Social complexity affects cognitive abilities but not brain structure in a Poeciliid fish

Abstract

Some cognitive abilities are suggested to be the result of a complex social life, allowing individuals to achieve higher fitness through advanced strategies. However, most evidence is correlative. Here, we provide an experimental investigation of how group size and composition affect brain and cognitive development in the guppy (Poecilia reticulata). For six months, we reared sexually mature females in one of three social treatments: a small conspecific group of three guppies, a large heterospecific group of three guppies and three splash tetras (Copella arnoldi)-a species that co-occurs with the guppy in the wild, and a large conspecific group of six guppies. We then tested the guppies' performance in self-control (inhibitory control), operant conditioning (associative learning) and cognitive flexibility (reversal learning) tasks. Using X-ray imaging, we measured their brain size and major brain regions. Larger groups of six individuals, both conspecific and heterospecific groups, showed better cognitive flexibility than smaller groups, but no difference in self-control and operant conditioning tests. Interestingly, while social manipulation had no significant effect on brain morphology, relatively larger telencephalons were associated with better cognitive flexibility. This suggests alternative mechanisms beyond brain region size enabled greater cognitive flexibility in individuals from larger groups. Although there is no clear evidence for the impact on brain morphology, our research shows that living in larger social groups can enhance cognitive flexibility. This indicates that the social environment plays a role in the cognitive development of guppies.

Full text

Behavioral
Ecology
The ocial journal of the
ISBE
International Society for Behavioral Ecology
Behavioral Ecology (2024), 35(3), arae026. https://doi.org/10.1093/beheco/arae026
Original Article
Social complexity aects cognitive abilities
but not brain structure in a Poeciliid fish
Zegni Triki1,2,*,, Tunhe Zhou3, Elli Argyriou2, Edson Sousa de Novais4, Oriane Servant2,
Niclas Kolm2,
1Behavioral Ecology Division, Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6,
3012 Bern, Switzerland
2Department of Zoology, Stockholm University, Svante Arrheniusväg 18 B, 10691, Stockholm, Sweden
3Brain Imaging Centre, Stockholm University, Svante Arrheniusväg 16 A, 10691, Stockholm, Sweden
4Behavioural Ecology Laboratory, Faculty of Science, University of Neuchâtel, Emile-Argand 11, 2000
Neuchâtel, Switzerland
Received 20 September 2023; revised 16 February 2024; editorial decision 7 March 2024; accepted 1 April 2024
Some cognitive abilities are suggested to be the result of a complex social life, allowing individuals to achieve higher fitness through
advanced strategies. However, most evidence is correlative. Here, we provide an experimental investigation of how group size and
composition affect brain and cognitive development in the guppy (Poecilia reticulata). For 6 months, we reared sexually mature fe-
males in one of 3 social treatments: a small conspecific group of 3 guppies, a large heterospecific group of 3 guppies and 3 splash
tetras (Copella arnoldi)—a species that co-occurs with the guppy in the wild, and a large conspecific group of 6 guppies. We then
tested the guppies’ performance in self-control (inhibitory control), operant conditioning (associative learning), and cognitive flexibility
(reversal learning) tasks. Using X-ray imaging, we measured their brain size and major brain regions. Larger groups of 6 individuals,
both conspecific and heterospecific groups, showed better cognitive flexibility than smaller groups but no difference in self-control and
operant conditioning tests. Interestingly, while social manipulation had no significant effect on brain morphology, relatively larger tel-
encephalons were associated with better cognitive flexibility. This suggests alternative mechanisms beyond brain region size enabled
greater cognitive flexibility in individuals from larger groups. Although there is no clear evidence for the impact on brain morphology,
our research shows that living in larger social groups can enhance cognitive flexibility. This indicates that the social environment plays
a role in the cognitive development of guppies.
Key words: associative learning, brain morphology, executive functions, group size, group composition, inhibitory control,
reversal learning, X-ray.
Introduction
Animals display impressive cognitive abilities, from simple associ-
ative learning (Bielecki et al. 2023) to complex and sophisticated
cognitive skills, such as tool use (Finn etal. 2009), problem-solving
(Damerius et al. 2017) and theory of mind (Call and Tomasello
2008). Nevertheless, there is tremendous variation in their perfor-
mance. Large-scale phylogenetic comparisons have revealed pat-
terns and generated hypotheses as to why such variation exists.
They suggest that species may evolve to adjust their brain mor-
phology to their cognitive needs based on the ecological conditions
(Shultz and Dunbar 2006; van Schaik and Burkart 2011). Over
decades of research into this question, the positive correlations be-
tween the brain, cognitive traits and multiple ecological conditions
led to the emergence of several “intelligence” hypotheses. For in-
stance, the “social brain hypothesis” (Dunbar 1998) states that the
brain or specific brain regions have enlarged due to selective social
pressures linked to factors such as group size (Dunbar 1992, 1993;
Barton 1996; Kudo and Dunbar 2001; Beauchamp and Fernández-
Juricic 2004; Shultz and Dunbar 2006; Street etal. 2017), mating
systems (Pawłowski etal. 1998; Iwaniuk 2001; Barton 2006), and
social bonds (Dunbar and Shultz 2007; Emery etal. 2007). On the
other hand, the “ecological intelligence hypothesis” suggests that
environmental conditions, like foraging ecology, are the best cor-
relates of brain morphology and cognitive abilities (Clutton-Brock
and Harvey 1977; Iwaniuk and Nelson 2001; Hutcheon etal. 2002;
DeCasien et al. 2017; Rosati 2017). There is an ongoing debate
*Corresponding author: Behavioral Ecology Division, Institute of
Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012 Bern,
Switzerland. Email: zegni.triki@gmail.com
© The Author(s) 2024. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits
unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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Triki et al.
on the relative importance of these hypotheses (Powell etal. 2017;
González-Forero and Gardner 2018), primarily due to the varying
and conflicting research outcomes when testing various clades and
taxa with varying biology and ecology (DeCasien and Higham
2019; Kappeler 2019). Therefore, studying species of closely re-
lated species of the same clade would eliminate some of these in-
herent biological and ecological variables.
With their ability for continuous adult neurogenesis and neu-
ronal regeneration (Zupanc 2008), teleost fishes are an ideal study
clade for understanding the ecological pressures that drive brain
and cognition evolution (Bshary and Triki 2022). Such plasticity
also oers the possibility to adopt a within-species approach to in-
vestigate brain and cognitive development to complement compar-
ative phylogenetic studies. For instance, the social brain hypothesis,
originally emerging from between-species comparisons, can be used
to explain how social pressures impact individual brain morphology
(Kotrschal etal. 2012; Fischer et al. 2015; Triki et al. 2019) and
cognitive performance (Brown and Braithwaite 2005; Ashton etal.
2018; Triki etal. 2020). This gives rise to an ontogenetic version of
the social brain hypothesis, with the potential to put the social brain
hypothesis to empirical testing. There is currently limited evidence
of how living in a socially rich environment can shape fish brain
morphology (Gonda etal. 2009; Kotrschal etal. 2012; Fischer etal.
2015; Triki etal. 2019), with a knowledge gap regarding the cogni-
tive correlates. In addition, most research on social enrichment fails
to consider other social pressures that can arise from interactions
with dierent species (Bijl and Kolm 2016; Oliveira and Bshary
2021). Therefore, it is crucial to adopt an integrative approach with
an experimental framework that simultaneously investigates brain
morphology and cognitive performance to understand how fish ad-
just to social pressures arising not only from living in larger groups
but also from social interactions within vs between species.
Here, we used the guppy (Poecilia reticulata) as a study system. In
the wild, guppies live in shoals varying in size from only two fish
to up to 50 per shoal, with frequent fission-fusion events allowing
them to form complex and well-structured social networks (Croft
et al. 2003, 2004). Also, guppies often coexist and may compete
with other fish species over resources (Anaya-Rojas etal. 2021). In
our experiment, we reared sexually mature female guppies in the
same- and mixed-species groups that varied in size for 6 months.
We established 3 dierent social treatments: (1) a small conspecific
group of 3 guppies living together, (2) a large heterospecific group
of 3 guppies living with 3 other female fish of a dierent species,
the splash tetra (Copella arnoldi)—a species that coexist with gup-
pies in nature (Phillip 1998), and (3) a large conspecific group of 6
guppies living together. This allowed us to simultaneously test the
eects of group size and same- vs mixed-species group composi-
tion. In order to gain insights into how the social treatment may
have impacted the social interactions of guppies, we recorded their
behavior to determine if living in larger groups would lead to escal-
ated conflicts.
To evaluate whether the guppies’ cognitive abilities were af-
fected by the social treatment, we chose 3 cognitive tasks: inhibitory
control (cylinder test), associative learning and reversal learning.
Associative learning tests basic operant conditioning abilities, while
inhibitory control and reversal learning tasks test for the two exec-
utive function abilities, self-control and cognitive flexibility. These
are two top-down executive functions that regulate several cognitive
subprocesses and, hence, modulate complex cognition dynamics
(Miyake et al. 2000; Diamond 2013). The cylinder test has been
widely used to evaluate animal self-control capabilities (Kabadayi
etal. 2018), from primates and birds (MacLean etal. 2014) to fish
(Lucon-Xiccato etal. 2017; Triki etal. 2023a; Guadagno and Triki
2024). It consists of placing a food reward inside a transparent cyl-
inder. The performance is then evaluated by recording whether
an animal would delay their gratification and move around the
cylinder without touching it—an indicator of inhibitory control
ability—or whether they would bump into the cylinder in an at-
tempt to retrieve the food immediately, which indicates a lack of
inhibitory control (Kabadayi et al. 2018). In the associative and
reversal learning tests, researchers in the field of animal cognition
often employ the 2-color discrimination paradigm. The test evalu-
ates the animal’s abilities in associating a color cue with a food re-
ward. Once this association is formed, the test then reverses the
color-reward contingency (reversal learning), and it allows us to
estimate the animal performance by unlearning the previous rule
and updating it with the new color-reward association. Such ca-
pacity to update a learning rule is an indicator of possessing cog-
nitive flexibility abilities (Uddin 2021). Furthermore, to investigate
whether there is a link between brain morphology and cognitive
performance in the tested fish, we used X-ray imaging technology
(see Methods) to generate fine-tuned and high-quality volume data
of the major fish brain regions (White and Brown 2015). These re-
gions included the telencephalon, diencephalon, mesencephalon,
cerebellum, and brain stem (Fig. 1).
Our choice of the cognitive tests builds on the comparative re-
search indicating that species living in larger groups tend to have
larger brains (Dunbar and Shultz 2007), where larger brains and
specific brain regions exhibit greater abilities in inhibitory control
and cognitive flexibility (Deaner etal. 2007; MacLean et al. 2014).
Telencephalon Mesencephalon
Cerebellum
Brain Stem
Diencephalon
(a) (b) (c)
Fig. 1. Segmented fish brain X-ray images. (a) Transversal, (b) coronal, and (c) sagittal planes of a brain scan. The major five brain parts were segmented
and displayed in dierent colours: yellow–telencephalon, purple–mesencephalon, green–diencephalon, blue–cerebellum, and red–brain stem.
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Behavioral Ecology, 2024, Vol. 35(3)
It also builds on Ashton etal.‘s work (2018) on magpie birds, re-
vealing a positive correlation between group size and performance
in the 3 cognitive tests: inhibitory control, associative learning, and
reversal learning. Our experiment aimed to test whether the social
brain hypothesis applies to individual development over an onto-
genetic timescale within an experimental framework. We predict
that living in a larger group will have a positive impact on cogni-
tive performance in tasks related to inhibitory control and reversal
learning. However, we do not predict to see the same eect in as-
sociative learning, since research suggests that forming associations
does not necessarily require a complex neural system (Bielecki etal.
2023). The expected positive relationship between group size and
performance in inhibitory control and reversal learning will be fa-
cilitated by rapid changes in brain morphology, specifically, the en-
largement of certain brain regions.
Materials and methods
Study animals and experimental set-up
We conducted the study between December 2019 and January
2021 in the fish laboratory facilities at Stockholm University in
Sweden. Our study animals were laboratory-bred guppies Poecilia
reticulata, descendants from an initial population of more than 500
fish caught in 1998 from Quare River in Trinidad. To create a new
generation of naïve guppies, we set up 75 breeding pairs in sepa-
rate 2 L tanks. We regularly isolated the fry and housed them in 2 L
tanks with a maximum capacity of 6 per tank. We periodically re-
moved the ones that developed into males by displaying secondary
sexual traits like color patterns, housing them in separate tanks,
and ensuring that the remaining female numbers were adjusted to
6 per tank. On average, guppies reach sexual maturation within 3
months of age. We also used another fish species, the splash tetra,
Copella arnoldi, an introduced species that is widespread across the
native Trinidadian rivers where the guppy typically resides (Kenny
2008). We obtained the splash tetras from an aquarium fish sup-
plier in Stockholm. Finally, we used 144 female guppies from
our new generation and 36 female splash tetras (divided into two
batches) to create 3 social treatments with dierent fish densities
and compositions.
The 3 social treatments were: (1) a small conspecific group of 3
female guppies, (2) a large heterospecific group of 3 female gup-
pies and 3 female splash tetras, and (3) a large conspecific group
of 6 female guppies. We used only females to avoid mating and
reproduction occurring and potential male–male aggression. Every
treatment had 12 replicate tanks, but some replicates had a later
development of apparent males whose color patterns had not yet
developed when we established the treatments at the age of about
3 months. This led to eliminating one replicate in the treatment of
the small conspecific group, one in the large heterospecific group,
and 4 in the large conspecific group. After that, the sample size was
33 fish (11 tank replicates) in the small conspecific group, 33 fish (11
tank replicates) in the large heterospecific group, and 48 fish (8 tank
replicates) in the large conspecific group treatments. All housing
tanks were of 6 L capacity and contained identical enrichment of
2 cm of gravel, one plastic plant in the middle and an air filter (see
Supplementary Material). We ensured an ambient temperature of
~26 °C and a light:dark cycle of 12:12 h with an ad libitum feeding
schedule alternating between fish flakes and live Artemia (brine
shrimp) hatchlings 6 days per week. Furthermore, we conducted
behavioral observations on the social and foraging interactions
of guppies in 17 of the tanks, finding limited evidence of behav-
ioral variation between the social treatments (see Supplementary
Material).
After 6 months, we terminated the 3 social treatments and allo-
cated our focal individuals, i.e. the female guppies, to “baseline” and
“test” sets while transferring the splash tetras to a 200 L housing
tank. The baseline set served to test for brain morphology changes
right after the termination of the social treatment but before the
cognitive tests. In contrast, we subjected the test set first to a bat-
tery of cognitive tasks, which lasted about 50 days, and then meas-
ured their brains (see below). In the baseline set, there were ten fish
from the small conspecific group, ten from the large heterospecific
group, and 9 from the large conspecific group treatments. The
remaining fish (23 from the small conspecific group, 23 from the
large heterospecific group, and 39 from the large conspecific group
treatments, see Supplementary Material) were housed individually
in experimental aquaria (L × W × H; 40 × 15 × 15 cm). To avoid
potential observer bias, the test fish had running number labels (1,
2, 3, etc.) to conceal their social treatment identity throughout the
experiment. Each experimental aquarium had an identical enrich-
ment of 2 cm of gravel and an artificial plant and had continuously
aerated water; it also had two adjacent guillotine doors, one see-
through and one opaque, dividing the space into a housing com-
partment and a test compartment. The experimental room had an
ambient temperature of ~26 °C with a light schedule of 12 h light
and 12 h dark. We fed the guppies ad libitum with defrosted adult
brine shrimps delivered with a 1 mL transparent plastic pipette
6 days per week. This facilitated acclimating the fish to receive
food from plastic pipettes, later used to provide food as a positive
reinforcement in the learning tests. During the weekdays, when we
ran cognitive tasks, fish acquired food solely from test trials. The
tests started after an acclimation period of 5 days, with no trials on
the weekends. During the tests, the between-trial interval for every
fish was about 60 min, and one test trial per fish took about 1 min.
Furthermore, there was always at least one day break between
every two cognitive tests. Unfortunately, 5 fish out of the total 85
died during the experiment after jumping out of the experimental
tanks during the night. That left 22 fish from the small conspecific
group treatment, 21 fish from the large heterospecific group treat-
ment, and 37 fish from the large conspecific group treatment.
Cognitive tests
Inhibitory control task (detour task)
After the acclimation period, we trained fish to associate the color
green with a food reward. To do so, we placed a green disc in the
test compartment and delivered a defrosted adult artemia placed
right on top of the disc. We repeated this exposure twice a day for
4 consecutive days. On the following day, we introduced a trans-
parent Plexiglas cylinder (5 cm in length and 4 cm ⌀) open on ei-
ther side in the test compartment. The cylinder contained a food
reward placed on top of a green spot drawn inside the cylinder.
This was a one-time acclimation opportunity for the fish to explore
the transparent barrier. After that, fish received 3 trials on test Day
1, 3 trials on Day 2, and 4 trials on Day 3. A trial started when the
experimenter simultaneously pulled up the opaque and transparent
guillotine doors and allowed the fish to detour the physical bar-
rier, here as the cylinder walls, and swim inside the cylinder to re-
trieve the food reward. The experimenter recorded whether the fish
touched the cylinder (failure) or not (success) before retrieving the
food (see Supplementary Videos). Finally, we ranked the proportion
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Triki et al.
of correct detours (the number of successes divided by test trials) in
descending order (80 to 1), where the largest proportion was ranked
80, reflecting thus the highest performance in our fish given that
the sample size was 80 fish.
Associative and reversal learning tasks
In the associative learning task, we exposed the fish to a 2-color
choice test to estimate fish learning abilities through operant con-
ditioning in associating a food reward with a color cue, yellow vs
red. These colors were chosen based on the guppies’ ability to de-
tect them (Archer etal. 1987). In the test compartment, we placed
two 1 mL plastic pipettes covered with either yellow or red adhesive
tape, and each contained a defrosted adult artemia. An opaque gray
Plexiglas rectangle plate separated the two pipettes, thus creating two
zones of choice. A test trial started with the experimenter pulling up
the opaque sliding door followed by the see-through door, giving the
fish a few seconds to see the set-up before entering the test compart-
ment and choosing one of the two pipettes. The experimenter scored
a choice as “correct” if a fish entered the zone of the rewarding color
with its body length at its first attempt and “failure” if it chose the
non-rewarding color at its first attempt (see Supplementary Videos).
We balanced the color and side of the rewarding pipette across fish
and trials. As such, half of the fish had red as the rewarding color
while the other half had yellow with 50–50 presentation of the re-
warding color on the left and right side of the test compartments
(with no more than 3 presentations on the same side in succession)
(following the protocol by Triki etal. (2022b) for guppies).
Once a fish learned the color-reward association in the associative
learning phase, we tested its abilities in a reversal learning phase. It
consisted of reversing the reward contingency, and the previously
unrewarding color became the new rewarding cue. For example, if
a fish learned the yellow-reward association in the previous task, in
the reversal task, it had to learn the red-reward association instead.
For associative and reversal learning phases, the fish received twenty
test sessions, with one session per day (one session = 6 trials). We set
the learning criteria in each test to a score of either 6 correct choices
out of 6 consecutive trials or 5 correct choices out of 6 trials in two
consecutive sessions. The probability of learning by chance with
these criteria is P < 0.05 (with a binomial test).
Finally, for the associative learning performance, we ranked
fish success and the number of sessions needed to learn the task
in descending order (from 80 to 1), where the smallest number of
sessions to succeed was ranked as 80, reflecting thus the highest
performance in our fish. In the reversal learning performance,
we ranked fish success and the number of sessions needed to pass
first the associative learning phase and then the reversal phase in
descending order (from 80 to 1), where the smallest number of
sessions to succeed was ranked as 80, reflecting thus the highest
performance in our fish given that the sample size was 80 fish.
Brain staining and 3D-image acquisition
We prepared the 29 female guppies from the baseline group and
80 from the treatment group for X-ray brain scans by first euthan-
izing them with an overdose of benzocaine (0.4 g L−1). With a dig-
ital caliper, we estimated the fish body size as standard length (SL)
to the nearest 0.01 millimeter. We then fixated their whole heads
in 4% paraformaldehyde phosphate-buered saline (PBS) for
5 days. After that, we washed the samples twice in PBS for 10 min
and kept them in PBS. We then followed the Phosphotungstic acid
(PTA) staining protocol by Lesciotto et al. (2020) to prepare our
samples for X-ray scanning. In this protocol, we first dehydrated the
samples by placing them in a series of solutions as follows: one day
in 30% ethanol in PBS; one day in 50% ethanol in PBS; one day
in 70% ethanol in PBS; one hour in a solution with a ratio of 4:4:3
volumes of ethanol, methanol, and H2O; one hour in 80% meth-
anol in H2O; and 1 h in 90% methanol in H2O. After that, we
proceeded with the staining phase by placing the samples in 0.7%
PTA in 90% methanol in H2O for 23 days. Twenty-three days was
the optimal staining duration for our samples based on pilot rapid
X-ray scans to check the staining quality (see below). We then re-
hydrated the samples by placing them in 90% methanol for 6 h;
80% methanol overnight; 70% methanol for 6 h; 50% methanol
overnight; 30% methanol for a day; in PBS for one day; and finally
storage in 0.01% sodium azide in PBS.
We transferred the samples to the Stockholm University Brain
Imagery Center (SUBIC) for image acquisition. We scanned the
samples using a Zeiss Xradia Versa 520, with the X-ray source at
a voltage of 100 kV and a power of 9W. We used the 0.4× ob-
jective coupled with a scintillator. The source-to-sample distance
was 30 mm, and the sample-to-detector distance was 81 mm. The
eective voxel size was 9.17 μm with a compensated optical and
geometrical magnification. The scan consisted of 1201 projections
over 360 degrees with 1 s exposure time for each projection. Each
scan took 1 h and 36 min on average, including reference images
and the readout time of the CCD camera. Given the small size of
fish heads and to optimize the scan time, we arranged 4 samples
per scan (see Supplementary Material). We obtained 3-dimensional
images of the brain scans through an automatized tomography re-
construction with Zeiss Scout-and-Scan software.
Brain morphology measurements
To segment the 3D brain images, we first aligned the images dig-
itally in Dragonfly (Dragonfly 2020.2 [Computer software] 2020)
in 3 planes—transversal, coronal, and sagittal to the cardinal axes
(Fig. 1). To ensure accuracy, we either adjusted the voxel size of
the dataset through resampling using bicubic interpolation or made
note of any changes in voxel size and corrected the volume accord-
ingly. This was necessary due to the potential for minor changes in
voxel sizes caused by alignment. We obtained full head images in
the scans and then cropped them to include only the brain tissue.
This was done consistently across all planes to improve segmenta-
tion and accurately measure the volumes.
For the segmentation per se, we first generated a semi-manually
segmented brain into 5 regions (telencephalon, mesencephalon, di-
encephalon, cerebellum, and brain stem) (Fig. 1). This was achieved
by employing Biomedisa (Lösel etal. 2020) using random walker
interpolation between sparsely manually segmented slices. In total,
we semi-manually segmented 23 samples and used them as an
Elastix template (Klein et al. 2009). We then manually checked
these samples and corrected potential errors, then used them as a
training dataset for the following deep-learning-based segmentation
on the rest of the samples. We used the deep-learning algorithm
from Schoppe etal. (2020), which is U-net-like (Ronneberger etal.
2015). Finally, we computed the brain regions’ volumes by multi-
plying the voxel number and voxel size from the segmented labels.
Data analysis
We used the open-access software R, version 4.2.1 (R Core Team
2022), to run all statistical analyses and generate the figures.
Overall, we implemented 4 dierent statistical analysis approaches.
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Behavioral Ecology, 2024, Vol. 35(3)
First, we tested whether the fish exposed to dierent social
treatments may have developed dierent cognitive abilities. We
analyzed the rank performance data for all 3 abilities: inhibitory
control, associative learning, and reversal learning. Given that this
data violates the overdispersion assumption for count data, we fitted
3 generalized linear mixed models using a template model builder
(glmmTMB) with a negative binomial distribution. In one model,
we fitted inhibitory control rank performance as the dependent
variable, social treatment as the predictor and batch as a random
factor. For the associative and reversal learning models, we fitted
rank performance as the dependent variable, treatment as a pre-
dictor, and the color of the pipette as a covariate to control statisti-
cally for potential color bias, while the batch was the random factor.
Second, we tested whether brain morphology (total brain size
and brain region sizes) diered across the 3 social treatments in
both the baseline and test groups. To do so, we ran 2 linear mixed
eect models (LMMs), one for baseline data and one for test data,
where we fitted log-transformed brain size (mm3) as the dependent
variable, social treatment (with 3 levels: small conspecific group,
large heterospecific group, and large conspecific group) as the
fixed predictor, log-transformed and standardized body size (mm)
as a covariate, and batch number (we had 2 batches, see above) as
a random factor. Similarly, for the 5 brain regions, telencephalon,
diencephalon, mesencephalon, cerebellum, and brain stem, we
fitted 2 multivariate analyses of variance (MANOVA) for baseline
and test data. We fitted the dependent variable as a matrix of all
5 brain region sizes log-transformed and standardized (e.g. Triki
et al. 2019), with treatment as a predictor and body size as a
covariate. We also fitted batch as a predictor since MANOVA does
not support mixed eects.
Third, we tested whether cognitive performance was influ-
enced by individual brain size and region size. In preparation for
these analyses, we extracted the residuals of log brain size on log
body size, as well as the residuals of every brain region size (log-
transformed) on log-transformed and standardized brain and body
sizes. Then, we fitted a set of glmmTMB models with the desig-
nated cognitive ability as the dependent variable and social treat-
ment and the designated brain measurement as predictors. We also
fitted batch as the random factor in all these models, while for the
models testing for associative and reversal learning, we added the
color of the pipette as a covariate.
Finally, we checked that the fitted models met their corre-
sponding assumptions, such as normality of residuals and homo-
geneity of variance. For further details, we provide a step-by-step
code and data used to generate the findings in the present study
(see the Data and Code accessibility statement).
Results
Social treatment effect on cognitive performance
Among the 3 cognitive tests we ran, reversal learning emerged as the
only one being aected by the social treatment (glmmTMB: N = 80,
χ2 = 12.852, P = 0.002, explained variance: marginal-R2 = 0.35,
conditional-R2 = 0.46). Fish from the large conspecific group and
the large heterospecific group outperformed fish from the small
conspecific group (posthoc test emmeans: large conspecific group
vs small conspecific group, estimate = 1.971, P = 0.004; large
heterospecific group vs small conspecific group, estimate = 1.309,
P = 0.047), with no statistically significant dierences between
the large conspecific group and the large heterospecific group
(estimate = 0.662, P = 0.584) (Fig. 3). The other 2 tests, perfor-
mance in the inhibitory control, and associative learning tasks did
not significantly dier across the 3 social treatments (glmmTMB:
inhibitory control, N = 80, χ2 = 1.952, P = 0.376; associative
learning, N = 80, χ2 = 1.611, P = 0.446) (Fig. 2).
Social treatment effect on brain morphology
Guppies’ brain morphology did not change as a function of social
treatment. Neither fish sampled before (N = 29) nor those sampled
after (N = 80) the cognitive tests showed a significant (P > 0.05)
change in overall brain size or the 5 regions quantified here (tel-
encephalon, diencephalon, mesencephalon, cerebellum, and brain
stem) (Fig. 3) (see detailed statistics in Table 1). All measurements
were corrected for body size since body growth across treatments
varied significantly. Guppies from the large conspecific group
were significantly smaller than guppies in both the small conspe-
cific group and the large heterospecific group (LMM: from base-
line data: χ2 = 7.843, P = 0.019, R2 = 0.22; from test data: χ2 =
129.5, P < 0.001, marginal-R2 = 0.55; conditional-R2 = 0.66, see
Supplementary Fig. S4).
The link between cognitive performance and
brain morphology as a function of social
treatment
By looking at individual performance in every test (Figs. 4–6) and
correlating it to brain morphology and social treatment, we found
that relative telencephalon size was positively associated with im-
proved performance in the reversal learning task (glmmTMB:
N = 80, telencephalon size residuals, χ2 = 6.374, P = 0.011,
marginal-R2 = 0.45, conditional-R2 = 0.55, Fig. 6), but independ-
ently of social treatment (glmmTMB: N = 80, telencephalon size
residuals × social treatment, χ2 = 1.319, P = 0.517) (Fig. 4). For the
other brain measurements as well as the other 2 cognitive tasks,
i.e. inhibitory control and associative learning, we did not find any
statistically significant relationships (Fig. 4, see detailed statistics in
Table 2).
Discussion
Our study tested the social brain hypothesis within an ontogenetic
timescale. We asked whether social group size and group compo-
sition aect brain morphology and cognitive performance across
dierent cognitive domains in a guppy. The key findings were:
(1) living in a large group of 6 individuals, either of conspecifics
or heterospecifics, yielded improved performance in the reversal
learning task than those in the small conspecific group of 3 indi-
viduals; (2) social treatment did not aect associative learning per-
formance and inhibitory control; (3) social treatment did not aect
brain morphology; and (4) independently of social treatment, rela-
tive telencephalon size correlated positively with individual perfor-
mance in the reversal learning task. We discuss each finding in turn
in the following paragraphs.
Living in larger groups can create richer social environments and
lead to the development of more sophisticated strategies to cope
with daily challenges (Dunbar and Shultz 2007). This, in turn, can
result in more advanced cognitive abilities. Our study found that
guppies living in larger groups of 6 individuals exhibited better
cognitive flexibility, as expected based on the social brain hypo-
thesis. This aligns with previous studies finding a positive corre-
lation between social complexity and cognitive flexibility (Byrne
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Triki et al.
and Whiten 1989; Bond et al. 2007; Ashton etal. 2018). In larger
groups, individuals must be able to switch attention and adjust be-
haviors with changing demands more eectively than when co-
habiting with fewer individuals. Interestingly, our study found that
regardless of whether a guppy lived in larger groups of conspecifics
or heterospecifics, their cognitive capacities developed similarly, a
finding that is comparable to that by Fischer etal. (2021) on cich-
lids. It seems that the amount of social interactions that occur when
living in larger groups is more important for cognitive flexibility
than the exact nature of these interactions. Alternatively, the dier-
ences in social interactions between the two species included here
might not be large enough to generate dierences in the cogni-
tive tasks we quantified. Indeed, our analysis of specific behaviors,
specifically aggression and aggregation, did not provide any clear
explanations for the dierences observed in group performance
during the reversal learning task. This result suggests that a more
comprehensive ecological framework, encompassing social inter-
actions between dierent species, may be necessary to comprehend
the eect of the social environment on cognitive development. It
would be highly interesting to extend this finding and incorporate
between-species social interactions in the comparative phyloge-
netic analyses looking into cognitive evolution. Beyond predator–
prey interactions (Bijl and Kolm 2016), this has not been done yet.
Moreover, an important applied outcome of our finding is that
social enrichment could be highly eective when created through
the inclusion of other species, for instance, in zoos and sanctuaries
(Dorman and Bourne 2010).
Unsurprisingly, there were no dierences in associative learning
performance due to social treatment. The test assesses basic op-
erant conditioning abilities, and simple cognitive processes are suf-
ficient for forming associations (Savage 1980; Bielecki et al. 2023;
Triki et al. 2023a). The task is often excluded when researchers
look into complex cognitive abilities, like the “general intelligence”
factor (Damerius et al. 2017; Aellen et al. 2022). In sum, social
complexity would unlikely impact how an individual forms basic
associations, such as between a color cue and a food reward.
The ability to pause and override motor impulses in response to
a specific stimulus is known as inhibitory control. When executed
correctly, this results in adaptive, goal-oriented behaviors requiring
complex cognitive processes (Diamond 2013). We used the detour
task to test fish performance in this cognitive capacity, and the re-
sults showed that social treatment did not aect their inhibitory
control abilities. This finding goes against our original predictions
as previous studies have found that living in complex social envir-
onments correlates positively with enhanced inhibitory control abil-
ities for individuals and species (Amici et al. 2008; Ashton et al.
0.00
0.25
0.50
0.75
1.00
Correct detours
Inhibitory control
(a)
0
20
40
60
80
Performance rank
0
10
20
Test session
Associative learning
(b)
0
20
40
60
80
Performance rank
0
10
20
Test session
Reversal learning
(c)
0
20
40
60
80
Performance rank
*
**
small conspecific group large heterospecific group large conspecific group
Fig. 2. Cognitive performance of the guppies from the 3 social treatments. The panels on the left show the raw scores per cognitive task as dot plots for
inhibitory control, associative, and reversal learning. Data points above the dashed line in associative and reversal learning refer to individual guppies that
failed to reach the learning criterion within 120 test trials (20 test sessions where 1 session = 6 trials). Panels on the right show the estimate and 95% CI
of model marginal eects, combined with boxplots of median and interquartile of performance rank (N = 80) for (a) inhibitory control, (b) associative
learning, and (c) reversal learning. The highest ranks refer to the highest performance. The reversal learning test shows an eect of social treatment on fish
performance (*P < 0.05, **P < 0.01), but not the other 2 tests (P > 0.05).
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Behavioral Ecology, 2024, Vol. 35(3)
2018; Johnson-Ulrich and Holekamp 2020). However, it is worth
noting that other ecological factors, besides social complexity, may
also have significant impacts on self-control (see review by Rosati
(Rosati 2017)). Currently, we rely heavily on comparative and cor-
relative research to study the relationship between social complexity
and inhibitory control. Therefore, we need more experimental data
at both the species and population levels to draw convincing con-
clusions about whether social complexity directly enhances inhibi-
tory control capabilities.
Regarding the brain morphology analysis, there were no evident
changes caused by the social treatment. Still, we noticed that the
brain allometry was dierent, with fish from the treatment of 6 gup-
pies having relatively steeper allometry slopes for overall brain size but
also for most of the brain regions on body size, compared to the other
2 treatments (Fig. 2). It is clear that dierences in body growth drove
this (see Supplementary Fig. S4). Although we fed all fish ad libitum
and we expected that their body growth would be density-dependent
(Lorenzen and Enberg 2002), there were dierences across treatments.
Telencephalon Diencephalon Mesencephalon Cerebellum Brain stem Total brain
3.0 3.1 3.2 3.3 3.0 3.1 3.2 3.3 3.0 3.1 3.2 3.3 3.0 3.1 3.2 3.3 3.0 3.1 3.2 3.0 3.1 3.2 3.3
0.9
1.0
1.1
1.2
1.3
1.1
1.0
0.9
0.8
0.7
1.0
0.9
0.8
0.7
0.6
0.2
0.0
0.2
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
log volume (mm3) log volume (mm3)
Baseline data
(a)
(b)
Telencephalon Diencephalon Mesencephalon Cerebellum Brain stem Total brain
3.0 3.1 3.2 3.3 3.4 3.0 3.1 3.2 3.3 3.4 3.0 3.1 3.2 3.3 3.4 3.0 3.1 3.2 3.3 3.4 3.0 3.1 3.2 3.3 3.4 3.0 3.1 3.2 3.3 3.4
0.6
0.8
1.0
1.2
1.4
1.2
1.0
0.8
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0.8
0.6
0.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
log standard length (mm)
small conspecific group large heterospecific group large conspecific group
Test data
Fig. 3. Brain morphology of the guppies from the 3 social treatments. Scatter plot and regression lines of log-normal transformed volume (mm3) of the
brain measurement on the log-normal transformed body size (standard length in mm) as a function of social treatment from (a) the baseline dataset of the 29
female guppies sampled before the cognitive tests and (b) test dataset (80 female guppies) after the cognitive tests. There were no significant eects of social
treatment on brain morphology (P > 0.05).
Table 1. Summary table of the outcomes of brain morphology as a function of social treatment.
Dataset Explanatory variable test N F-value P-value
Total brain size
Baseline Social treatment LMM 29 1.458 0.482
Body size 13.421 <0.001
Test Social treatment LMM 80 0.359 0.835
Body size 59.764 <0.001
Telencephalon, diencephalon, mesencephalon, cerebellum, and brain stem sizes
Baseline Social treatment MANOVA 29 0.362 0.955
Body size 2.542 0.065
Batch identity 1.920 0.140
Social treatment × batch identity 0.389 0.943
Test Social treatment MANOVA 80 0.908 0.527
Body size 14.598 <0.001
Batch identity 2.344 0.050
Social treatment × batch identity 0.499 0.887
Values in bold refer to statistically significant results with a P-value ≤ 0.05.
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Triki et al.
Cerebellum Brain stem Total brain
Telencephalon Diencephalon Mesencephalon
–0.10 –0.05 0.00 0.05 0.10 –0.15 –0.10 –0.05 0.00 0.05 0.10 –0.2 –0.1 0.0 0.1
–0.10 –0.05 0.00 0.05 0.10 –0.04 0.00 0.04 0.08 –0.04 0.00 0.04
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
Brain region volume residuals
Performance rank
small conspecific group large heterospecific group large conspecific group
Inhibitory control
Fig. 4. The relationship of inhibitory control performance and brain morphology of the guppies from the 3 social treatments. Scatter plot and regression
lines of performance rank (where highest ranks refer to highest performance) on brain measurement residuals (x-axes) (N = 80). No significant eect was
detected (P > 0.05).
Cerebellum Brain stem Total brain
Telencephalon Diencephalon Mesencephalon
–0.10 –0.05 0.00 0.05 0.10 –0.15 –0.10 –0.05 0.00 0.05 0.10 –0.2 –0.1 0.0 0.1
–0.10 –0.05 0.00 0.05 0.10 –0.04 0.00 0.04 0.08 –0.04 0.00 0.04
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
Brain region volume residuals
Performance rank
small conspecific group large heterospecific group large conspecific group
Associative learning
Fig. 5. The relationship of associative learning performance and brain morphology of the guppies from the 3 social treatments. Scatter plot and regression
lines of performance rank (where highest ranks refer to highest performance) on brain measurement residuals (x-axes) (N = 80). No significant eect was
detected (P > 0.05).
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Behavioral Ecology, 2024, Vol. 35(3)
–0.10 –0.05 0.00 0.05 0.10 –0.15 –0.10 –0.05 0.00 0.05 0.10 –0.2 –0.1 0.0 0.1
–0.10 –0.05 0.00 0.05 0.10 –0.04 0.00 0.04 0.08 –0.04 0.00 0.04
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
Cerebellum Brain stem Total brain
Telencephalon Diencephalon Mesencephalon
Brain region volume residuals
Performance rank
small conspecific group large heterospecific group large conspecific group
Reversal learning
Fig. 6. The relationship of reversal learning performance and brain morphology of the guppies from the 3 social treatments. Scatter plot and regression
lines of performance rank (where highest ranks refer to highest performance) on brain measurement residuals (x-axes) (N = 80). Only the telencephalon
relative size correlated significantly with performance (*P < 0.05).
Table 2. Summary table of the outcomes of cognitive performance as a function of brain morphology and social treatment.
Inhibitory control Associative learning Reversal learning
Explanatory variable χ2P
marginal R2/
conditional R2χ2P
marginal R2/
conditional R2χ2P
marginal R2/
conditional R2
Telencephalon 0.665 0.415 0.054/ NA 0.034 0.852 0.056/ NA 6.374 0.011 0.45/ 0.55
Social treatment 2.449 0.294 1.691 0.429 19.509 < 0.001
Telencephalon × Social
treatment
0.505 0.777 0.450 0.798 1.319 0.517
Diencephalon 0.157 0.692 0.043/ NA 0.793 0.373 0.113/ 0.151 2.392 0.121 0.40/ 0.55
Social treatment 2.010 0.366 2.183 0.336 16.342 < 0.001
Diencephalon × Social
treatment
0.376 0.828 0.736 0.692 0.132 0.936
Mesencephalon 0.299 0.584 0.050/ NA 0.933 0.334 0.051/ NA 3.790 0.051 0.42/ 0.64
Social treatment 1.934 0.380 1.861 0.394 18.759 < 0.001
Mesencephalon × Social
treatment
0.600 0.741 0.429 0.807 3.421 0.181
Cerebellum 0.249 0.618 0.042/ NA 0.575 0.448 0.042/ NA 1.231 0.267 0.39/ 0.61
Social treatment 1.969 0.374 1.635 0.442 15.436 < 0.001
Cerebellum × Social
treatment
0.305 0.859 0.114 0.944 3.521 0.172
Brain stem 0.474 0.491 0.043/ NA 0.010 0.919 0.045/ NA 0.034 0.853 0.39/ 0.56
Social treatment 2.278 0.320 1.508 0.471 15.056 < 0.001
Brain stem × Social
treatment
0.134 0.935 0.190 0.909 4.688 0.096
Total brain 0.033 0.856 0.100/ NA 0.292 0.588 0.044/ 0.049 2.387 0.122 0.38/ 0.55
Social treatment 2.610 0.271 1.476 0.478 15.042 < 0.001
Total brain × Social
treatment
3.711 0.156 0.094 0.954 0.922 0.631
Values in bold refer to statistically significant results with a P-value ≤ 0.05. The sample size is N = 80 guppies. NA: not applicable.
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Triki et al.
Guppies in the small conspecific group and the large heterospecific
group were of similar body sizes, but they were substantially larger
than those in the large conspecific group. It suggests that guppies were
more successful foragers than splash tetras in the large heterospecific
group because they attained larger body sizes as if they were alone in
the tank compared to those growing relatively smaller when they were
competing against their peers in the large conspecific groups. Despite
these body growth dierences across treatments, there were no evident
size changes in the brain or the 5 major brain regions when corrected
for body size. We can only speculate on why we did not find any eects
when other studies on fish have found substantial dierences in brain
morphology associated with variation in the social environment. For
instance, cleaner fish living at higher population densities possess larger
forebrains (telencephalon and diencephalon) (Triki etal. 2019), while
9-spined sticklebacks reared in groups develop a larger optic tectum
and a smaller olfactory bulb than those reared individually (Gonda
etal. 2009) (see review by Gonda etal. (2011) for more examples and
detailed discussion). It is possible that we did not see any changes across
the social treatments because increasing the group size from 3 to 6 was
not enough to create the necessary social eects that lead to brain mor-
phology changes. Yet, it is also possible that the social treatment gen-
erated eects on a dierent scale that could not be detected with our
X-ray scan methods. For instance, it could be that changes in neural
connectivity, neuronal activity or gene expression, while not essentially
leading to volume changes, were responsible for the observed group
performance dierences (Weitekamp and Hofmann 2014; Herculano-
Houzel 2017; Wallace and Hofmann 2021). Another possible reason
for the lack of social treatment eect on brain morphology in our data
is that we only manipulated group size, ignoring other key factors that
exist in the wild and aect brain development, such as predation pres-
sure, mating strategies, and feeding ecology (Brown and Braithwaite
2005; Kolm etal. 2009; Powell etal. 2017).
We found that relative telencephalon size explained, to some ex-
tent, fish performance in the reversal learning test with no apparent
dierences in this brain region volume across social treatments.
Specifically, relative telencephalon size correlated positively with
reversal learning performance within each social treatment (see
Fig. 4c), but it did not explain performance dierences across treat-
ments. Individual-level improvement in reversal learning perfor-
mance due to larger telencephalon has already been demonstrated
in guppies by Triki et al. (2022b, 2023b). Additionally, Triki etal.
(2022a, 2023b) found a positive correlation between the size of this
brain part and inhibitory control abilities, which was not observed
in the current study. One possible reason for this could be that Triki
etal.’s studies (2022a, 2023b) involved the use of guppies that were
selectively bred to reach a divergence in relative telencephalon size
over several generations. Often, it is dicult to detect brain mor-
phology eects on behavior in wild-type strains of laboratory-held
animals fed ad libitum and where predation selection pressures
are removed (see discussion on this topic in (McNeil etal. 2021)).
Hence, while not general across all cognitive abilities assayed here,
we find it interesting that the eect of relative telencephalon size on
cognitive flexibility is consistent both for wild-type guppies and for
artificial selection line guppies targeted for telencephalon size.
In summary, our research suggests that social complexity af-
fects cognitive flexibility but not inhibitory control or basic operant
conditioning skills. This impact was likely driven by mechanisms
beyond plastic changes in the 5 major brain regions. Although
there may not be any apparent changes in brain morphology,
the findings suggest that living in larger social groups can aect
an individual’s cognitive development, specifically their cognitive
flexibility. However, further research, using experimental methods,
is necessary to fully understand how social and environmental fac-
tors shape cognitive development.
SUPPLEMENTARY MATERIAL
Supplementary material is available at Behavioral Ecology online.
ACKNOWLEDGMENTS
We thank Mirjam Amco for all the help and support with the logistics
needed for this study.
AUTHOR CONTRIBUTIONS
Zegni Triki (Conceptualization [Lead], Data curation [Lead], Funding
acquisition [Lead], Investigation [Lead], Methodology [Lead], Project
administration [Lead], Supervision [Equal], Visualization [Lead],
Writing—original draft [Lead], Writing—review & editing [Lead]), Tunhe
Zhou (Data curation [Equal], Methodology [Equal], Writing—review &
editing [Supporting]), Elli Argyriou (Data curation [Equal], Writing—re-
view & editing [Supporting]), Edson Sousa de Novais (Data curation
[Supporting]), Oriane Servant (Data curation [Supporting]), and Niclas
Kolm (Conceptualization [Equal], Resources [Lead], Supervision [Equal],
Writing—review & editing [Supporting]).
ETHICS APPROVAL
This work was approved by the ethics research committee of the Stockholm
Animal Research Ethical Per mit Board [permit numbers: Dnr 17362-2019,
17402-2020].
FUNDING
This work was supported by the Swiss National Science Foundation [grant
numbers P2NEP3_188240, P400PB_199286 and PZ00P3_209020 to ZT]
and the Swedish Research Council [grant numbers 2016-03435 and 2021-
04476 to NK]. Brain data acquisition was supported by a grant from the
Stockholm University Brain Imaging Centre [grant number SU FV-5.1.2-
1035-15 to ZT].
CONFLICT OF INTEREST
All authors declare that they have no conflict of interest.
DATA AVAILABILITY
Analyses reported in this article can be reproduced using the data provided
by Triki etal. (2024).
Handling Editor: Aliza le Roux
REFERENCES
Aellen M, Burkart JM, Bshary R. No evidence for general intelligence in a
fish. Ethology. 2022:128(5):424–436. https://doi.org/10.1111/eth.13275
Amici F, Aureli F, Call J. Fission-fusion dynamics, behavioral flexibility,
and inhibitory control in primates. Curr Biol. 2008:18(18):1415–1419.
https://doi.org/10.1016/j.cub.2008.08.020
Anaya-Rojas JM, Bassar RD, Potter T, Blanchette A, Callahan S, Framstead
N, Reznick D, Travis J. The evolution of size-dependent competitive inter-
actions promotes species coexistence. J Anim Ecol. 2021:90(11):2704–
2717. https://doi.org/10.1111/1365-2656.13577
Archer SN, Endler JA, Lythgoe JN, Partridge JC. Visual pigment polymor-
phism in the guppy Poecilia reticulata. Vision Res. 1987:27(8):1243–1252.
https://doi.org/10.1016/0042-6989(87)90200-8
Ashton BJ, Ridley AR, Edwards EK, Thornton A. Cognitive performance
is linked to group size and aects fitness in Australian magpies. Nature.
2018:554(7692):364–367. https://doi.org/10.1038/nature25503
Page 10 of 12
Downloaded from https://academic.oup.com/beheco/article/35/3/arae026/7639089 by guest on 20 April 2024

Behavioral Ecology, 2024, Vol. 35(3)
Barton RA. Neocortex size and behavioural ecology in primates. Proc Biol
Sci. 1996:263(1367):173–177.
Barton RA. Olfactory evolution and behavioral ecology in primates. Am J
Primatol. 2006:68(6):545–558. https://doi.org/10.1002/ajp.20251
Beauchamp G, Fernández-Juricic E. Is there a relationship between fore-
brain size and group size in birds? Evol Ecol Res. 2004:6(6):833–842.
Bielecki J, Nielsen SKD, Nachman G, Garm A. Associative learning in the
box jellyfish Tripedalia cystophora. Curr Biol. 2023:33:4150–4159.
Bijl W van der, Kolm N. Why direct eects of predation complicate the
social brain hypothesis. Bioessays. 2016:38(6):568–577. https://doi.
org/10.1002/bies.201500166
Bond AB, Kamil AC, Balda RP. Serial reversal learning and the evo-
lution of behavioral flexibility in three species of North American
corvids (Gymnorhinus cyanocephalus, Nucifraga columbiana, Aphelocoma
californica). J Comp Psychol. 2007:121(4):372–379. https://doi.
org/10.1037/0735-7036.121.4.372
Brown C, Braithwaite VA. Eects of predation pressure on the cognitive
ability of the poeciliid Brachyraphis episcopi. Behav Ecol. 2005:16(2):482–
487. https://doi.org/10.1093/beheco/ari016
Bshary R, Triki Z. Fish ecology and cognition: insights from studies on wild
and wild-caught teleost fishes. Curr Opin Behav Sci. 2022:46:101174.
https://doi.org/10.1016/j.cobeha.2022.101174
Byrne R, Whiten A. 1989. Machiavellian Intelligence: Social Expertise
and the Evolution of Intellect in Monkeys, Apes, and Humans (Oxford
Science Publications). USA: Oxford University Press.
Call J, Tomasello M. Does the chimpanzee have a theory of mind?
30 years later. Trends Cogn Sci. 2008:12(5):187–192. https://doi.
org/10.1016/j.tics.2008.02.010
Clutton-Brock TH, Harvey PH. Primate ecology and social organization.
J Zool. 1977:183(1):1–39. https://doi.org/10.1111/j.1469-7998.1977.
tb04171.x
Croft DP, Arrowsmith BJ, Bielby J, Skinner K, White E, Couzin
ID, Magurran AE, Ramnarine I, Krause J. Mechanisms un-
derlying shoal composition in the Trinidadian guppy,
Poecilia reticulata. Oikos. 2003:100(3):429–438. https://doi.
org/10.1034/j.1600-0706.2003.12023.x
Croft DP, Krause J, James R. Social networks in the guppy (Poecilia
reticulata). Proc Biol Sci. 2004:271(Suppl 6):S516–S519.
Damerius LA, Graber SM, Willems EP, van Schaik CP. Curiosity boosts
orang-utan problem-solving ability. Anim Behav. 2017:134:57–70.
https://doi.org/10.1016/j.anbehav.2017.10.005
Deaner RO, Isler K, Burkart J, van Schaik C. Overall brain size, and not
encephalization quotient, best predicts cognitive ability across non-
human primates. Brain Behav Evol. 2007:70(2):115–124. https://doi.
org/10.1159/000102973
DeCasien AR, Higham JP. Primate mosaic brain evolution reflects se-
lection on sensory and cognitive specialization. Nat Ecol Evol.
2019:3(10):1483–1493. https://doi.org/10.1038/s41559-019-0969-0
DeCasien AR, Williams SA, Higham JP. Primate brain size is predicted
by diet but not sociality. Nat Ecol Evol. 2017:1(5):112. https://doi.
org/10.1038/s41559-017-0112. http://www.nature.com/articles/
s41559-017-0112.
Diamond A. Executive functions. Annu Rev Psychol. 2013:64:135–168.
https://doi.org/10.1146/annurev-psych-113011-143750
Dorman N, Bourne DC. Canids and ursids in mixed-species ex-
hibits. Int Zoo Yearb. 2010:44(1):75–86. https://doi.
org/10.1111/j.1748-1090.2009.00108.x
Dragonfly 2020.2 [Computer software]. 2020. Object Research Systems
(ORS) Inc, Montreal, Canada. https://www.theobjects.com/dragonfly/
publications-how-to-cite-dragonfly.html
Dunbar RIM. Neocortex size as a constraint on group size
in primates. J Hum Evol. 1992:22(6):469–493. https://doi.
org/10.1016/0047-2484(92)90081-J
Dunbar RIM. Coevolution of neocortical size, group size and lan-
guage in humans. Behav Brain Sci. 1993:16(04):681–694. https://doi.
org/10.1017/S0140525X00032325
Dunbar RIM. The social brain hypothesis. Evol Anthropol Issues
News Rev. 1998:6(5):178–190. https://doi.org/10.1002/
(SICI)1520-6505(1998)6:5<178::AID-EVAN5>3.0.CO;2-8
Dunbar RIM, Shultz S. Evolution in the social brain. Science.
2007:317(5843):1344–1347. https://doi.org/10.1126/science.1145463
Emery NJ, Seed AM, Bayern AMP von, Clayton NS. Cognitive adap-
tations of social bonding in birds. Philos Trans R Soc B Biol Sci.
2007:362(1480):489–505. https://doi.org/10.1098/rstb.2006.1991
Finn JK, Tregenza T, Norman MD. Defensive tool use in a coconut-
carrying octopus. Curr Biol. 2009:19(23):R1069–R1070. https://doi.
org/10.1016/j.cub.2009.10.052
Fischer S, Balshine S, Hadolt MC, Schaedelin FC. Siblings matter: family
heterogeneity improves associative learning later in life. Ethology.
2021:127(10):897–907. https://doi.org/10.1111/eth.13196
Fischer S, Bessert-Nettelbeck M, Kotrschal A, Taborsky B. Rearing-group
size determines social competence and brain structure in a coopera-
tively breeding cichlid. Am Nat. 2015:186(1):123–140. https://doi.
org/10.1086/681636
Gonda A, Herczeg G, Merilä J. Habitat-dependent and -independent
plastic responses to social environment in the nine-spined stickleback
(Pungitius pungitius) brain. Proc R Soc Lond B Biol Sci. 2009:276:2085–
2092. https://doi.org/10.1098/rspb.2009.0026
Gonda A, Herczeg G, Merilä J. Population variation in brain size of nine-
spined sticklebacks (Pungitius pungitius) - local adaptation or environmen-
tally induced variation? BMC Evol Biol. 2011:11(1):75. https://doi.
org/10.1186/1471-2148-11-75
González-Forero M, Gardner A. Inference of ecological and social drivers
of human brain-size evolution. Nature. 2018:557(7706):554–557.
https://doi.org/10.1038/s41586-018-0127-x
Guadagno A, Triki Z. Executive functions and brain morphology of male
and female dominant and subordinate cichlid fish. Brain Behav. 2024.
https://doi.org/10.1002/brb3.3484
Herculano-Houzel S. Numbers of neurons as biological correlates of cog-
nitive capability. Curr Opin Behav Sci. 2017:16(Supplement C):1–7.
https://doi.org/10.1016/j.cobeha.2017.02.004
Hutcheon JM, Kirsch JAW, Garland T Jr. A Comparative analysis of brain
size in relation to foraging ecology and phylogeny in the chiroptera. Brain
Behav Evol. 2002:60(3):165–180. https://doi.org/10.1159/000065938
Iwaniuk AN. Interspecific variation in sexual dimorphism in brain size in
Nearctic ground squirrels (Spermophilus spp.). Can J Zool. 2001:79(5):759–
765. https://doi.org/10.1139/cjz-79-5-759
Iwaniuk AN, Nelson JE. A Comparative analysis of relative brain size in
waterfowl (Anseriformes). Brain Behav Evol. 2001:57(2):87–97. https://
doi.org/10.1159/000047228
Johnson-Ulrich L, Holekamp KE. Group size and social rank predict inhibi-
tory control in spotted hyaenas. Anim Behav. 2020:160:157–168. https://
doi.org/10.1016/j.anbehav.2019.11.020
Kabadayi C, Bobrowicz K, Osvath M. The detour paradigm in animal
cognition. Anim Cogn. 2018:21(1):21–35. https://doi.org/10.1007/
s10071-017-1152-0
Kappeler PM. A framework for studying social complexity. Behav Ecol
Sociobiol. 2019:73(1):13. https://doi.org/10.1007/s00265-018-2601-8
Kenny JS. 2008. The Biological Diversity of Trinidad and Tobago: A
Naturalist’s Notes. Maravel, Port of Spain, Trinidad and Tobago:
Prospect Press/MEP.
Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. Elastix: a toolbox
for intensity-based medical image registration. IEEE Trans Med Imaging.
2009:29(1):196–205.
Kolm N, Gonzalez-Voyer A, Brelin D, Winberg S. Evidence for
small scale variation in the vertebrate brain: mating strategy
and sex aect brain size and structure in wild brown trout
(Salmo trutta). J Evol Biol. 2009:22(12):2524–2531. https://doi.
org/10.1111/j.1420-9101.2009.01875.x
Kotrschal A, Rogell B, Maklakov AA, Kolm N. Sex-specific plasticity in
brain morphology depends on social environment of the guppy, Poecilia
reticulata. Behav Ecol Sociobiol. 2012:66(11):1485–1492. https://doi.
org/10.1007/s00265-012-1403-7
Kudo H, Dunbar RIM. Neocortex size and social network size in pri-
mates. Anim Behav. 2001:62(4):711–722. https://doi.org/10.1006/
anbe.2001.1808
Lesciotto KM, Perrine SMM, Kawasaki M, Stecko T, Ryan TM, Kawasaki
K, Richtsmeier JT. Phosphotungstic acid enhanced microCT: optimized
protocols for embryonic and early postnatal mice. Dev Dyn O Publ Am
Assoc Anat. 2020:249(4):573–585. https://doi.org/10.1002/dvdy.136
Lorenzen K, Enberg K. Density-dependent growth as a key mechanism in
the regulation of fish populations: evidence from among-population com-
parisons. Proc Biol Sci. 2002:269(1486):49–54. https://doi.org/10.1098/
rspb.2001.1853
Lösel PD, van de Kamp T, Jayme A, Ershov A, Faragó T, Pichler O, Tan
Jerome N, Aadepu N, Bremer S, Chilingaryan SA, et al. Introducing
Biomedisa as an open-source online platform for biomedical image seg-
mentation. Nat Commun. 2020:11(1):5577.
Page 11 of 12
Downloaded from https://academic.oup.com/beheco/article/35/3/arae026/7639089 by guest on 20 April 2024

Triki et al.
Lucon-Xiccato T, Gatto E, Bisazza A. Fish perform like mammals and birds
in inhibitory motor control tasks. Sci Rep. 2017:7(1):13144. https://doi.
org/10.1038/s41598-017-13447-4
MacLean EL, Hare B, Nunn CL, Addessi E, Amici F, Anderson RC,
Aureli F, Baker JM, Bania AE, Barnard AM, etal. The evolution of self-
control. Proc Natl Acad Sci USA. 2014:111(20):E2140–E2148. https://
doi.org/10.1073/pnas.1323533111
McNeil RM, Devigili A, Kolm N, Fitzpatrick JL. Does brain size aect
mate choice? an experimental examination in pygmy halfbeaks. Behav
Ecol. 2021:32(6):1103–1113.
Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD.
The unity and diversity of executive functions and their contributions to
complex “frontal lobe” tasks: a latent variable analysis. Cogn Psychol.
2000:41(1):49–100.
Oliveira RF, Bshary R. Expanding the concept of social behavior to inter-
specific interactions. Ethology. 2021:127(10):758–773.
Pawłowski B, Lowen CB, Dunbar RIM. Neocortex Size, Social Skills and
Mating Success in Primates. Behaviour. 1998:135(3):357–368.
Phillip DAT. 1998. Biodiversity of freshwater fishes of Trinidad and Tobago,
West Indies [Thesis]. University of St Andrews. [accessed 2023 April 26].
https://research-repository.st-andrews.ac.uk/handle/10023/2832
Powell LE, Isler K, Barton RA. Re-evaluating the link between
brain size and behavioural ecology in primates. Proc Biol Sci.
2017:284(1865):20171765. https://doi.org/10.1098/rspb.2017.1765
R Core Team. 2022. A language and environment for statistical computing.
https://www.R-project.org/
Ronneberger O, Fischer P, Brox T. 2015. U-net: Convolutional networks
for biomedical image segmentation. In: Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2015: 18th International
Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III
18. Springer. p. 234–241.
Rosati AG. Foraging cognition: reviving the ecological intelligence hypo-
thesis. Trends Cogn Sci. 2017:21(9):691–702. https://doi.org/10.1016/j.
tics.2017.05.011
Savage GE. 1980. The fish telencephalon and its relation to learning. In:
Ebbesson SOE, editor. Comparative Neurology of the Telencephalon.
Boston (MA): Springer US. p. 129–174. [accessed 2021 August 23].
http://link.springer.com/10.1007/978-1-4613-2988-6_6
Schoppe O, Pan C, Coronel J, Mai H, Rong Z, Todorov MI, Müskes A,
Navarro F, Li H, Ertürk A, etal. Deep learning-enabled multi-organ seg-
mentation in whole-body mouse scans. Nat Commun. 2020:11(1):5626.
Shultz S, Dunbar RIM. Both social and ecological factors predict ungu-
late brain size. Proc Biol Sci. 2006:273(1583):207–215. https://doi.
org/10.1098/rspb.2005.3283
Street SE, Navarrete AF, Reader SM, Laland KN. Coevolution of cul-
tural intelligence, extended life history, sociality, and brain size in
primates. Proc Natl Acad Sci USA. 2017:114(30):7908–7914. https://
doi.org/10.1073/pnas.1620734114
Triki Z, Emery Y, Teles MC, Oliveira RF, Bshary R. Brain mor-
phology predicts social intelligence in wild cleaner fish. Nat Commun.
2020:11(1):6423. https://doi.org/10.1038/s41467-020-20130-2
Triki Z, Fong S, Amco M, Kolm N. Artificial mosaic brain evolution of
relative telencephalon size improves inhibitory control abilities in the
guppy (Poecilia reticulata). Evolution. 2022a:76(1):128–138. https://doi.
org/10.1111/evo.14405
Triki Z, Fong S, Amco M, Vàsquez-Nilsson S, Kolm N. Experimental ex-
pansion of relative telencephalon size improves the main executive func-
tion abilities in guppy. PNAS Nexus. 2023a:2(6):pgad129. https://doi.
org/10.1093/pnasnexus/pgad129
Triki Z, Granell-Ruiz M, Fong S, Amco M, Kolm N. Brain morphology
correlates of learning and cognitive flexibility in a fish species (Poecilia
reticulata). Proc Biol Sci. 2022b:289(1978):20220844. https://doi.
org/10.1098/rspb.2022.0844
Triki Z, Levorato E, McNeely W, Marshall J, Bshary R. Population densities pre-
dict forebrain size variation in the cleaner fish Labroides dimidiatus. Proc Biol
Sci. 2019:286(1915):20192108. https://doi.org/10.1098/rspb.2019.2108
Triki Z, Zhou T, Argyriou E, Sousa de Novais E, Servant O, Kolm N. Social
complexity aects cognitive abilities but not brain structure in a Poecilid
fish. bioRxiv. 2023b. https://doi.org/10.1101/2023.08.20.554009
Triki Z, Zhou T, Argyriou E, Sousa de Novais E, Servant O, Kolm N.
Social complexity aects cognitive abilities but not brain structure in a
Poecilid fish. Dryad. Behav Ecol. 2024. https://doi.org/10.5061/dryad.
hx3bgn5
Uddin LQ. Cognitive and behavioural flexibility: neural mechanisms and
clinical considerations. Nat Rev Neurosci. 2021:22(3):167–179. https://
doi.org/10.1038/s41583-021-00428-w
van Schaik CP, Burkart JM. Social learning and evolution: the cul-
tural intelligence hypothesis. Philos Trans R Soc Lond B Biol Sci.
2011:366(1567):1008–1016. https://doi.org/10.1098/rstb.2010.0304
Wallace KJ, Hofmann HA. Decision-making in a social world: integrating
cognitive ecology and social neuroscience. Curr Opin Neurobiol.
2021:68:152–158. https://doi.org/10.1016/j.conb.2021.03.009
Weitekamp CA, Hofmann HA. Evolutionary themes in the neurobiology
of social cognition. Curr Opin Neurobiol. 2014:28:22–27. https://doi.
org/10.1016/j.conb.2014.06.005
White GE, Brown C. Variation in brain morphology of intertidal go-
bies: a comparison of methodologies used to quantitatively assess brain
volumes in fish. Brain Behav Evol. 2015:85(4):245–256. https://doi.
org/10.1159/000398781
Zupanc GKH. Adult neurogenesis and neuronal regeneration in the brain
of teleost fish. J Physiol Paris. 2008:102(4–6):357–373. https://doi.
org/10.1016/j.jphysparis.2008.10.007
Page 12 of 12
Downloaded from https://academic.oup.com/beheco/article/35/3/arae026/7639089 by guest on 20 April 2024