Understanding the limits to animal cognition.

The thriving field of comparative cognition examines the behaviour of diverse animals in cognitive terms. Comparative cognition research has primarily focused on the abilities of animals - what tasks they can do - rather than on the limits of their cognition - tasks that exceed an animal's cognitive abilities. We propose that understanding and identifying cognitive limits is as important as demonstrating the capacities of animal minds. Here, we identify challenges that have deterred the study of cognitive limits related to epistemic, practical and publication problems. The epistemic problem is concerned with how we can confidently infer a cognitive limit from null or negative results. The practical problem is how can we be certain our research has identified a cognitive limit rather than failures in tasks due to methodological or experimental design issues. The publication problem outlines the publication bias toward positive and exciting results over negative or null results in animal cognition. We propose solutions to these three challenges and examples of how to conduct research to confidently identify and confirm cognitive limits in animals. We believe a refocus on the cognitive limits of animals is the next step in the field of comparative cognition. Knowing the limits to the intelligence of different animals will aid us in appreciating the diversity of animal intelligence, and will resolve outstanding questions of how cognition evolves.

Comparing cognition across major transitions using the hierarchy of formal automata.

The evolution of cognition can be understood in terms of a few major transitions-changes in the computational architecture of nervous systems that changed what cognitive capacities could be evolved by downstream lineages. We demonstrate how the idea of a major cognitive transition can be modeled in terms of where a system's effective computational architecture falls on the well-studied hierarchy of formal automata (HFA). We then use recent work connecting artificial neural networks to the HFA, which provides a way to make the structure-architecture link in natural systems. We conclude with reflections on the power and the challenges of traditional thinking when applied to neural architectures. This article is categorized under: Cognitive Biology > Evolutionary Roots of Cognition Psychology > Comparative Philosophy > Foundations of Cognitive Science.

Environmental exposure to metallic pollution impairs honey bee brain development and cognition.

Laboratory studies show detrimental effects of metallic pollutants on invertebrate behaviour and cognition, even at low levels. Here we report a field study on Western honey bees exposed to metal and metalloid pollution through dusts, food and water at a historic mining site. We analysed more than 1000 bees from five apiaries along a gradient of contamination within 11 km of a former gold mine in Southern France. Bees collected close to the mine exhibited olfactory learning performances lower by 36% and heads smaller by 4%. Three-dimensional scans of bee brains showed that the olfactory centres of insects sampled close to the mine were also 4% smaller, indicating neurodevelopmental issues. Our study raises serious concerns about the health of honey bee populations in areas polluted with potentially harmful elements, particularly with arsenic, and illustrates how standard cognitive tests can be used for risk assessment.

Transitions in cognitive evolution.

The evolutionary history of animal cognition appears to involve a few major transitions: major changes that opened up new phylogenetic possibilities for cognition. Here, we review and contrast current transitional accounts of cognitive evolution. We discuss how an important feature of an evolutionary transition should be that it changes what is evolvable, so that the possible phenotypic spaces before and after a transition are different. We develop an account of cognitive evolution that focuses on how selection might act on the computational architecture of nervous systems. Selection for operational efficiency or robustness can drive changes in computational architecture that then make new types of cognition evolvable. We propose five major transitions in the evolution of animal nervous systems. Each of these gave rise to a different type of computational architecture that changed the evolvability of a lineage and allowed the evolution of new cognitive capacities. Transitional accounts have value in that they allow a big-picture perspective of macroevolution by focusing on changes that have had major consequences. For cognitive evolution, however, we argue it is most useful to focus on evolutionary changes to the nervous system that changed what is evolvable, rather than to focus on specific cognitive capacities.

The relationship between cognition and brain size or neuron number.

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.

How honey bees make fast and accurate decisions.

Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

In the natural world, decision-making processes are often intricate and challenging. Animals frequently encounter situations where they have limited information on which to rely to guide them, yet even simple choices can have far-reaching impact on survival. Each time a bee sets out to collect nectar, for example, it must use tiny variations in colour or odour to decide which flower it should land on and explore. Each 'mistake' is costly, wasting energy and exposing the insect to potential dangers. To learn how to refine their choices through trial-and-error, bees only have at their disposal a brain the size of a sesame seed, which contains fewer than a million neurons. And yet, they excel at this task, being both quick and accurate. The underlying mechanisms which drive these remarkable decision-making capabilities remain unclear. In response, MaBouDi et al. aimed to explore which strategies honeybees adopt to forage so effectively, and the neural systems that may underlie them. To do so, they released the insects in a 'field' containing artificial flowers in five different colours. The bees were trained to link each colour with a certain likelihood of receiving either a sugary liquid (reward) or bitter quinine (punishment); they were then tested on this knowledge. Next, MaBouDi et al. recorded how the bees would navigate a 'reduced evidence' test, where the colour of the flowers were ambiguous and consisted in various blends of the originally rewarded or punished colours; and a 'reduced reward likelihood' test, where the sweet recompense was offered less often than before. Response times and accuracy rates revealed a complex pattern of decision-making processes. How quickly the insects made a choice, and the types of mistakes they made (such as deciding to explore a non-rewarded flower, or to ignore a rewarded one) were dependent on both the quality of the evidence and the certainty of the reward. Such sophistication and subtlety in decision-making is comparable to that of primates. Next, MaBouDi et al. developed a computational model which could faithfully replicate the pattern of decisions exhibited by the bees, while also being plausible biologically. This approach offered insights into how a small brain could execute such complex choices 'on the fly', and the type of neural circuits that would be required. Going forward, this knowledge could be harnessed to design more efficient decision-making algorithms for artificial systems, and in particular for autonomous robotics.

Feeding and Amines Stimulate the Growth of the Salivary Gland following Short-Term Starvation in the Black Field Cricket, Teleogryllus commodus.

The salivary gland of the black field cricket, Teleogryllus commodus Walker changed size between being starved and fed. Crickets without access to food for 72 h showed a reduction in both wet and dry mass of the glands compared with the glands from continuously fed animals at 72 h. Glands returned to size following ingestion within 10 min. Salivary glands of starved crickets (72 h) were incubated in saline containing either serotonin (5-HT) or dopamine (DA). Glands increased to pre-starvation size after 1 h incubation in situ with either 10-4 moles L-1 5-HT or 10-4 moles L-1 DA, although lower concentrations (10-5 moles L-1) did not affect gland size. From immunohistochemistry, amines appeared to shift from zymogen cells during starvation to parietal cells following feeding. High-performance liquid chromatography showed that serotonin concentration is higher than dopamine in the salivary gland removed from starved and fed crickets, but the quantity of these compounds was not dependent upon feeding state; the amine quantities increased as gland size increased. Further work is necessary to determine what might be the stimulus for gland growth and if dopamine and serotonin play a role in the stimulation of salivary gland growth after a period of starvation.

The diverging epigenomic landscapes of honeybee queens and workers revealed by multiomic sequencing.

The role of the epigenome in phenotypic plasticity is unclear presently. Here we used a multiomics approach to explore the nature of the epigenome in developing honey bee (Apis mellifera) workers and queens. Our data clearly showed distinct queen and worker epigenomic landscapes during the developmental process. Differences in gene expression between workers and queens become more extensive and more layered during the process of development. Genes known to be important for caste differentiation were more likely to be regulated by multiple epigenomic systems than other differentially expressed genes. We confirmed the importance of two candidate genes for caste differentiation by using RNAi to manipulate the expression of two genes that differed in expression between workers and queens were regulated by multiple epigenomic systems. For both genes the RNAi manipulation resulted in a decrease in weight and fewer ovarioles of newly emerged queens compared to controls. Our data show that the distinct epigenomic landscapes of worker and queen bees differentiate during the course of larval development.

Vision: Flies move their eyes.

As you are reading this text, your eyes scan across the page, even if you keep your head perfectly still. New research reveals that flies can perform analogous retinal movements, despite their eyes being rigidly fixed to their heads.

Bumblebees retrieve only the ordinal ranking of foraging options when comparing memories obtained in distinct settings.

Are animals' preferences determined by absolute memories for options (e.g. reward sizes) or by their remembered ranking (better/worse)? The only studies examining this question suggest humans and starlings utilise memories for both absolute and relative information. We show that bumblebees' learned preferences are based only on memories of ordinal comparisons. A series of experiments showed that after learning to discriminate pairs of different flowers by sucrose concentration, bumblebees preferred flowers (in novel pairings) with (1) higher ranking over equal absolute reward, (2) higher ranking over higher absolute reward, and (3) identical qualitative ranking but different quantitative ranking equally. Bumblebees used absolute information in order to rank different flowers. However, additional experiments revealed that, even when ranking information was absent (i.e. bees learned one flower at a time), memories for absolute information were lost or could no longer be retrieved after at most 1 hr. Our results illuminate a divergent mechanism for bees (compared to starlings and humans) of learned preferences that may have arisen from different adaptations to their natural environment.

The involvement of a floral scent in plant-honeybee interaction.

Volatile odors from flowers play an important role in plant-pollinator interaction. The honeybee is an important generalist pollinator of many plants. Here, we explored whether any components of the odors of a range of honeybee-pollinated plants are commonly involved in the interaction between plants and honeybees. We used a needle trap system to collect floral odors, and GC-MS analysis revealed nonanal was the only component scent detected in 12 different honeybee-pollinated flowers and not present in anemophilous plant species. For Ligustrum compactum, blooming flowers released significantly more nonanal than buds and faded flowers. For Sapium sebiferum, nonanal release through the day correlated with nectar secretion. Experimentally increasing nectar load in flowers of Sapium sebiferum, Ligustrum compactum, and Castanea henryi increased nonanal levels also. Nonanal was also detected in flower nectar and honeys from experimental colonies. Electroantennogram recordings and behavioral observations showed that untrained honeybees could detect and were strongly attracted to nonanal. We argue that nonanal persists in both honey and nectar odors facilitating a learned association between nonanal and food reward in honeybees.

Extent and complexity of RNA processing in honey bee queen and worker caste development.

The distinct honeybee (Apis mellifera) worker and queen castes have become a model for the study of genomic mechanisms of phenotypic plasticity. Here we performed a nanopore-based direct RNA sequencing with exceptionally long reads to compare the mRNA transcripts between queen and workers at three points during their larval development. We found thousands of significantly differentially expressed transcript isoforms (DEIs) between queen and worker larvae. These DEIs were formatted by a flexible splicing system. We showed that poly(A) tails participated in this caste differentiation by negatively regulating the expression of DEIs. Hundreds of isoforms uniquely expressed in either queens or workers during their larval development, and isoforms were expressed at different points in queen and worker larval development demonstrating a dynamic relationship between isoform expression and developmental mechanisms. These findings show the full complexity of RNA processing and transcript expression in honey bee phenotypic plasticity.

The best of both worlds: Dual systems of reasoning in animals and AI.

Much of human cognition involves two different types of reasoning that operate together. Type 1 reasoning systems are intuitive and fast, whereas Type 2 reasoning systems are reflective and slow. Why has our cognition evolved with these features? Both systems are coherent and in most ecological circumstances either alone is capable of coming up with the right answer most of the time. Neural tissue is costly, and thus far evolutionary models have struggled to identify a benefit of operating two systems of reasoning. To explore this issue we take a broad comparative perspective. We discuss how dual processes of cognition have enabled the emergence of selective attention in insects, transforming the learning capacities of these animals. Modern AIs using dual systems of learning are able to learn how their vast world works and how best to interact with it, allowing them to exceed human levels of performance in strategy games. We propose that the core benefits of dual processes of reasoning are to narrow down a problem space in order to focus cognitive resources most effectively.

Honey bees cannot sense harmful concentrations of metal pollutants in food.

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.

Traces of a neonicotinoid pesticide stimulate different honey bee colony activities, but do not increase colony size or longevity.

How neonicotinoid contamination affects honey bees remains controversial. Studies have yielded contradictory results, and few have examined effects on colony development. Here we report the results of a comprehensive five-year study of the effects of the neonicotinoid imidacloprid on honey bee colonies. Colonies fed 5 ng/g (ppb) imidacloprid in sugar syrup showed increased brood production, lower temperature variability, higher CO2 production and had more foragers compared to control colonies fed unadulterated syrup, but treatment did not affect adult bee numbers or average hive temperatures, and did not increase food stores, daily food acquisition or colony survivorship. These results suggest that imidacloprid contamination increased colony metabolism without improving colony productivity, and helps explain why some studies have reported no, or even positive, effects of neonicotinoids. Effect sizes were generally small but that could be attributed at least in part to variability in uncontrolled factors such as weather. We provide an explanation for the diverse effects of pesticide contamination on honey bees, and an improved understanding of how colonies are impacted.

Non-additive gene interactions underpin molecular and phenotypic responses in honey bee larvae exposed to imidacloprid and thymol.

Understanding the cumulative risk of chemical mixtures at environmentally realistic concentrations is a key challenge in honey bee ecotoxicology. Ecotoxicogenomics, including transcriptomics, measures responses in individual organisms at the molecular level which can provide insights into the mechanisms underlying phenotypic responses induced by one or more stressors and link impacts on individuals to populations. Here, fifth instar honey bee larvae were sampled from a previously reported field experiment exploring the phenotypic impacts of environmentally realistic chronic exposures of the pesticide imidacloprid (5 µg.kg-1 for six weeks) and the acaricide thymol (250 g.kg-1 applied via Apiguard gel in-hive for four weeks), both separately and in combination. RNA-seq was used to discover individual and interactive chemical effects on larval gene expression and to uncover molecular mechanisms linked to reported adult and colony phenotypes. The separate and combined treatments had distinct gene expression profiles which represented differentially affected signaling and metabolic pathways. The molecular signature of the mixture was characterised by additive interactions in canonical stress responses associated with oxidative stress and detoxification, and non-additive interactions in secondary responses including developmental, neurological, and immune pathways. Novel emergent impacts on eye development genes correlated with long-term defects in visual learning performance as adults. This is consistent with these chemicals working through independent modes of action that combine to impact common downstream pathways, and highlights the importance of establishing mechanistic links between molecular and phenotypic responses when predicting effects of chemical mixtures on ecologically relevant population outcomes.

A model of resource partitioning between foraging bees based on learning.

Central place foraging pollinators tend to develop multi-destination routes (traplines) to exploit patchily distributed plant resources. While the formation of traplines by individual pollinators has been studied in detail, how populations of foragers use resources in a common area is an open question, difficult to address experimentally. We explored conditions for the emergence of resource partitioning among traplining bees using agent-based models built from experimental data of bumblebees foraging on artificial flowers. In the models, bees learn to develop routes as a consequence of feedback loops that change their probabilities of moving between flowers. While a positive reinforcement of movements leading to rewarding flowers is sufficient for the emergence of resource partitioning when flowers are evenly distributed, the addition of a negative reinforcement of movements leading to unrewarding flowers is necessary when flowers are patchily distributed. In environments with more complex spatial structures, the negative experiences of individual bees on flowers favour spatial segregation and efficient collective foraging. Our study fills a major gap in modelling pollinator behaviour and constitutes a unique tool to guide future experimental programs.

Current permissible levels of metal pollutants harm terrestrial invertebrates.

The current decline of invertebrates worldwide is alarming. Several potential causes have been proposed but metal pollutants, while being widespread in the air, soils and water, have so far been largely overlooked. Here, we reviewed the results of 527 observations of the effects of arsenic, cadmium, lead and mercury on terrestrial invertebrates. These four well-studied metals are considered as priorities for public health and for which international regulatory guidelines exist. We found that they all significantly impact the physiology and behavior of invertebrates, even at levels below those recommended as 'safe' for humans. Our results call for a revision of the regulatory thresholds to better protect terrestrial invertebrates, which appear to be more sensitive to metal pollution than vertebrates. More fundamental research on a broader range of compounds and species is needed to improve international guidelines for metal pollutants, and to develop conservation plans to protect invertebrates and ecosystem services.

Metal pollutants have additive negative effects on honey bee cognition.

Environmental pollutants can exert sublethal deleterious effects on animals. These include disruption of cognitive functions underlying crucial behaviours. While agrochemicals have been identified as a major threat to pollinators, metal pollutants, which are often found in complex mixtures, have so far been overlooked. Here, we assessed the impact of acute exposure to field-realistic concentrations of three common metal pollutants, lead, copper and arsenic, and their combinations, on honey bee appetitive learning and memory. All treatments involving single metals slowed down learning and disrupted memory retrieval at 24 h. Combinations of these metals had additive negative effects on both processes, suggesting common pathways of toxicity. Our results highlight the need to further assess the risks of metal pollution on invertebrates.