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The ability to generalize over naturally occurring variation in cues indicating food or predation risk is highly useful for efficient decision-making in many animals. Honeybees have remarkable visual cognitive abilities, allowing them to classify visual patterns by common features despite having a relatively miniature brain. Here we ask the question whether generalization requires complex visual recognition or whether it can also be achieved with relatively simple neuronal mechanisms. We produced several simple models inspired by the known anatomical structures and neuronal responses within the bee brain and subsequently compared their ability to generalize achromatic patterns to the observed behavioural performance of honeybees on these cues. Neural networks with just eight large-field orientation-sensitive input neurons from the optic ganglia and a single layer of simple neuronal connectivity within the mushroom bodies (learning centres) show performances remarkably similar to a large proportion of the empirical results without requiring any form of learning, or fine-tuning of neuronal parameters to replicate these results. Indeed, a model simply combining sensory input from both eyes onto single mushroom body neurons returned correct discriminations even with partial occlusion of the patterns and an impressive invariance to the location of the test patterns on the eyes. This model also replicated surprising failures of bees to discriminate certain seemingly highly different patterns, providing novel and useful insights into the inner workings facilitating and limiting the utilisation of visual cues in honeybees. Our results reveal that reliable generalization of visual information can be achieved through simple neuronal circuitry that is biologically plausible and can easily be accommodated in a tiny insect brain.

Honey bees display remarkable visual learning abilities, providing insights regarding visual information processing in a miniature brain. It was discovered that bees can solve a task that is generally viewed as spatial concept learning in primates, specifically the concept of “above” and “below.” In these works, two pairs of visual stimuli were shown in the two arms of a Y-maze. Each arm displayed a “referent” shape (e.g., a cross, or a horizontal line) and a second geometric shape that appeared either above or below the referent. Bees learning the “concept of aboveness” had to choose the arm of the Y-maze in which a shape–any shape–occurred above the referent, while those learning the “concept of belowness” had to pick the arm in which there was an arbitrary item beneath the referent. Here, we explore the sequential decision-making process that allows bees to solve this task by analyzing their flight trajectories inside the Y-maze. Over 368 h of high-speed video footage of the bees' choice strategies were analyzed in detail. In our experiments, many bees failed the task, and, with the possible exception of a single forager, bees as a group failed to reach significance in picking the correct arm from the decision chamber of the maze. Of those bees that succeeded in choosing correctly, most required a close-up inspection of the targets. These bees typically employed a close-up scan of only the bottom part of the pattern before taking the decision of landing on a feeder. When rejecting incorrect feeders, they repeatedly scanned the pattern features, but were still, on average, faster at completing the task than the non-leaners. This shows that solving a concept learning task could actually be mediated by turning it into a more manageable discrimination task by some animals, although one individual in this study appeared to have gained the ability (by the end of the training) to solve the task in a manner predicted by concept learning.

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Low energy electron diffraction, Auger electron spectroscopy, X-ray photoelectron spectroscopy and line of sight mass spectrometry have been used to study the adsorption and desorption of dimethyldisulfide (DMDS) on Au(111). At 300 K adsorption is dissociative, forming a chemisorbed adlayer of methylthiolate with a 1/3 ML, (sq rt 3 x sq rt 3)R30 degrees, structure. At 100 K adsorption is molecular, with dissociation to form the 1/3 ML (sq rt 3 x sq rt 3)R30 degrees methylthiolate structure occurring at 138-160 K. A physisorbed DMDS layer, with a coverage of 1/6 ML of DMDS, forms on top of the (sq rt 3 x sq rt 3)R30 degrees chemisorbed MT surface for T < or = 180 K, with multilayers forming for T < or = 150 K. In temperature programmed desorption, multilayers of DMDS desorbed with zero order kinetics and an activation energy of 41 kJ mol(-1); the physisorbed layer desorbed with first order kinetics, exhibiting repulsive lateral interactions with an activation energy which varied from 63 kJ mol(-1) (theta = 0) to 51 kJ mol(-1) (theta = 1); the chemisorbed methylthiolate layer desorbed associatively as DMDS via the physisorbed layer, the activation energy for the reaction, 2 methylthiolate --> physisorbed DMDS, exhibiting repulsive lateral interactions with an activation energy which varied from 65 kJ mol(-1) (theta = 0) to 61 kJ mol(-1) (theta = 1). The physisorbed disulfide layer explains the pre-cursor state adsorption kinetics observed in sticking probability measurement, while its relatively facile formation provides a mechanism by which thiolate self-assembled monolayers can become mobile at room temperature.