oy, Finland) were used
to record heart rate. For each heifer, the mode of her heart rate across all measurements
Table 2
Behaviour during locomotion
Variable Levels Description
Main gait Walk, trot, gallop/canter The gait that dominates the clip
Other gait None, walk, trot, gallop/canter Additional gait that occurs during the clip (if several
additional gaits occur, the one that appears most)
Jump Yes, no The front legs are lifted together, top line descends
sharply from back to front and all feet are then in the air
Buck Yes, no Both hind legs are lifted simultaneously and the top line
ascends sharply from front to back
Kick Yes, no One of the hind legs is extended back- and sideward in a
sharp movement
K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213 207
Table 3
Derivation of gait index from raw scores of ‘main gait’ and ‘other gait’
Possible combinations Gait index
Main gait Other gait
Walk None 1
Trot 2
Gallop 3
Trot Walk 3
None 4
Gallop 5
Gallop Walk 5
Trot 6
None 7
was calculated as an individual baseline. For further analysis, the mean deviation from an
individual’s mode was calculated for functional phases of the experiment: before a heifer
entered the start area (a); during the last 15s before the exit gate was opened (b); during the
first 10s after gate has been opened (c); 10–20s after gate had been opened (d); from 20 s
after gate had been opened until the heifer stopped eating (e); after the heifer had stopped
eating (f). The deviation of the heart rate in each phase from an individual’s mode was
expressed as a percentage higher or lower than the mode, i.e. deviation = ((mean/mode)
1) × 100.
As a consequence of the experimental design, pairwise differences between experimen-
tal subjects and their matched controls were of particular interest as dependent variables.
Pairwise differences were always calculated as experimental-control, i.e. the pairwise heart
rate differences were the differences, in each functional phase of each trial, between an
experimental heifer’s heart rate deviation from mode, and her matched control’s heart
rate deviation from mode. Only cases where both were available were included in the
Prior to non-parametric tests following Siegel and Castellan (1988), proportions and
numerical values were averaged to one value per subject per treatment level to ensure
independence of the data. Effectson heart rate were investigated with a general linear mixed
model calculated in R (Ihaka and Gentleman, 1996). The repeated measures design and
individualdifferences were taken into account by including the individual pairs as a random
factor. Normality of the residuals’ distributions was tested with the Kolmogorov–Smirnov
test and homogeneity of variances was tested with the Bartlett test.
3. Results
Thelatencytopressthe panel variedfrom upto 3min (at thebeginningofthe experiment)
to 3 s when the heifers had learned the task well. Fig. 2 showsthe performance index and the
derived learning index for the experimental heifers. The figure also shows the occurrence
of panel investigation or panel-directed play behaviour after the gate has opened, and of
208 K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213
Fig. 2. Performanceindexon each day foreach of theheifers. Numbers within squares indicate where the resulting
learning indexis 1. Asterices indicate that the heifer showed investigativeor play-like behaviour towards the panel
after the gate had opened. Plus signs indicate that the heifer went back into the race and investigated the gate after
K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213 209
gate investigation after the heifer has eaten the food. Only one of the control group heifers
showed such behaviour on one occasion.
The time taken to move down the race, from when a heifer’s head was through the
exit gate until it was in the food trough, ranged from 4 to 71s. After a decrease from the
first day, the values remained close to a median of 8 s. There was no difference between
experimental and control animals (Wilcoxon signed-ranks test: T = 9, N = 5, P = 0.81),
and the differences in speed between experimental and controlheifers within matchedpairs
were not influenced by the binary learning index (T = 12, N = 5, P = 0.32).
The main gait when moving down the race was walk in 88 cases (74%), trot in 26 cases
(22%), and gallop in 5 cases (4%). The distribution of the gait index was thus strongly
skewed towards lower values. It was not correlated with the speed when moving down the
race (Spearman rank correlation: r
=−0.05, N = 112, P = 0.59). Gait scores increased
over the experiment (Page test: L = 1232, k = 7, N = 10, P<0.001), but they did
not differ between experimental and control groups (Wilcoxon signed-ranks test: T = 10,
N = 5, P = 0.63). There was a trend for a relationship (T = 15, N = 5, P = 0.062)
between the binary learning index and the pairwise differences in gait scores between the
experimental and control heifers: when the learning index was 1, the experimental heifers
tended to be more likely to score higher than their matched controls, than when it was
0. Jumping occurred in five cases: heifer 4 on day 13, heifer 12 on days 6 and 12, heifer
8 on day 2 and heifer 5 on day 5; bucking in three cases: heifers 12 and 5 on the same
occasions as when they jumped; and kicking in one case: heifer 4 on day 6 (see Fig. 2 for
comparison with their learning curves). In the control group, neither jumping, bucking or
kicking occurred on any occasion.
Individuals’ median heart rate values differed but were not correlated with body weight
or age. There were no overall treatment group differences (Wilcoxon signed-ranks test:
T = 8, N = 5, P = 0.89). Heart rate varied across functional phases (Friedman two-way
Fig. 3. Deviation of heart rate from mode (mean percentage ± S.E.) pooled over days for experimental (, solid
line) and control (
, dashed line) groups, in the functional phases of the experiment: a, up to 15 s before gate
opens; b, last 15 s before gate opens; c, first 10 s after gate has opened; d, 10–20 s after gate has opened; e, while
eating reward; f, after eating.
210 K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213
Fig. 4. Deviationofheart ratefrom mode(mean percentage± S.E.) pooledoverfunctional phases for experimental
, solid line) and control (, dashed line) groups, on each day of the experiment.
analysis of variance: F
= 33.7, d.f. = 5, N = 10, P<0.001; Fig. 3) and increased with
time (days of the experiment) in the control group (Spearman rank correlation: r
= 0.48,
N = 60, P<0.001), with a trend for increase in the experimental group (r
= 0.25,
N = 60, P = 0.055). Inspection of Fig. 4 shows that the groups only differed on the first 2
days, and that the relationship between heart rate and time was relatively linear from day 3
to day 11.
Heart rate while, and shortly after, going down the race was not correlated with the
number of seconds taken to go down the race (Spearman rank correlation: r
= 0.10,
N = 54, P = 0.46 for control group in phase c; r
= 0.04, N = 48, P = 0.80 for
experimental group in phase c; r
= 0.22, N = 55, P = 0.12 for control group in phase d;
Fig. 5. Pairwise differences in deviation of heart rate from mode (mean percentage ± S.E.) in relation to the
learning index for experimental heifers.
K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213 211
=−0.08, N = 49, P = 0.59 for experimental group in phase d). It did correlate weakly
with the gait index in the control group (r
= 0.31, N = 52, P = 0.026 for control group
in phase c; r
= 0.07, N = 52, P = 0.23 for experimental group in phase c; r
= 0.36,
N = 52, P = 0.019 for control group in phase d; r
= 0.17, N = 53, P = 0.23 for
experimental group in phase d). A stronger correlation was found between gait index and
heart rate for both groups in phase b, i.e. just before the gait opened (r
= 0.46, N = 52,
P = 0.001 for control group; r
= 0.31, N = 52, P = 0.024 for experimental group).
Nine extreme values (heart rate deviation of more than 50%) were excluded to achieve
a normal distribution of residuals in the general mixed model for effects on the differences
in heart rate deviation between experimental group and control group. The only significant
factor in the fitted model was the learning index (ANOVA: F
= 6.98, P = 0.0087,
Fig. 5).
4. Discussion
Five of the six experimental heifers acquired the operant tasks and reached levels of
reliable and quick performance within 12 trials. This ease of trainingcattle to operanttasks,
provided that they are not scared, is in line with previous findings (Kiley-Worthington and
Randle, 1998).
As the heifers had no prior experience of the start box and maze, it is not surprising
that on their first trials they took longer to go down the race than on subsequent trials. In
the later course of the experiment, speed remained stable and was not correlated with the
gait index, which increased in both treatment groups. On days when learning performance
increased, heifers tended to have higher gait scores than their matched controls. Jumps,
bucks and kicks only occurred in the experimental group. Taken together, the behaviours
while moving down the race indicate more agitation in the experimental group when the
learning curve was steep.
Heart rate dropped in the two groups on days 1 and 2, respectively, and then rose in both
groups until day 11. This may reflect the stress of habituation to the learning apparatus, in
similar way as has been observed in the context of visual discrimination learning in dwarf
goats (Langbein et al., 2003), although in their case, a similar tendency was observed not
during learning itself, but in periods of rest between learning sessions. In our study, heart
rate also varied with functional phases, with peaks around the time of the main locomotor
activity and a low during eating, reflecting the amount of movement. However, heart rate
was only weakly correlated with the gait index. Metabolic activity could therefore not
explain the variation in heart rate. Pairwise differences between experimental heifers and
theirmatched controls with regardto heart rate deviationfrom individualmode were greater
when the learning index was 1, than when it was 0. This indicates that there was a treatment
effect on heart rate which corresponds to the effect on movement.
The experimental animals got more excited than the control animals, not generally, but in
a temporal relation to the only difference between the groups: that the experimental heifers
experienced an operant learning process whereas the control heifers did not. There are two
experimental heifers were reacting to their own learning process and thus in a sense to their
212 K. Hagen, D.M. Broom/Applied Animal Behaviour Science 85 (2004) 203–213
own achievement. Secondly, it could also be argued that increased arousal or motivational
levels occurred randomly, but led to better performance. We speculate that the arousal
leading to improved performance may well be the result of a process of understanding after
the previous trial, rather than occurring randomly.
The investigation of non-verbal ‘self’ and of consciousness in animals and in infants have
been focused on cognitive abilities and self-recognition in mirrors (Marten and Psarakos,
1995; Mitchell, 1997; De Veer and Van den Bos, 1999; Shillito et al., 1999; Swartz et al.,
1999). In the light of theories of self-consciousness that make attempts at explaining its
evolution and ontogeny in terms of emotional and bodily responses rather than cognition
only (Neisser, 1991, 1997; Bermúdez et al., 1995; Bermúdez, 1998; Damasio, 2000), there
is a challenge to devise alternative empirical approaches (Rochat and Hespos, 1997; Rochat
andGoubet,2000).The present study represents anattemptatestablishing such an approach
to investigating ways in which there may be non-verbal self-referral, as there would be in
the emotional response to one’s own understanding.
One assumption underlying the experimental design was that the operant acquisition
task involved such a process of understanding. However, the yoked control design is
not in itself sufficient to demonstrate instrumental learning (Church, 1988; Church et al.,
1989). The idea of a process agency, and of understanding the causal connection between
a response and a reward, can be accommodated in theories of goal-directed action and
incentive learning (Dickinson and Balleine, 1994). Further investigation of emotional re-
actions to learning would involve validation of the instrumental nature of the learning
To our knowledge, our experimental approach to investigate whether animals might
respond emotionally to a process of understanding, has not been used before. The yoked
control design was first used to show that rats with control over the termination of electric
shocks developed fewer stomach ulcers than their yoked controls (reviewed by Drugan
et al., 1997). In the present experiment, the purpose was to control for all variables that
might influence emotional responses other than the learning process.
In conclusion, the yoked control design was effective to some extent in separating the
effectsof the operant learningprocess from other variableslike habituation and expectation
of reward. The study indicated that cattle might be more agitated when they are just about
to acquire a task, i.e. understand, and thus that they may have an emotional perspective on
their own agency. However, because of the novelty of the approach and the small number
of animals, this study should be seen as a first step towards further investigation of the
EricAllen and GavinHughesatCambridge UniversityFarm for lettingus work with their
Touma and Sophie Prowse for practical help. Richard Kirkden for help with programming
and electronics, and Irene Rochlitz for advice and for reading an earlier draft of this paper.
KH was supported by the Overseas Research Students’ Awards Scheme, the Cambridge
Overseas Trust and a St. John’s College Benefactors’ Scholarship.
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... Even the "aha" moment itself might be accessible to study in non-verbal subjects, given the expected physiological emotional response that follows it. We know that many animals show an emotional response while learning how to solve tasks (independent from the presence of a reward; e.g., cows, Hagen and Broom, 2004;goats, Langbein et al., 2004;horses, Mengoli et al., 2014;dogs, McGowan et al., 2014;dolphins, Clark et al., 2013). Studying insight through the presentation of a solution would thus require both a behavioral analysis (as in traditional contrafreeloading tests or yoked experimental designs; e.g., Hagen and Broom, 2004;Rosenberger et al., 2020) as well as a physiological one. ...
... We know that many animals show an emotional response while learning how to solve tasks (independent from the presence of a reward; e.g., cows, Hagen and Broom, 2004;goats, Langbein et al., 2004;horses, Mengoli et al., 2014;dogs, McGowan et al., 2014;dolphins, Clark et al., 2013). Studying insight through the presentation of a solution would thus require both a behavioral analysis (as in traditional contrafreeloading tests or yoked experimental designs; e.g., Hagen and Broom, 2004;Rosenberger et al., 2020) as well as a physiological one. Artificially altering the transparency of the path toward the solution, and altering the time spent at an apparent impasse, may allow us to predict and modify the intensity of the respective physiological (as it would be an increased heart rate; Hill and Kemp, 2018) and behavioral responses (e.g., in dogs, we would predict pupil dilation, tail wagging, and increased general activity; McGowan et al., 2014;Webb et al., 2019;Salvi et al., 2020). ...
Full-text available
Despite countless anecdotes and the historical significance of insight as a problem solving mechanism, its nature has long remained elusive. The conscious experience of insight is notoriously difficult to trace in non-verbal animals. Although studying insight has presented a significant challenge even to neurobiology and psychology, human neuroimaging studies have cleared the theoretical landscape, as they have begun to reveal the underlying mechanisms. The study of insight in non-human animals has, in contrast, remained limited to innovative adjustments to experimental designs within the classical approach of judging cognitive processes in animals, based on task performance. This leaves no apparent possibility of ending debates from different interpretations emerging from conflicting schools of thought. We believe that comparative cognition has thus much to gain by embracing advances from neuroscience and human cognitive psychology. We will review literature on insight (mainly human) and discuss the consequences of these findings to comparative cognition.
... In great apes undergoing cognitive testing, behaviors directed 'off-task' (i.e., towards themselves, conspecifics, or the wider environment) such as rough-scratching and aggression have been positively correlated with task difficulty and failure (Elder and Menzel 2001;Leavens et al. 2001;Yamanashi and Matsuzawa 2010) and, therefore, appear to be predictive of shortterm emotional response to challenge. The existence of the 'Eureka moment' in cattle and domestic dogs highlights the welfare value of spontaneous problem-solving (Hagen and Broom 2004;McGowan et al. 2014) but is yet to be studied in other taxa (e.g., recently no evidence was found in bottlenose dolphins; Alexander et al. 2021). Pure researchers who study animal problem-solving and innovation, particularly in primates and birds (Griffin and Guez 2014) have a unique opportunity to formally capture and quantify animals' emotional responses at the point of innovation. ...
Cognitive enrichment is a growing subset of environmental enrichment for captive animals. However, it has been difficult for practitioners to design, implement, and evaluate relevant and appropriate cognitive challenges. Even though pure comparative cognition researchers focus on fundamental evolutionary questions, their knowledge and expertise can also shape the future of cognitive enrichment. This paper describes the motive, means, and opportunity to do so. Taxon-specific summaries of animal cognition (including inter-individual variation in skill and effects of motivation), and experimental designs (including the task itself, training, and reward) need to be accessible to practitioners in applied settings, such as farms, zoos, and sanctuaries. Furthermore, I invite pure researchers to directly evaluate their cognitive research program as enrichment and thus bridge the disciplines of animal cognition and welfare.
... Initial results suggest that cognitive stimulation via enrichment devices has positive effects on activity budgets and social interactions in primates (Yamanashi & Hayashi, 2011;Whitehouse et al., 2013;Jacobson et al., 2019) and the potential to increase exploration and reduce fear in farmed animals (Puppe et al., 2007;Zebunke, Puppe & Langbein, 2013). Positive emotions through the engagement in a solvable task (Hagen & Broom, 2004;Langbein, Nürnberg & Manteuffel, 2004;Meehan & Mench, 2007;Puppe et al., 2007;Manteuffel, Langbein & Puppe, 2009) and the reinforcing effect of the successful completion of a task have been suggested as potential explanations for these positive effects on animal welfare (Jensen, 1963;Hughes & Duncan, 1988). Others suggest that welfare is increased by the increased control over the environment (Meehan & Mench, 2007;Langbein, Siebert & Nürnberg, 2009). ...
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Current evidence suggests that frequent exposure to situations in which captive animals can solve cognitive tasks may have positive effects on stress responsiveness and thus on welfare. However, confounding factors often hamper the interpretation of study results. In this study, we used human-presented object-choice tests (in form of visual discrimination and reversal learning tests and a cognitive test battery), to assess the effect of long-term cognitive stimulation (44 sessions over 4-5 months) on behavioural and cardiac responses of female domestic goats in subsequent stress tests. To disentangle whether cognitive stimulation per se or the reward associated with the human-animal interaction required for testing was affecting the stress responsiveness, we conditioned three treatment groups: goats that were isolated for participation in human-presented cognitive tests and rewarded with food ('Cognitive', COG treatment), goats that were isolated as for the test exposure and rewarded with food by the experimenter without being administered the object-choice tests ('Positive', POS treatment), and goats that were isolated in the same test room but neither received a reward nor were administered the tests ('Isolation', ISO treatment). All treatment groups were subsequently tested in four stress tests: a novel arena test, a novel object test, a novel human test, and a weighing test in which goats had to enter and exit a scale cage. All treatment groups were tested at the same two research sites, each using two selection lines, namely dwarf goats, not selected for production traits, and dairy goats, selected for high productivity. Analysing the data with principal component analysis and linear mixed-effects models, we did not find evidence that cognitive testing per se (COG-POS contrast) reduces stress responsiveness of goats in subsequent stress tests. However, for dwarf goats but not for dairy goats, we found support for an effect of reward-associated human-animal interactions (POS-ISO contrast) at least for some stress test measures. Our results highlight the need to consider ontogenetic and genetic variation when assessing stress responsiveness and when interacting with goats.
... Due to its unpredictable nature, studying the Eureka moment is challenging, but first attempts have been made to identify behavioural and physiological signatures in non-human animals. In a yoked control experiment, Hagen and Broom (2004) found that heifers who learned to press a panel to obtain access to a food reward had higher heart rates and tended to show more vigorous behaviour at the very moment in which they learned the contingency. This finding was interpreted as a first indication of an emotional reaction to the heifers' own learning performance. ...
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The concept of flow, a state of complete absorption in an intrinsically rewarding activity, has played a pivotal role in advancing notions of human well-being beyond minimising suffering towards promoting flourishing and thriving. While flow has played a fundamental role in human positive psychology, it has not yet been explored in non-human animals, leaving an enormous void in our understanding of intrinsic motivation in animals. As ethology and related fields keep progressing in uncovering complex cognitive and affective capacities of non-human animals, we propose the time is ripe to translate the concept of flow to animals. We start by embedding flow in the topic of intrinsic motivation and describe its impact on positive human psychology and potentially positive animal welfare. We then disambiguate flow from related concepts discussed in the animal literature. Next, we derive experimental approaches in animals from the canonical characteristics of flow in humans and provide guidelines for both inducing and assessing flow by focusing on two characteristics that do not necessarily depend on self-report, namely resistance to distraction and time distortion. Not all aspects of the human flow experience are (yet) translatable, but those that are may improve quality of life in non-human animals.
... In humans, the degree of curiosity can also be highly correlated with the degree of emotional intelligence (89,90), thus highlighting the potential interdependence between enrichment provision to stimulate neural development and future engagement with enrichments. Livestock will also actively engage with cognitive enrichments [e.g., pigs: (91); laying hens: (92); dwarf goats: (93)], and show motivation to learn (94) as well as physiological evidence of learning processes being rewarding (95)(96)(97). This evidence of a natural desire for enrichment engagement supports the negative impacts that the absence of such stimulation would have. ...
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Livestock animals are sentient beings with cognitive and emotional capacities and their brain development, similar to humans and other animal species, is affected by their surrounding environmental conditions. Current intensive production systems, through the restrictions of safely managing large numbers of animals, may not facilitate optimal neurological development which can contribute to negative affective states, abnormal behaviors, and reduce experiences of positive welfare states. Enrichment provision is likely necessary to enable animals to reach toward their neurological potential, optimizing their cognitive capacity and emotional intelligence, improving their ability to cope with stressors as well as experience positive affect. However, greater understanding of the neurological impacts of specific types of enrichment strategies is needed to ensure enrichment programs are effectively improving the individual's welfare. Enrichment programs during animal development that target key neurological pathways that may be most utilized by the individual within specific types of housing or management situations is proposed to result in the greatest positive impacts on animal welfare. Research within livestock animals is needed in this regard to ensure future deployment of enrichment for livestock animals is widespread and effective in enhancing their neurological capacities.
... Emotion, which has long been viewed as necessarily separate from intellectual activity, is now shown to be a facilitator of learning and a consequence of learning. An indication of the possible awareness of own actions and functioning comes from the studies of Hagen and Broom (2004) on young cattle. The heifers were put in a pen with a gate that could be opened by pressing a panel with the nose, thus giving access to food 15m away. ...
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Donald M. Broom. Farm animal welfare: a key component of the sustainability of farming systems. Abstract Consumers of food and other products now demand sustainability of production methods and, for most people, the welfare of production animals is an important component of sustainability. Products are not considered to be of good quality unless the welfare of the production animals is good. This is part of a more general change in knowledge that there are few differences between humans and other animal species, with the conclusion that each individual life should be valued and that causing poor welfare to a farmed animal is morally wrong. All vertebrate animals and some invertebrates are now shown to be sentient, that is they have the capacity to have feelings. There have been major advances in animal welfare science so that housing and management systems that result in poor welfare of the animals are now identified and every producer needs to change their systems and methods to ensure good welfare and avoid all of the worst welfare problems.
... According to Meagher et al (2020) heifers are motivated to learn when provided with a range of learning opportunities, and it has been shown by Hagen and Broom (2004) that individuals can have an emotional response, including more energetic locomotion, after improving their performance during learning tasks related to food stimuli. Allowing cattle to have control over their own environment may also benefit them and elicit a positive response. ...
Historically, farm animal cognition has not always been considered on commercial enterprises, but it has emerged as an important aspect of managing livestock to enhance welfare and increase productivity. The aim of this review is to summarise literature on the subject of cognition in livestock and discuss techniques to stimulate the minds of animals to enhance welfare practices on farm.
... Lambs, Ovis aries, and rats responded with a heart rate increase to novel, sudden and unpredictable stimuli [47,48]. When engaging in learning tasks, heart rate increased in goats, Capra hircus [45], horses, Equus caballus [42] and cattle [35] but decreased in European starlings, Sturnus vulgaris [41]. In the future, improved biologging technology to measure heart rate in different contexts could allow more objective quantification of cognitive function in a wide range of species, as already shown by the use of neurologgers to measure electroencephalograms Phil. ...
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How individuals interact with their environment and respond to changes is a key area of research in evolutionary biology. A physiological parameter that provides an instant proxy for the activation of the automatic nervous system, and can be measured relatively easily, is modulation of heart rate. Over the past four decades, heart rate has been used to assess emotional arousal in non-human animals in a variety of contexts, including social behaviour, animal cognition, animal welfare and animal personality. In this review, I summarize how measuring heart rate has provided new insights into how social animals cope with challenges in their environment. I assess the advantages and limitations of different technologies used to measure heart rate in this context, including wearable heart rate belts and implantable transmitters, and provide an overview of prospective research avenues using established and new technologies, with a special focus on implications for applied research on animal welfare. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.
Concept definitions applicable to human and non-human animals should be usable for both. Awareness is a state during which concepts of environment, self, and self in relation to environment result from complex brain analysis of sensory stimuli or constructs based on memory. There are several proposed categories of awareness. The widespread usage of the term conscious is 'not unconscious' so a conscious individual is an individual that has the capability to perceive and respond to sensory stimuli. It is confusing and scientifically undesirable if conscious is also used to mean aware. Hence it is proposed that conscious should be used only as above. Fully functioning and adequately developed humans and members of many other animal species are sentient. Sentience means having the capacity, the level of awareness and cognitive ability, necessary to have feelings. The welfare of an individual is its state as regards its attempts to cope with its environment. This includes feelings, which are important coping mechanisms, and health. Since feelings involve awareness, there is overlap between welfare assessment and awareness assessment. Methods for assessing awareness, consciousness, sentience, and welfare and links to morality are briefly discussed.
Domestic pet dogs typically spend a large amount of time inside the home environment, including hours alone. An audio/food dispensing automated device (an artificial agent, hereafter agent) was evaluated for enrichment potential. The agent issued owner-spoken verbal cues to dogs, and food rewards for correct behavioural responses. The dogs’ welfare during interaction was examined using Qualitative Behavioural Assessment (QBA), and results were compared with quantitative behavioural measurements. QBA is a ‘whole animal’ measure, which describes and quantifies the emotionally expressive quality of an animal’s demeanour in a specific context, using descriptive terms such as ‘excited’ or ‘curious’. Adult pet domestic dogs (n = 17) were observed in two experimental conditions: 1) Agent; researcher and owner present (A+RO); 2) Agent; researcher and owner absent (A-RO). The agent was remotely triggered to call each dog by name and issue up to four repeated, randomised and previously trained verbal cues e.g., ‘spin’, ‘roll’, ‘sit’, ‘up’ (maximum 16 cues). In a baseline condition (Owner; researcher and agent present) (O+RA) the dog’s owner replaced the agent’s role. QBA using a fixed list of 20 descriptors was performed by three observers for each dog in each condition, using video recordings (51 clips) and Principal Component Analysis (PCA) applied. QBA principal component 1 (PC1) was the same in all conditions, characterising dogs as ranging from ‘attentive/interested/anticipating’ to ‘conflicted/apathetic’, with the majority of dogs at the former end. QBA PC2 revealed variability in dogs’ interactive styles, ranging from ‘calm/concentrating’ to ‘aroused/excited/persistent’ in agent conditions, and ‘calm/wary’ to ‘excited/aroused’ in baseline. Inter-observer agreement was high on all PCs (e.g., PC1 A+RO Kendall’s W = 0.88; PC1 A-RO W = 0.83; PC1 O+RA W = 0.77). Quantitative continuous behaviour sampling revealed no significant differences in state behaviour between conditions; ‘looking up’ (p = 0.494); ‘ears up’ (p = 0.662); ‘cued response behaviour’ (p = 0.630); these behaviour categories correlated significantly with QBA PC1 in each condition (average r = 0.800; r = 0.780; r = 0.793 respectively). Indicators of positive anticipation, sustained engagement, competence, and motivation were identified throughout testing and in all conditions. These findings suggest that positively reinforcing interactions with an agent or an owner are equally rewarding for dogs within the context of testing. This study is the first to assess welfare during interaction with an automated device and, using a novel application of QBA, suggests the overarching experience is pleasurable for dogs.
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We tested the ability of cattle to discriminate between socially familiar conspecifics in a Y-maze discrimination experiment. The discriminative stimuli were herd members tethered in the Y-maze side arms (stimulus heifers); approach to one of these was rewarded with food and approach to the other was unrewarded, and their positions were randomly swapped. Each of six experimental heifers was subjected to two pairs of stimulus heifers. All subjects reached the learning criterion (19 out of 20 consecutive choices to the rewarded stimulus heifer’s position in the Y-maze) with one pair of stimulus heifers. With the other pair, learning was slower and only three of the subjects reached criterion. All heifers that reached criterion chose correctly in at least five additional trials designed to control for cues emanating from the experimenter’s behaviour or from the food reward. We conclude that cattle can discriminate between individual familiar conspecifics, that they can learn discrimination tasks quickly, and that speed of learning and level of correct response can be influenced by the identity of the stimulus individuals. We also explored the experimental heifers’ behaviour during the learning process. Behaviours indicative of agitation were observed more often in the second task than in the first and also increased with time during learning tasks (P
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It is proposed that from birth, and long before mirror self-recognition, infants manifest a sense of self as a differentiated and situated entity in the environment. In support of this view, observations are reported suggesting that neonates discriminate between external and self-stimulation. Five newborns and 11 4-week-old infants were observed when they spontaneously brought one hand to their face, touching one of their cheeks (self-stimulation), or when the index finger of the experimenter touched one of the infant's cheeks (external stimulation). Microanalysis revealed that infants responded differentially to the two types of stimulation. Newborns tended to display significantly more rooting responses (i.e., head turn towards the stimulation with mouth open and tonguing) following external compared to self-stimulation. Four-week-old infants demonstrated an opposite pattern. These data are discussed as evidence of an innate ability to discriminate between self versus externally caused stimulation. The differential expression of this ability at birth and at 4 weeks is considered in relation to learning opportunities and the emergence of new functional goals guiding infant behaviour. ©1997 John Wiley & Sons, Ltd.
One of the attractions of Gibson’s concept of ecological perception is that it seems to provide a basic awareness of the bodily self that can serve as the core of a comprehensive account of full-fledged self-consciousness in thought and action. On the ecological understanding of perception, sensitivity to self-specifying information is built into the very structure of perception in such a way that, as Gibson famously put it, all perception involves co-perception of the (bodily) self and the environment. This paper shows how Gibson’s ecological account is not itself sufficient for self-awareness, even of a primitive form, but suggests what needs to be added to it in order to yield the basic awareness of the bodily self that I term possessing a nonconceptual point of view.
Implicit knowledge of the body in infancy It is proposed that long before two-year-old children show signs of mirror self- recognition, manifesting an explicit, conceptual sense of themselves, infants already have an implicit sense of their own body. This implicit sense of self corresponds to a sense of the body as a differentiated entity, situated and agent among other physical objects in the environment. It is also the sense of the body as a spatially and temporally organized entity, capable of feelings and emotions, capable in particular of modulating its affectivity in the reciprocal exchanges with other people. It is proposed that these different aspects of an early sense of the body form the basis for later development of a conceptual and explicit sense of self.
Since its inception as a topic of inquiry, mirror-self-recognition has usually been explained by two models: one, initiated by Guillaume, proposes that mirror-self-recognition depends upon kinesthetic-visual matching, and the other, initiated by Gallup, that self-recognition depends upon a self-concept. These two models are examined historically and conceptually. This examination suggests that the kinesthetic-visual matching model is conceptually coherent and makes reasonable and accurate predictions; and that the self-concept model is conceptually incoherent and makes inaccurate predictions from premises which are themselves inaccurate. From a theoretical (and empirical) standpoint, the kinesthetic-visual matching model is the better explanation of self-recognition.