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RESEARCH ARTICLE
Pull or Push? Octopuses Solve a Puzzle
Problem
Jonas N. Richter
¤
, Binyamin Hochner, Michael J. Kuba*
Department of Neurobiology, Alexander Silberman Institute of Life Sciences, Hebrew University of
Jerusalem, Givat Ram, Israel
¤Current address: Max Planck Florida Institute, Jupiter, Florida, United States of America
*michi.kuba@lobster.ls.huji.ac.il
Abstract
Octopuses have large brains and exhibit complex behaviors, but relatively little is known
about their cognitive abilities. Here we present data from a five-level learning and problem-
solving experiment. Seven octopuses (Octopus vulgaris) were first trained to open an L
shaped container to retrieve food (level 0). After learning the initial task all animals followed
the same experimental protocol, first they had to retrieve this L shaped container, presented
at the same orientation, through a tight fitting hole in a clear Perspex partition (level 1). This
required the octopuses to perform both pull and release or push actions. After reaching cri-
terion the animals advanced to the next stage of the test, which would be a different consis-
tent orientation of the object (level 2) at the start of the trial, an opaque barrier (level 3) or a
random orientation of the object (level 4). All octopuses were successful in reaching crite-
rion in all levels of the task. At the onset of each new level the performance of the animals
dropped, shown as an increase in working times. However, they adapted quickly so that
overall working times were not significantly different between levels. Our findings indicate
that octopuses show behavioral flexibility by quickly adapting to a change in a task. This
can be compared to tests in other species where subjects had to conduct actions comprised
of a set of motor actions that cannot be understood by a simple learning rule alone.
Introduction
Octopuses represent an important and interesting model for comparative cognition. They have
a unique morphology, intricate behavior and the most complex central nervous system among
all invertebrates. Animal cognition has advanced greatly in recent years and new findings in
both vertebrates and invertebrates elicited the interest in the evolution of cognition. Still, most
research focuses on primates, birds, and to some extent insects, and while cephalopods show
great potential, experimental data on cognition in octopus is scarce.
Innovative behavior or problem solving means finding a solution to a novel problem or a
novel solution to an old one [1]. Theory predicts that such cognitive abilities are favored in spe-
cies that exploit diverse food sources, have complex social structure, inhabit environments with
highly unpredictable resources and undergo relatively long developmental stages [2,3].
PLOS ONE | DOI:10.1371/journal.pone.0152048 March 22, 2016 1/16
OPEN ACCESS
Citation: Richter JN, Hochner B, Kuba MJ (2016)
Pull or Push? Octopuses Solve a Puzzle Problem.
PLoS ONE 11(3): e0152048. doi:10.1371/journal.
pone.0152048
Editor: Daniel Osorio, University of Sussex, UNITED
KINGDOM
Received: June 29, 2015
Accepted: March 8, 2016
Published: March 22, 2016
Copyright: © 2016 Richter et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All data is uploaded as
supporting files.
Funding: This work was supported by the European
Commission under the 7th Framework Programme in
the theme of the Future and Emerging Technologies
(FET, OCTOPUS IP, FP7-ICT 2007.8.5, FET)
[231608]—www.octopus.eu<http://www.octopus.eu>;
and STIFFness controllable Flexible and Learn-able
manipulator for surgical Operations [STIFF-FLOP]
FP7-ICT-2011-7 Project Number 287728 (http://www.
stiff-flop.eu/en/). The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Octopuses are reported to be exploratory and attracted by novel objects [4], opportunistic feed-
ers [5,6], forage large territories [7,8] and migrate vertically and horizontally [5]. While octo-
puses don’t have rich social structure or particularly long developmental stages, their ecology
and behavior at the adult benthic stage is very variable and complex, representing the dynamic
near shore environments in which O.vulgaris is found [9,10].
Innovation has been correlated to cephalization, i.e. cortex and pallium size, while this
could be shown in birds and monkeys [11], but, cognitive traits are shown in insects not pos-
sessing these brain areas [12–14]. The octopus brain with about 140 million neurons [15,16]is
large and complex compared to other invertebrate brains. Their brain-weight-to-body-weight
ratio is comparable to that of vertebrates [17] and the vertical lobe, which is involved in long
term memory [18], shares some functional features with the vertebrate hippocampus and the
insect mushroom bodies [19–21]. In many behavioral studies octopuses show learning and
memory abilities [22,23] and readily solve discrimination tasks [24–27].
However, despite the excellent learning abilities of octopuses, only a few cognitive abilities
have been investigated, e.g. play behavior [28], navigation [8,29,30] and detour task solving in
a maze [31,32]. Some reasons for this scarcity are technical difficulties, the lack of standardized
and practical training protocols, as well as technical devices and apparatuses. The special mor-
phology of octopuses, their eight highly flexible arms and soft body, makes it difficult to
restrain the animals for experimental set-ups. Classic cognition experiments like problem solv-
ing often incorporated applications (e.g. the trap-tube problem [33,34]), which are difficult to
test in octopuses because of their flexible arms. A lever-pulling experiment, similar to a skin-
ner-box, proved to be inapplicable to octopuses as they failed to learn the task [35]. Fiorito
et al. [36] overcame some of the difficulties using a jar-opening task, which exploited the ani-
mal’s natural exploration and manipulation instincts. In his experiments, Octopus vulgaris had
to solve a novel problem in form of a glass jar, which had a food item in it and was closed by a
rubber plug. Fiorito et al. [36] (also see [37,38]) showed a simple and successful problem solv-
ing experiment, in which the task remained constant throughout the entire experiment. How-
ever, several unanswered questions remain—did the animals solve the task by stimulus-
response association or by trial-and-error and whether octopuses solve more complex prob-
lems [39]?
In order to test flexible behavior and problem-solving strategies in octopuses, we developed
a series of experiments consisting of a learning and a systematically changing problem solving
task. All seven subjects solved the two basic tasks, to open the L-shaped container in level 0 and
to pull it through the separator hole in the subsequent puzzle-task levels, and thus showed
behavioral flexibility.
Materials and Methods
Subjects and holding
Subjects were seven wild-caught Octopus vulgaris (4 females, 3 males; between 250–500g body-
weight) collected by fishermen from the Israeli coast of the Mediterranean Sea. The animals
were housed individually in a semi closed system of glass aquaria (100cm x 40cm x 40cm) and
visually shielded from their conspecifics. Using a water chiller, temperature was held constant
at about 24°C. According to the guidelines for the EU Directive 2010/63/EU for cephalopod
welfare [40] aquaria were enriched with clay-pot dens, gravel, rocks and green algae (Caulerpa
prolifera). Animals were fed every other day with either shrimps or pieces of fish. All animals
acclimatized for at least 14 days in the holding tanks before experiments started. Animals were
monitored daily by either the authors of the study or by students from the local college for
aquaculture. Animals were preselected for both adaptation to human care and general health.
Problem Solving in Octopus
PLOS ONE | DOI:10.1371/journal.pone.0152048 March 22, 2016 2/16
Competing Interests: The authors have declared
that no competing interests exist.
This was judged by checking if the animals were regularly and readily feeding and by inspec-
tion for skin lesions, visible parasites and general health status. During the experiments all ani-
mals were fed a regular diet of dead fish and crustaceans.
Experiments
The unique morphology of octopuses can present a challenge for researchers and the experi-
mental design. Octopuses are typically very curious they pounce and manipulate novel moving
objects. They often forcefully manipulate lighter and moving objects or experimental appara-
tuses until they break. The presence of observers can influence the animals behavior, handling
of the animals can constitute an immense stress factor, especially since contact with an object
(e.g. a net or the researchers arm) can lead to a “tug of war”over it [39]. Motivation of the ani-
mals can wane quickly after an object was explored and yielded no food reinforcement for the
octopus [4]. The aim of the current study was to use the natural behavior of octopuses to
develop a complex problem-solving task with several levels, which built upon each other. To
that end we restricted each animal to a compartment of the tank (30 min before the start),
which was closed off by a Perspex separator (Fig 1B). This restrained the animal to an approxi-
mately 10×30×30 cm space, which in turn eased stimulus presentation and trial onset and lim-
ited the handling of the animal. Between trials there were at least ten minute inter-trial
intervals. Each day ten experimental trials were conducted. Experiments were carried out in
the home tank of each individual animal, which reduced time for adaptation to an experimen-
tal setting. After the experiments the partitions were removed and the animals had again access
to their entire home tank. Such temporary restraining has been successfully done in a previous
study [41] and did not have any measurable effect on the wellbeing of the animal. The follow-
ing tasks were presented to the subjects:
Container-opening task (level 0); a training level to form an association between the L-
shaped container (Fig 1A) and a food reward. A piece of shrimp was placed inside the L-shaped
container and presented to the animal, which then had to open the container within a 5 minute
time period. Success criterion was an opened container.
Puzzle task (level 1–4); individual subjects were restricted to one part of the tank and had to
manipulate the L-shaped container through a tightly fit hole in the separator in order to get the
food reward. The container was presented to them with one leg of the container put in the hole
and the other leg pointing upwards. In order to manipulate the container through the hole, it
had to be accurately leveraged around the corner of the container, as the tightly fit hole left no
room to manipulate the container through the hole otherwise. The tight fit of the container
made it impossible to open the container while in the hole.
The task was altered for each of the four levels: In level 1, the first stage of the puzzle task,
the separator was transparent and the orientation of the container was upwards (S1 Video). In
level 2 the transparent Perspex separator was exchanged for an opaque separator, but the con-
tainer-orientation remained upwards. The opaque separator was also used for the next two lev-
els. In level 3 the orientation of the container was reversed, the outside leg pointed down. In
level 4 the orientation of the container was randomized, using all four possible orientations (S2
Video). Randomization was done according to Fellows [42] for each animal, with the first trial
set to an upward orientation for motivational purposes.
The stimulus presentation through a small hole in the separator wall reduced further con-
tact and presented a cue for trial onset. Once an item is grabbed, octopuses hardly let go and
forcefully pull on it [39]. In order to get to the food-containing object, however, the animals
needed to learn to overcome their initial instinctive behavior and not pull, but lever the tight
fitted L-shaped box through the hole.
Problem Solving in Octopus
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We decided to apply a strict criterion to ensure that each animal passed to the next level
with equal mastery of the task. The success criteria for level 0–2 were three consecutive experi-
ment days of 80% successful trials. We assumed that these levels were the most difficult, and
that therefore it was important to establish equal proficiency in all subjects before proceeding
to levels 3 and 4. After the initial capacity for box manipulation had been established, criterion
for testing in levels 3 and 4 was one experiment day with 80% successful trials. Each experiment
day consisted of 10 trials, except level 4 with 12 trials to account for equal distribution of the
four orientations. Trials were marked successful in level 1 to 4 when the corner piece was pulled
to the other side of the separator. The trial was reset, when animals turned the container to dif-
ferent orientations, dropped the container, or after 5 minutes of no success.
Analysis
The sessions were recorded with a digital video camera (SONY Handycam HDR-XR550;
Tokyo, Japan) and later analyzed with The Observer XT 10 (Noldus Information Technologies,
Wageningen, Netherlands). Further data analysis was done with SPSS 19 (IBM Software;
Fig 1. Experiment set-up. (A) Two halves of the L-shaped container. Length of one arm 6 cm, width 1.4 cm,
height 2 cm. (B) Schematic set up of the experiment tank with an opaque Perspex separator (light blue) and
the stimulus hole in the center. Subjects were placed in the smaller compartment 30 min before the onset of
the experiments.
doi:10.1371/journal.pone.0152048.g001
Problem Solving in Octopus
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Armonk, New York, USA) and Microsoft Excel 2011 for Mac OS (Redmond, Washington,
USA).
Over all 1469 trials were analyzed, of which 240 (16.3%) were marked as fails and 1229 were
used for further analysis. Mean (± SD) number of experiment days for all levels and animals
was 20 (± 6.9) with a minimum of 11 days by experiment design (S1 Data).
To reduce high variance of trial durations, which was caused by animals not behaving dur-
ing the experiments, only behaviors scored as “working”were used for time analysis. Working
times consisted of durations that were subjectively scored while the animal showed active inter-
action with the container. The shape of the distribution of durations was examined and found
to be similar to the overall trial time. To calculate learning effects over the course of the experi-
ment, trial bins were created for level 1 to 4. Trials for level 1 and level 2 were divided into
three equally-sized trial bins per level and animal. Since the criterion for the test situations in
level 3 and level 4 was only one successful experiment day, each level formed one trial bin.
To analyze performance between different trial bins or experiment days a Wilcoxon signed
rank test was used. The regression of working times within level 1 and 2 was analyzed with
Kendall’s tau. To test for differences in the orientation of the container during level 4, a Krus-
kal-Wallis H test was employed.
Ethical Statement
All experiments took place before the passing of the EU directive 2010/63/EU on the protection
of laboratory animals. However, due to the involvement of the authors in the process of devel-
oping the guidelines regarding the use of cephalopods as laboratory animals, all experimenta-
tion and housing guidelines of the EU directive were followed as if the regulations were in
place. All experiments conducted were non-invasive behavioral experiments and no negative
reinforcement was used at any time (S1 and S2 Texts). Animals were kept in a flow-through
system and therefore always-in natural water conditions. After completing the experiments,
animals were released back into the wild. The animal protection law in Israel does not cover
invertebrates, thus no evaluation by an ethics commission was possible.
Results
Level 0–2: Learning and problem solving performances
All animals learned to solve the task in level 0 and showed a relatively short (1.94 ±1.81 sec)
mean contact latency (the time between the insertion of the container into the water and first
contact). Animals showed inter-individual differences in the amount of time it took them to
successfully accomplish the task in level 0 and the subsequent task in level 1 (Fig 2, blue and
green circles). For example, Animal C passed level 0 after four experiment days and level 1
after 24 days. On the other hand, Animal L passed level 0 after 16 experiment days and level 1
after seven days. After level 1 was passed, all animals had a significant decrease in trial numbers
to reach success criteria in level 2 (Wilcoxon signed-rank test; z = -2.032, p = 0.042; see Fig 2,
yellow circles).
To test and compare performance differences between animals and levels (i.e. how fast an
animal retrieved the container), over all working times were equally split into three trial bins
per level for level 1 and level 2 (Fig 3A). While overall there was no significant difference in
working times between level 1 and 2, a test on the single trial bins 1.3 and 2.1, i.e. the last trial
bin in level 1 versus the first trial bin in level 2, showed a significant increase in working times
for all animals combined (Wilcoxon signed-rank test; z = 3.018, p = 0.003; Fig 3A). However,
when analyzing the results of each animal individually, only Animal N had a significant
increase in working time between these two trial bins (z = 2.1, p = 0.036; see Fig 4).
Problem Solving in Octopus
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Fig 2. Success rates for individual animals over all experiment days. Blue circles: Pre-training level 0; food association with L-shaped container. Green
circles: training level 1; introduction of a transparent separator. Orange circles: level 2; introduction of an opaqueseparator. Black circles: test situation level
3; downward oriented container. White circles: level 4; container randomly oriented in 4 directions. Black line marks 80% success criterion.
doi:10.1371/journal.pone.0152048.g002
Problem Solving in Octopus
PLOS ONE | DOI:10.1371/journal.pone.0152048 March 22, 2016 6/16
Fig 3. Boxplot of working times over trial bins for grouped animals (A). Central line indicates median; boxes represent 2
nd
and 3
rd
quartiles and
whiskers 1
st
and 4
th
. Dots denote outliers. Asterisks indicate significance (p <0.05). Median working times (B-D) Median working times plotted for individual
animals show performance during the last day of a level and the single first trial of the subsequent level.
doi:10.1371/journal.pone.0152048.g003
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To investigate if animals adapted faster to the task alterations than detectable in averaged
trial bins, median working times were also compared between key experiment days (Fig 4) and
between the averaged last day of a level and the single first trial of the subsequent level (Fig 3B–
3D). Grouped animals showed a significant performance decrease again when the first day of
level 2 was compared to the last day of level 1 (z = 2.884, p = 0.004). This was probably mostly
driven by Animal C, which was the only animal showing a significant increase in working time
when compared between days (z = 2.31, p = 0.021; Fig 4). However, by visual inspection of Fig
3B, all but one animal showed a working time increase during the first trial of level 2, compared
to their median working time of the previous day, indicating that these animals were affected
by the change of the task in level 2, but adapted quickly throughout the experiment day.
Level 3: Motor learning
In level 1 and level 2 the orientation of the container and therefore the motor procedure was
constant, which allowed the possible facilitation of motor learning effects. Since increased per-
formance or a decrease in time could indicate motor learning during the puzzle task, the regres-
sion of working time was analyzed for level 1 and level 2 combined. While working times did
not decrease significantly for combined animals (Kendall’s tau τ
b
= 0.024, p = 0.37, N = 639;
see Fig 3), a single animal significantly decreased working time during these levels (τ
b
= -0.155,
p = 0.019, N = 105; see Fig 4, Animal E).
Fig 4. Median working times over trial bins per animal. Green: level 1, introduction of the puzzle-task.
Orange: level 2, opaque separator. Black: level 3, reversed container orientation. White: level 4, randomized
container orientation. Error bars show 95% CI. Asterisk represents statistical significance between trial bins
with p<0.05; triangle represents significance between single experiment days with p<0.05.
doi:10.1371/journal.pone.0152048.g004
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To further test motor learning effects during the problem solving task, the puzzle was
altered by reversing the orientation of the container in level 3. Two animals did not reach suc-
cess criteria in the first day in level 3 (Fig 2, black circles), and when performance was com-
pared between trial bins 2.3 and 3.1 (z = 2.72, p = 0.007; Fig 3A), grouped animals showed a
significant decrease in performance, i.e. an increase in working time. This was evident also in 4
individual animals out of the 7 total; Animal E (z = 2.073, p = 0.038), Animal L (z = 2.24,
p = 0.025), Animal M (z = 2.073, p = 0.038) and Animal P (z = 2.31, p = 0.021; Fig 4). Three
animals showed significant working time differences when compared between the single exper-
iment days of level 2 and level 3, Animal E (z = 2.31, p = 0.021), Animal J (z = 1.992, p = 0.046)
and Animal L (z = 2.24, p = 0.025). Five of the seven animals showed a performance decrease
on the first trial of level 3, compared to the median working time of the previous day (Fig 3C).
Taken together, the data suggest that the animals were affected by the change of orientation of
the container but adapted quickly to the new motoric procedure so that five of the seven ani-
mals passed the success criteria during a single experiment day.
Level 4: Behavioral flexibility
All animals quickly adapted to changes in the puzzle task. We aimed to test behavioral flexibil-
ity by randomizing the orientation of the container in four different positions (level 4). All ani-
mals, except Animal M, reached success criteria in the first experiment day (Fig 2, white
circles). Overall animals showed a high variance and no significant increase in working time
was found between level 3 and level 4. However, an increase in working time was observed for
5 animals between the first trial of level 4 and the last of level 3 (Fig 3D). No significant differ-
ences in success rates or working times were found between the four orientations at level 4
(Kruskal-Wallis H test; χ
2
= 1.415, p = 0.702 and χ
2
= 5.287, p = 0.152; Fig 5A and 5B), which
suggests that the animals used a generalized problem-solving strategy, instead of relying on
experience from previous levels.
Discussion
In order to test for flexible behavior in octopuses, we developed a series of experiments consist-
ing of a learning and a problem solving task. All seven subjects solved the two basic tasks, to
open the L-shaped container in level 0 and to pull it through the separator hole in the subse-
quent puzzle-task levels, and thus demonstrated problem-solving abilities. The animals’perfor-
mances differed significantly between level 1, level 2 and level 3, but not level 4 (Fig 3A),
suggesting, that they were less affected by the randomized container orientation in level 4 due
to a generalized problem solving strategy. Since performances systematically differed between
individual animals and tasks, we conclude that the octopuses did not use a trial-and-error strat-
egy throughout the experiment, which would have led to equal performances between tasks,
but rather showed individual problem-solving strategies.
Problem-solving strategies
Innovation, as a form of cognition, stands in relation to other cognitive traits, for example
learning, problem-solving and flexible behavior [43]. Performance in these tasks can vary
greatly between individuals within a species [44]. We analyzed individual performance differ-
ences in order to elucidate if and how octopuses used individual approaches to solve the tasks.
The octopuses of the present study generally showed the most variance in performance during
the tasks in level 0 and level 1. Level 0 presented a relatively simple problem with a larger frac-
tion being a learning task. Level 1, on the other hand, was more complex: in order to get the
object through the tightly fit hole, animals had to overcome their initial instinct to strongly pull
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on the object and instead, let loose to lever it through. While learning has been shown to be
positively related to problem-solving abilities in various birds [45–47], performances were not
consistent among octopuses between these levels: One animal (Animal L, see Fig 2) passed this
level with the minimum amount of experiment days needed (i.e. three experiment days), show-
ing 100% success rate per day. Animal C on the other hand showed the slowest progress on the
task and passed level 1 after 24 experiment days (see Fig 2). On the other hand, Animal C
showed over 80% success rate for all experiment days of level 0, while Animal L, with a perfect
success score in level 1, needed 16 experimental days in level 0. The individual differences
between these two tasks indicate that the classification of an animal into either a “slow”or
“fast”learner was not consistent between the levels but rather that solving the learning tasks
depended on individual problem-solving strategies. It is interesting to note, that after level 1,
success rates stayed relatively constant over 80% and it is plausible to assume that at this level a
certain understanding about the general solvability of the problem formed in the animals. It
furthermore shows that the animals were motivated to solve the task and that success rates
were not dependent on varying motivation.
Fig 5. Combined animals during level 4. Orientation of L-shaped box was randomized with equal number of presentations per orientation. (A) Percent
success per each of the four orientations. Error bars mark SE. (B) Working time for each orientation.
doi:10.1371/journal.pone.0152048.g005
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In level 2, in which by introducing opaque separators eliminated visual information about
the container, task-solving times differed between the individual animals. Two animals (Ani-
mal C and Animal N) showed an increase in duration to solve the puzzle task. Interestingly,
these two animals were the only ones that showed no significant performance decrease in level
3, but instead, showed even faster working times (Fig 4). These results suggest that these ani-
mals switched their problem solving strategies from a vision-based to a generalized strategy.
It is unclear if the animals used the visual cues to form a mental representation of the solu-
tion to the problem in level 2 or if the presentation of the container itself increased motivation
to solve the puzzle task. However, it is likely that the odor cues of the food item inside the
unsealed container increased motivation more than the visual cues [38], so that the impact on
task duration was probably due to missing visual cues. The other five animals either adapted
instantly to the new situation or used different problem solving strategies. Likewise, animals
that showed performance loss in level 3 probably adapted to a generalized strategy in level 4,
which–with very high working time variances between orientations–resembles what one would
expect during trial-and-error.
Behavioral flexibility has been linked to the ability to quickly adjust to novel situations in
response to environmental feedback [48,49]. Griffin and Guez [46] showed that innovation
and flexible behavior are not necessarily positively correlated in individual Indian mynas: they
showed to be fast learners and problem-solvers, but showed slow adaptation to a reversal task.
To test for behavioral flexibility in octopuses, the orientation of the container was reversed in
level 3, which led to fast adaptation within the first experiment day and probably the first few
trials, however, performance was significantly affected. This resulted in an overall successful
experiment day (except animal J; Fig 2) but significant working time increases (Fig 3A) and
might reflect the adaptation to a more flexible strategy. Furthermore, in level 4, in which the
four different orientations were randomized for every trial, all animals except animal M passed
success criteria after the first experiment day and decreased median task duration (but note
variance; Fig 3A), which shows that the animals developed a fast and flexible problem solving
strategy throughout the previous levels.
Interestingly, in the comparison of the four orientations of the container in level 4, the pre-
viously trained orientations ‘up’and ‘down’were not necessarily the most successful or the
fastest, which hints towards a more generalized puzzle solving strategy, which did not rely on
previous knowledge of the orientations. A generalized and hence flexible strategy might consist
of motoric elements (e.g. trying to leverage the container in all directions) and intellectual ele-
ments (e.g. knowledge about the general shape of the container or mental rotation). A trial-
and-error strategy would grant the highest success rate during the constantly changing and
unpredictable task, while different strategies might have been advantageous in earlier stages. It
is also possible that the animals used sensory cues about the orientation of the containers (e.g.
due to slight tilts of the container), which they did not use in earlier levels. At least the common
trend towards very short task durations for the ‘right’orientation would support the use of
such strategy. However, although octopuses are able to visually discriminate shapes and to
some extent shape rotation [50], tactile shape discrimination seems to be far more complicated.
While they are able to discriminate between different surface profiles, orientation and object
shapes were indistinguishable [51,52]. The data on the mechanics of visual shape discrimina-
tion remain inconclusive [50]. Taken together, it is very likely that animals resolved to a trial-
and-error strategy and it is yet unknown if octopuses have mental representations of objects
and if they, with better methodological approaches, would be able to learn to discriminate
between different orientations or mirror images, as was shown in e.g. pigeons [53].
Another possible explanation and a potential avenue for further testing, is that performance
differences could be due to individual personalities or ‘cognitive styles’which shaped the
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animals’problem-solving strategies (e.g. boldness, activity levels or neophilia) [54]. Personality
traits have been shown before in octopuses [55,56] and while individual personality traits have
not been tested during this experiment, some of the results may be explainable by personality
differences: While an aggressive personality might have been advantageous in the container-
opening task of level 0, it would have been a disadvantage in level 1, which required a more del-
icate approach. However, the present number of animals is far too small to make any conclu-
sive statement personal traits.
Motor learning
While solving a problem by insight is an ideal display of cognitive abilities, it is a rarely
observed phenomenon in non-primate animals, [57] and at least some knowledge about a cer-
tain problem is necessary. A more commonly observed solution to a complex problem is a
trial-and-error-approach. One could argue that after repeated presentations to a physical prob-
lem, motor learning effects contribute to the solution of a principally similar problem. New
Caledonian crows, for example, that were familiar with just the use of short sticks, used the
poking-skill to overcome similar problems in a serial, three-step modified trap-tube problem
[58].
It has been argued before that motor stereotypy, i.e. the tendency to produce only a narrow
range of motor actions, limits the chance to produce high cognitive abilities [59]. It is thought
that high motor plasticity and the ability to express novel behaviours during novel situations, is
central to innovation [43,59]. Indeed it has been shown that greater motor diversity can be a
predictor for higher problem-soling abilities [60–62]. This is particularly interesting in light of
the octopuses’bodily features: It has been shown that there is some separation of labor between
the periphery and the central nervous system [63] and it has been hypothesized, that the central
nervous system does not use proprioceptive feedback from the arms [5,64] and furthermore,
that there is a conflict of sensory and tactile information [65,66]. It has therefore been dis-
cussed if octopuses are able to use sensory feedback to control their movements [23,67], how-
ever, recent findings suggest that the animals use at least visual feedback to guide their arms
[68] and are able to change motor patterns to adapt to complex motor tasks [41]. One of the
most important questions that arises from these theories is, how octopuses learn new motor
skills, and adapt to a diverse environment. To test for motor learning effects, the orientation of
the container was reversed in level 3, while visual information was still limited. Our working
hypothesis was that animals that rely on entrenched motoric procedures to solve the task
would then experience a change in performance, either in terms of task duration or success
rate. Five of the seven animals were significantly affected by the reversed container (Fig 4). Fur-
thermore, these five animals did not show significant differences in level 2, suggesting, that
they were using a motor-oriented strategy early in the experiment.
Motor learning should have a measurable effect on performance, leading to higher effective-
ness and hence decrease task duration. Therefore, task duration of experiment days of level 1
and level 2 were analyzed. In the two levels, the orientation of the container and hence motor
procedure were the same and each animal had a minimum number of 60 presentations and at
least six successive experiment days of 80% success at this point. However, only one animal
showed a significant decrease in task duration in this period, which also showed significant dif-
ferences in level 3, both for trial-bin comparison and day-to-day comparison (Fig 4, Animal E).
The trial duration during level 1 and 2 did not show a significant trend to shorter task dura-
tions for combined animals, which implies that motor learning does not have a major effect on
the animals during the experiments and in turn suggests that performance differences of the
five animals rely on certain strategies, instead of motor learning. Octopuses use stereotypic and
Problem Solving in Octopus
PLOS ONE | DOI:10.1371/journal.pone.0152048 March 22, 2016 12 / 16
invariant movement patterns during reaching and fetching movements [69,70], but it could be
shown that they are able to adapt their movement patterns during novel situations [41]. While
it remains unclear if octopuses can learn new movements or optimize movement patterns in a
repeated motor learning task, they are able to adapt their motor programs. This enables them
to solve a wide range of motor tasks and hence the ability to solve complex problems, which
would fit the prediction of a positive correlation between motor plasticity and cognition [59].
In conclusion, we show that octopuses exhibit flexible behaviors by being able to quickly
adapt to a changing problem. Individual analysis of performance revealed different problem-
solving approaches, which exceeded simpler learning mechanisms and did not rely on single
fixed strategies like trial-and-error or a stimulus-response-association. However, these strate-
gies can be adapted according to the task and the octopuses probably resort to trial-and-error
in unpredictable and changing situations. We furthermore present a non-invasive operant con-
ditioning task, proposing a new way of thinking about complex cognitive experiments on
octopuses.
Supporting Information
S1 Data. A SPSS.sav file containing the original data.
(SAV)
S1 Text. This document contains the NC3Rs ARRIVE Guidelines Checklist Richter et al.
(DOCX)
S2 Text. This document contains the NC3Rs ARRIVE Guidelines Checklist (filled).
(PDF)
S1 Video. A video clip of an octopus during a successful trail at level 1, the orientation of
the L shaped container is “up”.
(MOV)
S2 Video. A video clip of an octopus during a successful trail at level 3, the orientation of
the L shaped container is “down”.
(MOV)
Acknowledgments
We express our sincere gratitude to Vered Kellner for helpful comments on this study and
Tamar Gutnick for help on statistical software and data analysis.
Author Contributions
Conceived and designed the experiments: JNR BH MJK. Performed the experiments: JNR
MJK. Analyzed the data: JNR MJK. Contributed reagents/materials/analysis tools: BH MJK.
Wrote the paper: JNR BH MJK.
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