Social experience drives the development of holistic face processing in paper wasps.

Most recognition is based on identifying features, but specialization for face recognition in some taxa relies on a different mechanism, termed 'holistic processing' where facial features are bound together into a gestalt which is more than the sum of its parts. Although previous work suggests that extensive experience may be required for the development of holistic processing, we lack experiments that test how age and experience interact to influence holistic processing. Here, we test how age and experience influence the development of holistic face processing in Polistes fuscatus paper wasps. Previous work has shown that P. fuscatus use facial patterns to individually identify conspecifics and wasps use holistic processing to discriminate between conspecific faces. We tested face processing in three groups of P. fuscatus: young (1-week-old), older, experienced (2-weeks-old, normal experience), and older, inexperienced (2-weeks-old, 1 week normal social experience and 1 week social isolation). Older, experienced wasps used holistic processing to discriminate between conspecific faces. In contrast, older inexperienced wasps used featural rather than holistic mechanisms to discriminate between faces. Young wasps show some evidence of holistic face processing, but this ability was less refined than older, experienced wasps. Notably, wasps only required 2 weeks of normal experience to develop holistic processing, while previous work suggests that humans may require years of experience. Overall, P. fuscatus wasps rapidly develop holistic processing for conspecific faces. Experience rather than age facilitates the transition between featural and holistic face processing mechanisms.

Individual consistency in the learning abilities of honey bees: cognitive specialization within sensory and reinforcement modalities.

The question of whether individuals perform consistently across a variety of cognitive tasks is relevant for studies of comparative cognition. The honey bee (Apis mellifera) is an appropriate model to study cognitive consistency as its learning can be studied in multiple elemental and non-elemental learning tasks. We took advantage of this possibility and studied if the ability of honey bees to learn a simple discrimination correlates with their ability to solve two tasks of higher complexity, reversal learning and negative patterning. We performed four experiments in which we varied the sensory modality of the stimuli (visual or olfactory) and the type (Pavlovian or operant) and complexity (elemental or non-elemental) of conditioning to examine if stable correlated performances could be observed across experiments. Across all experiments, an individual's proficiency to learn the simple discrimination task was positively and significantly correlated with performance in both reversal learning and negative patterning, while the performances in reversal learning and negative patterning were positively, yet not significantly correlated. These results suggest that correlated performances across learning paradigms represent a distinct cognitive characteristic of bees. Further research is necessary to examine if individual cognitive consistency can be found in other insect species as a common characteristic of insect brains.

Different mechanisms underlie implicit visual statistical learning in honey bees and humans.

The ability of developing complex internal representations of the environment is considered a crucial antecedent to the emergence of humans' higher cognitive functions. Yet it is an open question whether there is any fundamental difference in how humans and other good visual learner species naturally encode aspects of novel visual scenes. Using the same modified visual statistical learning paradigm and multielement stimuli, we investigated how human adults and honey bees (Apis mellifera) encode spontaneously, without dedicated training, various statistical properties of novel visual scenes. We found that, similarly to humans, honey bees automatically develop a complex internal representation of their visual environment that evolves with accumulation of new evidence even without a targeted reinforcement. In particular, with more experience, they shift from being sensitive to statistics of only elemental features of the scenes to relying on co-occurrence frequencies of elements while losing their sensitivity to elemental frequencies, but they never encode automatically the predictivity of elements. In contrast, humans involuntarily develop an internal representation that includes single-element and co-occurrence statistics, as well as information about the predictivity between elements. Importantly, capturing human visual learning results requires a probabilistic chunk-learning model, whereas a simple fragment-based memory-trace model that counts occurrence summary statistics is sufficient to replicate honey bees' learning behavior. Thus, humans' sophisticated encoding of sensory stimuli that provides intrinsic sensitivity to predictive information might be one of the fundamental prerequisites of developing higher cognitive abilities.

Individual recognition is associated with holistic face processing in Polistes paper wasps in a species-specific way.

Most recognition is based on identifying features, but specialization for face recognition in primates relies on a different mechanism, termed 'holistic processing' where facial features are bound together into a gestalt which is more than the sum of its parts. Here, we test whether individual face recognition in paper wasps also involved holistic processing using a modification of the classic part-whole test in two related paper wasp species: Polistes fuscatus, which use facial patterns to individually identify conspecifics, and Polistes dominula, which lacks individual recognition. We show that P. fuscatus use holistic processing to discriminate between P. fuscatus face images but not P. dominula face images. By contrast, P. dominula do not rely on holistic processing to discriminate between conspecific or heterospecific face images. Therefore, P. fuscatus wasps have evolved holistic face processing, but this ability is highly specific and shaped by species-specific and stimulus-specific selective pressures. Convergence towards holistic face processing in distant taxa (primates, wasps) as well as divergence among closely related taxa with different recognition behaviour (P. dominula, P. fuscatus) suggests that holistic processing may be a universal adaptive strategy to facilitate expertise in face recognition.

Evidence of cognitive specialization in an insect: proficiency is maintained across elemental and higher-order visual learning but not between sensory modalities in honey bees.

Individuals differing in their cognitive abilities and foraging strategies may confer a valuable benefit to their social groups as variability may help them to respond flexibly in scenarios with different resource availability. Individual learning proficiency may either be absolute or vary with the complexity or the nature of the problem considered. Determining whether learning ability correlates between tasks of different complexity or between sensory modalities is of high interest for research on brain modularity and task-dependent specialization of neural circuits. The honeybee Apis mellifera constitutes an attractive model to address this question because of its capacity to successfully learn a large range of tasks in various sensory domains. Here, we studied whether the performance of individual bees in a simple visual discrimination task (a discrimination between two visual shapes) is stable over time and correlates with their capacity to solve either a higher-order visual task (a conceptual discrimination based on spatial relationships between objects) or an elemental olfactory task (a discrimination between two odorants). We found that individual learning proficiency within a given task was maintained over time and that some individuals performed consistently better than others within the visual modality, thus showing consistent aptitude across visual tasks of different complexity. By contrast, performance in the elemental visual-learning task did not predict performance in the equivalent elemental olfactory task. Overall, our results suggest the existence of cognitive specialization within the hive, which may contribute to ecological social success.

Oviposition in the onion fly Delia antiqua (Diptera: Anthomyiidae) is socially facilitated by visual cues.

Ovipositional decisions in herbivorous insects may be affected by social information from conspecifics. Social facilitation of oviposition has been suggested for the onion fly Delia antiqua. In the current study, we found that D. antiqua oviposition was unequal between paired oviposition stations of equal quality and that more eggs were laid on an oviposition station baited with decoy flies than on the control. The increased oviposition toward the decoys continued over time >8 h. When decoys were placed upside down, the number of eggs laid did not differ between the decoy and control sides of oviposition stations, suggesting that social facilitation of oviposition is mediated by visual cues. Based on these findings, mechanisms of social facilitation of oviposition in D. antiqua were discussed.

Sparse Neural Representation of Odor Predicts Learning.

The way in which neurons encode information remains a hotly debated topic in neuroscience. Lin and colleagues in a 2014 article in the journal Nature Neuroscience demonstrate how sparse coding in the olfactory learning and memory center of Drosophila can influence learning behavior. Coding sparsity is the idea that only a small number of neurons in a network represent any given stimulus. Using neurogenetics, computational neuroscience, and cognitive approaches, they outline the discovery of an inhibitory feedback circuit responsible for differentiating the neuronal response to different odors. Manipulating this feedback circuit, they demonstrate how the sparseness in neural networks (how easily neurons are activated) can correspond to the ability to learn a sensory discrimination more easily. From a research perspective, this paper was important as it was the first causal demonstration of the role of sparseness in learning. From a teaching point of view, this paper is valuable because it is a simple but effective introduction to artificial neural network theory, where both the abstract theory and the importance of its application is apparent to those without a mathematical or computational background.

Honeybees use absolute rather than relative numerosity in number discrimination.

Various vertebrate species use relative numerosity judgements in comparative assessments of quantities for which they use larger/smaller relationships rather than absolute number. The numerical ability of honeybees shares basic properties with that of vertebrates but their use of absolute or relative numerosity has not been explored. We trained free-flying bees to choose variable images containing three dots; one group ('larger') was trained to discriminate 3 from 2, while another group ('smaller') was trained to discriminate 3 from 4. In both cases, numbers were kept constant but stimulus characteristics and position were varied from trial to trial. Bees were then tested with novel stimuli displaying the previously trained numerosity (3) versus a novel numerosity (4 for 'larger' and 2 for 'smaller'). Both groups preferred the three-item stimulus, consistent with absolute numerosity. They also exhibited ratio-dependent discrimination of numbers, a property shared by vertebrates, as performance after 2 versus 3 was better than after 3 versus 4 training. Thus, bees differ from vertebrates in their use of absolute rather than of relative numerosity but they also have some numeric properties in common.

Copy if dissatisfied, innovate if not: contrasting egg-laying decision making in an insect.

The use of conspecific cues as social information in decision making is widespread among animals; but, because this social information is indirect, it is error-prone. During resource acquisition, conspecific cues also indicate the presence of competitors; therefore, decision makers are expected to utilize direct information from resources and modify their responses to social information accordingly. Here, we show that, in a non-social insect, unattractive egg-laying resources alter the behavioural response to conspecific cues from avoidance to preference, leading to resource sharing. Females of the adzuki bean beetle Callosobruchus chinensis avoid laying eggs onto beans that already have conspecific eggs. However, when we provided females with bean-sized clean glass beads with and without conspecific eggs, the females preferred to add their eggs onto the beads with eggs. The glass beads, once coated with water extracts of adzuki beans, enabled the females to behave as if they were provided with the beans: the females preferred bean-odoured glass beads to clean glass beads and they avoided the substrates with eggs. When females are provided with unattractive egg-laying substrates only, joining behaviour (i.e. copying) might be advantageous, as it takes advantage of information about positive attributes of the substrate that the focal animal might have missed. Our results suggest that given only unsatisfactory options, the benefits of copying outweigh the costs of resource competition. Our study highlights the importance of integrating multiple information sources in animal decision making.

A neural network model for familiarity and context learning during honeybee foraging flights.

How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.

Facial patterns in a tropical social wasp correlate with colony membership.

Social insects excel in discriminating nestmates from intruders, typically relying on colony odours. Remarkably, some wasp species achieve such discrimination using visual information. However, while it is universally accepted that odours mediate a group level recognition, the ability to recognise colony members visually has been considered possible only via individual recognition by which wasps discriminate 'friends' and 'foes'. Using geometric morphometric analysis, which is a technique based on a rigorous statistical theory of shape allowing quantitative multivariate analyses on structure shapes, we first quantified facial marking variation of Liostenogaster flavolineata wasps. We then compared this facial variation with that of chemical profiles (generated by cuticular hydrocarbons) within and between colonies. Principal component analysis and discriminant analysis applied to sets of variables containing pure shape information showed that despite appreciable intra-colony variation, the faces of females belonging to the same colony resemble one another more than those of outsiders. This colony-specific variation in facial patterns was on a par with that observed for odours. While the occurrence of face discrimination at the colony level remains to be tested by behavioural experiments, overall our results suggest that, in this species, wasp faces display adequate information that might be potentially perceived and used by wasps for colony level recognition.

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.

Conceptual learning by miniature brains.

Concepts act as a cornerstone of human cognition. Humans and non-human primates learn conceptual relationships such as 'same', 'different', 'larger than', 'better than', among others. In all cases, the relationships have to be encoded by the brain independently of the physical nature of objects linked by the relation. Consequently, concepts are associated with high levels of cognitive sophistication and are not expected in an insect brain. Yet, various works have shown that the miniature brain of honeybees rapidly learns conceptual relationships involving visual stimuli. Concepts such as 'same', 'different', 'above/below of' or 'left/right are well mastered by bees. We review here evidence about concept learning in honeybees and discuss both its potential adaptive advantage and its possible neural substrates. The results reviewed here challenge the traditional view attributing supremacy to larger brains when it comes to the elaboration of concepts and have wide implications for understanding how brains can form conceptual relations.

Numerical discrimination in Drosophila melanogaster.

Sensitivity to numbers is a crucial cognitive ability. The lack of experimental models amenable to systematic genetic and neural manipulation has precluded discovering neural circuits required for numerical cognition. Here, we demonstrate that Drosophila flies spontaneously prefer sets containing larger numbers of objects. This preference is determined by the ratio between the two numerical quantities tested, a characteristic signature of numerical cognition across species. Individual flies maintained their numerical choice over consecutive days. Using a numerical visual conditioning paradigm, we found that flies are capable of associating sucrose with numerical quantities and can be trained to reverse their spontaneous preference for large quantities. Finally, we show that silencing lobula columnar neurons (LC11) reduces the preference for more objects, thus identifying a neuronal substrate for numerical cognition in invertebrates. This discovery paves the way for the systematic analysis of the behavioral and neural mechanisms underlying the evolutionary conserved sensitivity to numerosity.

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 Gut-Brain-Microbiome Axis in Bumble Bees.

The brain-gut-microbiome axis is an emerging area of study, particularly in vertebrate systems. Existing evidence suggests that gut microbes can influence basic physiological functions and that perturbations to the gut microbiome can have deleterious effects on cognition and lead to neurodevelopmental disorders. While this relationship has been extensively studied in vertebrate systems, little is known about this relationship in insects. We hypothesized that because of its importance in bee health, the gut microbiota influences learning and memory in adult bumble bees. As an initial test of whether there is a brain-gut-microbiome axis in bumble bees, we reared microbe-inoculated and microbe-depleted bees from commercial Bombus impatiens colonies. We then conditioned experimental bees to associate a sucrose reward with a color and tested their ability to learn and remember the rewarding color. We found no difference between microbe-inoculated and microbe-depleted bumble bees in performance during the behavioral assay. While these results suggest that the brain-gut-microbiome axis is not evident in Bombus impatiens, future studies with different invertebrate systems are needed to further investigate this phenomenon.

Cockroaches Show Individuality in Learning and Memory During Classical and Operant Conditioning.

Animal personality and individuality are intensively researched in vertebrates and both concepts are increasingly applied to behavioral science in insects. However, only few studies have looked into individuality with respect to performance in learning and memory tasks. In vertebrates, individual learning capabilities vary considerably with respect to learning speed and learning rate. Likewise, honeybees express individual learning abilities in a wide range of classical conditioning protocols. Here, we study individuality in the learning and memory performance of cockroaches, both in classical and operant conditioning tasks. We implemented a novel classical (olfactory) conditioning paradigm where the conditioned response is established in the maxilla-labia response (MLR). Operant spatial learning was investigated in a forced two-choice task using a T-maze. Our results confirm individual learning abilities in classical conditioning of cockroaches that was reported for honeybees and vertebrates but contrast long-standing reports on stochastic learning behavior in fruit flies. In our experiments, most learners expressed a correct behavior after only a single learning trial showing a consistent high performance during training and test. We can further show that individual learning differences in insects are not limited to classical conditioning but equally appear in operant conditioning of the cockroach.

The Drivers of Heuristic Optimization in Insect Object Manufacture and Use.

Insects have small brains and heuristics or 'rules of thumb' are proposed here to be a good model for how insects optimize the objects they make and use. Generally, heuristics are thought to increase the speed of decision making by reducing the computational resources needed for making decisions. By corollary, heuristic decisions are also deemed to impose a compromise in decision accuracy. Using examples from object optimization behavior in insects, we will argue that heuristics do not inevitably imply a lower computational burden or lower decision accuracy. We also show that heuristic optimization may be driven by certain features of the optimization problem itself: the properties of the object being optimized, the biology of the insect, and the properties of the function being optimized. We also delineate the structural conditions under which heuristic optimization may achieve accuracy equivalent to or better than more fine-grained and onerous optimization methods.

Transfer of Visual Learning Between a Virtual and a Real Environment in Honey Bees: The Role of Active Vision.

To study visual learning in honey bees, we developed a virtual reality (VR) system in which the movements of a tethered bee walking stationary on a spherical treadmill update the visual panorama presented in front of it (closed-loop conditions), thus creating an experience of immersion within a virtual environment. In parallel, we developed a small Y-maze with interchangeable end-boxes, which allowed replacing repeatedly a freely walking bee into the starting point of the maze for repeated decision recording. Using conditioning and transfer experiments between the VR setup and the Y-maze, we studied the extent to which movement freedom and active vision are crucial for learning a simple color discrimination. Approximately 57% of the bees learned the visual discrimination in both conditions. Transfer from VR to the maze improved significantly the bees' performances: 75% of bees having chosen the CS+ continued doing so and 100% of bees having chosen the CS- reverted their choice in favor of the CS+. In contrast, no improvement was seen for these two groups of bees during the reciprocal transfer from the Y-maze to VR. In this case, bees exhibited inconsistent choices in the VR setup. The asymmetric transfer between contexts indicates that the information learned in each environment may be different despite the similar learning success. Moreover, it shows that reducing the possibility of active vision and movement freedom in the passage from the maze to the VR impairs the expression of visual learning while increasing them in the reciprocal transfer improves it. Our results underline the active nature of visual processing in bees and allow discussing the developments required for immersive VR experiences in insects.

Do Insects Have Emotions? Some Insights from Bumble Bees.

While our conceptual understanding of emotions is largely based on human subjective experiences, research in comparative cognition has shown growing interest in the existence and identification of "emotion-like" states in non-human animals. There is still ongoing debate about the nature of emotions in animals (especially invertebrates), and certainly their existence and the existence of certain expressive behaviors displaying internal emotional states raise a number of exciting and challenging questions. Interestingly, at least superficially, insects (bees and flies) seem to fulfill the basic requirements of emotional behavior. Yet, recent works go a step further by adopting terminologies and interpretational frameworks that could have been considered as crude anthropocentrism and that now seem acceptable in the scientific literature on invertebrate behavior and cognition. This change in paradigm requires, therefore, that the question of emotions in invertebrates is reconsidered from a cautious perspective and with parsimonious explanations. Here we review and discuss this controversial topic based on the recent finding that bumblebees experience positive emotions while experiencing unexpected sucrose rewards, but also incorporating a broader survey of recent literature in which similar claims have been done for other invertebrates. We maintain that caution is warranted before attributing emotion-like states to honey bees and bumble bees as some experimental caveats may undermine definitive conclusions. We suggest that interpreting many of these findings in terms of motivational drives may be less anthropocentrically biased and more cautious, at least until more careful experiments warrant the use of an emotion-related terminology.