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.

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.

Does Holistic Processing Require a Large Brain? Insights From Honeybees and Wasps in Fine Visual Recognition Tasks.

The expertise of humans for recognizing faces is largely based on holistic processing mechanism, a sophisticated cognitive process that develops with visual experience. The various visual features of a face are thus glued together and treated by the brain as a unique stimulus, facilitating robust recognition. Holistic processing is known to facilitate fine discrimination of highly similar visual stimuli, and involves specialized brain areas in humans and other primates. Although holistic processing is most typically employed with face stimuli, subjects can also learn to apply similar image analysis mechanisms when gaining expertise in discriminating novel visual objects, like becoming experts in recognizing birds or cars. Here, we ask if holistic processing with expertise might be a mechanism employed by the comparatively miniature brains of insects. We thus test whether honeybees (Apis mellifera) and/or wasps (Vespula vulgaris) can use holistic-like processing with experience to recognize images of human faces, or Navon-like parameterized-stimuli. These insect species are excellent visual learners and have previously shown ability to discriminate human face stimuli using configural type processing. Freely flying bees and wasps were consequently confronted with classical tests for holistic processing, the part-whole effect and the composite-face effect. Both species could learn similar faces from a standard face recognition test used for humans, and their performance in transfer tests was consistent with holistic processing as defined for studies on humans. Tests with parameterized stimuli also revealed a capacity of honeybees, but not wasps, to process complex visual information in a holistic way, suggesting that such sophisticated visual processing may be far more spread within the animal kingdom than previously thought, although may depend on ecological constraints.