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Happy hamsters? Enrichment induces positive judgement bias for mildly (but not truly) ambiguous cues to reward and punishment in Mesocricetus auratus

July 2015
Royal Society Open Science 2(140399)

DOI:10.1098/rsos.140399

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CC BY

Authors:

Emily June Bethell

Liverpool John Moores University

Nicola F Koyama

Liverpool John Moores University

Recent developments in the study of animal cognition and emotion have resulted in the ‘judgement bias’ model of animal welfare. Judgement biases describe the way in which changes in affective state are characterized by changes in information processing. In humans, anxiety and depression are characterized by increased expectation of negative events and negative interpretation of ambiguous information. Positive wellbeing is associated with enhanced expectation of positive outcomes and more positive interpretation of ambiguous information.Mood-congruent judgement biases for ambiguous information have been demonstrated in a range of animal species, with large variation in the way tests are administered and in the robustness of analyses. We highlight and address some issues using a laboratory species not previously tested: the Syrian hamster (Mesocricetus auratus). Hamsters were tested using a spatial judgement go/no-go task in enriched and unenriched housing. We included a number of controls and additional behavioural tests and applied a robust analytical approach using linear mixed effects models. Hamsters approached the ambiguous cues significantly more often when enriched than unenriched. There was no effect of enrichment on responses to the middle cue. We discuss these findings in light of mechanisms underlying processing cues to reward, punishment and true ambiguity, and the implications for the welfare of laboratory hamsters.

Timeline for the study, conducted over five weeks. Day numbers are shown: discrimination training (days 1–15); judgement bias testing (days 17–19 and 22–24; maintenance trials) days 16, 20 and 21). ○, Enrichment manipulation; additional behavioural tests—△, OF and LDE tests (days 19 and 24) and ×, neophobia test (Neo) (day 25).

…

The judgement bias task. During training (Phase A) hamsters learned to approach the sugar drinker (‘go’) and not to approach (‘no go’) the QHCl drinker. At the end of Phase A, all animals received a basic provision of novel enrichment items. Training on the go/no-go task was maintained in Phase B with the inclusion of the empty drinker on some trials to create a 50% VRR (empty drinker trials: ‘Control +’ and ‘Control −). Towards the end of Phase B half of the sibling groups received the maximum complement of enrichment, while the novel enrichment items were removed from the other half of the sibling groups. Animals were tested on the judgement bias task on the following week (Phase C) with the presentation of the empty drinker at all five locations, including the three ambiguous intermediate probe locations. Enrichments were then switched and animals tested again in Phase D.

…

Mean proportion (± s.e.) of approaches to each of the probe and control locations when enrichment had been added (open circles) or reduced (closed circles). Hamsters approached the outer probes (P− and P+) significantly more often following addition of enrichment compared to when enrichment had been reduced. Hamsters approached the middle probe equally often in both treatments. CQHCl control; P− probe closest to the QHCl location; Pmid middle probe; P+ probe closest to the sugar location; C+ sugar control.

…

Mean latency (±s.e.) for hamsters to approach the sugar drinker with and without a novel object present. E, Enrichment added; R, Enrichment removed.

…

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Research

Cite this article: Bethell EJ, Koyama NF. 2015

Happy hamsters? Enrichment induces positive

judgement bias for mildly (but not truly)

ambiguous cues to reward and punishment in

Mesocricetus auratus.R. Soc. open sci.

2: 140399.

http://dx.doi.org/10.1098/rsos.140399

Received: 22 October 2014

Accepted: 3 July 2015

Subject Category:

Psychology and cognitive neuroscience

Subject Areas:

behaviour/cognition/psychology

Keywords:

animal welfare, judgement bias, emotion,

environmental enrichment, psychological

wellbeing, Syrian hamster

Author for correspondence:

Emily J. Bethell

e-mail: e.j.bethell@ljmu.ac.uk

Electronic supplementary material is available

at http://dx.doi.org/10.1098/rsos.140399 or via

http://rsos.royalsocietypublishing.org.

Happy hamsters?

Enrichment induces positive

judgement bias for mildly

(but not truly) ambiguous

cues to reward and

punishment in Mesocricetus

auratus

Emily J. Bethell and Nicola F. Koyama

School of Natural Sciences and Psychology,Liverpool John Moores University,

Byrom Street, Liverpool L3 3AF, UK

Recent developments in the study of animal cognition and

emotion have resulted in the ‘judgement bias’ model of

animal welfare. Judgement biases describe the way in which

changes in affective state are characterized by changes in

information processing. In humans, anxiety and depression

are characterized by increased expectation of negative events

and negative interpretation of ambiguous information. Positive

wellbeing is associated with enhanced expectation of positive

outcomes and more positive interpretation of ambiguous

information. Mood-congruent judgement biases for ambiguous

information have been demonstrated in a range of animal

species, with large variation in the way tests are administered

and in the robustness of analyses. We highlight and address

some issues using a laboratory species not previously

tested: the Syrian hamster (Mesocricetus auratus). Hamsters

were tested using a spatial judgement go/no-go task in

enriched and unenriched housing. We included a number

of controls and additional behavioural tests and applied a

robust analytical approach using linear mixed effects models.

Hamsters approached the ambiguous cues signiﬁcantly more

often when enriched than unenriched. There was no effect of

enrichment on responses to the middle cue. We discuss these

ﬁndings in light of mechanisms underlying processing cues to

reward, punishment and true ambiguity, and the implications

for the welfare of laboratory hamsters.

2015 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.

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1. Introduction

Accurate assessment of animal emotions is an important goal of welfare science [1,2] and central to the

reﬁnement of the use of animals in scientiﬁc research [3,4]. In the last decade, methods used to assess the

link between affective state and cognitive processes in humans [5,6] have been successfully adapted and

applied to animals. The judgement bias model of animal welfare [7–10] argues that emotion states may

be measured in animals, as they are in humans, as a function of altered cognitive judgements about the

likely rewarding or punishing nature of ambiguous stimuli. The value of judgement bias measures over

traditional physiological and behavioural approaches is their potential to distinguish emotional valence

(positive versus negative emotions). Given the large number of laboratory rodents used each year, it is

important to develop proxy measures of affective state to aid in welfare assessment. Thus far, judgement

bias studies on laboratory rodents have focused on rats (e.g. [7]), with two studies on mice [11,12]and

none on hamsters.

Judgement bias in animals has been most often measured using a ‘go/no-go’ task in which animals

are trained to discriminate between a ‘positive’ and a ‘negative’ cue and then tested on their responses

to ambiguous cues [7]. Ambiguous cues possess characteristics intermediate to both the rewarded and

non-rewarded stimuli, and it is responses (‘go’ or ‘no-go’) to these that quantify judgement bias. Recent

studies with animals including rats [7,13–16], mice [11,12], cats [17], dogs [18–24], dairy calves [25,26],

sheep [27–38], chickens [39–42], honeybees [43], rhesus macaques [44], marmosets [45], starlings [46], pigs

[47–49], horses [50] and goats [51] demonstrate that emotion-mediated judgement biases may be detected

using species-speciﬁc variants of the go/no-go task. So far, studies have revealed that manipulations

presumed to create a negative state (such as disrupted housing conditions [7] or dehorning in calves [26])

lead to reduced responses (more ‘no-go’s) to the ambiguous probes. This negative shift in judgement

bias is presumed to arise from a negative shift in underlying emotion state. Positive manipulations (e.g.

addition of environmental enrichment [46]) generally lead to increased responses (more ‘go’s) at one or

more of the ambiguous probes. This is presumed to arise from positive shifts in judgement bias and

underlying emotion state.

A criticism of the go/no-go approach (discussed more fully in [9]) is that results may not reﬂect

judgements about ambiguity, but instead reveal changes in motivation (due to satiation or learning),

arousal or activity (due to treatment effects or task fatigue). A solution to this is to include control

trials and a variable reinforcement ratio (VRR) to maintain motivation and reduce learning effects.

Triangulating judgement bias with traditional measures of affect (e.g. open ﬁeld (OF) test and light–

dark emergence (LDE) tests) and recording behaviour during testing would allow treatment effects and

task fatigue to be considered.

The active choice task is an alternative to the go/no-go task. The response (‘go’ or ‘go’) is the same

to both learned stimuli, providing a built-in control for arousal effects. The active choice test has been

validated with rats [52–64], starlings [65,66], pigs [67], chickens [68], grizzly bear [69] and capuchin

monkey [70]. Results from these studies are varied, and several report arousal effects (e.g. [67,68]).

Care must be taken to ensure that the perceived affective difference between the two reinforcers is

sufﬁciently great to detect any shift in judgement bias [9]. For example, in the study of Parker et al. [64],

the ‘positive’ cue signalled two pellets and the ‘negative’ cue signalled one pellet. In this case, repeated

training might more rapidly lead to satiation, a decrease in reward value or reduced ability to distinguish

between high and low reward. A variant of the active choice task is to use negative reinforcement, e.g.

electric shock [52,55–57,59–63], but ethical issues and confounding effects of fear and stress make this

method unsuitable for welfare assessment. Results from studies using negative reinforcement, which

tends to be pharmacological manipulations conducted in rats, support the judgement bias model of

emotion–cognition interaction in non-human animals.

Both approaches are susceptible to a number of limitations. These include: (i) cueing effects (e.g.

odour cues from food), (ii) the contribution of other aspects of cognition such as risk-taking behaviour or

attention for threat cues, (iii) learning the reward value of the ambiguous probes, (iv) lack of a consistent

and robust statistical approach, and (v) a lack of consistency in aprioripredictions about the direction of

change in bias and associated emotion state (for example, increased responding to probes in the ‘stress’

condition is interpreted as indicating relief following the termination of the stressor in Doyle et al. [27]

and Sanger et al. [31]). These limitations are rarely addressed in study design, but failure to do so may

lead to misinterpretation of results.

Of the two task designs, the spatial go/no-go task has been the most widely validated and applied

across species. Within Mendl et al.’s [10] integrative framework, emotional valence is directly associated

with approach towards reward (associated with excitement and happiness) and avoidance of (or

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inhibition of response to) threat or punishment (associated with fear and anxiety). These underlying

associations may facilitate training on the ‘go/no-go’ task (compared to learning to discriminate high

versus low reward in the active choice task), making go/no-go an ideal method to develop a judgement

bias task in a new species. Both tasks have been developed using auditory cues (e.g. [48,67]) and visual

cues (e.g. [44,70]). In addition, the go/no-go task has been validated with spatial cues (e.g. [7,39,50]),

tactile cues (e.g. [53,58]) and odour cues (e.g. [43]). Among rodents, spatial cues [7,13–16] and auditory

tones [52,55–57,60–63] have been used successfully with rats, while spatial cues [39,41] and odour

cues [12] have been used successfully with mice. Spatial cues are therefore suitable for ﬁrst development

of a task with a new species of rodent.

For the purposes of welfare assessment, the addition of environmental enrichment has been the most

common positive manipulation in judgement bias studies. The majority of studies where enrichment

was added reported a positive shift in judgement bias. Studies have differed in the type of enrichment

added (e.g. increased space, perches, shelters, foraging substrate [44,46]), the duration of treatment

(e.g. 2 h in grizzly bears [68], one week in rats [16,53], 10 days in rhesus macaques [44]), whether

enrichment is removed (starlings [46]) and whether a switch in housing occurs, for example from

enriched to basic and back to enriched (starlings [66] and pigs [48]). There are some exceptions: no

judgement bias shift was detected in laying hens [40]; however, this study used a between-subjects

design and the individual differences in fearfulness, body condition and neophobia appeared to have

a greater impact on performance than the enrichment treatment. Of the four studies that employed

a switch in housing conditions [46,48,65,66], three detected a treatment-induced shift in judgement

bias [46,48,65]. In starlings, a pessimistic bias was noted following a reduction in enrichment but no

change was noted following addition of enrichment [46]; however, a later study using similar enrichment

items and increased space found an optimistic bias in enriched cages [65]. Both these studies used

a cross-over design with half the subjects experiencing enriched then unenriched housing and the

other half unenriched then enriched housing. Douglas et al. [48] also used a cross-over design in their

study of pigs, with half experiencing the switch enriched-to basic-to enriched and the other half the

opposite housing manipulation. Pigs currently in enriched housing interpreted the ambiguous cues

more optimistically. Moreover, there was an interaction between current and past environment: pigs

that were ﬁrst in enriched housing reacted more negatively when subsequently housed in a barren

environment. In the fourth study involving a switch in housing conditions [66], individual differences in

stereotypies were found to predict pessimism. These studies highlight the importance of within-subjects

study design, appropriate enrichment to create a shift of sufﬁcient magnitude that is not masked by

individual differences, and using a cross-over design to control for order effects.

Here, we aim to validate a judgement bias test for use in hamsters (Mesocricetus auratus) and to tackle

the above criticisms within spatial judgement bias paradigm [9,13].

We measured judgement bias using a balanced, cross-over design [48] using changes in enrichment to

validate the task. Hamsters were initially housed in standard laboratory cages with basic enrichment and

then received highly enriched housing (E) ﬁrst or experienced removal of some of the basic enrichment

(R). Enrichment devices were selected according to published data from hamster preference tests and

natural behaviour [71–73]. A week later hamsters moved to the opposite housing conditions allowing

comparison of the two groups (ER or RE). In response to our critique above we proceeded as follows.

(i) We excluded olfactory cueing effects by introducing a VRR during training. (ii) We statistically

controlled for confounding effects of arousal, by including day and block of testing in our model

(see below), and controlling for other aspects of cognition by triangulating data with traditional tests

of affect in rodents such as OF and LDE [74], neophobia [75] tests and recording in-trial behaviours

traditionally used to measure emotion state or arousal in laboratory rodents (e.g. [7,16,73–77]). (iii) To

reduce the likelihood of hamsters learning that probes were never rewarded we used a VRR during

training, and included unreinforced control trials at the positive and negative stimulus locations during

testing. (iv) We applied a robust information-theory approach (multi-model inference) using linear mixed

effects models [78–81] to assess the relative contribution of different variables to responses on the task.

Generalized linear mixed models incorporate random effects, test ﬁxed effects using generalized least

squares results in more powerful tests, handle non-normal data by using link functions and exponential

family distributions (e.g. normal, Poisson or binomial), and are able to handle missing data and unequal

spacing over time [78,79]. Linear mixed effects models are beginning to appear in judgement bias studies

(e.g. [22,51,82]) and provide a robust and ﬂexible approach to analysis that will aid interpretation of

output in light of potential confounding factors. (v) We predicted that the addition of enrichment would

be associated with a positive affective state and corresponding optimistic judgement bias, while the

removal of enrichment would be associated with a more negative affective state and judgement bias.

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2. Material and methods

2.1. Study animals and housing

Thirty captive-born male Syrian hamsters (Mesocricecitus auratus) took part in the study. Hamsters were

weaned at 21 days and retained in eight same-sex sibling groups throughout the study. Hamsters were 8

weeks old at the start of the study and 13 weeks old by the end of the study. The study was conducted

with one cohort of hamsters in 2011 (n=17) and a second cohort in 2013 (n=13). In both years, sibling

groups were housed in the same style of caging (56 ×38 ×22 cm) in the same air-conditioned room

kept at a constant 20–24◦C on a 12 L : 12 D cycle, with humidity maintained between 45 and 70%.

Hamsters had ad libitum access to water and dry rodent pellets (Eurodent Diet 22% 5LF5, PMI Nutrition

International Plc) and were handled twice per week from weaning for routine cage cleaning and health

inspections by care staff. All animals that completed the study were assessed by care staff as healthy

throughout.

2.2. Apparatus

Training and testing sessions were conducted in a high-sided blue opaque plastic arena (100 ×80 ×

85 cm) in the housing room during the ﬁrst hours of the active (dark) phase using low level light. The

arena had ﬁve holes drilled along one wall, with attachments on the outside so that on each trial a drinker

could be attached at any location with only the spout protruding into the arena. Three drinkers were

used during the experiment. Two drinkers were used for training and maintenance during testing: one

drinker contained 0.3 M sugar water and one drinker contained 0.01 M quinine hydrochloride (QHCl)

solution. A third drinker was used for experimental trials (‘control’ and ‘probe’ trials) and was kept

empty throughout.

2.3. Habituation

The timeline for the study is shown in ﬁgure 1. Initially, all animals were familiarized with the arena. One

familiarization session was conducted on each of four days (Tuesday–Friday) for each sibling group. The

sibling group was placed in the arena with scattered food and a bottle containing sugar water attached

at the rewarded location (left or right) for that group. Animals were monitored for 5 min to ensure all

animals had drunk from the drinker. At the end of each daily session, the number of faecal pellets left in

the arena was counted.

2.4. Training

2.4.1. Phase A (weeks 1 and 2: days 1–10)

We employed a spatial judgement task used previously ([13], ﬁgure 2). In brief, hamsters were trained

to approach a drinker at one location (left or right) to obtain a reward (‘go’ towards sugar water), and

to refrain from approaching a drinker at the other location (right or left) to avoid an aversive liquid

(‘no go’ towards QHCl). A trial began when the hamster was placed at a start point at the centre

back of the arena. The trial ended when the hamster approached the drinker, or after a predetermined

number of seconds, whichever occurred sooner. On the ﬁrst day of Phase A, each hamster took

part in 8 ×120 s trials. As hamsters learned the task and became faster to approach, sessions were

sequentially adjusted to 8 ×60 s trials and then 10 ×30s trials. This adjustment was made according to

individual response latencies so that trial duration was reduced quickly for fast-responding hamsters,

and more slowly for hamsters that needed the extra time to approach. If the hamster approached

to within 3 mm of the drinker in the predetermined trial duration this was scored as a ‘go’. If the

hamster did not approach the drinker in the predetermined trial duration this was scored as a ‘no-

go’. An equal number of sugar and QHCl trials were run within each daily training session for each

hamster. The ﬁrst and last trials were always sugar trials so that each session began and ended with a

positive association with the arena (same throughout all subsequent phases). A hamster was removed

once he had ﬁnished drinking. Criterion for learning the discrimination task was average latency to

approach the sugar drinker being shorter than average latency to approach the QHCl drinker on each of

three consecutive days.

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judgement bias test

Tues, Weds, Thurs Neo

(Fri am)

judgement bias test

Tues, Weds, Thurs

Phase C

testing

weekend

6–10 11–15

Phase C

testing

16 17 18 19 20

Phase B

training

enrichment removed ‘R’

cages 1–4

enrichment added ‘E’

cages 5–8

(Thursday pm)

enrichment added ‘E’

cages 1–4

enrichment removed ‘R’

cages 5–8

(Thursday pm)

OF, LDE

(Thurs pm)

Phase D

testing

21 22–24 25

1–5

Phase A

training

novel enrichments added

to all cages

(Friday pm)

OF, LDE

(Thurs pm)

Figure 1. Timeline for the study,conduc tedover ve weeks. Day numbers are shown: discrimination training (days1–15); judgement bias

testing (days 17–19 and 22–24; maintenance trials) days16, 20 and 21). , Enrichment manipulation; additional behavioural tests—,

OF and LDE tests (days 19 and 24) and ×, neophobia test (Neo) (day 25).

2.4.2. Phase B (week 3; training days 11–15)

Training continued as for Phase A with the introduction of control trials to create a 60% variable

reinforcement ratio. A control trial was a trial on which the empty drinker was presented at either of

the two trained (sugar/QHCl) locations. These trials were included to ensure animals were responding

to drinker location and not to odour cues. A daily training session in Phase B comprised 16 ×30 s trials

divided into two blocks (table 1). Block 1 contained nine trials: ﬁve reinforcement trials (2 sugar : 2 QHCl,

plus one) pseudorandomized with four control trials (2C+:2C−). Block 2 comprised a total of seven

trials: three reinforcement trials (1 sugar: 1 QHCl, plus one) pseudorandomized with four control trials

(2C+:2C−). Care was taken to remove the hamster as soon as he reached the empty drinker on control

trials to reduce the possibility of animals becoming frustrated if left to attempt to drink from an empty

drinker.

2.5. Judgement bias experiment

2.5.1. Phases C and D (weeks 4 and 5; days 16–25)

The judgement bias testing phases followed the design used in Phase B with the addition of experimental

probe trials on the Tuesday, Wednesday and Thursday of each week (Phase C testing days: 17–19;

Phase D testing days: 22–24). An experimental probe trial was a trial on which the empty drinker

was presented at one of the three intermediate locations. A daily testing session in Phase C comprised

16 ×30 s trials divided into two blocks. Block 1 comprised four reinforcement trials (2 sugar : 2 QHCl,

pseudorandomized with two control trials (C+,C−) and three probe trials (P+, Pmid and P−). Block 2

comprised two control trials (C+,C−), pseudorandomized with three probe trials (P+, Pmid and P−),

and two reinforcement trials (Q−,S+). Hamsters therefore had six experimental probe trials (two at each

location: P+, Pmid and P−) in each daily session. Hamsters were removed as soon as they reached the

empty drinker on all probe and control trials. Phase C comprised three testing days which generated

six data points per probe location per hamster. On the Monday and Friday (days 16 and 20), hamsters

received sessions as in Phase B. Phase D (week 5) was an exact repetition of Phase C, except that housing

treatments were switched as explained below.

2.6. Housing treatments

At the start of the study hamsters were housed in their home cages provisioned with a thin layer

of aspen chip substrate and lignocel bedding material (both from IPS, London, UK), a free-standing

barred running wheel (diameter 15 cm) and two spirally wound cardboard tubes (20 ×10 cm). In the

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Phase C testing

trial: sugar reinforcer

or Control+

Ambiguous probes

Pmid

QHCl punisher

or Control–

P+P–

response:

max. enrichment added (cages 1–4)

(+ extra bedding, substrate, huts)

enrichment reduced (cages 5–8)

(– plastic wheel, ledge, chews, tent)

introduce novel enrichment items to all cages

(plastic wheel, ledge, chews, tent)

Phase A training

trial: sugar reinforcer QHCl punisher

response: ‘go’ = approach drinker ‘no go’ = do not approach drinker

Phase B training

trial: sugar reinforcer

or Control+

QHCl punisher

or Control–

response: ‘go’ ‘no go’

enrichment reduced (cages 1–4)

(– plastic wheel, ledge, chews, tent, extra

bedding, substrate and huts)

Phase D testing (repeat Phase C)

max. enrichment added (cages 5–8)

(+ plastic wheel, ledge, chews, tent, extra

bedding and substrate, huts)

Figure 2. The judgement bias task. During training (Phase A) hamsters learned to approach the sugar drinker (‘go’) and not to approach

(‘no go’) the QHCl drinker. At the end of Phase A, all animals received a basic provision of novel enrichment items. Training on the go/no-

go task was maintained in Phase B with the inclusion of the empty drinker on some trials to create a 50% VRR (empty drinker trials:

‘Control +’ and ‘Control −). Towards the end of Phase B half of the sibling groups received the maximum complement of enrichment,

while the novel enrichment items were removed from the other half of the sibling groups. Animals were tested on the judgement bias

task on the following week (Phase C) with the presentation of the empty drinker at all ve locations, including the three ambiguous

intermediate probe locations. Enrichments were then switched and animals tested again in Phase D.

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Table 1. The blocked design for training in Phase B and testing in Phases C and D. Each daily session began (block 1: trials 1 and 2) with a

rewarding sugar trial followedby a QHCl trial to remind hamsters of the task contingencies. Each daily session ended (block 2: trial 16) with

a sugar trial to end on a positive association and encourage maintained performance over subsequent days. Order of all other trials was

pseudorandomized within each of block. S+=Sugar; Q−=QHCl; C+=Control at sugar location; C−=Control at QHCl location;

P+=ambiguous probe closest to the sugar location; Pmid =middle probe; P−=ambiguous probe closest to the QHCl location. Trial

numbers are shown in parentheses.

block

Phase 1 2

B(trialn) S+,Q−(1, 2) 2C+, 2C−,S+,Q−(3–9) 2C+, 2C−,S+,Q−(10–15) S+(16)

.........................................................................................................................................................................................................................

C/D (trial n) S+,Q−(1, 2) C+,C−,P+,Pmid,P−S+,Q−(3–9) C+,C−,P+,Pmid,P−,Q−(10–15) S+(16)

current study, emotion state was manipulated by adding or reducing enrichment. Choice of enrichment

devices was informed by published work showing that their presence leads to changes in behaviour

assumed to indicate preferences and improved welfare in hamsters [71–73]. Additional enrichment items

comprised deeper aspen substrate and extra nesting material [71], two coloured transparent plastic huts

(10 ×12 cm), a suspended hamster tent (15 ×12 cm), four hamster gnaw sticks and a wooden ledge

(18 ×13 cm: all of which increase opportunity for natural and exploratory behaviour [72,73,77]). The

metal barred wheel was replaced with a larger solid-ﬂoor plastic silent running ball-bearing wheel

(16.5 cm, Silent Spinner Regular [83]).

Enrichment items that hamsters had not encountered previously (hamster gnaw sticks, tent, ledge

and plastic running wheel) were added to all home cages on the Friday afternoon of Phase A (day

10 after training: ﬁgure 1). Animals were left to habituate to the novel enrichment devices over the

weekend and throughout the next week (Phase B on the following Monday–Friday). Introducing novel

enrichment items during training should reduce potentially confounding context-dependent learning

effects on animals’ performance in the two treatments during later testing sessions [84]. After training

on the Thursday of Phase B, the maximum complement of enrichment items was added to half of the

cages (Treatment E: ‘enrichment added’) while the novel enrichment items were removed from the other

half of the cages (Treatment R: ‘enrichment removed’). Discrimination training was then maintained on

the Friday and the following Monday. This ensured all animals had undergone some form of ‘change’

in housing environment prior to the start of testing in Phase C). Testing in Phase C was conducted on

three days (Tuesday–Thursday), which corresponded to the ﬁfth, sixth and seventh days of having a

full complement of enrichment devices added to the home cage for four sibling groups, and the ﬁfth,

sixth and seventh days following reduction in enrichment from the home cage for four sibling groups.

Following the third day of testing in Phase C (Thursday), housing treatments were switched so that

the hamsters receiving Treatment E now received Treatment R, and vice versa. Maintenance on the task

following the procedure described for Phase C was conducted on the Friday and following Monday.

Hamsters were then tested again in Phase D on the following Tuesday–Thursday.

2.7. Behavioural measures

Behaviour during experimental trials (probe and control trials on the Tuesday–Thursday in Phases C and

D) was recorded to explore possible treatment-induced differences in arousal and stress-related activity.

Arousal was assessed by calculating a ‘locomotion score’: the arena ﬂoor was marked out as a grid of

12 squares, and the number of squares the hamsters entered prior to reaching the drinker (or until the end

of the 30 s trial on ‘no-go’ trials) was recorded. General stress-related activity was assessed by calculating

an ‘anxiety score’: behaviours considered to indicate stress or anxiety in hamsters (autogrooming,

attempting to climb arena walls to ‘escape’, freeze, stereotypy, rearing and snifﬁng [7,74,76,77]) were

recorded using 1/0 scoring [85] (range 0–6) during each trial.

OF, LDE and neophobia tests were conducted during Phase C and D (ﬁgure 1). Each hamster took

part in one 2 min trial on each of an OF and an LDE test on the Thursday afternoon after judgement bias

testing in each phase. The OF apparatus measured 1 m2. Total number of squares entered and time spent

in the middle square were recorded. In the LD emergence test, the hamster was placed in a wooden hut

(15 cm3) and time taken to emerge into a well-lit exterior was recorded. Neophobia was tested on the

Friday of Phase D. Each hamster took part in nine trials in the arena, during which the time to approach

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the sugar drinker when a novel object was next to the drinker (n=4 trials) or not present (n=5trials)

was recorded. A different novel object was used on each trial and objects varied in shape and colour and

were unfamiliar to the hamsters.

2.8. Data analysis

Each hamster took part in a total of 60 experimental trials (30 in each housing treatment): 36 probe trials

(12 at each of the three probe locations) and 24 control trials (12 at each of the C+and C−locations).

These were dispersed amongst a total of 36 reinforcement trials (Sugar and QHCl). A criterion of more

than 50% correct responses (‘go’ to the C+and ‘no-go’ to the C−) on control trials across testing sessions

was set for each hamster to be included in the analyses. We used a generous criterion for inclusion in

the analyses since we wanted to explore the potential inﬂuence of a range of factors on performance

in the judgement bias task. We did not want to bias ﬁndings by selectively removing hamsters who

were perceived to be poor performers if changes in performance reﬂected meaningful responses to the

enrichment treatments. However, we wanted to remove hamsters who stopped responding on the task

altogether.

2.8.1. Model development

All recorded explanatory variables were considered for inclusion in the maximal model. These were:

treatment (E, R), trial type (C+,P+, Pmid, P−,C−), testing block (blocks 1 and 2), year of testing (2011,

2013), day of testing (Tuesday, Wednesday or Thursday), order of testing (E ﬁrst, R ﬁrst), trial number

(trial 1–16), experimenter ID, hamster ID (24 hamsters), sibling group (eight groups) and speed to learn

the discrimination task (3–16 days). Preliminary assessment for colinearity between these variables was

conducted in the statistical package R v. 3.0.1 [86]. Where two or more variables covaried, these were

serially entered into a series of generalized linear models (GLMs) and the variable resulting in the GLM

with the lowest AICcvalue was retained [80,81].

Preliminary analyses revealed that testing block correlated with trial number (r=0.63); block had the

lowest AICcand was retained. Week of testing correlated strongly with day of testing (r>0.9); day was

retained. Sibling group, year in which the data were collected, experimenter ID and speed to learn the

discrimination task were correlated with each other (all rs>0.36); sibling group was retained. Order of

testing was correlated with sibling group and hamster ID (as siblings were housed together and therefore

underwent the same housing treatment together: rs>0.55). Because previous studies have found an

effect of order on responses during judgement bias tests, we conducted a glm (order ×treatment) to

explore possible order effects. This revealed a signiﬁcant order ×treatment interaction (estimate =0.512,

error =0.23, z=2.19, p=0.029). To control for this effect in the ﬁnal model order of testing was retained

with sibling group and male ID as nested random factors.

A maximal model was deﬁned using the glmer package in R [86]. Go/no-go responses were the

dependent binary variable. Treatment, trial type and block were the ﬁxed factors; day of testing was

included as a random factor and order of testing, sibling group and male ID were included as nested

random factors. Treatment, trial type and block were also included as interaction terms:

glmer(NoGo ∼Treatment ×Trial Type ×Block +(1|Day Test) +(1|Order/Siblgrp/Male ID),

family =binomial).

These explanatory variables were used to create a total of 15 candidate models all with a logit link

function and a binomial error distribution (electronic supplementary material, table S1). Models were

compared and selected using the AICc, with the lowest AICcvalue indicating the best model ﬁt. All

models within 2 AICcof the best model were retained as the ﬁnal subset of models [80,81].

To test for an effect of ﬁxed factors featured in the best-ﬁt model(s) at each of the probe and control

locations separately, test models were created for each probe and control location (with random factors

held constant), and with each ﬁxed factor from the best model(s) included separately. Test models were

then tested against the null model for each probe and control location using the anova function in R [86].

Criterion for signiﬁcance was p<0.05.

An example of the null model used with data for trial location separately is as follows:

NoGo ∼(1|Day Test) +(1|Order/Siblgrp/Male ID).

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2.9. Behavioural measures

Behavioural data for hamsters while in the arena were analysed separately as they were collected on a

subset of the experimental trials. For each hamster, the locomotion score was divided by the trial duration

(latency to approach the drinker, or 30 s in the case of no-gos) to give a locomotion index for each trial. For

behaviour in the arena, anxiety score was divided by the trial duration. Data were tested for normality

and sqrt transformed where necessary. Locomotion and anxiety indices were then entered separately as

dependent variables in the 15 candidate models and the best-ﬁt models selected as described above.

LD emergence, OF and neophobia test data were also analysed separately as these data were collected

at the end of each treatment week (Phases C and D—Phase D only for neophobia data). Owing to the

small number of trials, these data were analysed using non-parametric Wilcoxon tests. Neophobia data

were tested following a model selection procedure as outlined above using the lmer function in R [87].

Time to approach the drinker was the dependent variable (since hamsters approached the drinker on all

novelty trials and most sugar trials), with ﬁxed factors treatment (E or R) and trial type (sugar or sugar +

novel) with nested random factors sibling group and hamster ID.

3. Results

3.1. Habituation

Animals defecated in the arena on the ﬁrst day of familiarization, but not thereafter. Hamsters readily

approached and drank from the drinker within each 5 min session.

3.2. Learning the discrimination task (days 1–16)

Performance data for hamsters during Phases A and B are given in the electronic supplementary material,

table S2 and table S3. On average, hamsters reached criterion for learning the discrimination task in 8

days (79 trials; range 3–16 days. Eighteen hamsters reached criterion for learning the discrimination task

before the end of Phase A (day 10). Five hamsters reached criterion before the end of Phase B (day 15).

One hamster reached criterion on the ﬁrst day in Phase C (day 16).

3.3. Performance on control trials in Phase B

Group-level accuracy for discrimination and control trials during phase B are given in the electronic

supplementary material, table S3. Hamsters correctly approached the sugar location on 84% of trials and

the sugar control (C+) on 71% of trials. They correctly did not approach the QHCl location on 64% of

trials and the QHCl control (C−) on 69% of trials.

Maintenance of performance on the discrimination task during testing in Phases C and D: of the 30

hamsters that took part in testing, 24 hamsters (n=15 from 2011 and n=9 from 2013) from eight sibling

groups performed to criterion on control trials during the judgement bias test in Phases C and D and were

included in the ﬁnal analysis. Performance data for these 24 are given in the electronic supplementary

material, table S4. Of the six hamsters that were not included, one was removed from the study due to

health reasons, and the remaining ﬁve failed to approach the C+ during the enrichment treatment (n=3)

or during both treatments (n=2).

3.4. Model selection

The factors treatment, trial type and block of testing appeared in the best-ﬁtting models, explaining 84%

of the variance in the data (table 2).

3.5. Localizing signicant eects

3.5.1. Probe trials

Anovas comparing the effect of treatment against the null model were conducted for each probe

separately (table 3). These revealed a strong effect of treatment at the two outer probes, with no effect at

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Table 2. AICc-ranked candidate model set showing the relative importance of xed eects (treatment, trial type, block) and random

eects (order of testing, sibling group, hamster ID and day) in explaining tendency to approach drinkers of known and uncertain reward

and punishment value.

model xed eects random eects (/nested) d.f. log likelihood AIC delta weight

treatment, trial ×block (order/sibling grp/ID), day 15 −772.932 1576.2 0.00 0.622

treatment, trial (order/sibling grp/ID), day 11 −778.072 1578.3 2.11 0.216

Table 3. Comparison of test models (xed factor of either treatment or block) with the null model for each of the three probes, two

control locations and the reinforcing sugar or QHCl trials. Values for the null model are given in parentheses. Test models which were

shown by ANOVA to deviate signicantly from the null are indicated by asterisks.

trial intercept d.f. log likelihood AICcdelta weight

probe (+)

treatment −1.268 6 −143.38 299.1∗0.00 0.589

block −2.743 6 −143.98 300.3∗1.19 0.325

null (−0.879) (5) (−146.35) (302.9) (3.84) (0.09)

probe (mid)

treatment −0.512 6 −166.48 345.3 6.37 0.036

block −2.381 6 −163.30 338.9∗0.00 0.870

null (−0.453) (5) (−166.58) (343.4) (4.46) (0.094)

probe (−)

treatment −0.508 6 −173.17 358.7∗0.00 0.745

block 0.244 6 −175.46 363.3 4.59 0.075

null (−0.202) (5) (−175.64) (361.5) (2.85) (0.18)

control (+)

treatment −1.707 6 −129.28 270.9 0.25 0.40

block −1.477 6 −130.20 272.7 2.10 0.16

null (−1.484) (5) (−130.20) (270.6) (0.00) (0.45)

control (−)

treatment 0.571 6 −161.51 335.4 0.71 0.29

block −0.130 6 161.49 335.3 0.66 0.30

null (0.732) (5) (−162.20) (334.6) (0.00) (0.41)

reinforcer (S+)

treatment −2.807 6 −138.62 289.4 9.32 0.00

block −3.990 6 −133.96 280.1∗∗ 0.00 0.97

null (−2.695) (5) (−138.84) (287.8) (7.70) (0.02)

punisher (QHCl)

treatment 0.509 6 −226.53 465.3 1.38 0.23

block 0.246 6 −226.25 464.7 0.82 0.31

null (0.602) (5) (−226.88) (463.9) (0.00) (0.46)

∗p<0.05; ∗∗p<0.01.

the middle probe: hamsters approached the two outer probes more often in treatment E than R (ﬁgure 3).

Comparison of the effect of block against the null model for each probe separately revealed a strong

effect of block on responses to the Pmid and P+, but not the P−: hamsters made fewer approaches to the

Pmid and P+in the second compared to the ﬁrst block.

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0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

C– P– P+ C+

proportion trials approached drinker

Pmid

Figure 3. Mean proportion (±s.e.) of approaches to each of the probe and control locations when enrichment had been added (open

circles) or reduced (closed circles). Hamsters approached the outer probes (P−and P+) signicantly more often following addition of

enrichment compared to when enrichment had been reduced.Hamsters approached the middle probe equally often in both treatments.

CQHCl control; P−probe closest to the QHCl location; Pmid middle probe; P+probe closest to the sugar location; C+sugar control.

Table 4. Best-t models for locomotion and anxiety indices for hamsters during test trials in the arena. All models within 2AICcof the

best model are shown for each behavioural index.

model xed eects intercept d.f. log likelihood AIC delta weight

locomotion index

trial 0.347 12 −188.11 400.4∗∗ 0.00 0.762

null (0.443) (6) (−255.45) (523.0) (122.51) (0.000)

anxiety index

block 0.2732 7 712.43 −1410.8 0.00 0.412

null (0.3398) (6) (711.01) (−1410.0) (0.83) (0.273)

block +treatment 0.2828 8 712.65 −1409.2 1.58 0.187

∗∗p<0.01.

3.5.2. Control and reinforcement trials

There was no effect of treatment or block at either of the two control locations (or on the QHCl trials:

table 3). There was a signiﬁcant effect of block on approaches to the sugar drinker with hamsters

approaching less often in the second block of testing compared to the ﬁrst. Comparison of bottle weights

revealed hamsters drank equivalent amounts of sugar water in the two treatments (t15 =1.56, p=0.14).

3.6. Behavioural measures

Trial type explained more than 76% of the variance in locomotion in the arena (table 4). Hamsters

locomoted more per second on sugar, C+and P+trials compared to the C−baseline (table 5). For anxiety

index, no model explained the variance any better than the null model.

Non-parametric tests revealed no difference between treatments in performance in the OF (nsquares

entered: W=105, p=0.50; time in centre: W=129, p=0.95) or LDE (W=87, p=0.12). Model selection

performed on data from neophobia tests revealed that trial type explained 70% of the variance in the

data (all other models greater than 2 AICcpoints from best model). Hamsters were faster to approach

the sugar drinker when there was a novel object situated next to it than when the sugar drinker alone

was presented (ﬁgure 4). This was not affected by housing treatment.

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novelty + sugar sugar only

latency to approach drinker (s)

Figure 4. Mean latency (±s.e.) for hamsters to approach the sugar drinker with and without a novel object present. E, Enrichment

added; R, Enrichment removed.

Table 5. The linear relationship between trial type and locomotion in the arena. Hamsters locomoted most per second on sugar trials

and least on QHCl trials. Values calculated using C−used as reference.

locomotion estimate s.e. t-value

intercept 0.35 0.09 3.91

sugar 0.22 0.02 8.17

C+0.21 0.02 7.95

P+0.18 0.02 6.74

Pmid 0.06 0.02 2.31

P−0.02 0.02 0.67

QHCl −0.01 0.02 −0.50

4. Discussion

We developed a spatial judgement bias test for use with hamsters, using short-term changes in

enrichment to validate the task. Firstly, hamsters learned to discriminate between drinkers at two

locations: they were more likely to approach the positive location and less likely to approach the

negative location. Therefore, the spatial judgement bias task is suitable for further development with

hamsters. Secondly, hamsters were more likely to approach an empty drinker at ambiguous locations

when enrichment had been added to their cage for the previous week, than when it had been removed.

Hamsters approached more often as the probe neared the positive location. Importantly, there was no

effect of treatment on approaches at the extreme cue locations. This difference in response to ambiguity

is consistent with the judgement bias model of emotion–cognition interaction previously reported for a

range of species [82]. This is the ﬁrst study to report evidence for emotion-mediated judgement bias in a

species of hamster.

Our unique pattern of ﬁndings has implications for continued reﬁnement in the design of the

judgement bias task by: (i) using a VRR during training to remove cueing effects of reinforcers; (ii)

statistically controlling for potentially confounding effects by including additional factors (e.g. time,

order of testing) as random effects in mixed models, and using behavioural measures to distinguish

valenced judgements from arousal, risk-taking or perceptual factors; (iii) including a VRR during

training, and control trials during testing to reduce rate of learning that probes were never rewarded;

and (iv) using robust statistical approaches such as mixed effects models to control for potentially

confounding factors in studies that often have small sample sizes. Our ﬁndings also have theoretical

implications for: the value of cognitive measures of emotion over behavioural and experimental

measures alone; the study of positive emotions; and further understanding the mechanisms underlying

responses to ambiguous cues to reward and punishment.

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4.1. Control trials and behavioural measures distinguish judgement bias from alternative

explanations

Increased responding to two of the probes may be interpreted as valenced judgements of likelihood of

reward or punishment because hamsters showed no difference between treatments in (i) proportion of

approaches on control trials at the sugar drinker location and (ii) proportion of approaches on control

trials at the QHCl location. If changes in responses were due to a generalized increase in arousal (or a

shift in perception of the empty drinker, or risk-taking behaviour), then we would expect to see greater or

fewer responses on the control trials as well as at the probes. Behavioural indices of arousal (locomotion

and activity) collected during trials further support this interpretation. Hamsters showed no difference

between treatments in rate of locomotion or anxiety-related behaviour during trials. Control trials and

additional behavioural measures are therefore an effective way of controlling and accounting for possible

arousal effects on responses.

4.2. Linear mixed eects models with random and nested factors provide robust analysis of data

The information-theoretic approach using generalized linear mixed models (GLMMs) provided a ﬂexible

and highly informative means of exploring our data. By testing the effect of ﬁxed factors (here treatment

and block) at each drinker location separately, we were able to tease apart the effects of different factors

on responses at each probe. An advantage of the multi-model inference approach is that it provides

one or more ‘best-ﬁt’ models with information on the relative ﬁt of these models to the spread of data.

Using a hierarchical nested approach, we were able to control for a range of additional factors including

individual differences in rate of learning, relatedness within sibling groups, order of testing, as well

as for changes in performance over testing days. Using mixed effects models prevents problems of

interpretation when there are temporal effects on performance (for example, in Scollo et al. [49]ashiftin

judgement bias was reported in pigs on the third day of testing, but no difference was found on previous

days or when a mean across days was considered).

The results we report here meet the performance criteria suggested elsewhere for applying mixed

models to judgement bias data [82], but we argue the model selection approach we present provides a

more informative and intuitive analysis than that proposed by Gygax [82]. By testing our best-ﬁt models

against the null model at each probe location, we were able to identify the location of mood effects

on responses to the probes. Probes are categorically different from the control locations (about which we

have different predictions). The approach we advocate may be more useful for distinguishing the location

of effects when identifying different emotion states, or processes involved in approach and avoidance

(discussed below). Furthermore, a uniﬁed statistical approach to judgement bias studies would allow

better comparison between studies and meta-analyses.

4.3. Learning and motivation may inuence responses to ambiguous probes

In some previous studies, it has not been possible to rule out possible learning or motivational effects

on responses to probes, especially if the effect of treatment is small [22,67,69], animals took part in a

relatively large number of trials (more than 50 trials in some studies [26,44,62,65]) or where changes in

responses at the learned locations were also detected (e.g. [7,30,32,39,41,43]). To reduce the likelihood, or

speed, of learning that the probes were never rewarded, we used variable reinforcement during training.

We then controlled for learning effects in the analyses by including day of testing as a random factor in

our models. Block featured in the best model only in interaction with trial type. The main effect of block

was found at the S+, P+ and Pmid (hamsters approached less often in the second of the two experimental

blocks), but not at the other locations. There is therefore a possibility that learning may affect responses

to ambiguous cues to reward, or that learning occurred across all locations but the lack of reduction in

responses in the second block for either of the P−,C−or QHCl trials was concealed by a ﬂoor effect in

the data. Alternatively, reduced responding to locations at the positive end may reﬂect reduced sugar

motivation. In either case, any changes due to learning or motivation did not differ between treatments:

hamsters drank equal amounts of sugar water during the two treatments, and there was no interaction of

block with treatment. For hamsters, at least, it may therefore not be suitable to run more than one or two

trials per probe location in a daily testing session. Including a measure of time (here, block and day) in

analyses can provide a way of examining potential changes in responding over trials. This may be useful

for reﬁning protocols to optimize aspects of study design such as number and spacing of trials within

and across days.

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4.4. Not all probes are equal: the spatial judgement task may reveal qualitatively dierent

emotion–cognition interactions

Our study presents a unique pattern of ﬁndings: a shift in bias at the two outer probes with no evidence

of a bias at the middle probe. Changes in enrichment explained 59% and 76% of the variance in responses

to the P+and P−, respectively, yet treatment failed to explain variance at the middle probe. There are

several possible reasons for the lack of bias at the middle probe: the middle probe block explained

87% of the variance which may suggest a greater inﬂuence of learning effects at this (perhaps more

highly) ambiguous location; the middle probe, being truly ambiguous, may have induced a greater level

of approach–avoidance conﬂict and anxiety which masked any other emotion effects arising from the

housing treatments; or hamsters may process mildly ambiguous cues to punishment (P−)orreward

(P+) differently from ambiguous cues with no partial negative or positive association.

Responses at the two outer probes have been proposed to distinguish ‘anxiety’ and ‘depression’ in

animal models [9,39,41,62]. Humans suffering from anxiety have an increased expectation of negative

events, while people suffering from depression have both an increased expectation of negative events

and a reduced expectation of positive events [88]. In the animal judgement bias task, responses to

the rewarded stimulus and adjacent probe are considered to tap into brain mechanisms associated

with seeking, approaching and gaining reward; responses to the punished stimulus are considered to

tap into brain mechanisms associated with avoidance of threat. Reinforcement sensitivity theory [89]

identiﬁes that emotional responses to stimuli arise from the interaction of three motivational systems

sensitive to reward (dopaminergic and associated systems relating to positive emotions and approach),

punishment (amygdala, anterior cingulate and serotonergic systems relating to negative emotions

and avoidance) and reward–punishment conﬂict [90,91]. Different emotional and cognitive processes

may underlie responses to the ambiguous probes, reﬂecting differences in degree of ambiguity,

approach, avoidance and approach–avoidance anxiety. Studies involving genetic models [12,16,55]or

pharmaceutical manipulation of brain systems [35,36,41,55,59,62] may elucidate the mechanisms driving

responses on the judgement bias test. At the least, we suggest several probes should be included in

the test when used with hamsters (cf. for example, single ambiguous probe used with rats [58,59,63]and

pigs [48]), and each probe should be examined separately in analyses (cf. [36]): in the example we present

here, this was done by testing against the null model at each probe location.

4.5. Optimistic judgement bias in hamsters?

Mapping shifts in judgement bias on to emotional ‘states’ [9] remains a challenge. Hamsters responded to

all probes at least as often as expected by chance in both treatments; the most parsimonious explanation

is therefore that the addition of extra cage enrichment leads to a more ‘optimistic’ judgement bias in the

hamsters. Removal of the additional enrichment items (back to standard enrichment condition which

hamsters were used to) resulted in a negative shift (possibly back to the standard baseline?). It is difﬁcult

to interpret animals as categorically ‘optimistic’ or ‘pessimistic’ as there is no reference baseline against

which to assess this.

4.6. Judgement bias tests may be more sensitive to changes in emotion than traditional

behavioural tests

Finally, while the judgement bias task revealed a change in responses to ambiguous cues to possible

reward and possible punishment, we found no differences in responses between treatments on the

additional standard behavioural tests. Hamsters performed similarly on the OF, LDE and neophobia

tests in both housing treatments. In the latter test, hamsters were equally interested in investigating the

novel objects in both treatments and were much faster on trials when a novel object was present than

for sugar drinker only trials. There was therefore no difference in exploratory tendency or fear of new

stimuli (e.g. a drinker spout appearing at a new location) between treatments. These ﬁndings lend weight

to the argument that the judgement bias test may detect shifts in emotion state not discernable from,

or apparently contra to, traditional behavioural measures alone ([7,13,27,29,34,51,70], cf. [24,40,49,66]);

may detect shifts earlier (the housing treatments were one week in duration and therefore relatively

short-term); or may detect these shifts at different points in the time course of emotional response

(e.g. [32,51,58]).

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This study adds to the rapidly expanding body of literature which supports the judgement bias task

as a means of detecting affect-mediated changes in cognition in non-human animals. This literature

shows that shifts in judgement bias may be detected following a range of affect manipulations in a

range of species, now including hamsters; and that cognitive measures may detect shifts in emotion not

apparent from behavioural observation or traditional behavioural tests of emotion alone. The unique

pattern of results reported in this paper adds weight to suggestions that different underlying brain

systems may contribute to differences in responses to ambiguous cues to reward or punishment that

may have value in distinguishing between different, but similarly valenced, emotional states. We cannot

say whether the hamsters in our study felt happy in their enriched housing, but the changes in cognitive

processing of ambiguous cues certainly suggests enriched hamsters became more optimistic about the

likelihood of future reward when faced with uncertain information. Judgement bias tasks present a

unique and valuable approach to assessing emotion in laboratory rodents, including hamsters. Future

development of these approaches (for example, incorporating automated systems, exploring long-term

and developmental effects of captive animal husbandry) should lead to improved welfare assessment

across species.

Ethics. This research was conducted at Liverpool John Moores University and conformed to the current

UK Home Ofﬁce Guidelines and Codes of Practice for housing and husbandry of laboratory animals:

http://www.homeofﬁce.gov.uk/ science-research/animal-research/ and the Association for the Study of Animal

Behaviour’s guidelines on the use of animals in research: http://asab.nottingham.ac.uk/ethics/guidelines.php.All

procedures were approved by the local ethics committee prior to the start of the study.

Data accessibility. The datasets and R code supporting this article have been uploaded as part of the electronic

supplementary material, tables S1–S5. R is a freely available statistical software package and is available to download

at: http://www.r-project.org/.

Authors’ contributions. E.J.B. and N.F.K. jointly designed the study, participated in data analysis, carried out the statistical

analyses and drafted the manuscript. Both authors give ﬁnal approval for publication.

Competing interests. We have no competing interests.

Funding. Data collection was part-funded by a small grant from Liverpool John Moores University, School of Natural

Sciences and Psychology. Training in R programming was funded by the Liverpool John Moores University Research

Ofﬁce.

Acknowledgements. We would like to thank Steve Broadfoot and Roy Williams for care of the animals; Alice Maher, Katie

Garland, Samantha Noonan and Charlotte Buckley for assistance with data collection; Kevin Arbuckle, Hazel Nichols

and Richard Brown for statistical advice. We are grateful to two anonymous reviewers for their helpful feedback.

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ESM4 Performance on control trials and reinforced trials during Phase C and D

Data

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ESM3 Performance on control trials and reinforced trials during Phase B with R code

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Emily June Bethell · Nicola F Koyama

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ESM2 Individual rates of learning the discrimination task

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Emily June Bethell · Nicola F Koyama

Fearful snake pictures make monkeys pessimistic

Article

Full-text available

Aug 2023

Judgment bias is the cognitive tendency of animals experiencing negative (or positive) affect to expect undesirable (or favorable) outcomes in ambiguous situations. The lack of examination of judgment biases induced by ecologically relevant stimuli hampers our understanding of the adaptive role of these biases. We examined whether predator-related stimuli, i.e., pictures of snakes, induce a pessimistic judgment bias in Japanese macaques (Macaca fuscata). Our subjects underwent a touchscreen-based Go/No-go judgment bias test. We found that the subjects were less likely and slower to make Go responses to ambiguous stimuli after viewing the snake pictures, indicating that pictures of snakes induce a pessimistic evaluation of ambiguous stimuli. In environments with high levels of threat, behavioral strategies that reduce risk-taking would be evolutionarily advantageous. Hence, an affective response system that lowers expectations of favorable outcomes in ambiguous situations after encountering threat-related stimuli would serve adaptive purposes, such as curbing excessive exploratory behavior.

Structural environmental enrichment and the way it is offered influence cognitive judgement bias and anxiety-like behaviours in zebrafish

Article

Full-text available

Oct 2022
ANIM COGN

Environmental enrichment in zebrafish generally reduces anxiety-related behaviours, improves learning in maze trials and increases health and biological fitness. However, certain types of enrichment or certain conditions induce the opposite effects. Therefore, it is essential to study the characteristics of environmental enrichment that modulate these effects. This study aims to investigate if structural environmental enrichment and the way it is offered influence cognitive judgement bias and anxiety-like behaviours in adult zebrafish. The fish were assigned to six housing manipulations: constant barren, constant enrichment, gradual gain of enrichment, gradual loss of enrichment, sudden gain of enrichment and sudden loss of enrichment. We then transposed the cognitive judgment bias paradigm, formerly used in studies on other animals to measure the link between emotion and cognition, to objectively assess the impact of these manipulations on the zebrafish’s interpretation of ambiguous stimuli, considering previous experiences and related emotional states. We used two battery tests (light/dark and activity tests), which measured anxiety-related behaviours to check if these tests covariate with cognitive bias results. The fish with a sudden gain in enrichment showed a pessimistic bias (interpreted ambiguous stimuli as negative). In addition, the fish that experienced a sudden gain and a gradual loss in enrichment showed more anxiety-like behaviours than the fish that experienced constant conditions or a gradual gain in enrichment. The data provide some proof that structural environmental enrichment and the way it is presented can alter zebrafish's cognitive bias and anxiety-like behaviours.

Optimistic ants: Positive cognitive judgement bias but no emotional contagion in the ant Lasius niger

Preprint

Full-text available

Nov 2022

Understanding the emotional states of animals is key for informing their ethical treatment, but very little attention has been directed towards the emotional lives of invertebrates. As emotions influence information processing, one way to assess emotional states is to look for an individual's cognitive bias, i.e., their tendency to make optimistic or pessimistic judgements. Here we developed a free-running judgment bias task for the ant Lasius niger, and applied the judgement bias to assess ants' reactions towards positive and negative stimuli. After an initial learning phase in which individuals were trained to associate two odour stimuli with positive or negative reinforcement, their reaction towards ambiguous stimuli, i.e., a mixture between both odours, was assessed. We also explored our study species' capacity to socially transmit emotional states ('emotional contagion') by investigating whether social information could elicit emotional responses. We find L. niger to be optimistic, showing a baseline positive judgement bias, with 65-68% of ants preferring an ambiguous 1:1 mix of positive and negative cues over no cues. Providing an unexpected food reward prior to the judgement bias task increases positive judgement bias (c. 75% positive). There was a non-significant tendency towards a negative judgement bias after experiencing a mild electric shock (c. 75% negative). Neither positive nor negative social information (trail and alarm pheromones, respectively) affected the ants' judgement biases, thus providing no indication for emotional contagion. The development of a powerful, simple, and ecologically relevant cognitive judgement task, deployable in the lab and in the field, opens the door to systematic comparative studies of the evolutionary and ecological causes of judgement bias.

Transcriptional profiles in the mouse amygdala after a cognitive judgment bias test largely depend on the genotype

Article

Full-text available

Oct 2022

Background: The amygdala is crucial for emotional cognitive processing. Affective or emotional states can bias cognitive processes, including attention, memory, and decision-making. This can result in optimistic or pessimistic behaviors that are partially driven by the activation of the amygdala. The resulting emotional cognitive bias is a common feature of anxiety and mood disorders, both of which are interactively influenced by genetic and environmental factors. It is also known that emotional cognitive biases can be influenced by environmental factors. However, little is known about the effects of genetics and/or gene-environment interactions on emotional cognitive biases. We investigated the effects of the genetic background and environmental enrichment on the transcriptional profiles of the mouse amygdala following a well-established cognitive bias test. Methods: Twenty-four female C57BL/6J and B6D2F1N mice were housed either in standard (control) conditions or in an enriched environment. After appropriate training, the cognitive bias test was performed on 19 mice that satisfactorily completed the training scheme to assess their responses to ambiguous cues. This allowed us to calculate an “optimism score” for each mouse. Subsequently, we dissected the anterior and posterior portions of the amygdala to perform RNA-sequencing for differential expression and other statistical analyses. Results: In general, we found only minor changes in the amygdala’s transcriptome associated with the levels of optimism in our mice. In contrast, we observed wide molecular effects of the genetic background in both housing environments. The C57BL/6J animals showed more transcriptional changes in response to enriched environments than the B6D2F1N mice. We also generally found more dysregulated genes in the posterior than in the anterior portion of the amygdala. Gene set overrepresentation analyses consistently implicated cellular metabolic responses and immune processes in the differences observed between mouse strains, while processes favoring neurogenesis and neurotransmission were implicated in the responses to environmental enrichment. In a correlation analysis, lipid metabolism in the anterior amygdala was suggested to influence the levels of optimism. Conclusions: Our observations underscore the importance of selecting appropriate animal models when performing molecular studies of affective conditions or emotional states, and suggest an important role of immune and stress responses in the genetic component of emotion regulation.

Negative expectations and vulnerability to stressors in animals

Article

Aug 2021
NEUROSCI BIOBEHAV R

Humans express stable differences in pessimism that render some individuals more vulnerable to stressors and mood disorders. We explored whether non-human animals express stable individual differences in expectations (assessed via judgment bias tests) and whether these differences relate to susceptibility to stressors. Judgment bias tests do not distinguish pessimism from sensitivity to reinforcers; negative expectations are likely driven by a combination of these two elements. The available evidence suggests that animals express stable individual differences in expectations such that some persistently perceive ambiguous situations in a more negative way. A lack of research prevents the drawing of firm conclusions on how negative expectations affect responses to stressors, but current evidence suggests a potential link between negative expectations and the adoption of avoidance coping strategies, stronger responses to uncontrollable stressors and risk of mood-related disorders. We explore implications for animals living in captivity and for research using animals as models for human disorders. 50 DAYS FREE ACCESS: https://authors.elsevier.com/c/1dgUeY3M3Wb-g

Short telomeres drive pessimistic judgement bias in zebrafish

Article

Full-text available

Mar 2021
BIOL LETTERS

The role of telomerase reverse transcriptase has been widely investigated in the contexts of ageing and age-related diseases. Interestingly, decreased telomerase activities (and accelerated telomere shortening) have also been reported in patients with emotion-related disorders, opening the possibility for subjective appraisal of stressful stimuli playing a key role in stress-driven telomere shortening. In fact, patients showing a pessimistic judgement bias have shorter telomeres. However, in humans the evidence for this is correlational and the causal directionality between pessimism and telomere shortening has not been established experimentally yet. We have developed and validated a judgement bias experimental paradigm to measure subjective evaluations of ambiguous stimuli in zebrafish. This behavioural assay allows classification of individuals in an optimistic-pessimistic dimension (i.e. from individuals that consistently evaluate ambiguous stimuli as negative to others that perceive them as positive). Using this behavioural paradigm we found that telomerase-deficient zebrafish (tert - / - ) were more pessimistic in response to ambiguous stimuli than wild-type zebrafish. The fact that individuals with constitutive shorter telomeres have pessimistic behaviours demonstrates for the first time in a vertebrate model a genetic basis of judgement bias.

Cognitive Bias Tasks: A New Set of Approaches to Assess Welfare in Nonhuman Primates

Chapter

Feb 2023

At the start of the new millennium, the “cognitive bias” paradigm emerged as a new approach to assessing animal emotion. In the animal welfare literature, cognitive bias describes how emotions such as anxiety and depression are associated with changes in the way the brain processes information. For example, studies with humans have long demonstrated that anxious people are more vigilant for negative cues and depressed people interpret the proverbial glass of water as “half empty” rather than “half full.” In this chapter, we review how methods developed to study cognitive bias in humans have been adapted to measure the interaction between emotion and cognition in nonhuman primates. We focus on judgment bias and attention bias tasks and discuss study design, controls, confounds, and advantages and limitations of each. We also indicate future research directions. This chapter is intended to introduce readers with little or no experience of cognitive bias tasks to theory and practical considerations around designing these tasks.

Can Early Environmental Enrichment Buffer Stress from Commercial Hatchery Processing in Laying Hens?

Article

Full-text available

Jun 2022

Under commercial conditions, laying hen chicks are exposed to a range of stressful events immediately after hatch. Here, we studied whether environmental enrichment was able to reduce the stress sensitivity of these chicks. A total of 50 hatchery chicks (HC) and 50 control chicks (CC) were raised in enriched pens (E), while 53 HC + 53 CC were raised in standard non-enriched floor pens (NE). HC weighed less on day one, but there was no effect of hatchery treatment at later ages. HC were more pessimistic in a cognitive judgement bias test and emitted more distress calls when socially isolated, indicating that hatchery stress affected behaviour and stress sensitivity. However, enrichment did not affect the behaviour in any of these tests. We found no effects of hatchery stress in a novel environment, but indications that enrichment may have increased fearfulness of HC. The sensitivity of the hypothalamic-pituitary-adrenal (HPA) axis was reduced in HC-E compared to HC-NE, indicating that enrichment buffered the physiological stress sensitivity in HC; however, the opposite pattern was found in CC. In conclusion, our results show complex and somewhat contradictory effects on the ability of enrichment to buffer the consequences of stress in commercial hatcheries.

A Behavioural Assay to Investigate Judgment Bias in Zebrafish

Article

Feb 2022

In this protocol, we describe for the first time a judgment bias paradigm to phenotype the way zebrafish assess ambiguous stimuli. We have developed and validated a protocol for a judgment bias test based on a Go/No-go task, and performed using a half radial maze. After a habituation phase, fish are trained to discriminate between two reference arms [positive (P) and negative (N)]. For this purpose, they experience a positive event (food reward in P), when presented with a specific location/color cue, and a negative event (chasing with net in N), when presented with a different location/color cue. Acquisition of the discrimination learning between P and N is revealed by the latencies to enter the experimental arms of the behavioral maze being significantly lower for the P arm than for the N arm. Once zebrafish are able to discriminate between P and N arms, their latency to enter other maze arms spatially located between P and N [(Near Positive (NP), Ambiguous (A) = half-way between P and N, and Near Negative (NN)] is analyzed. Latencies (L) to enter NP, A and NN maze arms are interpreted as indicating the individual expectancy to experience a reward/punishment on each of them. A judgment bias score (JBS) is calculated from the latencies to enter the P, N, and A arms for each fish [JBS = (LA-LP)*100/(LN-LP)], based on which fish can be classified into an optimistic/pessimistic axis. A JBS below 50 indicates that fish perceive the ambiguous stimulus as a positive one (optimistic bias), while JBS above 50 indicates that fish perceive the ambiguous stimulus as a negative one (pessimistic bias). However, for classification criteria, it could be advantageous to use the method of selecting extreme phenotypes (e.g., upper and lower quartiles of the JBS), since JBS in zebrafish falls into a bimodal distribution (unpublished data). Therefore, this protocol provides a unique, inexpensive, and effective alternative to other methods of measuring affective states in zebrafish that might be of great interest to a broad target audience and have a large number of applications. Graphic abstract: Flow chart of the judgment bias protocol in zebrafish.

Social Buffering as a Tool for Improving Rodent Welfare

Article

Dec 2021
J AM ASSOC LAB ANIM

The presence of a conspecific can be calming to some species of animal during stress, a phenomenon known as social buffering. For rodents, social buffering can reduce the perception of and reaction to aversive experiences. With a companion, animals may be less frightened in conditioned fear paradigms, experience faster wound healing, show reduced corticosterone responses to novelty, and become more resilient to everyday stressors like cage-cleaning. Social buffering works in diverse ways across species and life stages. For example, social buffering may rely on specific bonds and interactions between individuals, whereas in other cases, the mere presence of conspecific cues may reduce isolation stress. Social buffering has diverse practical applications for enhancing rodent wellbeing (some of which can be immediately applied, while others need further development via welfare-oriented research). Appropriate social housing will generally increase rodents’ abilities to cope with challenges, with affiliative cage mates being the most effective buffers. Thus, when rodents are scheduled to experience distressing research procedures, ensuring that their home lives supply high degrees of affiliative, low stress social contact can be an effective refinement. Furthermore, social buffering research illustrates the stress of acute isolation: stressors experienced outside the cage may thus be less impactful if a companion is present. If a companion cannot be provided for subjects exposed to out-of-cage stressors, odors from unstressed animals can help ameliorate stress, as can proxies such as pieces of synthetic fur. Finally, in cases involving conditioned fear (the learned expectation of harm), newly providing social contact during exposure to negative conditioned stimuli (CS) can modify the CS such that for research rodents repeatedly exposed to aversive stimuli, adding conspecific contact can reduce their conditioned fear. Ultimately, these benefits of social buffering should inspire the use of creative techniques to reduce the impact of stressful procedures on laboratory rodents, so enhancing their welfare.

Measuring judgement bias and emotional reactivity in sheep following long-term exposure to unpredictable and aversive events

Article

Full-text available

Jan 2011

Package Lme4: Linear Mixed-Effects Models Using Eigen and S4

Technical Report

Full-text available

Jan 2014
J STAT SOFTW

Description Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' ``glue''.

R: A Language and Environment for Statistical Computing

Book

Jan 2015

Core R Team

lme4: Linear mixed-effects models using Eigen and S4

Article

Jan 2014
J STAT SOFTW

Data Analysis with R Statistical Software: A Guidebook for Scientists

Book

Sep 2015

Robert J Thomas

Introduction Why do scientists need statistics? The ability to understand, use and interpret statistics is one of the most empowering skills that a scientist can possess, because it enables the researcher to address any kind of scientific question in a rigorous and quantitative manner. When using statistics, scientists need to be sure that they are collecting the right sort of data in well-designed experiments, using the most appropriate statistical tests, and interpreting the results properly. Statistical analysis does not necessarily come easily to many scientists, but it is an increasingly important and useful part of the toolkit of techniques that are available for understanding the world about us. Therefore, investing some time and effort in getting to grips with statistics will pay dividends for the rest of your scientific career. What is R? R is a very powerful statistical software package that will enable you to analyse more or less any dataset. It is compatible with all of the common computer operating systems (Windows, Mac & Linux). Remarkably, R is completely free to download and use on your own computer. R is actually a computing environment and programming language, rather than a statistics package in the usual sense; unlike most of the familiar statistics packages (e.g. Minitab & SPSS) you tell R what to do by typing in commands, rather than clicking on options in a menu. This short guide is designed to help you quickly to become familiar with R and to explore its potential as a powerful tool for analysing your data, whatever your field of research. Many of the world’s top statisticians and scientists now use R. Because it is “open source”, they frequently contribute new data analysis techniques as add-on “packages” that anyone can download for free from the internet. Consequently, R is generally more up-to-date than traditional statistics packages, as there is less lag time in development, and its functionality can be expanded dramatically as soon as each new method becomes available. These benefits can offset the relatively steep learning curve involved in getting started with R. As Mick Crawley says in his books about R; “Learning R is not easy, but you will not regret investing the effort to master the basics” As one of our students (who for some reason wished to remain anonymous) has testified; “Learning R is an absolute nightmare, but apparently it is in some way good for me” The aim of this guidebook is to convince you that learning R really is good for you, to give you a basic grounding in the things that R can do for you, and to give you the confidence to learn more.

The Principles of Humane Experimental Technique

Article

Jan 1959

An integrative and functional framework for the study of animal emotion and mood

Article

Jan 2010

Affective Neuroscience. The Foundations Of Human and Animal Emotions

Book

Jan 1998

Jaak Panksepp

Wang Z, Goonewardene LA. The use of MIXED models in the analysis of animal experiments with repeated measures data. Canadian Journal of Animal Science

Article

Mar 2004
CAN VET J

Z. Wang and L. A. Goonewardene

The analysis of data containing repeated observations measured on animals (experimental unit) allocated to different treatments over time is a common design in animal science. Conventionally, repeated measures data were either analyzed as a univariate (split-plot in time) or a multivariate ANOVA (analysis of contrasts), both being handled by the General Linear Model procedure of SAS. In recent times, the mixed model has become more appealing for analyzing repeated data. The objective of this paper is to provide a background understanding of mixed model methodology in a repeated measures analysis and to use balanced steer data from a growth study to illustrate the use of PROC MIXED in the SAS system using five covariance structures. The split-plot in time approach assumes a constant variance and equal correlations (covariance) between repeated measures or compound symmetry, regardless of their proximity in time, and often these assumptions are not true. Recognizing this limitation, the analysis of contrasts was proposed. If there are missing measurements, or some of the data are measured at different times, such data were excluded resulting in inadequate data for a meaningful analysis. The mixed model uses the generalized least squares method, which is generally better than the ordinary least squares used by GLM, if the appropriate covariance structure is adopted. The presence of unequally spaced and/or missing data does not pose a problem for the mixed model. In the example analyzed, the first order ante dependence [ANTE(1)] covariance model had the lowest value for the Akaike and Schwarz’s Bayesian information criteria fit statistics and is therefore the model that provided the best fit to our data. Hence, F values, least square estimates and standard errors based on the ANTE (1) were considered the most appropriate from among the five models demonstrated. It is recommended that the mixed model be used for the analysis of repeated measures designs in animal studies. Key words: Repeated measures, General Linear Model, Mixed Model, split-plot, covariance structure

Cognitive bias, hand preference and welfare of common marmosets

Article

Mar 2015

Common marmosets (Callithrix jacchus) have hand preferences for grasping pieces of food and holding them while eating and these are stable throughout adult life. We report here that left-handed marmosets have negative cognitive bias compared to right-handed marmosets. Twelve marmosets were trained to expect a food reward from a bowl with a black lid and not from one with a white lid, or vice versa. In probe tests with ambiguous, grey-lidded bowls a left-handed group (N=7) were less likely to remove the lid to inspect the bowl than a right-handed group (N=5). This difference between left- and right-handed marmosets was not dependent on rate of learning, sex or age. In fact, hand-preference was not associated with rate of learning the task. Furthermore, retrospective examination of colony records of 39 marmosets revealed that more aggression was directed towards left- than right-handed marmosets. Hence, hand preference, which can be measured easily, could serve as an indicator of cognitive bias and may signal a need for particular care in laboratory environments. We explain the results on the grounds that hand preference reflects more frequent (or dominant) use of the opposite hemisphere and this predisposes individuals to behave differently. Copyright © 2015. Published by Elsevier B.V.