Higher-order discrimination learning by honey bees in a virtual environment

Non-elemental learning constitutes a cognitive challenge because, contrary to elemental learning forms, it does not rely on simple associations, as events to be learned are usually ambiguous in terms of reinforcement outcome. Negative patterning constitutes a paradigmatic case of non-elemental learning, as subjects have to learn that single elements A and B are reinforced while their conjunctive representation AB is not reinforced (A+, B+ vs. AB-). Solving this problem requires treating the compound AB as being different from the linear sum of its components in order to overcome stimulus ambiguity (A+/A- and B+/B-). The honey bee is the only insect capable of mastering negative patterning as shown by numerous studies restricted mainly to the olfactory domain. Here we studied the capacity of bees to solve a negative patterning discrimination in the visual domain and used to this end a virtual reality (VR) environment in which a tethered bee walking stationary on a treadmill faces visual stimuli projected on a semicircular screen. Stimuli are updated by the bee’s movements, thus creating an immersive environment. Bees were trained to discriminate single-colored gratings rewarded with sucrose solution (blue, green; A+, B+) from a non-rewarded composite grating (blue-green, AB-). Bees learned this discrimination in the VR environment and inhibited to this end linear processing of the composite grating, which otherwise is treated as the sum of its components. Our results show for the first time mastering of a non-linear visual discrimination in a VR environment by honey bees, thus highlighting the value of VR for the study of cognition in insects.

Neural substrate for higher-order learning in an insect: Mushroom bodies are necessary for configural discriminations

Learning theories distinguish elemental from configural learning based on their different complexity. Although the former relies on simple and unambiguous links between the learned events, the latter deals with ambiguous discriminations in which conjunctive representations of events are learned as being different from their elements. In mammals, configural learning is mediated by brain areas that are either dispensable or partially involved in elemental learning. We studied whether the insect brain follows the same principles and addressed this question in the honey bee, the only insect in which configural learning has been demonstrated. We used a combination of conditioning protocols, disruption of neural activity, and optophysiological recording of olfactory circuits in the bee brain to determine whether mushroom bodies (MBs), brain structures that are essential for memory storage and retrieval, are equally necessary for configural and elemental olfactory learning. We show that bees with anesthetized MBs distinguish odors and learn elemental olfactory discriminations but not configural ones, such as positive and negative patterning. Inhibition of GABAergic signaling in the MB calyces, but not in the lobes, impairs patterning discrimination, thus suggesting a requirement of GABAergic feedback neurons from the lobes to the calyces for nonelemental learning. These results uncover a previously unidentified role for MBs besides memory storage and retrieval: namely, their implication in the acquisition of ambiguous discrimination problems. Thus, in insects as in mammals, specific brain regions are recruited when the ambiguity of learning tasks increases, a fact that reveals similarities in the neural processes underlying the elucidation of ambiguous tasks across species.

Elemental Learning and the Pyramid of Fidelity

One of the emerging issues for educators who recognize the importance of digital games and virtual worlds is fidelity to learning outcomes, both intentional and incidental. In this chapter, from the perspective of an educator, the author introduces an integrated framework that emphasizes elemental learning. The model, based on learning analysis and direct measurement of learning is iterative, as opposed to a front-end-only approach, and includes five major cognitive learning outcomes: actual elements, simulated elements, procedural understanding, conceptual understanding, and related knowledge. For each of the learning outcomes, the author provides design propositions and an example.