what is problem solving behavior in animals

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Problem Solving in Animals: Proposal for an Ontogenetic Perspective

Misha k. rowell.

1 College of Science and Engineering, James Cook University, P. O. Box 6811, Cairns, Queensland 4870, Australia; [email protected]

2 Centre for Tropical Environmental and Sustainability Sciences, James Cook University, P. O. Box 6811, Cairns, Queensland 4870, Australia

Neville Pillay

3 School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Johannesburg 2000, South Africa; [email protected]

Tasmin L. Rymer

Simple summary.

Animals must be able to solve problems to access food and avoid predators. Problem solving is not a complicated process, often relying only on animals exploring their surroundings, and being able to learn and remember information. However, not all species, populations, or even individuals, can solve problems, or can solve problems in the same way. Differences in problem-solving ability could be due to differences in how animals develop and grow, including differences in their genetics, hormones, age, and/or environmental conditions. Here, we consider how an animal’s problem-solving ability could be impacted by its development, and what future work needs to be done to understand the development of problem solving. We argue that, considering how many different factors are involved, focusing on individual animals, and individual variation, is the best way to study the development of problem solving.

Problem solving, the act of overcoming an obstacle to obtain an incentive, has been studied in a wide variety of taxa, and is often based on simple strategies such as trial-and-error learning, instead of higher-order cognitive processes, such as insight. There are large variations in problem solving abilities between species, populations and individuals, and this variation could arise due to differences in development, and other intrinsic (genetic, neuroendocrine and aging) and extrinsic (environmental) factors. However, experimental studies investigating the ontogeny of problem solving are lacking. Here, we provide a comprehensive review of problem solving from an ontogenetic perspective. The focus is to highlight aspects of problem solving that have been overlooked in the current literature, and highlight why developmental influences of problem-solving ability are particularly important avenues for future investigation. We argue that the ultimate outcome of solving a problem is underpinned by interacting cognitive, physiological and behavioural components, all of which are affected by ontogenetic factors. We emphasise that, due to the large number of confounding ontogenetic influences, an individual-centric approach is important for a full understanding of the development of problem solving.

1. Introduction

Increasing concerns over human-induced rapid environmental change has led to a corresponding increase in interest in understanding how animals will cope with these challenges. Rapid and unpredictable changes may have significant effects on survival and coping ability [ 1 ]. In order to survive, animals need to gain information about the environment (e.g., relative predation risk and food availability). While this might sometimes be easily attained, such as directly observing fruit on a tree, obtaining resources or avoiding predation may require an ability to solve a problem, such as obtaining fruit that is out of reach.

Problem solving has been documented in all major vertebrate taxa, including mammals (e.g., food-baited puzzles in various mammalian carnivores, [ 2 ]), birds (e.g., food-baited puzzles given to multiple parrot and corvid species [ 3 , 4 ]), reptiles (e.g., multiple species of monitor lizards Varanus spp. are capable of solving food-baited puzzle boxes, [ 5 ]), amphibians (e.g., detour task, where the animal had to move around an obstacle in brilliant-thighed poison frogs Allobates femoralis , [ 6 ]), fishes (e.g., foraging innovation in guppies Poecilia reticulata , [ 7 ]), and some invertebrates (e.g., overcoming a physical barrier in leaf-cutting ants Atta colombica [ 8 ]).

Currently, there is no universally accepted definition of problem solving ( Table 1 ). From our literature search (see below), most definitions consider mechanical (i.e., movements required to solve problems), morphological (i.e., physical structure to manipulate objects to solve a problem) and/or cognitive (i.e., assessing, learning, storing information about problem) components as part of problem-solving ability. We consider problem solving to be the ability of an individual to integrate the information it has gained (knowledge or behaviour) to move itself, or manipulate an object, to overcome a barrier, negative state or agent, and access a desired goal or incentive, such as a resource [ 9 , 10 ]. Most reports of problem solving are based on experimental evidence where animals are presented with a feeding motivation task (e.g., a puzzle box or detour task), in which an animal manipulates an object, or moves itself around the object, to access the food. Occasionally, animals are experimentally presented with an obstacle blocking access to a location, and the animal needs to move the obstacle to access a refuge or their nest. These solutions can be achieved by innovation (the use of a new behaviour, or existing behaviour in a new context [ 11 ]) and/or by refining behaviour over repeated sessions with the stimulus (e.g., trial-and-error learning). Our literature search has also demonstrated that problem solving is sometimes assessed simply as a dichotomous skill, in which an animal either can or cannot solve a problem, but other studies have focused on how animals vary in the way they solve problems, and how efficiently they solve problems. Our definition encompasses all of these aspects.

Definitions of problem solving and innovation quoted from the literature and associated references. We highlight the drivers (i.e., whether the ability to problem solve is linked to internal (e.g., physiology, cognition) or external (e.g., environmental) factors) and the properties of the animal (mechanical/morphological abilities or cognitive abilities) that authors attribute to problem solving.

Successful problem solving has been theorised to be important for survival, as it allows animals to adjust to changing environmental conditions [ 24 ] and even invade new environments (e.g., bird species introduced to New Zealand, [ 25 ]), or to cope with harsh or extreme conditions [ 26 ]. However, the ability of animals to solve problems [ 27 ], and the specific strategy/manoeuvre that they use to solve problems [ 28 ], is highly variable, and this variation can be observed at all taxonomic levels, including between families (e.g., Columbida vs. Icteridae, [ 29 ]), genera (e.g., Molothrus vs. Quiscalus [ 30 ]), and species (jaguar Panthera onca vs. Amur tiger P. tigris , [ 2 ]). It is even possible that problem solving is phylogenetically conserved, with some groups having a greater potential to solve problems than others [ 31 ]. However, variation in problem-solving ability also occurs within species, including between populations (e.g., house finches Haemorhous mexicanus given extractive foraging tasks [ 32 ]), and individuals (e.g., meerkats Suricata suricatta given food-baited puzzle boxes [ 27 ]). Likely causes of this variation are the conditions that arise during an individual’s development. This variation could then allow problem-solving ability to be acted upon by natural selection [ 33 ], possibly impacting individual fitness. Therefore, understanding the influence of developmental factors on problem-solving ability is important.

An individual’s behaviour, physiology and morphology may change as it grows and ages due to developmental changes in life history traits [ 34 , 35 ]. Furthermore, interactions and experiences with other individuals and the immediate environment further feedback into these systems [ 36 ]. These intrinsic and extrinsic factors, either independently or synergistically, influence the individual’s ability to cope with, and respond to, environmental challenges [ 37 ], although their outcomes are likely difficult to predict because of myriad interacting factors.

Although aspects of behaviour, physiology and cognition have been studied in an ontogenetic context [ 38 , 39 ], little is currently known about how problem-solving abilities develop and change as individuals grow and age. Developmental differences between individuals could fine tune or modulate the ability to solve problems, causing individual variation in this ability. Importantly, this inter-individual variation in problem solving could have fitness consequences by influencing survival and/or reproductive success. However, untangling the relative influence of intrinsic (genetic, neuroendocrine and aging) and extrinsic (environmental) factors on the development of problem solving is challenging [ 40 , 41 ]. We propose that an integrated approach, focusing on the development of problem solving, is needed to fully appreciate the ability and propensity of animals to solve novel problems. Our aim was to review the literature on problem solving to document and then construct the links between intrinsic and extrinsic factors that influence the development of problem-solving.

We therefore conducted a literature search using Google Scholar and the Web of Science database. We included the general search terms “problem solving” “innovation” and “animal” in all searches and excluded all articles with the word “human”. This produced 6100 hits. We further refined the search by including the following as specific terms in individual searches: “development”, “ontogeny”, “heritability”, “personality”, “cognition”, “learning”, “experience”, “age”, “hormone”, “brain”, and “environment”. Articles that were repeated in subsequent searches were ignored. Articles were excluded if: (1) the researchers trained the animals to solve the problem before testing (and, therefore, tested memory rather than natural problem-solving ability); (2) the authors referred to a type of problem solving that did not meet our definition (e.g., relational problems where animals needed to extract and transfer rules between tests); and/or (3) development of problem solving was not investigated. If two papers found similar results (e.g., neophobia hinders problem solving in a bird species), we only reported on one study to avoid repetition and to reduce the overall number of citations.

Numerous studies have shown that animals can problem solve [ 42 ], and several studies have explored the fitness consequences of problem solving in animals (e.g., [ 10 ]). However, how problem solving develops is an area that has been little explored. In this paper, we first discuss how intrinsic and extrinsic factors influencing the ontogeny of individuals could affect the development of problem-solving ability. We focus on genetic (direct and indirect), neuroendocrine, and environmental (physical and social) factors, as well as age, learning and experience. Given the relative paucity of empirical studies investigating the development of problem solving in general (42 publications found of seven developmental factors), we demonstrate first how these factors impact other traits in order to create a conceptual framework for addressing problem solving. We acknowledge that limited information currently makes it challenging to separate developmental factors underlying problem-solving ability from other causal mechanisms (e.g., hormones, genetic effects). We then explore how interactions between intrinsic and extrinsic factors during an individual’s development could influence problem solving indirectly. Specifically, we focus on how personality (individual differences in behaviour) and behavioural flexibility (ability to change behaviour in response to environmental cues) contribute to differences in problem-solving ability. Finally, we briefly discuss aspects that have been overlooked in studies investigating the development of problem solving, providing hypotheses for future testing. Throughout this paper, we advocate for an individual-centric approach to study the ontogeny of problem solving, where individual variation in solving ability is considered, rather than only using simple population-level averages. Future studies should be tailored to focus on individual differences within and between tests, as well as consider a longitudinal approach to track how individuals change over their lifetimes. Analyses of these experiments should then include individual data points as a measure of individual ability and variation, and should not exclude outliers because these account for the species- or population-level variation.

2. Factors Affecting the Development of Problem Solving

Problem solving is influenced by direct [ 43 ] and indirect (epigenetic and transgenerational) genetic [ 44 ], and neuroendocrine [ 45 ] factors ( Figure 1 ). Furthermore, extrinsic factors, including both the physical and social environments, can also affect the development of problem solving ( Figure 1 ). However, the development, and ultimately expression, of problem solving is more likely impacted by complex interactions between these intrinsic and extrinsic factors ( Figure 1 ), and is also likely to change as the animal ages and experiences (i.e., learns) new situations (e.g., ravens Corvus corax [ 28 ]; North Island robins Petroica longipes , [ 46 ]). Untangling these effects is likely to be challenging.

Intrinsic (genetic, neuroendocrine, and aging), extrinsic (environment) and acquired (learning and experience) factors influencing an individual’s development directly (solid arrows) or indirectly (dashed arrows). Arrow heads indicate direction of influence.

2.1. Instrinic Factors

2.1.1. direct genetic effects.

Heritable genetic effects influence the development of phenotypic traits. For example, physiological stress (barn swallows Hirudo rustica , [ 47 ]), parental care (African striped mice Rhabdomys pumilio [ 48 ]), exploratory behaviour (great tits Parus major [ 49 ]), multiple aspects of cognition in chimpanzees Pan troglodytes [ 50 ], learning in hens Gallus gallus domesticus [ 51 ] and spatial learning ability (C57BL/6Ibg and DBA/2Ibg mice Mus musculus [ 52 ]) all have a heritable component (but see [ 53 ]).

Heritable genetic effects may also affect the development of problem solving ( Figure 1 ), although this has received little attention in the literature. Elliot and Scott [ 43 ] found that different dog Canis lupus familiaris breeds solved a complex barrier problem in different ways, and Audet et al. [ 54 ] showed that an innovative species of Darwin’s finches Loxigilla barbadensis had higher glutamate receptor expression (correlated with synaptic plasticity) than a closely related, poorly innovative species Tiaris bicolor . Tolman [ 55 ] and Heron [ 56 ] also indicated underlying genetic effects on maze-learning ability in rats, although the ability to learn a maze may not necessarily imply an ability to solve a problem (see [ 57 ]). In contrast, Quinn et al. [ 58 ] and Bókony et al. [ 59 ] found little measurable heritability of innovative problem-solving performance in great tits in a food-baited puzzle box and an obstacle-removal task, respectively. These studies suggest that the genetic architecture underlying problem solving may provide a rich area for future research.

2.1.2. Indirect Genetic Effects

Indirect genetic factors, specifically epigenetic and transgenerational effects, influence how genes are read (e.g., DNA methylation, [ 60 ]) or expressed (e.g., hormones activating genes during sexual maturation, [ 61 ]) without altering the underlying DNA. These epigenetic changes are underpinned by biochemical mechanisms that affect how easily the DNA can be transcribed [ 62 ], subsequently influencing the development of different systems. For example, the activation of thyroid receptor genes (TRα and β) in the cerebellum of 0–19 day old chicks causes hormone-dependent neuron growth and development [ 63 ]. No studies to date have explored the effects of epigenetic factors on the development of problem solving, although this relationship can be postulated ( Figure 1 ), since epigenetic factors influence the development of behaviour (e.g., maternal care, [ 64 ]), and cognition (e.g., memory, [ 44 ]). Memory is an important component of problem solving [ 65 ]. Consequently, two possible routes could be inhibited via transcriptional silencing of the memory suppressor gene protein phosphatase 1 (PP1), and demethylation and transcriptional activation of the synaptic plasticity gene reelin, both of which enhance long-term potentiation. These could lead to increased memory formation (e.g., in male Sprague Dawley rats Rattus norvegicus domesticus , [ 44 ]).

Transgenerational epigenetic effects can also influence development. These effects result from parental or grandparental responses to prevailing environmental conditions, which influence how offspring and grand offspring ultimately respond to their own environment [ 66 ]. For example, embryonic exposure to the endocrine disruptor vinclozilin in female Sprague Dawley rats resulted in epigenetic reprogramming of hippocampal and amygdala genes for at least three generations, with the resulting F3 males showing decreased, and F3 females showing increased, anxiety-like behaviour, as adults [ 67 ]. An interesting avenue for research into transgenerational effects on the development of problem solving is the NMDA (N-methyl-D-aspartate) receptor/cAMP (cyclic adenosine monophosphate)/p38 MAP kinase (P38 mitogen-activated protein kinases) signalling cascade. Exposure of newly weaned Ras-GRF1 (growth regulating factor) knockout mice to an enriched environment enables this latent signalling pathway, rescuing defective long-term potentiation and learning ability [ 68 ]. These epigenetic effects may therefore influence problem-solving ability indirectly by affecting the individual’s learning ability, or possibly directly by affecting the development of particular brain regions.

2.1.3. Neuroendocrine Effects—Brain Morphology

Many developmental processes are driven by neuroendocrine factors that are, themselves, impacted by other developmental processes [ 63 ]. While the development of many of the brain’s circuits (e.g., those located near the sensory or motor periphery), are governed by innate mechanisms [ 69 ], other parts (e.g., the basolateral nucleus of the amygdala and the cerebellar cortex [ 70 ]; the CA1 region of the mammalian hippocampus [ 71 ]; the avian hippocampus [ 72 ]) are considerably more plastic and more responsive to external stimuli, maintaining a high degree of neural plasticity throughout life. As these brain regions can be important for the expression of particular behaviours (e.g., the cerebellum is necessary for tool use, [ 73 ]), this plasticity has particular relevance for problem solving. For example, North American bird species with relatively larger forebrains were more likely to innovate when foraging than bird species with smaller forebrains [ 74 ] and New Caledonian crows Corvus moneduloides , which are renowned for their tool use and problem-solving abilities, had relatively larger brains than other bird species [ 75 ]. Similarly, C57BL/6J laboratory mice that received lesions to the hippocampus and medial prefrontal cortex initially showed impairments in solving a puzzle box task, although the mice ultimately solved the task over time, indicating the importance of experience and learning with repeated presentation of the task [ 76 ].

2.1.4. Neuroendocrine Effects—Hormones

The brain is also the central control of endocrine responses that can influence an individual’s development ( Figure 1 ). For example, the hypothalamic-pituitary-gonadal (HPG) axis activates gonadotropin-releasing hormone (GnRH), which stimulates the pituitary to produce luteinizing hormone (LH) and follicle-stimulating hormone (FSH, [ 77 ]). These hormones regulate the production of steroid hormones (testosterone and oestrogen) via the gonads [ 78 ], stimulating sexual maturity [ 79 ]. Fluctuations in steroids also influence cognitive function [ 80 , 81 ]. For example, female rats injected neonatally with testosterone show heightened learning of a Lashley III maze (contains start box, maze, and goal box; used to test learning and memory) as adults compared to non-injected females, although the underlying impacts on neural development or neuroendocrine processes were not discussed [ 82 ].

Endocrine responses can also feedback to brain morphology ( Figure 1 ), affecting neural structure and function, which can impact behaviour, cognition, and development. The hypothalamic-pituitary-adrenocortical (HPA) axis regulates the secretion of adrenocorticotropic hormone (ACTH), which in turn regulates the secretion of glucocorticoid stress hormones (e.g., corticosterone, [ 83 ]) from the adrenal glands [ 84 ]. Short-term exposure to corticosterone can improve learning, since it allows important associations to be formed, such as between threat and a behavioural response [ 85 ]. However, prolonged increased corticosterone concentrations (chronic physiological stress) reduce hippocampal neuron survival [ 86 ], which interferes with learning [ 87 , 88 ], memory retrieval [ 89 ] and problem solving. For example, house sparrows Passer domesticus with prolonged elevated corticosterone concentrations were less efficient problem solvers of puzzle boxes than birds with lower corticosterone concentrations, as stress impairs working memory and cognitive capacity [ 45 ]. Prolonged physiological stress can also cause detrimental developmental changes in morphology (e.g., chickens [ 90 , 91 ]) and behaviour (e.g., rats [ 83 ]).

In contrast to stress hormones, the mesolimbic dopaminergic system [ 92 ], which consists of the substantia nigra and ventral tegmental region [ 93 ], regulates the production of dopamine, a hormone associated with motivation and reward-seeking [ 94 ]. Motivation is a physiological process [ 94 ] that increases persistence and thereby increases the likelihood of successfully solving a problem [ 95 ]. Persistence is important for problem solving in foraging tasks in house sparrows [ 96 ], common pheasants Phasianus spp . [ 97 ] and Indian mynas Acridotheres tristis [ 98 ], and in puzzle box tasks in spotted hyenas Crocuta crocuta and lions P. leo [ 99 ]. Changes to dopamine production can also negatively impact the development of sensorimotor integration [ 100 ], disrupting approach, seeking and investigatory behaviours [ 101 ] and acquisition of spatial discrimination [ 102 ]. Disruption to dopamine production, or other circuits, may also lead to an individual persisting with an inadequate strategy if the individual lacks inhibitory control [ 103 ] and cannot recognise when to terminate the behaviour [ 104 ]. Disruptions to these behaviours and cognitive functioning therefore impact foraging and exploratory behaviours [ 87 , 104 ], which can lead to undernutrition, and consequent negative impacts on growth and physical, behavioural, and cognitive development [ 105 ].

Other hormones have also been implicated in the expression of problem solving. For example, both norepinephrine and serotonin likely impact problem solving, since they are related to cognitive flexibility (e.g., rhesus macaques Macaca mulatta [ 106 , 107 ]), with serotonin activating, and norepinephrine deactivating, the prefrontal cortex [ 108 ]. However, although some studies have investigated the role of these hormones in problem solving, these relationships are not clearly defined. For example, dietary deficiency in n-3 fatty acids during development increased serotonin receptor density and reduced dopamine receptor binding in the frontal cortex of rats, and it also altered dopamine metabolism [ 109 , 110 ]. This dietary n-3 fatty acid deficiency also impaired problem solving in a delayed matching-to-place task in the Morris water maze [ 111 ]. However, whether problem-solving ability was impacted specifically by down-regulation of dopamine receptor binding, or up-regulation of serotonin receptor binding, is unclear.

2.2. Extrinsic Factors

2.2.1. physical environmental factors.

The physical environment varies in structural complexity and quality across both spatial and temporal scales [ 112 ]. Throughout its lifetime, an individual will experience daily and/or seasonal variation in environmental conditions (e.g., rainfall, temperature, food availability, [ 113 ]), and/or when it disperses [ 114 ], migrates [ 115 ] or travels into different areas. This variability changes the likelihood of an individual encountering positive (e.g., food [ 116 ]) or negative (e.g., predator [ 117 ]) stimuli, consequently influencing its development ( Figure 1 ). For example, a higher density and abundance of aquatic snails results in the development of larger pharyngeal jaw muscles and stronger bones in predatory pumpkinseed sunfish Lepomis gibbosus [ 111 ].

Some studies have investigated the interplay between physical environmental conditions and problem-solving ability. Favourable environmental conditions can reduce stress [ 118 ], promote active and exploratory behaviours [ 119 ] and enhance cognition [ 120 ], but harsh conditions may promote problem solving. For example, mountain chickadees Poecile gambeli living in harsher high elevation montane habitats with longer winters solved novel foraging problems significantly faster than chickadees living at lower elevations, most likely because finding food in these habitats was more challenging, and survival depends on plastic responses to these challenges [ 26 ]. However, this effect on food-motivated problem-solving ability was not seen in great tits experiencing similar harsh conditions [ 40 ], suggesting that species-dependent developmental factors may be constrained by environmental effects. Urban environments may also promote the development of problem solving since they are expected to contain a higher frequency of novel problems for animals to solve. For example, house sparrows [ 121 ] and house finches [ 32 ] in urban environments were more adept food-motivated problem solvers than birds from rural areas, particularly when the problem was difficult to solve [ 96 ].

2.2.2. Social Environmental Factors

The social environment also changes throughout an individual’s lifetime, and has the capacity to influence its development ( Figure 1 ). Any positive (e.g., offspring suckling from mothers) or negative interactions (e.g., siblings fighting over food) between individuals can be considered social, and can vary over time scales (e.g., from daily interactions between individuals in a group, to shorter interactions between parents and offspring or mating partners [ 122 ]).

For mammals, females are constrained to care for their offspring through pregnancy and suckling [ 123 ]. Consequently, the mother’s physiological state and access to resources can impact offspring embryonic development prenatally through direct transfer of maternal hormones or nutrients across the placenta [ 124 ]. For example, pregnant female Sprague Dawley rats exposed to unpredictable, variable stress (e.g., restraint, food restriction) during the final week of gestation produced anxious daughters and sons with impaired cognitive function (contextual memory [ 125 ]). Furthermore, maternal care during postnatal development [ 64 ], particularly the mother’s diet quality, can also influence development. For example, protein deficiency in African striped mouse Rhabdomys dilectus chakae mothers during early postnatal development of offspring resulted in these offspring showing increased anxiety, decreased novel object recognition and increased aggression as adults compared to mice raised by mothers that did not experience nutrient deficiency [ 126 ]. Thus, detrimental developmental effects such as these may go on to impede offspring problem solving abilities.

For some species, a key developmental milestone is dispersal. Interactions with other conspecifics during this phase are often driven by dramatic developmental changes often associated with reproduction [ 114 ]. For example, male vervet monkeys Chlorocebus pygerythrus leave their natal group at sexual maturity and attempt to attain dominance in another group [ 127 ], which could lead to increased access to food resources that can be channeled further into growth and development. This process of leaving the natal territory, and any social interactions during this time, can feedback to the individual to further affect its development. For example, in many species (e.g., brown rats), dispersing juveniles undergo a period of heightened exploration and learning, allowing them to rapidly adjust to new environmental conditions [ 128 ]. However, it is unknown how dispersal and other associated events impact an individual’s problem-solving abilities.

Problem solving is most often studied in social animals [ 122 ], possibly because they are more conspicuous than solitary species. In some species, such as European starlings Sturnus vulgaris with a foraging task [ 129 ], coyotes Canis latrans with a puzzle box task [ 130 ] and rhesus macaques in an associative learning task [ 131 ], dominant individuals are better learners and problem solvers. Similarly, the presence of an alpha individual impedes problem solving success in subordinate spotted hyenas presented with a puzzle box [ 132 ] and ravens in a string-pulling task [ 28 ] due to direct interference and increased aggression from the dominant. However, in other species, such as blue tits Cyanistes caeruleus [ 133 ], adult meerkats [ 27 ] and chimpanzees [ 134 ], subdominants tend to be better solvers of puzzle boxes, since their lower competitive ability makes them more reliant on alternative methods for accessing resources [ 26 ]. Group size may also influence problem solving, although results are equivocal. For example, larger groups of house sparrows [ 121 ] and Australian magpies Gymnorhina tibicen [ 135 ] in extractive foraging tasks and zebra fish Danio rerio in an avoidance task [ 136 ] were better problem solvers than individuals in small groups, possibly because larger groups contained more reliable demonstrators. However, orange-winged amazons Amazona amazonica had similar solving success in a string-pulling task when tested in groups or in isolation [ 137 ]. Social carnivore species, such as banded mongoose Mungos mungo , were also less successful problem solvers of a puzzle box compared to solitary species, such as black bears Ursus americanus and wolverines Gulo gulo , suggesting that relative brain size may be more important for cognitive abilities than social environment [ 33 ].

Problem solving studies in solitary species are generally lacking, making it difficult to assess how social interactions may impact the development of problem solving in these species. However, it is evident that individual animals can solve problems in the absence of conspecifics. For example, black-throated monitor lizards V. albigularis albigularis [ 138 ], eastern grey squirrels Sciurus carolinensis [ 139 ], and orangutans Pongo pygmaeus [ 140 ] can individually solve puzzle boxes using flexible behaviours (i.e., switching strategies when necessary), persistence and learning. Similarly, North Island robins [ 46 ] and brilliant-thighed poison frogs [ 8 ] can solve detour problem tasks when tested in their home territories. How solitary species solve problems in the presence of conspecifics, however, is an area for future investigation.

3. Interacting Factors that Influence the Development of Problem Solving

3.1. gene × environment interactions.

Genotype × environment interactions can also have a profound effect on the development of individuals ( Figure 1 ). For example, the gene monoamine oxidase A ( MAOA ) encodes for an enzyme that impacts serotonergic activity in the central nervous system, leading to increased impulsivity and anxiety [ 141 ]. Stressful life events, or changes in social structure or status can alter the expression of this gene, leading to developmental changes during adulthood. For example, rhesus macaques raised in the absence of their parents showed increased aggression due to low MAOA enzymatic activity [ 142 ].

Although genotype × physical environment interactions have not been explored in the context of problem solving, environmental enrichment in captive bi-transgenic CK-p25 Tg laboratory mice is associated with the activation of plasticity genes, inducing chromatin modification via histone acetylation and methylation of histones 3 and 4 in the hippocampus and cortex, leading to increased numbers of dendrites and synapses [ 143 ]. This cascade of genetic and neuroendocrine processes functions to help restore learning and memory [ 143 ], both of which are important for problem solving [ 65 , 95 ].

Parents may also alter the environment (e.g., amount of parental care or food) their offspring experience [ 66 ], which could be a consequence of genetic variation between mothers [ 144 ] or a result of other factors (e.g., variability in resource availability [ 145 ]). When an offspring’s development is impacted by this nongenetic parental environment, these effects are known as parental effects [ 146 ], which are specific types of indirect genetic effects (IGEs, [ 144 ]). For example, female Long-Evans hooded rats that provided high levels of tactile stimulation (e.g., grooming and nursing [ 64 ]) to their young produced daughters that also displayed higher levels of maternal care to their own offspring [ 147 ], indicating an IGE.

Maternal care also regulates the expression of the hippocampal glucocorticoid receptor gene by changing the acetylation of histones H3-K9 and the methylation of the NGFI-A consensus sequence on the exon 17 promoter [ 148 ]. Young rats that experienced low levels of maternal tactile stimulation showed reductions in hippocampal neuron survival [ 149 ] and decreased hippocampal glucocorticoid receptor mRNA expression [ 148 ], leading to chronic corticosterone release as adults [ 150 ]. Offspring also showed decreased exploratory behaviour [ 151 ] and impairments in spatial learning and memory [ 64 ] and object recognition [ 149 , 152 ] as adults. As for genotype × physical environment interactions, how the social environment × genotype interaction affects problem solving is a promising avenue for future research.

3.2. Neuroendocrine × Environment Interactions

Habitat complexity, resource availability and social complexity can influence development via effects on neuroendocrine systems, which can also result in changes to the social environment that may then feedback to further impact development. For example, nine-spined sticklebacks Pungitius pungitius preferentially shoal together in marine environments with high predation risk and patchy food resources, but prefer to swim alone when these constraints are relaxed in freshwater ponds [ 153 ]. Marine fish with more social interactions had significantly larger olfactory bulbs and optic tecta, parts of the brain associated with sensory perception, compared to solitary fish from freshwater ponds that experienced fewer social interactions [ 154 , 155 ]. Rhesus macaques from larger social groups also had more grey matter and greater neural activity in the mid-superior temporal sulcus and rostral prefrontal cortex than macaques from smaller groups [ 156 ]. Similarly, structurally complex, changing environments improve survival of hippocampal cells and neurons by increasing the level of nerve growth factor in the hippocampus [ 112 ], which increases hippocampal volume [ 83 ], leading to increased neural plasticity [ 157 ] and a greater capacity to adjust to new environmental conditions [ 158 ]. Environmental enrichment has also been shown to enhance long-term potentiation in the hippocampus, which facilitates learning and memory [ 159 ], two important processes for problem solving [ 23 , 95 ]. Environmental enrichment has been associated with increased problem-solving ability in C57/BL6J mice in an obstruction puzzle task [ 160 ] and Labrador retrievers in puzzle box tasks [ 161 ]. This suggests causal links between the environment, the neuroendocrine system, and problem solving which are likely mediated by underlying genotype × environment interactions.

3.3. Age Effects

Separating out the effects of aging and neuroendocrine or genetic effects on development is challenging. Nevertheless, age-specific effects on development, regardless of the underlying mechanisms, are an important consideration.

The nervous system shows age-dependent decreases in neurogenesis and plasticity, particularly in the dentate gyrus of the hippocampus [ 162 ], and the subventricular zone of the lateral ventricle [ 163 ], and these age-dependent changes can alter cognitive ability and behaviour (e.g., beagles [ 164 ]). Other neuroendocrine processes also naturally change with age. For example, as brown rats age, the ACTH response increases, glucocorticoid receptor binding capacity in the hippocampus and hypothalamus decreases, corticotropin releasing hormone (CRH) mRNA expression decreases in the paraventricular nucleus, and mineralocorticoid mRNA expression in the dentate gyrus of the hippocampus is reduced [ 165 ]. These changes result in an associated attenuation of the corticosterone response to novelty [ 164 ], as well as declines in spatial learning and memory [ 166 ].

Depending on the age of the individual, changes to both the physical and social environments also impact development [ 167 ]. When raised in small cages with limited space, juvenile rats showed increased anxiety, and lower activity and exploration, whereas older rats did not [ 167 ]. Similarly, older rats reared in larger groups were more active than juveniles, mostly likely due to increased frequency of social interactions and establishment of their rank within the social hierarchy [ 167 ].

Several studies have shown that juveniles are better problem solvers than adults, although the underlying mechanisms are currently not known. For example, juvenile Chimango caracaras Milvago chimango were more successful at solving a puzzle box task than adults [ 168 ], and juvenile canaries Serinus canaria solved a vertical-string pulling task, whereas adults did not [ 169 ]. Similarly, juvenile Chacma baboons Papio ursinus solved a hidden food task more often than adults [ 170 ], and juvenile kakas N. meridionalis showed higher innovation efficiency than adults across different tasks and contexts [ 171 ]. Juveniles are often prone to higher levels of exploration [ 159 ], and are more playful [ 172 ], than adult animals, allowing juveniles to rapidly gain motor skills [ 172 ]. This could possibly improve problem solving abilities of juveniles despite their lack of experience at solving tasks. However, results are species-specific, as Indian mynas [ 173 ] and spotted hyenas [ 174 ] show no age-specific effects on problem solving in foraging tasks, while adult meerkats [ 27 ] and black-capped chickadees [ 175 ] were better innovators than juveniles in extractive foraging tasks.

3.4. Learning and Experience

As an animal ages, it encounters predators and food resources, and interacts with conspecifics. These experiences provide a rich potential for learning, which is a critical component of problem solving. However, separating out the effects of the experience itself on development from other extrinsic and intrinsic factors, or their interactions, is challenging. Nevertheless, as in aging, an animal’s development can be impacted by its experiences, particularly via learning, suggesting that experience must be considered when attempting to understand how problem solving develops.

To survive, use new resources, or avoid predators, individuals must learn to associate the experience with its significance (e.g., threat of a predator [ 176 , 177 ]). Learning enables animals to acquire information about the state of their environment [ 178 ] and learning through experience allows for adjustments in physiological and behavioural responses [ 176 ]. For example, repeated foot shock in a specific environmental location caused increases in norepinephrine and epinephrine in Sprague Dawley rats, eliciting fear and resulting in rats avoiding that location [ 179 ]. Similarly, guppies decreased their time foraging in the presence of a predatory convict cichlid Cichlasoma nigrofasciatum [ 180 ]. Animals can learn to solve problems in different ways, such as through trial and error (e.g., rooks C. frugilegus across multiple foraging extraction tasks [ 181 ]) or socially through local enhancement (e.g., common marmosets Callithrix jacchus in a foraging extraction task [ 182 ]), social facilitation (e.g., capuchin monkeys Cebus apella in a foraging extraction task [ 183 ]) or copying/imitation (e.g., laboratory rats in an extractive foraging task [ 184 ]). Learning from previous experience is also an important component for successful problem solving. For example, grey squirrels improve their ability to solve a food-baited puzzle box with repeated exposures to the problem [ 23 ]. Similarly, North Island Robins became more efficient problem solvers of new food-extraction tasks with experience [ 46 ].

3.5. Behavioural Flexibility and Personality

Although development is governed by several unifying genetic and physiological mechanisms, and these processes are impacted by age and environmental effects [ 185 ], the development of one individual differs considerably from that of another individual. Some of this variation can be attributed to the behavioural flexibility of each individual [ 29 ] and/or its personality [ 168 ], which also undergo developmental changes over the course of an individual’s lifetime [ 36 ].

Behavioural flexibility is the ability to switch behavioural responses (likely due to cognitive flexibility [ 95 ]) to adjust to new situations or states [ 186 ], and is likely governed by both genetic and non-genetic mechanisms [ 187 ]. The degree of behavioural and cognitive flexibility, and corresponding learning ability, is important for problem solving, as seen in tropical anoles ( Anolis evermanni in an obstruction task [ 188 ]; A. sagrei in a detour task [ 189 ]), spotted hyenas in a puzzle box task [ 174 ], grey squirrels in a food-extraction task [ 139 ] and keas Nestor notabilis in a foraging extraction task [ 190 ]. However, the degree of flexibility varies between species. For example, Indian mynas are more flexible, and are better innovative foraging problem solvers, than noisy miners Manorina melanocephala across a range of tasks [ 173 ]. Importantly, individual differences in behavioural and cognitive flexibility, particularly learning ability, are often attributed to physiological effects occurring during development (e.g., corticosterone exposure in nestling Florida scrub jays Aphelocoma coerulescens [ 191 ]).

An individual’s development and experiences can also affect its personality [ 192 ], defined as consistent individual differences in behaviour shown across contexts and situations, and over time [ 193 ]. Personalities are often measured along different axes (e.g., bold/shy [ 194 ]; proactive/reactive [ 195 ]), and are mediated by hormones [ 196 ]. Although personality itself is influenced by intrinsic (e.g., hunger [ 197 ]) and extrinsic (e.g., environmental quality [ 119 ]) developmental factors, personality can further feedback on an individual’s development through its effects on exploration [ 167 ]. For example, avoidant individuals may be less willing to investigate their environment than exploratory individuals, which reduces their chances of being predated, but also reduces foraging rate, which affects growth, as seen in grey treefrog tadpoles Hyla versicolor [ 198 ].

Personality can also impact problem solving [ 40 ]. Exploratory individuals have higher interaction rates with problems, increasing their likelihood of solving innovative tasks. For example, brushtail possums Trichosurus vulpecula that were exploratory, active and vigilant were more likely to solve an escape-box task during the first trial, and were capable of solving a difficult task, compared to less exploratory, less active and less vigilant individuals [ 199 ]. Similarly, exploratory fawn-footed mosaic-tailed rats Melomys cervinipes were faster problem solvers, and solved more problems, than avoidant individuals when tested with food- and escape-motivated tasks [ 200 ]. Exploratory Carib grackles were also faster learners and more likely to innovate in a foraging-extraction task than avoidant individuals [ 201 ]. However, this relationship is not always clearly defined. For example, both bold and shy chacma baboons improved their solving of a food extraction problem after watching a demonstrator [ 170 ]. Similarly, bold meerkats that approached a puzzle box first were not always the first to solve it [ 27 ], and neophobia did not significantly influence problem-solving ability in Barbary macaques Macaca sylvanus presented with puzzle boxes [ 202 ]. Although relationships between personality, behavioural flexibility and problem solving are not clearly defined, such individual variation should be taken into consideration when investigating developmental effects on problem solving.

4. Forgotten Components Limiting Our Understanding of Problem Solving and Its Development

Problem solving has been considered to rely almost exclusively on complex cognitive processes involving insightful thinking (i.e., just knowing what to do, rather than arriving at it through trial and error learning [ 181 , 203 ]), understanding of functionality [ 204 ] or causal understanding (i.e., being able to understand rules and consequences of actions [ 27 ]). Consequently, complex problem solving is often considered to be a consequence of relative brain size (e.g., birds and primates [ 169 ]). However, there is little evidence that problem solving involves complicated cognitive processes [ 28 ]. For example, introduced black rats R. rattus in Australia have caused extensive damage to macadamia Macadamia sp. orchards [ 205 ]. As rodents are evolutionarily constrained to gnaw due to the unrooted nature of their incisors [ 206 ], gnawing is an effective strategy for accessing novel food resources behind barriers or hard seed coats. To solve the problem of accessing the new food, black rats required only persistence, motivation and the appropriate mechanical apparatus rather than complex cognitive abilities. While each animal’s brain consists of a set of information-processing circuits that have evolved by natural selection to solve particular problems in their environment and increase their reproductive fitness [ 207 ], without the appropriate mechanical apparatus, the animal cannot solve the problem [ 208 ]. The ability to solve particular problems may therefore be species-specific, and morphologically constrained, specifically involving mechanical problem solving, unless animals can overcome these mechanical shortcomings (e.g., by developing tool use [ 28 ]).

Although problem solving has been studied in a wide variety of taxa, studies of the development of problem solving specifically have largely been restricted to birds [ 43 ], laboratory rats and mice [ 73 , 82 , 209 ], dogs [ 44 ], and primates that have been housed in captivity [ 131 ]. This is largely due to difficulties associated with observing free-living individuals [ 210 ] and accounting for their previous experience [ 95 ]. Consequently, studies rarely follow problem solving abilities over the development of individuals, instead comparing problem-solving ability between different age cohorts [ 168 ]. Such studies have shed light on the effects of intrinsic factors on the development of problem solving, but fail to consider individual variation in development.

Furthermore, the majority of studies on problem solving concern social species. Both solitary and social species need to problem solve, but the social environment could possibly influence how individuals develop their problem solving abilities. For example, social individuals may use social learning to problem solve, whereas solitary individuals would require persistence and motivation to achieve trial-and-error learning, or would rely on innovation because they are most likely unable to rely on social demonstrators for assistance [ 122 , 170 ], at least after weaning. Current studies therefore provide a limited view of the relevance of social conditions on problem solving development.

Finally, while the influences of environmental quality on problem-solving ability are documented, they are not well understood [ 27 , 40 ]. Animals tend to innovate under harsh conditions in times of necessity [ 24 ], yet good environmental conditions benefit problem solving by promoting neuroendocrine development [ 120 ] and reducing stress [ 118 ]. The effects of the physical or social environment tend to be studied either through manipulation studies during early development, with subsequent tests occurring later on as adults in static environments [ 165 ] or via correlative studies, where individuals from different habitats are compared [ 26 ]. Similarly, studies have investigated the impact of social rank [ 132 ], social isolation [ 211 ], group size [ 121 , 136 ], and group composition [ 2 , 27 ] on problem solving, but the majority of these studies have not explored the underlying developmental processes. To our knowledge, only one longitudinal study has tracked an individual’s problem-solving ability in response to changing physical environments. Cole et al. [ 40 ] found that individual performances in free-living great tits were consistent across time (seasonal variation). How problem-solving ability changes in response to changing social environments, such as when a subordinate changes dominance rank, has rarely been studied.

5. An Individual-Centric Focus can be Beneficial

The ability to solve a problem relies on a combination of genetic and non-genetic factors [ 44 ], physiology [ 97 ], behavioural flexibility [ 95 ], general cognitive ability [ 27 ], personality [ 129 ] and mechanical ability [ 212 ]. In addition, age and experience further influence problem-solving ability. Aging results in natural neuroendocrine system changes [ 213 ], which further affect behaviour and cognition [ 163 ]. However, every individual develops along its own unique developmental trajectory within the phylogenetic constraints of the species, and the relative contribution of these intrinsic and extrinsic factors and their interactions are likely to vary considerably between individuals. Therefore, we cannot assume that individuals from the same environment [ 214 ], or even the same clutch/litter [ 215 ], will behave or respond to the environment in the same way. We only have to look at genetic clones (e.g., identical human twins displaying linguistic differences [ 216 ]) to realise the uniqueness of individual developmental trajectories. This considerable variation argues strongly for focusing on individuals, particularly as they develop, learn and experience new things over their lifetimes in the context of problem solving. Therefore, when investigating problem solving abilities in the future, it may be beneficial to consider individual variation as an important aspect of the data analyses, and not just rejected as statistical ‘white noise’ (see [ 40 , 46 ] for examples). Using this approach may enable future research to identify key predictors, or clusters of common predictors, of problem-solving ability.

6. Conclusions

Individuals experience developmental changes over the course of their lifetimes, which impact their problem-solving abilities. The external environment, including the physical and social environments, can affect the development of problem solving via its impact on underlying genetic, non-genetic and neuroendocrine mechanisms. Problem solving has a heritable component in some species, while complex neuroendocrine processes are also involved in the development of problem solving. However, untangling the influence of these different factors on the development of problem solving is challenging, given their interdependence and complexity. Our understanding of how problem solving develops would benefit from studies of solitary species, to allow for comparisons of general causal mechanisms, since solitary species cannot rely on social learning about problems, at least after weaning. Furthermore, because environments are not static, future studies should consider the effects of changing environmental conditions over the course of an individual’s lifetime on the development of problem solving. Importantly, investigating individual variation in problem-solving ability is necessary for a full understanding of the development of problem solving, which will allow us to assess the relative contributions of different developmental factors on this ability in different individuals.

Acknowledgments

We would like to thank Ben Hirsch and Brad Congdon for providing helpful comments on the manuscript.

Author Contributions

Conceptualization, M.K.R. and T.L.R.; Writing—Original Draft Preparation, M.K.R.; Writing—Review and Editing, T.L.R. and N.P.; Supervision, T.L.R.; Project Administration, M.K.R.; Funding Acquisition, M.K.R. All authors have read and agreed to the published version of the manuscript.

We would like to thank the Australian Government for providing a Research Training Program Scholarship to MKR, and James Cook University for funding this project.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Original research article, assistance and therapy dogs are better problem solvers than both trained and untrained family dogs.

• 1 Department of Ethology, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
• 2 Instituto de Ciencias Biológicas y Biomédicas del Sur (INBIOSUR), Departamento de Biología Bioquímica y Farmacia, Universidad Nacional del Sur (UNS)- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Argentina
• 3 Grupo de Investigación del Comportamiento en Cánidos (ICOC), Instituto de Investigaciones Médicas (IDIM), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad de Buenos Aires, Buenos Aires, Argentina
• 4 MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary

When faced with unsolvable or difficult situations dogs use different behavioral strategies. If they are motivated to obtain rewards, they either try to solve the problem on their own or tend to interact with a human partner. Based on the observation that in problem situations less successful and less perseverant dogs look more at the humans' face, some authors claim that the use of social strategies is detrimental to attempting an independent solution in dogs. Training may have an effect on dogs' problem-solving performance. We compared the behavior of (1) untrained, (2) trained for recreational purposes, and (3) working dogs: assistance and therapy dogs living in families ( N = 90). During the task, dogs had to manipulate an apparatus with food pellets hidden inside. We measured the behaviors oriented toward the apparatus and behaviors directed at the owner/experimenter, and ran a principal component analysis. All measures loaded in one factor representing the use of the social strategy over a more problem-oriented strategy. Untrained dogs obtained the highest social strategy scores, followed by dogs trained for recreational purposes, and assistance and therapy dogs had the lowest scores. We conclude that assistance and therapy dogs' specific training and working experience (i.e., to actively help people) favors their independent and more successful problem-solving performance. General training (mainly obedience and agility in this study) also increases problem-oriented behavior.

Introduction

Problem-solving behaviors involve a diverse set of cognitive processes, such as perception, learning, memory and decision making, among others ( 1 , 2 ).

Several studies have focused on dogs' problem-solving abilities using a wide variety of tasks (e.g., puzzle boxes in Frank and Frank ( 3 ) and Marshall-Pescini et al. ( 4 ); unsolvable task in Miklósi et al. ( 5 ); string pulling in Osthaus et al. ( 6 ); interactive dog toy in Shimabukuro et al. ( 7 ). Different kinds of tasks require different skills, thus allowing the thorough study of the diverse strategies that dogs use to solve problems [see e.g., Polgár et al. ( 8 )]. While some studies focus on the manipulation of the physical environment, others analyse social strategies, including communicative interactions. With regard to the latter, dogs' gazing behavior has received the most attention. One frequently used protocol to assess dogs' communicative intents toward people is the so-called unsolvable task. In this situation, dogs try to obtain a reward from an apparatus that cannot be opened. When faced with this problem, most dogs tend to gaze at their owners, which can be interpreted as a referential request for assistance by the human partner [e.g., Miklósi et al. ( 5 ), for a review see Cavalli et al. ( 9 )].

Dogs' selection for socio-cognitive abilities during the domestication process might have had a detrimental effect on their physical cognition ( 3 ). This hypothesis has been supported by several comparative studies in which dogs privileged the use of social strategies such as gazing to the human face, while wolves spent more time manipulating an apparatus and were thus more successful in solving the problem ( 10 , 11 ). However, other authors have highlighted that this discrepancy in the performance of the two species may not be (only) due to differences in their ability to solve physical problems, but other factors, such as motivation and persistence ( 12 – 14 ), and vague definitions ( 15 ). Persistence is a reliable predictor of problem-solving ability, and might be linked to trial and error learning strategies ( 16 ). In this regard, persistence has been operationally defined as the time spent interacting with an apparatus ( 17 ). Accordingly, those individuals that persist longer in their problem-solving attempts are more likely to solve a problem than those that give up earlier [e.g., ( 16 )].

Several other factors appear to influence dogs' problem-solving abilities, including their relationship with humans ( 18 ), their living conditions ( 19 , 20 ), and their breed. For example, compared to Siberian huskies, border collies looked more at the owner in an unsolvable problem situation ( 21 ), and herding dogs tended to look more at the person than working and mastiff like dog breeds when confronted with a puzzle box ( 22 ). However, herding dogs did not interact more with the apparatus than other breed groups in this study, and when taking into consideration both breed and training experience, training had a major influence on dogs' orientation to the apparatus ( 22 ).

In line with this, many studies have focused on the role of training experience. This is of particular relevance, considering the importance of training in working dogs' performance and the increased number of tasks in which dogs participate nowadays. For instance, Marshall-Pescini et al. ( 4 ) tested the performance of untrained family dogs and highly trained family dogs that participated in different activities (i.e., agility, schutzhund, retrieving, search and rescue, freestyle performances). All dogs were exposed to a commercial feeding box which could be opened by pressing a paw pad or nosing the lid. While untrained dogs spent significantly more time looking at either the experimenter or their owner; trained dogs interacted significantly longer with the apparatus and were more successful in opening it. Marshall-Pescini et al. ( 22 ) observed similar results using the same apparatus, as dogs with training experience (i.e., agility, police, search and rescue, and man-trailing) were more successful in the task and looked less to people than untrained dogs. It is important to note that in both of the aforementioned studies trained dogs' groups were heterogeneous given that the subjects differed in the types of training they received and their everyday experiences. While some dogs were trained working dogs, others were trained for recreational or sporting purposes such as agility. Thus, to disentangle the relative effects of training for recreational purposes and for specific work, we aimed to compare the performance of dogs trained for assistance and therapy work with family dogs which had been trained for recreational purposes (see subjects' details). Assistance and therapy dogs differed from trained family dogs in the purpose of their training, their everyday tasks and in the methods of training.

Range et al. ( 23 ) carried out a similar experiment, using a wooden box with a handle which could be opened by pushing it down with the mouth or a paw. In line with previous results, trained dogs (i.e., agility and search and rescue) spent more time interacting with the apparatus and were able to open it significantly more often than untrained ones ( 23 ). On the contrary, Brubaker and Udell ( 24 ) found no significant differences between search and rescue dogs and untrained family dogs in gazing or persistence in a similar task. However, significantly more search and rescue dogs opened the container when they received encouragement ( 24 ). The divergence between these studies may be related to differences in the training the dogs from each sample had received [i.e., agility and rescue dogs in Range et al. ( 23 ); only rescue dogs in Brubaker and Udell ( 24 )]. Furthermore, the encouragement in Brubaker and Udell ( 24 ) may have also influenced the results and this difference in the protocols hinders a straightforward comparison. All in all, results regarding the effects of training on dogs' problem-solving skills and strategies are contradicting. This could be due to differences in the protocols and tasks used, samples, the dogs' breed, and the training received as discussed above.

Professional working dogs represent a special group of dogs which, unlike family dogs, are specifically trained to regularly perform a specific activity such as detection of substances, search and rescue or helping disabled people, among others ( 25 ). Importantly, working dogs face a variety of cognitive challenges during their training and working activities which may influence their behavior and performance during cognitive tests. Even more, as different working roles require different sets of skills, it would be expected that working dogs vary in their performance during such tasks according to the specific activities they carry out ( 26 ). In line with this, it must be taken into account that there are variations in the goals of training, the methods employed for it and the frequency in which those abilities need to be performed, which add to the expected variability among working dogs as a whole. Thus, it is important to assess dogs with different training and working experiences to further understand how these aspects influence dogs' problem-solving skills.

In this study we focused on two types of working dogs: assistance and therapy dogs. Assistance dogs are individually trained to perform tasks for the benefit of their owner with a disability affecting everyday life situations ( 27 ). Therapy dogs participate with their owners in planned, goal-oriented therapeutic interventions directed by providers of health and human service ( 28 ). Both types of working dogs need to be sensitive to their owners' wishes, but at the same time they have to be independent in order to solve problems on their own and flexibly adjust to new scenarios.

Gácsi et al. ( 29 ) studied the interactions between assistance dogs and their owners during a carrying task. They observed joint attention during different parts of the task as well as the use of both verbal and non-verbal communication to guide the dogs' actions. In the case of a task that was impossible to perform, they observed that assistance dogs did not give up easily and were very persistent before they showed communicative signals directed at the owner ( 29 ). The results suggest that assistance dogs are not only persistent, but also able to switch between different strategies, such as communicating with the owner, if they failed in independent problem-solving.

Thus, in this study we aimed to compare the problem-solving performance of dogs with different levels of training and working experience. To this end, we tested three groups of dogs in a problem-solving task; untrained family dogs, family dogs trained for specific tasks (e.g., obedience, agility, herding), and working assistance and therapy dogs. For the sake of simplicity, we will refer to dogs working in assistance and therapy as “working dogs.” We expected working dogs to perform better at independent problem-solving and thus to obtain more food rewards than family pet dogs. Also, we expected untrained family dogs to depend more on their owners and prefer the use of a social strategy such as gazing toward people. In the case of trained family dogs, training experience may increase their independent problem-solving abilities [e.g., ( 4 )]. If this is the case, they should behave similarly to the working dog group. Alternatively, the trainings these dogs had (mainly obedience and agility) may have not prepared them for independent problem-solving, thus their performance may be indistinguishable from that of untrained family dogs.

Materials and Methods

Ethical statement.

The procedures comply with national and EU legislation and institutional guidelines and in accordance with the recommendations in the International Society for Applied Ethology guidelines ( www.applied-ethology.org ). In Hungary, the behavioral observations conducted in this study were not identified as animal experiments by the Hungarian Animal Protection Act (“1998. évi XXVIII. Törvény,” 3. §9.), which identifies animal experiments, as this study was non-invasive. The application number of the ethical commission by the Pest County Government Office is PE/EA/2019-5/2017. Each owner filled in a consent form stating that they have been informed of the tests. Our Consent Form was based on the Ethical Codex of Hungarian Psychologists (2004).

We tested a total of 90 dogs between 1 and 12 years of age, of different breeds and mixed-breeds (see below). Owners volunteered to participate in the test and were recruited through the Family Dog Project database of Eötvös Loránd University, Budapest, Hungary. All dogs had been living with their owners for at least 6 months before the test. Dogs were assigned to three groups according to their work and training experience. Size, sex, and breed were balanced across groups:

1. Untrained family dogs had no certification exams. N = 30, 14 males, 16 females, mean age = 4.05, SD ± 2.74, breeds: 1 beagle, 7 border collies, 3 German shepherd dogs, 4 golden retrievers, 3 Labrador retrievers, 1 Maltese, 10 mixed, 1 English cocker spaniel.

2. Trained family dogs are dogs trained for recreational purposes. They had 1–4 certification exams (27 obedience, 23 agility, 11 herding, 5 guarding, 9 other: rescue dog, frisbee, dog dancing, K99). N = 30, 15 males, 15 females, mean age = 4.66, SD ± 2.67, Breeds: 8 Border Collies, 1 Bouvier, 1 Dobermann, 2 Golden Retrievers, 1 groenendael, 1 kelpie, 1 Labrador retriever, 2 malinois, 8 mixed, 1 mudi, 1 sheltie, 2 Hungarian vizslas, 1 Yorkshire terrier.

3. Working dogs worked as certified assistance or therapy dogs. assistance dogs were trained to aid individuals with disabilities by the dogs for human charity ( http://kea-net.hu/ ). Therapy dogs were all certified trained dogs, and lived with their owners at their homes. N = 30, 15 males, 15 females, mean age = 4.47, SD ± 3.32, 1 Airdale terrier, 3 border collies, 1 Cavalier King Charles spaniel, 4 golden retrievers, 1 groenendael, 1 Irish setter, 2 Labrador retrievers, 1 Malinois, 8 mixed, 2 standard poodles, 1 English cocker spaniel, 4 Tervuerens, 1 Hungarian vizsla.

Experimental Setup

All dogs had at least 1 h of fasting time before the testing. Dogs were tested in a room unfamiliar to them at the Eötvös University, Department of Ethology. Four cameras in each corner of the room videotaped all testing sessions. The room was 3 × 6 m 2 and there was a drawer where the problem box was stored before the start of the test and a chair for the owner to sit on ( Figure 1 ).

Figure 1 . The experimental setup. Written informed consents were obtained from the individuals for the publication of this image.

As a problem box we used a commercial wooden dog toy (Nina Ottosson® Dog Brick) that comprised a rectangular base with eight holes where treats could be hidden. A sliding wooden brick covered eight holes on both longer sides of the toy, so dogs had to slide the covers toward the middle with their paws or nose in order to get the treats. The bricks could not be lifted. Eight pellets of dry food in the eight holes on both longer sides were used as treats. None of the dogs were familiar with the apparatus prior to the task.

At the beginning of the test, the owner sat on a chair holding the dog on leash. The experimenter (female, 22 years old), who was the same for all dogs, took the interactive dog toy out from the drawers, placed it on the ground, and put a pellet of dry food inside each hole. Thus, dogs were able to see the baiting. When she was ready, the experimenter stepped back, the owner released the dog and the testing began. The dog had 2 min to obtain the food pellets from the apparatus. During this period, the owner was allowed to encourage the dog to find the pellets, verbally and by pointing at the apparatus, but we forbade the use of any previously trained or known commands relevant to the task such as “catch” or “nose.” The owner could not touch the apparatus nor the dog ( Figure 1 ). After the 2 min had elapsed, the experimenter put the toy back in the drawer. Dogs were allowed to eat only the food pellets they had recovered.

Behavioral Variables

We measured the duration of the vocalizations using a 0–3 score. We also measured the proportion of time dogs spent wagging their tail and the proportion of time oriented to the apparatus (including manipulating it, as gazing at the apparatus was often immediately followed by manipulation, therefore it would have been difficult to separate the two behaviors). We counted the number of times the dog gazed at the owner/experimenter, because gazing was generally a short event (just a glance) and provided more information than duration. We also counted the number of food pellets eaten after the behavior tests, on the spot. Other behavioral measures were coded from the videos using Solomon Coder (András Péter). See Table 1 for details and descriptive statistics.

Table 1 . Descriptive statistics of the measured raw variables and factor loadings of the standardized variables.

Statistical Analysis

We analyzed the inter-rater reliability of the variables using two-way random intraclass correlation, looking for absolute agreement between average measures. The inter-rater reliabilities were satisfactory (ICC > 0.741, N = 10).

After standardizing the variables, we ran principal component analysis and calculated factor scores. Cronbach alpha (CA) was used for checking the internal consistency of the factor. For investigating differences in the behavioral factor score (“social strategy” score, see below), as a function of group, sex (as fixed factors), and age (as covariate) we applied General Linear Model with Student–Newman–Keuls (SNK) post-hoc test, including all main effects and two way interactions. We used backward elimination to obtain the minimum adequate model. SPSS v25 ( 30 ) was used for the analyses.

Descriptive statistics of the variables and factor loadings are presented in Table 1 . Standardized variables loaded on a single factor. The total explained variance of the factor was 51.5%, CA = 0.8. The factor included looking at the owner, looking at the experimenter, tail wagging, and vocalization with positive loadings while orientation toward the apparatus and number of food pellets eaten had negative loadings. We labeled this factor as “social strategy,” because high score indicated that the dog uses communicative signals toward the human partners, including gazing, vocalization, tail wagging.

Only group affected the social strategy score [ F (2, 85) = 16.477, p < 0.001, partial eta squared = 0.275, Figure 2 ), age or sex had no effect and there were no interactions (all p > 0.05). According to the SNK post-hoc tests, all groups differed from each other (alpha = 0.05). Untrained dogs obtained the highest social strategy scores, trained dogs had lower scores, followed by working dogs.

Figure 2 . Social strategy factor scores of the three dog groups.

We set out to investigate the problem-solving abilities and related behaviors of dogs with different levels of training and working experience (trained and untrained family dogs as well as working assistance and therapy dogs) in a problem-solving task. Working assistance and therapy dogs displayed a less social and more problem-oriented strategy with a higher success rate than both untrained and trained family dogs. The frequent use of social strategies (i.e., gazing) is correlated with less persistence on the task (i.e., independent manipulation of the apparatus) and consequently with lower success ( 17 ). The results are also consistent with prior literature stating that animals persisting more on their problem-solving attempts are more successful in actually solving the task ( 16 ).

As it was mentioned in the introduction, the literature is mixed regarding the effects of training on dogs' persistence and gazing behavior during problem-solving tasks. For instance, Marshall-Pescini et al. ( 4 , 22 ) found differences in trained dogs' gazing and persistence patterns, but other authors did not find these differences ( 18 , 24 , 31 ). Results regarding working dogs' abilities should be taken with caution, as dogs from different studies vary in the type and amount of training they have received. For example, dogs in Marshall-Pescini et al. ( 22 ) were trained for different purposes (agility, police, search and rescue, and man-trailing), while Brubaker and Udell ( 24 ) tested search and rescue dogs, D'Aniello et al. ( 31 ) focused on water rescue dogs, and in Topál et al. ( 18 ) dogs were trained for basic obedience. A possible explanation is that specific training and working experience confounded the results. We have tried to independently assess ( 1 ) the effect of training for recreational purposes as dogs in our trained family group were trained for different hobby activities, mainly obedience and agility, and ( 2 ) the effect of specific training, as working dogs were trained as assistance and therapy dogs. Therefore, the type and methods of training could be an important aspect to take into account in future studies. Most probably the broad category “trained vs. untrained” is not precise enough to unravel the effect of training on problem-solving behaviors. Furthermore, working dogs may vary in their independence levels according to the context in which they work. For instance, water rescue dogs did not differ from pet dogs in their interaction with the apparatus during an unsolvable task, but they directed their first gaze significantly more often toward the owner and spent more time gazing at people than untrained pet dogs ( 31 ). Water rescue dogs are rewarded for looking at the handler during their training, and during their service they have to remain inactive for a long time in the vicinity of their owners in order not to cause any disturbance, and they take initiatives only upon command. These specific requirements probably affect their performance during problem-solving tasks.

A direct antecedent in the literature is the study of Mongillo et al. ( 32 ) who measured dogs' attention toward the owner in untrained family dogs, agility trained dogs, and assisted intervention animals. They assessed the number of gazes and the amount of time dogs spent watching their owner in a baseline condition where the owner walked alone in a room, and in a selective attention test where the owner's movements were mirrored by an experimenter. During the baseline phase, agility dogs shifted their gaze frequently toward the owner and were also the ones who spent the lesser amount of time looking at their owners, while assistance dogs gazed longer. In addition, assistance dogs gazed longer at their owners during the selective attention test. These results support the idea that different training and everyday activities may modify dogs' attentional patterns. Contrary to our results, Mongillo et al. ( 32 ) found that dogs participating in animal assisted interventions were the most attentive to their owners. This apparent contradiction could be due to the differences in the task. Unlike Mongillo et al. ( 32 ), we presented dogs with a problem-solving situation, in which dogs had to manipulate an apparatus to access a reward. In this latter scenario we observed that working dogs (which include dogs participating in animal assisted interventions) displayed less social strategies than the other group of dogs. Assistant and therapy dogs have to be attentive to their owners' needs but once they understand them or receive a specific command, they should be independent to succeed in their tasks. This interpretation is also supported by the fact that agility dogs in Mongillo et al. ( 32 ) shifted their gaze toward the owner more frequently than family dogs which is an important feature in the agility sport, but they do not need to solve novel problems independently during it. In our study trained family dogs (which include agility dogs) differed in the use of social strategies from untrained pet dogs. Thus, training for specific purposes may yield different patterns of social behavior depending on the context, emphasizing the plasticity and adaptability of dogs' behavior.

Importantly, according to the SNK post-hoc tests, trained family dogs had lower social strategy scores than untrained family dogs. Possibly, trained dogs were more used to facing novel situations and they could have generalized their training experience to this situation as well. It is possible that during training sessions dogs have to persevere and try different behaviors before getting the reward and that the contextual cues of the testing scenario trigger some of those responses. Indirect evidence supporting this idea comes from studies indicating that dogs are able to generalize and learn to follow novel and complex communicative signals faster when they have previously received a brief training phase with a simpler communicative cue ( 33 , 34 ).

Nevertheless, training for recreational purposes did not seem to be enough for dogs to reach the effectiveness of working assistance and therapy dogs, as the latter were more successful problem solvers and had lower scores in the use of the social strategies component. This result suggests that dogs' everyday experience is an uttermost important aspect to take into account when assessing their skills in a problem-solving situation. There are at least two possible, non-exclusive, explanations for this difference. First, it is possible that working assistance and therapy dogs were more comfortable in the presence of strangers and in novel situations given that they usually accompany their owners to a variety of places. Second, it is possible that dogs that have successfully accomplished the training as assistance or therapy dogs had pre-existing characteristics that distinguished them from other dogs. For instance, it has been shown that personality traits such as boldness are related with the successful training of working dogs ( 35 ). We propose that these two explanations are complementary, because it is possible that those dogs that became working dogs were encouraged during their everyday activities to behave in a more independent manner. Owners were allowed to encourage their dogs during the task, verbally or pointing to the apparatus, but without using commands or touch. Interestingly, Udell ( 11 ) reported that dogs, who were encouraged, spent more time in contact and looking at the puzzle box, but they were not significantly more successful in solving the task. Similarly, in Brubaker and Udell ( 24 ) encouraged family dogs interacted more with the apparatus but their performance was not significantly better. Conversely, encouragement did improve the performance of dogs trained for search and rescue ( 24 ). Given that in the present study we did not systematically manipulate the quantity and quality of the encouragement, we cannot derive unambiguous conclusions regarding this aspect. Udell's ( 11 ) results suggest that the use of encouragement and verbal instructions modulates problem-solving behavior, but their particular effects could depend on the context as well as working and training experience ( 11 ). In this regard, it is also possible that dogs react differently to verbal commands. Working dogs are trained to respond to a command by performing a specific action. For example, if the owner points to a particular object and asks the dog to do something with it, trained working dogs will manipulate the object instead of looking at the owner, while untrained pet dogs may be uncertain about what to do and will gaze at the owner in search for further clues [similarly to young dogs in Miklósi et al. ( 5 )]. Furthermore, not only the type of commands given by the owner affects dogs' performance, but also the bond between them. Topál et al. ( 18 ) compared the performance of dogs categorized according to their relationship with the owners. “Companion dogs” were defined as dogs living indoors as a member of the family and “working dogs” were kept outside the house as a guard or for some other purpose. In a simple manipulative task dogs had to manipulate an apparatus in order to get the reward while the owner could encourage them to retrieve the food. Companion dogs gazed more at the owner, started to manipulate the apparatus later and also retrieved less food than working dogs. The authors also found that obedience training did not affect dogs' performance or gazing patterns to their owners. These results are in line with our findings about the similar gazing patterns between trained and untrained family dogs.

One limitation of the study is that the dogs' characteristics before training were unknown. As it occurs in many studies assessing the effect of training on dogs' cognitive skills, the lack of a baseline measurement before training makes it impossible to guarantee that dogs were not selected for such work based on their pre-existing characteristics such as an increased persistence. Another limitation of these kind of studies is that training methods may differ between specific trainers and yield different results on dogs' problem-solving strategy. Thus, in future research, specific types and methods of previous training should also be taken into account when assessing dogs' problem-solving skills.

Summing up, we have shown that working assistance and therapy dogs were more independent problem solvers compared to both trained and untrained family dogs, who privileged a more social strategy. Thus, although assistance and therapy dogs need to show highly developed social understanding in their interactions with the owner, their special training and work may have increased their persistence and independent problem-solving skills. However, obtaining training certificates (mainly obedience and agility in this study) also increased the independent problem-solving tendency in our task, suggesting that trained family dogs generalize their training experience of facing novel situations and perseverance for obtaining rewards.

Data Availability Statement

All datasets generated for this study are included in the article/ Supplementary Material .

Ethics Statement

The animal study was reviewed and approved by Pest County Government Office, PE/EA/2019-5/2017. Written informed consent was obtained from the owners for the participation of their animals in this study.

Author Contributions

EK, ÁM, and MG designed the experiments and collected the data. EK analyzed the data. FC, CC, and EK wrote the first draft. All authors finalized the manuscript. ÁM and EK provided funding.

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 680040), the ERASMUS+ mobility program, the Hungarian Academy of Sciences (Grant F01/031), the National Research, Development and Innovation Office (Grant No. 132372K), and the National Brain Research Program (2017-1.2.1-NKP-2017-00002).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors are grateful for Barbara Gáspár, Borbála Turcsán, and Vera Konok in collecting behavioral data and Ivaylo B. Iotchev for language editing.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2020.00164/full#supplementary-material

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Keywords: human-animal interaction, canine-cognition, persistence, gazing, unsolvable task, working dogs

Citation: Carballo F, Cavalli CM, Gácsi M, Miklósi Á and Kubinyi E (2020) Assistance and Therapy Dogs Are Better Problem Solvers Than Both Trained and Untrained Family Dogs. Front. Vet. Sci. 7:164. doi: 10.3389/fvets.2020.00164

Received: 03 January 2020; Accepted: 06 March 2020; Published: 31 March 2020.

Reviewed by:

Copyright © 2020 Carballo, Cavalli, Gácsi, Miklósi and Kubinyi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Enikő Kubinyi, eniko.kubinyi@ttk.elte.hu

† These authors have contributed equally to this work

This article is part of the Research Topic

Humans in an Animal’s World – How Non-Human Animals Perceive and Interact with Humans

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5.1 Animal problem-solving: using tools

From the earliest, most primitive stick or piece of rock, to the most sophisticated supercomputer or jet aircraft of modern times, humans have been using tools to solve problems since prehistoric times.

Given the advantages of using tools, it is perhaps surprising that it's not more common for animals to use them. There are examples of tool use by other species: some otters use stones to break open shellfish; some monkeys do the same to break open nuts; and some chimpanzees ‘fish’ for termites with sticks (Emery and Clayton, 2009). But it appears to be a general pattern that all humans use tools and most other species do not. Is this because animal minds do not have the capability to use tools? Tool use does, after all, involve a number of aspects of executive function, including: working out what a tool can be used for; planning how to use it; and remembering what the tool has managed to do (and failed to do) before.

While other species may not have the same degree of neocortical development and executive function as humans, are they able to use tools to solve problems to some extent?

There is evidence that the nearest evolutionary neighbours of humans, the other great apes (gorillas, chimpanzees, bonobos and orangutans), are able to solve problems using tools. A typical laboratory experiment involves putting food into an apparatus where the animal cannot reach it using their bodies alone, e.g. if testing chimpanzees, the apparatus will prevent the chimpanzees from reaching the food with their fingers. Tools, such as sticks of varying lengths or shapes, are left near the apparatus that will, if used correctly, allow the animal to access the food. Visalberghi and colleagues (1995) showed that a variety of primate species could solve such problems, but great apes were better than other primates (monkeys) at selecting the best tools, and adapting tools to the needs of the task.

But possibly the best non-human tool users are, perhaps surprisingly, to be found in species without a neocortex: birds. Emery and Clayton (2009) and Seed and Byrne (2010) give examples of a number of bird species with impressive tool-using and problem-solving abilities, including crows, jays and finches. One of the star species, though, is the New Zealand kea (Figure 8).

This is a photograph of a kea − a type of parrot from New Zealand that has impressive problem-solving abilities. The kea has green and blue plumage and is perched on a window frame.

Keas have been shown to solve a fairly simple problem (where food is obtained by hauling up a string) on the first attempt − suggesting they had mentally worked out the solution before starting the task, rather than by trial and error (Werdenich and Huber, 2006). They have also been shown to solve ‘second-order’ tool-use tasks, where one tool must be used to acquire or adapt another, in order to then complete the task (Auersperg et al., 2010), and there is evidence that they can learn from observing other keas performing a problem-solving task (Huber et al., 2001). As well as being able to solve problems as individuals, keas have been shown to collaborate to solve problems too (Tebbich et al., 1996).

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Animal Cognition

Deep inside a hive, a honey bee ( Apis mellifera ) dances wildly. Other bees cluster around, touching her body with their forelegs and antennae as she dances. Then one by one, the observers leave the dance, head to the hive entrance, and take flight in the same direction. The dancing bee has just communicated the direction and distance to an abundant food source (Figure 1). Within the hour, the foragers are returning, ready to relay the whereabouts of the resource to other foragers through dances of their own.

Honey bees provide an intriguing example of how animals exhibit extraordinary abilities to navigate their worlds. Some of these behaviors remind us of our own abilities, whereas others extend beyond human cognition. Either way, these amazing behaviors beg a number of questions. Do honey bees possess a mental map of their environment? What about other animals? What kind of cognitive capacities do they need to process information they receive from their environment?

Studying Animal Cognition

Studying the animal mind poses unique challenges because we project ourselves onto animals, as seen in the example of Clever Hans. Hans was a German horse who became a global phenomenon in the early 1900s because of his ability to answer mathematical questions (Figure 2). His ability to add, subtract, multiply and divide (by stomping the correct number of times) was examined by a number of professionals and determined to be real.

Although many people were thoroughly convinced of Hans's mathematical prowess, some remained skeptical. Oskar Pfungst made careful observations of Hans's behavior and discovered that although Hans could correctly answer questions from a variety of people, he could do so only if the questioner were visible. Pfungst discovered that when people asked Hans a question, they slightly moved their heads when the correct answer was presented. Hans was indeed clever: he was attuned to the subtle subconscious body language of the people around him (Pfungst 1911).

Clever Hans taught comparative psychologists an important lesson. Avoiding pitfalls such as this requires carefully controlling experimental studies and following Lloyd Morgan's canon: accept the simplest explanation for a behavior in favor of a more complex cognitive process.

Cognitive Capacities

The physical world poses a number of problems for animals to solve. On a daily basis, animals must find food, avoid predators, and seek shelter. Solving these problems requires cognitive capacities. Cognition involves processing information, from sensing the environment to making decisions based on available information. Such cognitive capacities include, among others, the ability to navigate through space, account for the passage of time, determine quantity, and remember events and locations. Here, we explore a sample of the cognitive capacities animals use in their daily lives.

Where am I?

Most animal species move about in their habitat, which requires navigating between locations. Navigation occurs over different spatial scales, from centimetres to thousands of kilometres, and different mechanisms are used at different scales. At small scales, in which animals navigate around their home territory, they can use dead reckoning, landmarks, and cognitive maps to navigate.

Dead reckoning involves estimating the distance and direction one has traveled. For instance, desert ants ( Cataglyphis spp.) track how far away and in what direction they have traveled from home in order to return home after searching for food (Wehner 2003; Figure 3a).

Other species use landmarks to guide their movement. Animals can learn the relationships among landmarks such as rocks, trees, or other large objects to triangulate their position. Landmarks are often the primary cues that animals use to locate their nests. For example, after digger wasps ( Philanthus triangulum ) leave their nests they circle around the entrance to orient themselves to local landmarks. When the landmarks are moved several centimetres away, the returning wasps land where the nest entrance should be relative to the landmarks and have difficulty finding their nests (Tinbergen 1951; Figure 3b).

Finally, some animals may use a cognitive map to navigate. A cognitive map involves a mental map-like representation of the environment. Though controversial and difficult to demonstrate, honey bees show some evidence of using cognitive maps; when they are physically displaced to a new foraging location, they return home via a direct route. That is, they take a shortcut, suggesting that they possess a cognitive map of their territory (Menzel et al . 2005).

Many migrating species navigate over long distances. Arctic terns ( Sterna paradisaea ) travel nearly 80,000 km a year between feeding and mating areas (Egevang et al . 2010). How do Arctic terns and other migrating species navigate such enormous distances? Many species use something similar to global positioning systems that are based on a sun compass or the earth's magnetic field. A sun compass is the ability to use the sun's position in the sky to determine direction, accounting for both daily and seasonal changes in the sun's position (Alerstam et al. 2001). Honey bees appear to use a sun compass when navigating to their foraging sites (Figure 1). Birds, reptiles, amphibians, and molluscs have also been shown to orient themselves based on the earth's magnetic field (Lohmann & Lohmann 1993; Figure 4). The precise mechanisms enabling such navigation are still under investigation.

Telling Time

Time influences an animal's environment over periods ranging from milliseconds to decades. Annual cycles, in particular, are important for migration, hibernation, caching , mating, and raising young. Though temperature may influence the timing of these activities, photoperiod provides a more accurate cue and plays a large role in initiating or stopping seasonal behaviors. Photoperiod is so important in regulating behavior such as caching that researchers artificially manipulate the photoperiod for animals in captivity to induce this behavior (Pravosudov et al. 2010).

The day-night cycle also plays a key role in animal behavior. Some species are active during daylight, others at night, and still others only at dawn or dusk. Activity corresponds to diurnal variation in the availability of food sources, temperature requirements, and the presence or absence of major predators. Even without the cues of light and dark (e.g., in an all-light or all-dark environment), animals maintain a circadian rhythm approximately 24 h long (Roberts 1965), which suggests the existence of an internal circadian clock used to regulate daily activities.

Conditions also change over finer time scales, requiring another internal clock that works over seconds and minutes. Timing over the short term is particularly important for foraging. To forage efficiently, animals must be able to estimate time periods. This is particularly true for species that consume resources that refresh over time. For example, long-tailed hermit hummingbirds ( Phaethornis superciliosus ) forage on nectar, and birds must wait for flowers to refill before they return for another meal. Returning too soon would be a waste of time and energy for the hummingbird; waiting too long might mean losing the nectar to a competitor. So the birds learn to return within a few minutes of the time required for the flower to refill (Gill 1988). Experiments on timing in rats show that they can estimate short time intervals fairly precisely, but as the interval increases, their accuracy decreases (Gibbon 1977).

More or Less?

Many aspects of an animal's environment vary visibly in size and quantity. Peahens ( Pavo spp.), for instance, use the number of eye spots on a male's tail as a cue when selecting a mate (Petrie & Halliday 1994). To use this cue, females must have some way to assess the quantity of eye spots. The ability to discriminate numbers is also important for group-living, territorial animals. For example, black howler monkeys ( Alouatta pigra ), which are highly territorial, can assess relative group size based on the number of males howling in a rival troop. This ability allows the monkeys to avoid potentially injurious encounters with larger troops (Kitchen 2006).

How accurate are animals at discriminating quantities? One rather accurate method would be to count the number of items. Hauser and colleagues (2000) tested free-ranging rhesus macaques ( Macaca mulatta ) to determine whether animals can precisely distinguish between small numbers. They placed apple slices into each of two boxes in full view of a monkey (Figure 5). They then allowed the monkey to choose a box from which to feed. When the number of slices put into a box was four or less, the monkeys accurately chose the box that contained more slices, but when the number of slices exceeded four for both options, they chose randomly.

When the number of items is larger than three or four, the ability to distinguish precisely between amounts becomes more difficult. Yet in many situations, determining which of two options is "more" is important to an individual's fitness , so animals must use another mechanism to assess quantity. For example, many fish species group together in shoals ; a larger shoal should provide greater benefits by decreasing predation risk. Agrillo and colleagues (2007) tested mosquitofish ( Gambusia holbrooki ) and found that the fish could distinguish between shoals that varied by a 1:2 ratio (1:2, 2:4, 4:8, and 8:16), but they were unable to discriminate a ratio of 2:3 (Figure 6). Many species show this effect of reduced precision as the ratio increases, and, like timing, animals are less accurate at quantifying as the magnitude increases (Brannon & Roitman 2003).

Where did you put your keys? What is the capital of Germany? With whom did you speak at your last social gathering? How do you ride a bicycle? What was the first question asked here? Each of these questions represents a different type of memory, from short-term to long-term and including memory about places, facts, and experiences. Storing and retrieving information we have encountered previously can be useful when making predictions about the future. For animals as well, it is often beneficial to remember past information, and some animals seem to have enhanced memory for tasks that they face repeatedly in their natural environments.

Several species of birds cache seeds for the winter. For this to be an effective strategy, they must be able to remember the location of their caches months later, when they need the food. For example, captive black-capped chickadees ( Parus atricapillus ) are capable of recovering caches up to 28 d after caching (Hitchcock and Sherry 1990). Caching may have strong influences on at least two types of memory. First, caching species may have superior spatial memory . Clark's nutcrackers ( Nucifraga columbiana ) are corvids that cache up to 30,000 seeds each year (Vander Wall & Balda 1977). These seeds are an important winter food source for nutcrackers. Compared with non-caching corvid species, nutcrackers excel at remembering the locations of food when these species are tested in spatial memory tasks (Balda & Kamil 2006; Figure 7).

Caching may also impart an advantage to episodic memory. Episodic memory is the memory we use to recall experiences: the who, what, when, and where that we recall from specific episodes in our past. Though difficult to test in animals, there is evidence that some species have "episodic-like memory." Scrub jays ( Aphelecoma californica ), a relative of nutcrackers, also cache food (Figure 8). In experiments, they were allowed to cache nuts (a stable food source) and waxworms (a decaying food source). After both a 4-h and a 5-d delay, the jays were allowed to recover whichever food they wanted. Because they prefer waxworms, the jays retrieved more waxworms than nuts after the 4-h delay. After 5 d, however, the waxworms had decayed, so the jays retrieved more nuts than waxworms. The jays remembered what they cached, where, and when (either 4 h or 5 d ago) — these are the hallmarks of episodic-like memory (Clayton & Dickinson 1998).

caching : The act of storing food in a cache for later recovery

circadian : Daily; circadian rhythms refer to behaviors that occur at approximately the same time in a 24-h cycle

Corvid : A member of the family Corvidae, a group of birds that includes crows, jays, ravens, magpies, jackdaws and rooks

diurnal : Active primarily in the daytime

fitness : An individual's ability to pass on its genes; typically measured by the individual's lifetime reproductive success, fitness reflects both survival and reproduction

peahen : Female peafowl; counterpart of a peacock

photoperiod : Time period in a 24-h cycle during which organisms are exposed to light

shoal : A group of fish

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Innovative problem solving in nonhuman animals: the effects of group size revisited

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Andrea S. Griffin, David Guez, Innovative problem solving in nonhuman animals: the effects of group size revisited, Behavioral Ecology , Volume 26, Issue 3, May-June 2015, Pages 722–734, https://doi.org/10.1093/beheco/aru238

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Sociality is associated with a variety of costs and benefits, one of which can be to increase the likelihood of individuals solving novel problems. Several hypotheses explaining why groups show higher innovative problem-solving efficiencies than individuals alone have been proposed including the sharing of antipredator vigilance and the pool-of-competence effect, whereby larger groups containing a more diverse range of individuals are more likely to contain individuals with the skills necessary to solve the particular problem at hand. Interference between group members may cause groups to have lower problem solving abilities, however. Using a simulation approach, we model the shape of the relationship between group-level problem-solving probability and group size across a range of facilitation and inhibition scenarios, various population distributions of problem solving, and a task requiring 1 action or 2 actions to be solved. Simulations showed that both sharing of antipredator vigilance and the addition of competent individuals to an existing group lead to positive relationships between group-level problem solving and group size that reach 100% solving probability, whereas interference effects generate group-solving probabilities that rise to a maximum and decrease again, generating a group size for which problem solving is maximized. In contrast, both inhibition and facilitation scenarios generate identical patterns of individual efficiencies. Our results have important implications for our ability to understand the mechanisms that underpin group-size effects on problem solving in nonhumans.

Social systems range from simple aggregations of multiple individuals to complex societies where individuals recognize and negotiate a myriad of transient and lifelong social relationships ( Lott 1991 ; Krause and Ruxton 2002 ; Zuluaga 2013 ). Quantifying the costs and benefits of each of these increasingly more complex social arrangements is central to our understanding of the factors that drive the evolution of sociality ( Giraldeau and Caraco 2000 ; Silk 2007 ). Although the benefits of group living are most typically associated with reduced predation risk ( Elgar 1989 ; Roberts 1996 ; Janson 1998 ; Beauchamp 2010 ), sociality may also benefit individuals by allowing them access to the knowledge and skills of others ( Götmark et al. 1986 ; Galef and Giraldeau 2001 ; Griffin 2004 ). For example, animals that live in groups may use opportunistically the information produced by other, more knowledgeable individuals to detect and/or learn about novel resources ( Galef 1992 ; Reader and Laland 2000 ; Allen and Clarke 2005 ; Aplin et al. 2013 ). More coordinated social interactions may allow individuals to access larger or faster-moving prey, which are difficult to capture alone ( Creel 1995 ; Boesch 2002 ; Hayward and Kerley 2005 ; Lührs et al. 2012 ).

Accessing the skills and knowledge of others may also be beneficial when animals are faced with a novel problem ( Melis et al. 2006 ; Couzin 2009 ; Krause et al. 2010 ; Péron et al. 2011 ; Bräuer et al. 2013 ). For example, sociality may improve problem solving if individuals differ in their personalities, skills, and/or past experiences, such that they vary in their ability to solve particular problems ( Krause et al. 2011 ). Groups containing a more diverse range of individuals should be more likely to contain a problem solver with the knowledge and skills necessary to solve the problem at hand ( Hong and Page 2004 ; Burns and Dyer 2008 ). Once a knowledgeable individual has solved, its behavior becomes available to other individuals to copy. Group-size effects on problem-solving efficiency have long been known in humans ( Hastie 1986 ; Laughlin et al. 2006 ). For example, groups of 3 individuals outperform an equivalent number of single individuals attempting to solve a problem alone or in pairs ( Laughlin et al. 2006 ). Moreover, groups containing individuals with divergent skills have been found to outperform groups of high-performing individuals, suggesting that divergent humans interact synergistically to develop solutions more readily ( Hong and Page 2004 ; Laughlin et al. 2006 ).

Although research investigating the relationship between problem-solving efficiency and group size has yielded mixed results ( Liker and Bókony 2009 ; Overington et al. 2009 ; Morand-Ferron and Quinn 2011 ; Griffin et al. 2013 ), 2 studies in recent years have revealed that nonhuman animals may also show an increase in problem-solving efficiency with increased group size ( Liker and Bókony 2009 ; Morand-Ferron and Quinn 2011 ). In nonhuman animals, solving of novel problems, referred to as innovative problem solving, is most often operationalized by measuring an individual’s success in solving, or latency to solve, an extractive foraging task consisting of some kind of container that must be opened to access food ( Cole et al. 2011 ; Benson-Amram and Holekamp 2012 ; Thornton and Samson 2012 ; Griffin et al. 2013 , 2014 ; for a review, see Griffin and Guez 2014 ). Liker and Bókony (2009) measured the ability of wild-caught, captive-held house sparrows ( Passer domesticus ) to access 3.5-cm diameter wells containing food. Each well was covered with a plastic lid that needed to be removed to access the food. The proportion of individual birds that were successful in accessing a well was significantly larger in groups of 6 birds than in groups of 2. In addition, the per capita number of wells opened was significantly higher in the larger groups. In the second study, Morand-Ferron and Quinn (2011) measured the ability of free-ranging mixed species flocks of Passerines containing blue tits ( Cyanistes caeruleus ) and great tits ( Parus major ) to solve arrays of 6 lever-pulling devices. Two levers needed to be pulled sequentially, in any order, for the food to be released. Results showed that the proportion of devices solved by the group increased with increasing group size. Both studies concluded that increased group diversity offered the most likely explanation for the positive relationship between problem-solving efficiency and group size ( Liker and Bókony 2009 ; Morand-Ferron and Quinn 2011 ). Larger groups contained a more diverse range of individuals making them “more likely to contain individuals with specific skills, individual tendencies, or past experience, making them competent at solving the current problem,” a group-size effect on problem solving coined the “pool-of-competence” effect ( Morand-Ferron and Quinn 2011 ).

However, a prerequisite to establishing a pool-of-competence effect is to eliminate other potential alternative explanations for why problem-solving efficiency may increase in larger groups. First, if the presence of other individuals allows for antipredator vigilance to be shared, as has been found in the context of foraging behavior ( Elgar 1989 ; Beauchamp and Livoreil 1997; Beauchamp 1998; Lima and Bednekoff 1999; Beauchamp and Ruxton 2003; Bednekoff and Lima 2004 ), then problem-solving efficiency should increase in the presence of other individuals. Second, if the presence of other individuals socially facilitates approach and exploration, either via reduced neophobia or increased scramble competition, then individuals should also have higher solving probabilities in the presence of others (Coleman and Mellgren 1994; Visalberghi et al. 1998; Visalberghi and Addessi 2000).

Liker and Bókony (2009) ruled out that increased per capita problem-solving success in house sparrows was attributable to shared vigilance by showing that individual scan rates did not differ significantly among small and large groups. The authors also ruled out a mediating role of neophobia, exploration, and scramble competition by showing that neither individual latency to approach the problem-solving task nor the per capita attempt rates differed between 2-bird and 6-bird groups. Consequently, the possibility that larger groups were more likely to contain a problem solver and that the presence of a problem solver increased the solving rates of other members of the group remains a possible but untested explanation for the positive relationship between problem-solving efficiency and group size in this study ( Liker and Bókony 2009 ).

Morand-Ferron and Quinn (2011) proposed an alterative approach to disentangling a pool-of-competence effect from other facilitation effects. The crux of their argument was that antipredator benefits should diminish as group size becomes larger, whereas the pool-of-competence effect should lead to a linear increase of problem-solving efficiency with group size. This argument hinges on the finding that the antipredator benefits of group size in birds have been found to level off at intermediate group sizes ( Fernández-Juricic et al. 2007 ; Cresswell and Quinn 2011 ). Consequently, if antipredator benefits underpin the positive relationship between problem-solving efficiency and group size, then the relationship should also level off. In contrast, under the assumption that larger pools of individuals become increasingly more likely to contain problem solvers, a pool-of-competence effect should lead to problem-solving efficiency increasing linearly with group size.

Increases in group size may not always be associated with benefits. If problem solving is vulnerable to interference competition, from kleptoparasitism for example, then the frequency of problem solving should decrease in the presence of other individuals when compared with solitary conditions as has been found for food processing behaviors ( Overington et al. 2009 ). If problem solving is vulnerable to a “negotiation over risk,” then problem solving may also be reduced in larger groups ( Stöwe et al. 2006 ; Griffin et al. 2013 ).

Although there has been a substantial effort to model the effects of group size on foraging behavior in nonhuman animals ( Shaw et al. 1995 ; Bednekoff and Lima 1998a , 1998b ; Giraldeau and Caraco 2000 ; Bednekoff and Lima 2004 ), to our knowledge, there have been no previous attempts to model the effects of group size on innovative problem solving. Here, we use a theoretical modeling approach to simulate how the likelihood of a group solving a hypothetical 1-action extractive foraging task varies with group size under different facilitation and inhibitory scenarios. We simulated the effects of group size attributable to adding additional competent individuals to an existing group by assuming that the individual problem-solving probability remained stable as group size increased. Group-size benefits attributable to shared vigilance were modeled by increasing individual problem-solving probabilities each time an additional individual was added to the group, whereas inhibitory effects of group size on group problem solving were modeled by decreasing individual problem-solving probabilities each time an individual was added to the group. Our aim was to evaluate to what extent the relationship between problem-solving efficiency and group size varied in shape across a range of facilitation and inhibition scenarios, allowing for different processes to be identified. Current analyses of problem-solving ability have revealed both within-individual stability in problem-solving propensity and contextual variability ( Laland and Reader 1999 ; Reader 2003 ; Cole et al. 2011 ; Morand-Ferron et al. 2011 ; Griffin et al. 2013 ). Consequently, we modeled the case in which problem solving is assumed to be a stable individual trait, and populations contain both problem solvers and nonproblem solvers. We also modeled the case in which all individuals within a population have a low but equal probability of solving. Finally, we modeled the case in which individuals express specialized problem-solving abilities, and the solving skill of one individual is complementary to the solving skill of another individual.

We considered theoretical populations of animals with varying distributions of problem-solving propensity. We then simulated drawing random samples of individuals without repetition from these populations and calculated a solving probability for each sample. We varied the group size of each sample from 1 to 100 individuals, and for each group size, we calculated an average solving probability by averaging the solving probabilities of 50 independent sampling events.

Consistent with most recent studies on problem solving in nonhuman animals, which operationalize problem solving by measuring their success in opening a container to access food ( Benson-Amram and Holekamp 2012 ; Cole and Quinn 2012 ; Sol et al. 2012 ; Thornton and Samson 2012 ; Bókony et al. 2014 ), we varied the individual probability of solving a hypothetical extractive foraging task. We elected to model separately the effects of solving a 1-action task or a 2-action task. In a 1-action task, animals only need to perform 1 motor action to solve the task, whereas in the 2-action task, animals need to perform 2 motor actions. The 2-action task allowed us to model a scenario in which individuals specialized in 1 kind of motor action interact with another individual specialized a different motor action to solve a given problem. Two-action tasks have recently been proposed to provide a useful assay for measuring innovative problem-solving abilities in nonhumans ( Auersperg et al. 2012 ), so we considered it important for future research to model group-size effects on this type of task.

Theoretical populations

We considered 3 theoretical populations of 1000 individuals each. Each population had a distinct distribution of problem-solving propensity. The first theoretical population had a binomial distribution of problem-solving propensity. The solving probability of problem solvers was fixed at 0.1, whereas that of nonproblem solvers was fixed at 0.01. The frequency of problem solvers within the population was fixed at 10%. The second theoretical population had a bell-like distribution of problem-solving propensity. The population was generated by assuming a beta continuous probability distribution between 0 and 1. The parameters used to generate the population were α = 5 and β = 5. The final population is represented in Figure 1 , panel A. In our third theoretical population, we created a skewed distribution of problem-solving propensity. Once again, the population was generated by assuming a beta continuous probability distribution between 0 and 1, but this time, we assumed that problem solvers were much more rare within the population than nonproblem solvers. The parameters used to generate the population were α = 1 and β = 5. The final population is represented in Figure 1 , panel B. To explore to what extent our results were sensitive to variation in these particular population parameters, we created populations with other parameter sets and repeated our simulations (see below). The distributions of problem-solving propensity obtained using alternative parameter sets are presented in Supplementary Figures S1 and S5 .

Histogram of the distribution of problem-solving propensity within a range of different populations for a 1-action task (A and B) and a 2-action task (C and D). (A) Bell-like probability distribution of problem-solving ability for a 1-action task (beta distribution with α = 5 and β = 5). (B) Skewed distribution of problem-solving ability for a 1-action task (beta distribution with α = 1 and β = 5). (C) Bell-like probability distribution of problem-solving ability for a 2-action task (beta distribution with α = 30 and β = 30). (D) Skewed distribution of problem-solving ability for a 2-action task (beta distribution with α = 0.5 and β = 5).

Simulations

In a first series of simulations, we considered that our random sample of individuals was presented with a 1-action task that required only 1 motor action to be solved. This simulation was done under 3 different theoretical conditions. The first and simplest assumed that group size did not affect individual solving probability. In the second, we assumed that individual problem-solving probability increased with group size uniformly by a small amount. Under natural conditions, this effect would arise if individuals in groups shared antipredator vigilance and could allocate more attention to solving the task at hand. The third theoretical condition assumed that individual problem-solving abilities decreased uniformly by a small amount with group size. An example of a natural correlate of decreasing individual problem-solving probability would be that associated with an increased risk of interference competition (e.g., kleptoparasitism; intragroup aggression) with increasing group size. Increased density of conspecifics is known to be associated with reduced expression of behaviors prone to kleptoparasitism (i.e., food dunking; Morand-Ferron et al. 2004 ). Hence, we considered it reasonable to assume that individual solving probabilities may decrease with increasing group size because individuals would similarly withhold from problem solving.

In a second series of simulations, we considered that our random sample of individuals was presented with a 2-action task that could only be solved using 2 independent motor actions in any order ( Auersperg et al. 2012 ). In these simulations, we considered that each individual within our theoretical population had a different probability of performing each action. Because 2-action tasks involve 2 different actions, and not a repeat of the same action, and that empirical work has demonstrated that solving rates can vary across different kinds of tasks (e.g., Bókony et al. 2014 ), we considered it more realistic to assume that the probabilities of solving would be different for different actions. The probability distribution of the first action conformed to that described for the 1-action task described above. The probability distribution of the second motor action among the theoretical population with a bell-like distribution of problem-solving propensity is depicted in Figure 1 , panel C (beta distribution parameter α = 30 and β = 30), whereas the probability distribution of the second motor action within the theoretical population with skewed distribution of problem-solving propensity is depicted in Figure 1 , panel D (beta distribution parameter α = 0.5 and β = 5). In the case of the theoretical population with a binomial distribution of problem-solving propensity, the frequency of problem solvers within the population was still 10%, but problem solvers had a solving probability of 0.05, whereas nonproblem solvers had a solving probability of 0.005. Under these conditions, a given individual could have a high or low probability of solving via the first motor action, whereas having a high or low probability of solving via the second motor action. In other words, there was no link between the probability of using one action and the probability of using the other action. In the case of the 1-action task, the probability of the group solving was calculated by summing individual solving probabilities because the task could be solved by one group member or another. In the case of a 2-action task, the probability of the group solving was calculated as the product of the sum of the individual probabilities of solving each task. This is because solving the task required that both actions be performed in any order; however, either individual could perform either action. So, if Pa1 and Pa2 are the probabilities of solving using action A of individuals 1 and 2, respectively, and Pb1 and Pb2 are the probabilities of solving using action B for individuals 1 and 2, respectively, then the probability of solving the 2-action task can be calculated as P = (Pa1 + Pa2) × (Pb1 + Pb2).

In addition to the 3 theoretical conditions described above (stable, increasing, and decreasing individual problem-solving probabilities), we modeled group-level problem-solving probability under scenarios where the benefits (or costs) gained by (or imposed on) each additional individual changed exponentially (rather than being a uniform increase or a uniform decrease). First, we assumed that the problem-solving benefit decreased exponentially with each additional individual. Under natural conditions, this would arise if individuals in groups share vigilance, but these individual-level benefits level off beyond certain group sizes ( Elgar 1989 ; Beauchamp and Livoreil 1997; Beauchamp 1998; Lima and Bednekoff 1999; Beauchamp and Ruxton 2003; Bednekoff and Lima 2004 ). Second, we assumed that the problem-solving cost increased exponentially with each individual added to the group, as would arise if the probability of interference competition, such as kleptoparasitism, intragroup aggression, or vigilance toward competitors, increased with group size. Increasing penalties could arise because the probability of a thief being present becomes higher or because the number of individuals available to steal from becomes higher. For the sake of completeness, we also modeled an exponentially increasing benefit and an exponentially decreasing cost, even though we do not think that these conditions have any biological equivalent. As these simulations did not change our general conclusions, we provide the outcomes of these simulations in the supplementary materials (for exponentially decreasing benefits and costs, see Supplementary Figures S9–S11 ; for exponentially increasing benefits and costs, see Supplementary Figures S12–S14 ).

Finally, past empirical work has quantified group-size effects on problem solving either by measuring performance of increasingly large groups (e.g., proportion of devices solved by the group; Morand-Ferron and Quinn 2011 ) or by calculating a per capita solving performance by dividing each group’s performance by the number of group members (e.g., Liker and Bókony 2009 ; Griffin et al. 2013 ). Per capita solving measures (e.g., number of tasks solved per individual or number of tasks solved per individual per unit time) allow for the solving performances of groups of different sizes to be compared and are therefore taken to provide a measure of group efficiency ( Morand-Ferron and Quinn 2011 ). Given that they are calculated at the individual level, however, we refer to them here as “individual efficiencies.” In order to ensure that the outcomes of our models could be compared with empirical data sets, we modeled the effects of increasing group size not only on group-level solving probability but also on individual efficiency for a subset of our theoretical populations and simulation scenarios. Per capita solving performances were calculated for simulations involving binomial, bell-like, and skewed populations and uniformly increasing and decreasing individual solving probabilities, exponentially decreasing costs and benefits, and exponentially increasing costs and benefits for both a 1-action task and a 2-action task. Consistent with the literature where group efficiencies are calculated by dividing a group-level performance measure by the number of individuals in the group, per capita solving performances were calculated by dividing group-level solving probabilities by the number of individuals within the group at each stepwise increase in group size.

All simulations were run using Scilab 5.4.0 software for numerical computation available at www.scilab.org .

One-action task

In this series of simulations, we evaluated the solving probability of an extractive foraging task assuming 3 possible theoretical populations differing in the distribution of their problem-solving propensities (binomial; bell-like, Figure 1A ; skewed, Figure 1B ). We simulated the effect of group size on the probability of solving assuming 1) no variation of individual solving probabilities as a function of group size, 2) a uniformly increased probability of individual solving propensity with group size, and 3) a uniformly decreased probability of individual solving propensity with group size.

Assuming no change in individual solving probability as a function of group size, the probability of solving a 1-action task increased steadily with group size and reached 1 regardless of the distribution of problem-solving propensity within the population ( Figure 2 , left column). This pattern of results did not change when we assumed that the probability of individual problem solving increased with increasing group size, as would be the case if individuals shared antipredator vigilance. Specifically, in a scenario where individuals became slightly more likely to solve each time group size increased, the probability of solving a 1-action task increased steadily with group size and reached 1 regardless of the distribution of problem-solving propensity within the population ( Figure 2 , ✭ symbols). The larger the individual gain associated by increased group size (0.01, 0.005, or 0.0005 for the binomial distribution; 0.01, 0.005, or 0.015 for the bell-like or skewed distributions), the faster the positive relationship between group solving probability and group size increased and reached 1 ( Figure 2 ).

Average solving probability of a hypothetical 1-action problem-solving task as a function of group size in scenarios where individual solving probabilities remained constant (▼ symbols in left column), increased (✭; e.g., via shared antipredator vigilance), or decreased (○ symbols; e.g., via interference competition) with increasing group size. Solving probabilities were increased or decreased by a factor of 0.01, 0.005, and 0.0005 (binomial) or 0.01, 0.005, and 0.015 (bell-like and skewed). For each group size, averages were calculated across 50 independent samples drawn with no repetition from populations with a binomial Figure 1 panel (A), bell “like” Figure 1 panel (B), or skewed Figure 1 panel (C) distributions of problem-solving propensities.

Calculating individual efficiencies at each stepwise increase in group size assuming that individual problem-solving propensity remained stable (i.e., pool-of-competence effect) or increased (e.g., shared vigilance) with increased group size revealed that per capita solving performance consistently increased to a maximum and decreased again as group size increased, and this regardless of the distribution of problem solving within the original population ( Figure 3 , different rows), but also regardless of whether individual solving probability remained stable ( Figure 3 , left column) or increased steadily with group size by any given amount ( Figure 3 , ✭ symbols, different columns).

Individual solving efficiency of a hypothetical 1-action problem-solving task as a function of group size in scenarios where individual solving probabilities remained constant (▼ symbols in left column, e.g., via a pool-of-competence effect), increased (✭ symbols; e.g., via shared antipredator vigilance), or decreased (○ symbols; e.g., via interference competition) with increasing group size. Solving probabilities were increased or decreased by a factor of 0.01, 0.005, and 0.0005 (binomial) or 0.01, 0.005, and 0.015 (bell-like and skewed). For each group size, averages were calculated across 50 independent samples drawn with no repetition from populations with a binomial Figure 1 panel (A), bell-like Figure 1 panel (B), or skewed Figure 1 panel (C) distributions of problem-solving propensities in.

The shape of the relationship between group-solving probability and group size completely changed when we assumed that individual problem-solving propensity decreased with increased group size, as would be the case if the probability of interference competition increased with increasing group size. Under this assumption, group-solving probability increased and then decreased again either side of a group size whereby group-solving probability was maximized ( Figure 2 , ○ symbols). Considered with regard to problem solving, this “optimal” group size varied from 3 to around 40 individuals, depending on the distribution of individual problem-solving probabilities within the population and the amplitude of the reduction in individual problem-solving probability with increasing group size ( Figure 2 , ○ symbols).

In a case where individual problem-solving probability followed a bell-like distribution, group-solving probability reached 100% rapidly with increased group size ( Figure 2 , middle row, ○ symbols) before decreasing sharply when group size increased further. In contrast, in cases where the distribution of individual problem-solving propensity followed a binomial or skewed distribution within the population, maximum group-solving probability was clearly maximized for an optimal group size, but never reached 100%. Assuming a binomial distribution of individual solving probability within the population, and a 0.01 penalty for each additional individual in the group, the group size for which problem solving was maximized was 3–4 and the maximum solving probability reached approximately 5% ( Figure 2 , first row, second column). With a smaller penalty of 0.005 for each additional individual in the group, optimal group size increased to 4 and maximum solving probability to approximately 6% ( Figure 2 , first row, third column). Finally, with an even smaller penalty of 0.0005 for each additional individual in the group, optimal group size increased to between 35 and 40 and maximum solving probability to approximately 40% ( Figure 2 , first row, fourth column).

Assuming that the distribution of problem-solving propensity was skewed within the population, and a 0.005 penalty for each additional individual within the group, the optimal group size was around 18 individuals, and the maximum solving probability fell just short of 100% ( Figure 2 , third row, third column). With a larger penalty of 0.01 per individual added to the group ( Figure 2 , first row, second column), optimal group size decreased to around 12, and the maximum solving probability decreased to approximately 85%. With an even larger penalty of 0.015 ( Figure 2 , third row, fourth column), optimal group size decreased even further to approximately 9, and the maximum solving probability decreased to around 75%.

In sum, within the range of group sizes explored here, the simulations with decreasing individual problem-solving probabilities with increasing group sizes showed that both optimum group size and maximum solving probability changed when the penalty on individual problem solving changed, whether the distribution of problem-solving propensity was binomial ( Figure 2 , first row) or skewed ( Figure 2 , third row) within the population. In contrast, when the distribution of problem-solving propensity within the population followed a bell-like distribution ( Figure 2 , second row), only the optimal group size decreased with increasing penalties for adding additional individuals to the group.

Calculating individual efficiencies at each stepwise increase in group size in scenarios where we assumed that individual problem-solving propensity decreased with increased group size (i.e., interference competition) revealed that per capita solving performance consistently increased to a maximum and decreased again, and this regardless of the distribution of problem solving within the original population ( Figure 3 , different rows) and regardless of the amplitude of the individual penalty associated with increased group size ( Figure 3 , ○ symbols, different columns).

The patterns of group solving probability and individual efficiencies described above remained unchanged when we used alternative parameter sets to describe the distribution of problem solving within the initial populations (see Supplementary Figures S2 and S3 and S6 and S7 ). Our conclusions also remained unchanged when we repeated the simulations assuming that the benefit (or cost) associated with each additional individual decreased (or increased) exponentially as group size increased, as would be the case if individual antipredator vigilance benefits leveled off or if the probability of interference competition (e.g., kleptoparasitism; intraspecific aggression) became gradually higher, with increasing group sizes. These simulations are presented in Supplementary Figures S9–S14 .

Two-action task

The simulations considering that individuals were presented with a 2-action task revealed patterns of problem-solving probabilities that were strikingly similar to those revealed when considering that individuals were presented with a 1-action task. Regardless of the distribution of problem-solving propensity within the population, the probability of group-solving increased with increasing group size, and reached one, whether we assumed that individual problem-solving probability remained constant as additional individuals were added to the group ( Figure 4 , first column) or that individual problem-solving probability increased as additional individuals were added to the group ( Figure 4 , ✭ blue symbols). The only effect of increasing individual problem-solving probabilities as group size increased rather than maintaining them stable was to make the already positive relationship steeper. This effect was most visible when the distribution of problem-solving propensity within the population followed a binomial distribution ( Figure 4 , first row).

Average solving probability of a hypothetical 2-action problem-solving task as a function of group size in scenarios where individual solving probabilities remained constant (black ○ symbols in left column; e.g., via a pool-of-competence effect), increased (✭ blue symbols; e.g., via shared antipredator vigilance), or decreased (○ magenta symbols; e.g., via interference competition) with increasing group size. Solving probabilities were increased or decreased by a factor of 0.01, 0.005, and 0.0005 (binomial) or 0.01, 0.005, and 0.015 (bell-like and skewed). For each group size, averages were calculated as indicated in the legend of Figure 3 .

As for the 1-action task, when we assumed that individual problem-solving propensity decreased with additional individuals added to the group, the positive relationship between group problem solving and group size changed from a positive relationship to one with an optimal group size, for which group problem solving was maximized ( Figure 4 , ○ magenta symbols). The effects of decreasing individual solving probabilities with increasing group size were particularly dramatic if individual problem-solving propensity within the population for a 2-action task followed a binomial distribution. In this case, group-solving probability only rose substantially above 0 if the penalty of adding additional individuals was extremely low. For example, assuming a penalty of 0.0005, the maximal group solving probability plateaued at 0.025, with an optimal group size of around 23 individuals ( Figure 4 , fourth column).

In contrast, assuming that the distribution of individual problem-solving ability within the population followed a bell-like distribution, the requirement of a second motor action to solving generated relationships between solving probabilities and group size that are very similar to those generated using a 1-action task ( Figure 4 , second row, columns 2–4 vs. Figure 2 , second row).

Finally, in the case where the distribution of individual problem-solving ability followed a skewed distribution, introducing the requirement of a second motor action to solve the extractive foraging task decreased not only the optimum group size but also the maximum group-solving probabilities for each group size ( Figure 4 , third row, columns 2–4 vs. Figure 2 , third row).

Calculating individual efficiencies at each stepwise increase in group size when we assumed that individual problem-solving propensity for a 2-action task remained stable, increased uniformly, or decreased uniformly showed the same pattern as for a 1-action task. Per capita solving performance consistently rose to a peak and then decreased again, and this regardless of the distribution of problem solving within the original population ( Supplementary Figure S15 , different rows) and regardless of the amplitude of the individual penalty ( Supplementary Figure S15 , magenta symbols) or gain ( Supplementary Figure S15 , blue symbols) associated with increased group size ( Supplementary Figure S15 , different columns). Assuming that penalties/gains associated with increased group size increased/decreased exponentially, rather than uniformly, as a function of group size for populations with a skewed distribution of problem solving for a 1-action task did not change the shape of the relationship between individual efficiency and group size ( Supplementary Figure S16 ).

Using alternative parameter sets to describe the distribution of problem solving for 2-action tasks within the initial populations did not change the overall pattern of our results ( Supplementary Figures S4 and S7 ), nor did modeling group problem solving probability assuming exponentially increasing and decreasing costs and benefits ( Supplementary Figures S9–S14 ).

It has been suggested that increasing problem-solving efficiencies with increasing group sizes in nonhuman animals is mediated by a pool-of-competence effect. This is the idea that as group size increases, and with it, the diversity of individuals within the group, the presence of a problem solver with the skills suited to solving the particular task at hand becomes more likely, such that larger groups have a higher probability of problem solving than smaller groups ( Liker and Bókony 2009 ; Morand-Ferron and Quinn 2011 ). An alternative reason why performance may increase with increasing numbers of individuals is that each member is able to allocate less time to antipredator vigilance and hence more time to solving the task. In order to determine whether these mechanisms could be disentangled, we modeled the pool-of-competence effect by drawing individuals from a pool of competent and noncompetent individuals with varying distributions of problem-solving probability and adding them to a group without changing their individual problem-solving probabilities. We modeled antipredator benefits by drawing individuals from a pool of competent and noncompetent individuals once again, but this time increasing their problem-solving probabilities as they were added to the group. We found that regardless of the theoretical distribution of problem-solving propensity within the population, and regardless of whether individual problem-solving ability was maintained constant or increased as groups became larger, the relationship between group size and group problem–solving probability was consistently positive and rose to 1, with the only difference that increasing individual problem-solving probabilities at each stepwise increase in group size caused a steeper positive relationship. In addition, calculating per capita solving performance based on group solving probabilities revealed that regardless of the distribution of problem-solving ability within the population and regardless of whether individual problem-solving ability was maintained constant or increased as groups became larger, the relationship between group size and individual efficiency rose to a maximum and then dropped off again as group performance reached 1. These findings did not change when we considered an extractive foraging task that required 2 motor actions to be solved and in which individuals with different sets of skills could cooperate to solve the problem ( Péron et al. 2011 ). To examine what form the relationship between group size and solving probability would take in a case where group members interfered with each other as the group became larger, we simulated scenarios in which adding more individuals to a group decreased the probability of each individual solving. In this case, the likelihood of solving by the group increased to an optimal group size and then decreased again. These findings indicate that the shape of the relationship between group size and group problem solving can be used to distinguish competitive interference from group size–associated benefits. However, within the latter, a pool-of-competence effect cannot be disambiguated from shared antipredator vigilance benefit.

One might argue that individuals could benefit at first from being with others, but that those benefits may plateau as group size continues to increase, as has been found in the foraging context ( Fernández-Juricic et al. 2007 ; Cresswell and Quinn 2011 ). However, explicitly modeling this particular scenario by decreasing the antipredator benefit exponentially each time an individual was added to the group did not change the general pattern of our results. The relationship between group size and group problem–solving probability remained positive, gradually increasing to 1 ( Supplementary Figures S9–S14 ). Intuitively, this consistent increase occurs because even though individual benefits become gradually smaller, adding additional, potentially competent, individuals to the group continues to increase the likelihood of the group solving. In an alternative scenario, increased group size may be beneficial at first, but then become detrimental. For example, in humans, problem-solving performance increases up to groups with 3 members to above those levels exhibited by an equivalent number of individuals alone, and then stabilizes for groups of 4 and 5, an effect attributed to interference between group members ( Laughlin et al. 2006 ). The present results lead to the prediction that switching from benefits to costs at a specific group size would yield a group solving probability that increases at first and then decreases again. It is also important to note that an assumption of all our simulations was that all individuals in a group, independent of its size, had access to the problem to be solved. Failure to meet this assumption would be equivalent to drawing individuals solely from the pool of nonsolvers once the problem became inaccessible. This is the only scenario that produces a leveling off of group problem solving probability as group size increases beyond a certain upper limit.

Past empirical work measuring problem-solving performance of groups has quantified group-size effects using 2 distinct approaches ( Figure 5 ). The first quantifies performance of groups with increasing numbers of members (e.g., proportion of devices solved by the group; Morand-Ferron and Quinn 2011 ), whereas the second quantifies group performance and then calculates a per capita solving performance (e.g., Liker and Bókony 2009 ; Griffin et al. 2013 ). Per capita solving rates, which might be considered a measure of “group efficiency,” are expressed either as a number (or percentage) of problems solved per individual (e.g., Liker and Bókony 2009 ; Griffin et al. 2013 ) or a number (or percentage) of problems solved per individual per unit time ( Laughlin et al. 2006 ) and allow for the performance of groups of different sizes to be compared. Systematically varying individual solving probabilities to model a pool of competency effect, shared antipredator vigilance and interference competition, simulating group-level solving performance, and then back calculating per capita solving performances at each stepwise increase in group size revealed that per capita solving performances rose to a peak and then decreased again regardless of the theoretical distribution of problem-solving propensity within the population and regardless of whether individual problem-solving ability remained constant or changed exponentially as groups became larger. These results indicate that per capita solving performances calculated from measured group-level performance data do not allow for mechanisms underpinning group-size effects to be disentangled ( Figure 5 ). This contrasts with analyses of group level performance relative to group size, which, as discussed above, can be used to distinguish group size–associated costs (e.g., competitive interference) from group size–associated benefits, but within the later, cannot disambiguate pool-of-competence from a shared antipredator vigilance effects ( Figure 5 ).

Conceptual relations between empirical and simulated approaches to studying group-size effects on problem-solving performance. Empirical group performance measures and simulated group solving probabilities are equivalent. In contrast, per capita solving performances (at times used loosely to refer to the efficiency of a group in the literature) and individual solving probabilities are only equivalent if individual solving efficiencies are measured (not shown; e.g., Overington et al. 2009 ) rather than calculated on the basis of group level performance (shown; e.g., Morand-Ferron and Quinn 2011 ). Our simulations show that calculated individual efficiencies cannot be used to disentangle any type of group size–mediating mechanism, whereas group level measures allow for group size–associated costs (i.e., interference competition) to be distinguished from group size–associated benefits, but without identifying a benefit mechanism (i.e., pool of competence vs. shared antipredator vigilance).

The findings from this theoretical analysis are at odds with the prediction that group-size benefits attributable to an increasing number of problem solvers with a diverse range of skills can be distinguished from group-size benefits attributable to shared vigilance by examining the shape of the relationship between problem-solving efficiency and group size ( Morand-Ferron and Quinn 2011 ). It has been suggested that a pool-of-competence effect on problem solving should yield a linear increase in problem-solving efficiency as group size increases, whereas a shared antipredator vigilance should result in a relationship that increases at first and then levels off because the importance of antipredator benefits decrease as group size increases ( Morand-Ferron and Quinn 2011 ). Whereas the empirical approach involves measuring group performance and then calculating group efficiencies to disentangle mechanisms underpinning group-size effects (e.g., shared vigilance), here, we used a modeling approach in which we fixed individual solving probabilities assuming specific underpinning mechanisms and modeled their effect on group performance and individual efficiencies ( Figure 5 ). Modeled at the group level, simulations of both the pool-of-competence effect and shared antipredator vigilance both produced a positive relationship between group problem solving and group size, which reached a solving probability of 100%. Considered at the individual level, a pool-of-competence effect, that is, the increasing likelihood that a group will contain a competent individual as group size increases ( Morand-Ferron and Quinn 2011 ), will be reflected by individual solving rates that remain stable as group size increases. If competent individuals facilitate problem solving in other members of the group, then the individuals that learn will show an improved solving probability, leading, at the group level, to an even steeper increase of solving probability as group size increases compared with the case where no learning occurred. In contrast, at the individual level, a shared antipredator vigilance benefit will be reflected by increasing individual solving rates as groups become larger. If this benefit is maximum for a given group size, individual solving probability will plateau at this group size. However, our simulation shows that none of these distinct patterns of individual-level effects can be detected by calculating individual efficiencies based on group-level performance. This is because both the pool-of-competence effect and shared antipredator vigilance produce calculated individual efficiencies that rise to a peak and then decrease again. In order to demonstrate an antipredator vigilance effect, excluding a pool-of-competence effect, one would need to show that individual solving performances increase in the presence of increased numbers of conspecifics that cannot interact with the problem-solving task ( Overington et al. 2009 ). That is to say that individual solving performance variables need to be measured rather than calculated on the basis of group-level performance data ( Figure 5 ).

Overall, the outcomes of the simulations presented here are consistent with the suggestion that group diversity may underpin a positive relationship between group size and group performance in nonhumans but highlight the need for detailed measurement of individual solving performance in the presence of other individuals that cannot interact with the task ( Overington et al. 2009 ) rather than calculation of per capita solving performances based on group performance to establish with certainty the role of this mechanism. Individual specializations in foraging behavior are well documented across a broad range of vertebrates and can emerge as a consequence of the formation of search images, chance events, and individual learning biases. Skill pool effects have been predicted to maintain individual specializations and therefore diversity in foraging behaviors within species ( Giraldeau 1984 ). The mere addition of individuals with different foraging strategies to a group increases the availability of demonstrators within a group and opportunities for individuals to copy the behavior of others and thereby increase their own foraging efficiencies ( Galef and Giraldeau 2001 ). Liker and Bókony (2009) found that larger groups of sparrows had neither higher proportions of birds trying to solve nor higher attempt rates per capita. However, larger groups contained significantly larger proportions of individuals that succeeded among birds that were trying, indicating a higher conversion rate from trying to succeeding. Hence, with larger numbers of competent individuals available in the group, the pool-of-competence effect, individuals motivated to try presumably copied some aspects of the solving behavior of successful individuals. Social learning of this kind has been demonstrated in pigeons ( Columba livia ) ( Palameta and Lefebvre 1985 ) and in various species of tits ( Parus sp.) and thrushes ( Turdus sp.) ( Sasvari 1985 ; Aplin et al. 2013 ).

Our simulations were based on populations with either a binomial, bell-like, or skewed distribution of problem-solving propensity. Recent analyses indicate that innovative problem-solving ability is stable across time ( Cole et al. 2011 ), tasks ( Griffin and Diquelou 2015 ), and some ( Griffin et al. 2013 ), but not other ( Cole et al. 2011 ; Sol et al. 2012 ), contexts, suggesting that innovative problem solving should be regarded as a personality trait and hence underpinned by a specific genetic makeup. On the other hand, innovative problem solving is influenced by state-dependent variables ( Laland and Reader 1999 ) and motivational factors ( Benson-Amram and Holekamp 2012 ; Thornton and Samson 2012 ; Griffin et al. 2014 ), which most likely interact with personality-dependent expression biases to determine the final probability of problem solving. Although there are currently no descriptions of the distributions of innovation propensity within natural populations, these considerations together with the general view that inventing a solution to a new problem is a rare event within wild populations ( Reader and Laland 2003 ) suggest that either a skewed or a discrete binomial distribution of the type presented here is most likely to be representative of innovative problem-solving propensity within natural populations. For example, if problem-solving propensity was determined by a single gene with 2 alleles, one of which is common and associated with low innovation propensity, the other of which is rare and associated with high innovation tendency, then one would expect innovation propensity to follow a skewed binomial distribution. If, instead, we assume that this single gene has multiple alleles each associated with a discrete problem-solving propensity, with the rare variants being associated with higher innovation propensities, one would expect that innovation propensity would be distributed following a multinomial distribution skewed toward 0. Another possibility is to consider that innovation propensity is the result of the interaction of multiple genes. In this case, that innovation propensity would be best described by a continuous skewed distribution, similar to our skewed distribution of problem solving, with each individual problem-solving propensity the result of the interaction of these genes. Regardless of which of these distributions is appropriate in a given species, the fact that inventing a solution to a new problem appears to be a rare event, which is best described at the population level by a skewed distribution of innovation propensity, suggests that there is a fitness cost to high innovation propensity. These costs may be a consequence of exposure to the risks inherent to innovating ( Greenberg 2003 ). Alternatively, the costs may be of pleiotropic origin where one or more of the genes inducing improved innovation have a detrimental effect on seemingly unrelated phenotypic traits. Detailed studies combining behavioral and population genetic approaches will be useful for future work on mechanisms of innovative problem solving and the causes of group-size effects on this behavior.

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7 of the most impressive feats of animal intelligence

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Animals are far smarter than we ever realized.

If we've learned one thing from the past few decades of animal research, it's that many species have much more going on inside their brains than we previously thought. Experiments show that animals can solve puzzles, learn words, and communicate with each other in remarkably sophisticated ways.

Here are a few of the most impressive feats we've seen thus far.

1) Crows can solve puzzles as well as five-year-olds

A series of recent experiments have revealed crows' remarkably sophisticated problem-solving skills.

In one  study conducted at the University of Auckland, researchers found that when presented with tubes of water that contained a floating treat, crows figured out that dropping other objects into the tubes would cause the water level to rise, making the treat accessible. They also figured out that they could get the treats fastest if they chose tubes with higher water levels to start, and if they dropped objects that sank, rather than ones that floated.

Other research , meanwhile, has shown that crows can intentionally bend a piece of wire in order to fish a treat out of a narrow tube. On the whole, researchers put their problem-solving skills roughly on par with those of 5 to 7 year-old children.

2) Dolphins call each other by unique names

Dolphins are remarkably intelligent in  all sorts of ways .  In captivity, they can be quickly trained to complete tasks for treats and are known to mimic human behavior solely for the fun of it. In the wild, they're been observed putting sponges over their snouts to protect themselves from spiny fish while hunting, and killing spiny fish so they can use their spines to extract eels from crevices.

a dolphin's whistle seems to be much like its name

But one of the most striking examples of how smart they are is the fact that each dolphin seems to have a characteristic whistle that represents itself. In other words, a dolphin's whistle seems to be much like its name.

In experiments , dolphins will swim towards a speaker emitting the whistle of a family member much more often than an unknown dolphin's, and when a mother dolphin is separated from her calf, she'll emit the calve's whistle until they're reunited. Most recently,  researchers found that dolphins behave differently upon hearing the whistle of a dolphin they'd last seen 20 years earlier, compared to a stranger's — they're much more likely to approach the speaker and whistle at it repeatedly, trying to get it to whistle back.

3) Elephants can cooperate and show empathy

For years, researchers in the field have observed elephants cooperating in sophisticated ways . Families of related elephants travel together in clans, communicating via low-frequency rumbles. At times, they'll form circles around calves to protect them from predators, or carry out coordinated kidnappings of calves from competing clans in shows of dominance.

More recently, the same levels of coordination have been observed in controlled experiments. In  one , pairs of elephants quickly learned to pull on a rope at the same time to get a treat — and not to pull alone, as that would have ruined the chance of getting it.

Other work seems to suggest that elephants can show genuine empathy.

In general, animals show little interest in dead members of their species — typically, they briefly sniff them before walking away or eating them.  Elephants, however, show a special interest in elephant remains, lingering near them and in some cases becoming agitated around them. One study quantified this behavior: when shown an elephant skull, African elephants spent twice as long looking at it as buffalo or rhino skulls, and they investigated sticks of ivory for six times as long as pieces of wood.

Finally, field researchers  have observed elephants consoling each other — something seldom seen in other species. Typically, when an elephant becomes perturbed, it'll make squeaking noises and perk its ears up. Frequently, other elephants from the same clan will come and stroke its head with their trunks, or put their trunk in its mouth.

4) Dogs can learn hundreds of words

There are  many different examples of canine intelligence , but one of the most remarkable is a border collie named  Chaser . A psychology researcher named John Pilley  has trained Chaser to recognize the names of 1,022 different toys. When Pilley names a specific toy, Chaser is able to retrieve the correct one more than 95 percent of the time.

Recently, Pilley  taught Chaser verbs , in addition to nouns: she can follow instructions to pick a toy up, put her nose on it, or put her paw on it. All this took countless hours of training — and all dogs might not be capable of it — but it's still a remarkable achievement of canine intelligence.

5) Chimps are crazy good at memory puzzles

It may not be a huge surprise that chimps are smart, given that they're our closest relatives. But the degree of their intelligence — and, in some areas, the way it rivals human intelligence — is remarkable.

A chimp named Ayumu who lives at a research institute in Kyoto, Japan, for instance, has  become world-famous for his performance on a speed and memory-based game. As part of the game, 9 numbers are shown are shown in particular spots on a screen for a fraction of a second, and the player must remember their location and reproduce it afterward. You can  play a simplified version of the game here .

ayumu is better than any human who's challenged him thus far

Ayumu is not only capable of playing this game, but is  better than any human who's challenged him so far. When the numbers are shown for an extremely short amount of time (as little as 60 milliseconds), Ayumu is significantly more accurate than people, including college students and memory champions.

Scientists still don't entirely understand how he's so good, but they hypothesize he's doing something called subitizing — looking at a number of objects and immediately taking them in without sequentially counting them. Most humans can do this for up to four items, but Ayumu may be capable of doing it for many more.

6) Cockatoos can pick locks

Cockatoos, like crows, can solve difficult puzzles in order to get treats. And a  2013 study showed just how complex these puzzles can be: they required the birds to open a box (which contained a cashew) by removing a pin, unscrewing a screw, pulling out a bolt, turning a wheel, and finally sliding out a latch.

Obviously, this takes a long time for an animal that doesn't have opposable thumbs. But one cockatoo worked at it for a full two hours, ultimately solving the puzzle and showing that the birds are capable of striving towards goals that are much more distant than the researchers had previously thought.

Other birds in the experiment, meanwhile, learned from the first bird and completed the whole puzzle much more quickly. And when the puzzle was altered so that the five steps had to be completed in a different order, the birds seemed to understand this, and attacked it accordingly instead of trying to replicate the previous solution.

7) Octopuses are weirdly intelligent in ways we don't understand

(DeAgostini/Getty Images)

Octopus intelligence is tough to study for a few reasons: they're aquatic, difficult to keep alive in captivity, and most live relatively deep in the ocean. M ost importantly, octopuses inhabit an environment dramatically different than ours — so it stands to reason that their intelligence is directed at solving very different goals.

But some scientists believe that they're smart in ways that are qualitatively different from us and the other species on this list. One reason is that they have the largest brains of any invertebrate — but though they actually have more neurons than humans, sixty percent of these cells are in their arms, not their brains. As a result, t heir arms seem to be individually intelligent: when cut off, they can crawl away, grab food items, and lift them up to where the octopus' mouth would be if they were still connected.

Meanwhile, octopuses seem to have a keen sense of aesthetics, even though they're likely colorblind. F ield researchers have observed octopuses collect rocks of a specific color to camouflage their den, and many species can change color to blend in with their environment. The way they accomplish this, it's hypothesized, is that they actually sense color with their skin itself and respond accordingly.

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Instrumental Behavior, Problem-Solving, and Tool Use in Nonhuman Animals

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Goal-directed action ; Means/ends behavior

Instrumental behavior is action performed to reach a goal, such as to obtain a food item, achieve some other kind of reward, or remove a punishment; the behavior causes the desired outcome. Problem-solving is a subset of instrumental behavior, invoked when a direct action (such as reaching for an object) cannot achieve the goal and an indirect approach must be used (such as opening a container to get the object). To paraphrase Thorndike, a problem exists when the goal that is sought is not directly attainable by the performance of a simple act available in the animal’s repertoire. Instead, the solution calls for either a novel action or a new integration of available actions (Scheerer 1963, reprinted in Riopelle 1967 ). Tool use is a special kind of problem-solving involving use of an object in the problem-solving sequence. An individual uses a tool when it produces a spatial relation between the tool object and the target...

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Beck, B. (1980). Animal tool behavior: The use and manufacture of tools by animals . New York: Taylor and Francis.

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Brown, M., & Cook, R. G. (2006). Animal spatial cognition. Comparative, neural and computational approaches. [On-line]. www.pigeon.psy.tufts.edu/asc/

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Fragaszy, D., Liu, Q. (2012). Instrumental Behavior, Problem-Solving, and Tool Use in Nonhuman Animals. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_928

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