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We examined how bees solve a visual discrimination task with stimuli commonly used in numerical cognition studies. Bees performed well on the task, but additional tests showed that they had learned continuous (non-numerical) cues. A network model using biologically plausible visual feature filtering and a simple associative rule was capable of learning the task using only continuous cues inherent in the training stimuli, with no numerical processing. This model was also able to reproduce behaviours that have been considered in other studies indicative of numerical cognition. Our results support the idea that a sense of magnitude may be more primitive and basic than a sense of number. Our findings highlight how problematic inadvertent continuous cues can be for studies of numerical cognition. This remains a deep issue within the field that requires increased vigilance and cleverness from the experimenter. We suggest ways of better assessing numerical cognition in non-speaking animals, including assessing the use of all alternative cues in one test, using cross-modal cues, analysing behavioural responses to detect underlying strategies, and finding the neural substrate.

Brain structure and learning capacities both vary with experience, but the mechanistic link between them is unclear. Here, we investigated whether experience-dependent variability in learning performance can be explained by neuroplasticity in foraging honey bees. The mushroom bodies (MBs) are a brain center necessary for ambiguous olfactory learning tasks such as reversal learning. Using radio frequency identification technology, we assessed the effects of natural variation in foraging activity, and the age when first foraging, on both performance in reversal learning and on synaptic connectivity in the MBs. We found that reversal learning performance improved at foraging onset and could decline with greater foraging experience. If bees started foraging before the normal age, as a result of a stress applied to the colony, the decline in learning performance with foraging experience was more severe. Analyses of brain structure in the same bees showed that the total number of synaptic boutons at the MB input decreased when bees started foraging, and then increased with greater foraging intensity. At foraging onset MB structure is therefore optimized for bees to update learned information, but optimization of MB connectivity deteriorates with foraging effort. In a computational model of the MBs sparser coding of information at the MB input improved reversal learning performance. We propose, therefore, a plausible mechanistic relationship between experience, neuroplasticity, and cognitive performance in a natural and ecological context.

The honey bee mushroom bodies (MBs) are brain centers required for specific learning tasks. Here, we show that environmental conditions experienced as young adults affect the maturation of MB neuropil and performance in a MB-dependent learning task. Specifically, olfactory reversal learning was selectively impaired following early exposure to an impoverished environment lacking some of the sensory and social interactions present in the hive. In parallel, the overall number of synaptic boutons increased within the MB olfactory neuropil, whose volume remained unaffected. This suggests that experience of the rich in-hive environment promotes MB maturation and the development of MB-dependent learning capacities., (© 2017 Cabirol et al.; Published by Cold Spring Harbor Laboratory Press.)

Definitions of learning vary widely across disciplines, driven largely by different approaches used to assess its occurrence. These definitions can be better reconciled with each other if each is recognized as coherent with a common conceptualization of learning, while appreciating the practical utility of different learning definitions in different contexts., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The comparative approach is a powerful way to explore the relationship between brain structure and cognitive function. Thus far, the field has been dominated by the assumption that a bigger brain somehow means better cognition. Correlations between differences in brain size or neuron number between species and differences in specific cognitive abilities exist, but these correlations are very noisy. Extreme differences exist between clades in the relationship between either brain size or neuron number and specific cognitive abilities. This means that correlations become weaker, not stronger, as the taxonomic diversity of sampled groups increases. Cognition is the outcome of neural networks. Here we propose that considering plausible neural network models will advance our understanding of the complex relationships between neuron number and different aspects of cognition. Computational modelling of networks suggests that adding pathways, or layers, or changing patterns of connectivity in a network can all have different specific consequences for cognition. Consequently, models of computational architecture can help us hypothesise how and why differences in neuron number might be related to differences in cognition. As methods in connectomics continue to improve and more structural information on animal brains becomes available, we are learning more about natural network structures in brains, and we can develop more biologically plausible models of cognitive architecture. Natural animal diversity then becomes a powerful resource to both test the assumptions of these models and explore hypotheses for how neural network structure and network size might delimit cognitive function. [ABSTRACT FROM AUTHOR]

The capacity to learn abstract concepts such as ‘sameness’ and ‘difference’ is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly have this capacity. Here we report a model of the structures of the honey bee brain that can learn sameness and difference, as well as a range of complex and simple associative learning tasks. Our model is constrained by the known connections and properties of the mushroom body, including the protocerebral tract, and provides a good fit to the learning rates and performances of real bees in all tasks, including learning sameness and difference. The model proposes a novel mechanism for learning the abstract concepts of ‘sameness’ and ‘difference’ that is compatible with the insect brain, and is not dependent on top-down or executive control processing. [ABSTRACT FROM AUTHOR]

Honeybees are well known for their complex division of labor. Each bee sequentially performs a series of social tasks during its life. The changes in social task performance are linked to gross differences in behavior and physiology. We tested whether honeybees performing different social tasks (nursing versus foraging) would differ in their gustatory responsiveness and associative learning behavior in addition to their daily tasks in the colony. Further, we investigated the role of the biogenic amine tyramine and its receptors in the behavior of nurse bees and foragers. Tyramine is an important insect neurotransmitter, which has long been neglected in behavioral studies as it was believed to only act as the metabolic precursor of the better-known amine octopamine. With the increasing number of characterized tyramine receptors in diverse insects, we need to understand the functions of tyramine on its own account. Our findings suggest an important role for tyramine and its two receptors in regulating honeybee gustatory responsiveness, social organization and learning behavior. Foragers, which were more responsive to gustatory stimuli than nurse bees and performed better in appetitive learning, also differed from nurse bees in their tyramine brain titers and in the mRNA expression of a tyramine receptor in the brain. Pharmacological activation of tyramine receptors increased gustatory responsiveness of nurse bees and foragers and improved appetitive learning in nurse bees. These data suggest that a large part of the behavioral differences between honeybees may be directly linked to tyramine signaling in the brain. [ABSTRACT FROM AUTHOR]

Diverse invertebrate species have been used for studies of learning and comparative cognition. Although we have gained invaluable information from this, in this study we argue that our approach to comparative learning research is rather deficient. Generally invertebrate learning research has focused mainly on arthropods, and most of that within the Hymenoptera and Diptera. Any true comparative analysis of the distribution of comparative cognitive abilities across phyla is hampered by this bias, and more fundamentally by a reporting bias toward positive results. To understand the limits of learning and cognition for a species, knowing what animals cannot do is at least as important as reporting what they can. Finally, much more effort needs to be focused on the neurobiological analysis of different types of learning to truly understand the differences and similarities of learning types. In this review, we first give a brief overview of the various forms of learning in invertebrates. We also suggest areas where further study is needed for a more comparative understanding of learning. Finally, using what is known of learning in honeybees and the well-studied honeybee brain, we present a model of how various complex forms of learning may be accounted for with the same neural circuitry required for so-called simple learning types. At the neurobiological level, different learning phenomena are unlikely to be independent, and without considering this it is very difficult to correctly interpret the phylogenetic distribution of learning and cognitive abilities. WIREs Cogn Sci 2013, 4:561-582. doi: 10.1002/wcs.1248 For further resources related to this article, please visit the WIREs website. [ABSTRACT FROM AUTHOR]

Reward seeking is a major motivator and organizer of behavior, and animals readily learn to modify their behavior to more easily obtain reward, or to respond to stimuli that are predictive of reward. Here, we compare what is known of reward processing mechanisms in insects with the well-studied vertebrate reward systems. In insects almost all of what is known of reward processing is derived from studies of reward learning. This is localized to the mushroom bodies and antennal lobes and organized by a network of hierarchically arranged modulatory circuits, especially those involving octopamine and dopamine. Neurogenetic studies with Drosophila have identified distinct circuit elements for reward learning, 'wanting,' and possibly 'liking' in Drosophila, suggesting a modular structure to the insect reward processing system, which broadly parallels that of the mammals in terms of functional organization. [ABSTRACT FROM AUTHOR]

The article focuses on the instincts and epigenetics in the cellular and molecular mechanisms. Instincts which are viewed to be learned behavior and the way animals acquire the instincts for innate behavior is mentioned. The research on instinct that is evolved from learning is discussed. An overview on the plasticity model of evolution which states that plasticity can precede and facilitate evolutionary adaptation is presented.

Whether animals can actively avoid food contaminated with harmful compounds through taste is key to assess their ecotoxicological risks. Here, we investigated the ability of honey bees to perceive and avoid food resources contaminated with common metal pollutants known to impair behaviour at low concentrations. In laboratory assays, bees did not discriminate food contaminated with arsenic, lead or zinc and ingested it readily, up to estimated doses of 929.1 μg g−1 As, 6.45 mg g−1 Pb and 72.46 mg g−1 Zn. A decrease of intake and appetitive responses indicating metal detection was only observed at the highest concentrations of lead (3.6 mM) and zinc (122.3 mM) through contact with the antennae and the proboscis. Electrophysiological analyses confirmed that only high concentrations of the three metals in a sucrose solution induced a consistently reduced neural response to sucrose in antennal taste receptors (As: >0.1 μM, Pb: >1 mM; Zn: >100 mM). Overall, cellular and behavioural responses did not provide evidence for specific mechanisms that would support selective detection of toxic metals (arsenic, lead), as compared to zinc, which has important biological functions. Our results thus show that honey bees can avoid metal pollutants in their food only at high concentrations unlikely to be encountered in the environment. By contrast, they appear to be unable to detect low, yet harmful, concentrations found in flowers. Metal pollution at trace levels is therefore a major threat for pollinators. [Display omitted] • Pollinators can encounter food resources contaminated with toxic metal pollutants. • Honey bees do not avoid zinc, lead or arsenic present in food at field-realistic levels. • Recordings of taste receptors show no specific sensory detection of metals at these doses. • Thus bees may ingest high quantities of trace metals when foraging in polluted environments. [ABSTRACT FROM AUTHOR]