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Simple feature detectors in the visual system, such as edge-detectors, are likely to underlie even the most complex visual processing, so understanding the limits of these systems is crucial for a fuller understanding of visual processing. We investigated the ability of bumblebees ( Bombus terrestris ) to discriminate between differently angled edges. In a multiple-choice, “meadow-like” scenario, bumblebees successfully discriminated between angled bars with 7° differences, significantly exceeding the previously reported performance of eastern honeybees ( Apis cerana , limit: 15°). Neither the rate at which bees learned, nor their final discrimination performance were affected by the angular orientation of the training bars, indicating a uniform performance across the visual field. Previous work has found that, in dual-choice tests, eastern honeybees cannot reliably discriminate between angles with less than 25° difference, suggesting that performance in discrimination tasks is affected by the training regime, and doesn’t simply reflect the perceptual limitations of the visual system. We used high resolution LCD monitors to investigate bumblebees’ angular resolution in a dual-choice experiment. Bumblebees could still discriminate 7° angle differences under such conditions (exceeding the previously reported limit for Apis mellifera , of 10°, as well as that of A . cerana ). Bees eventually reached similar levels of accuracy in the dual-choice experiment as they did under multiple-choice conditions but required longer learning periods. Bumblebees show impressive abilities to discriminate between angled edges, performing better than two previously tested species of honeybee. This high performance may, in turn, support complex visual processing in the bumblebee brain.

In a photoinjector electron source, the initial transverse electron bunch properties are determined by the spatial properties of the laser beam on the photocathode. Spatial shaping of the laser is commonly achieved by relay imaging an illuminated circular mask onto the photocathode. However, the Gibbs phenomenon shows that recreating the sharp edge and discontinuity of the cut profile at the mask on the cathode is not possible with an optical relay of finite aperture. Furthermore, the practical injection of the laser into the photoinjector results in the beam passing through small or asymmetrically positioned apertures. This work uses wavefront propagation to show how the transport apertures cause ripple structures to appear in the transverse laser profile even when effectively the full laser power is transmitted. The impact of these structures on the propagated electron bunch has also been studied with electron bunches of high and low charge density. With high charge density, the ripples in the initial charge distribution rapidly wash‐out through space charge effects. However, for bunches with low charge density, the ripples can persist through the bunch transport. Although statistical properties of the electron bunch in the cases studied are not greatly affected, there is the potential for the distorted electron bunch to negatively impact machine performance. Therefore, these effects should be considered in the design phase of accelerators using photoinjectors. Wavefront propagation studies show that apertures in the laser injection path into photoinjector electron guns result in spatial ripples in relay‐imaged cut‐Gaussian profiles. The effect on electron beam properties depends on the extent to which space charge washes out the corresponding ripples in the emitted electron bunch, but could significantly negatively impact the quality of the electron bunch.

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.

Previous studies suggest that social learning in bumblebees can occur through second-order conditioning, with conspecifics functioning as first-order reinforcers. However, the behavioural mechanisms underlying bumblebees’ acquisition of socially learned associations remain largely unexplored. Investigating these mechanisms requires detailed quantification and analysis of the observation process. Here we designed a new 2D paradigm suitable for simple top-down high-speed video recording and analysed bumblebees’ observational learning process using a deep-learning-based pose-estimation framework. Two groups of bumblebees observed live conspecifics foraging from either blue or yellow flowers during a single foraging bout, and were subsequently tested for their socially learned colour preferences. Both groups successfully learned the colour indicated by the demonstrators and spent more time facing rewarding flowers—whether occupied by demonstrators or not—compared to non-rewarding flowers. While both groups showed a negative correlation between time spent facing non-rewarding flowers and learning outcomes, the observer bees in the blue group benefited from time spent facing occupied rewarding flowers, whereas the yellow group showed that time facing unoccupied rewarding flowers by the observer bees positively correlated with their learning outcomes. These results suggest that socially influenced colour preferences are shaped by the interplay of different types of observations rather than merely by observing a conspecific at a single colour. Together, these findings provide direct evidence of the dynamical viewing process of observer bees during social observation, opening up new opportunities for exploring the details of more complex social learning in bumblebees and other insects.

Automated sorption balances are widely used for characterizing the interaction of water vapor with hygroscopic materials. This paper is part of an interlaboratory study investigating the stability and performance of automated sorption balances. A previous paper in this study investigated the mass, temperature, and relative humidity (RH) stability of automated sorption balances by looking at the mass change of a non-hygroscopic sample over time. In this study, we examine the mass stability of wood samples held at constant RH for seven to ten days after a step change. The reason for the long hold times was to collect data to “operational equilibrium” where the change in mass is on the order of the inherent operational stability of the instrument. A total of 80 datasets were acquired from 21 laboratories covering absorption with final RH levels ranging from 10 to 95%. During these long hold times, several unusual behaviors were observed in the mass-vs-time curves. Deviations from expected sorption behavior were examined by fitting the data to an empirical sorption kinetics model and calculating the root mean square error (RMSE) between the observed and smoothed behavior. Samples that had a large RMSE relative to the median RMSE of the other datasets often had one of several types of errors: abrupt disturbances, diurnal oscillations, or long-term mass decline during an absorption step. In many cases, mass fluctuations were correlated with changes in the water reservoir temperature of the automated sorption balance. We discuss potential errors in sorption measurements on hygroscopic materials and suggest an acceptable level of RMSE for sorption data.

Bees’ remarkable visual learning abilities make them ideal for studying active information acquisition and representation. Here, we develop a biologically inspired model to examine how flight behaviours during visual scanning shape neural representation in the insect brain, exploring the interplay between scanning behaviour, neural connectivity, and visual encoding efficiency. Incorporating non-associative learning—adaptive changes without reinforcement—and exposing the model to sequential natural images during scanning, we obtain results that closely match neurobiological observations. Active scanning and non-associative learning dynamically shape neural activity, optimising information flow and representation. Lobula neurons, crucial for visual integration, self-organise into orientation-selective cells with sparse, decorrelated responses to orthogonal bar movements. They encode a range of orientations, biased by input speed and contrast, suggesting co-evolution with scanning behaviour to enhance visual representation and support efficient coding. To assess the significance of this spatiotemporal coding, we extend the model with circuitry analogous to the mushroom body, a region linked to associative learning. The model demonstrates robust performance in pattern recognition, implying a similar encoding mechanism in insects. Integrating behavioural, neurobiological, and computational insights, this study highlights how spatiotemporal coding in the lobula efficiently compresses visual features, offering broader insights into active vision strategies and bio-inspired automation.

Active vision, the sensory-motor process through which animals dynamically adjust visual input to sample and prioritise relevant information via photoreceptors, eyes, head, and body movements, is well-documented across species. In small-brained animals like insects, where parallel processing may be limited, active vision for sequential acquisition of visual features might be even more important. We investigated how bumblebees use active vision to distinguish between two visual patterns: a multiplication sign and its 45°-rotated variant, a plus sign. By allowing bees to freely inspect patterns, we analysed their flight paths, inspection times, velocities and regions of focus through high-speed videography. We observed that bees tended to inspect only a small region of each pattern, with a preference for lower and left-side sections, before accurately accepting target or rejecting distractor patterns. The specific pattern areas scanned differed between the plus and multiplication signs, yet flight behaviour remained consistent and specific to each pattern, regardless of whether the pattern was rewarding or punishing. Transfer tests showed that bees could generalise their pattern recognition to partial cues, maintaining scanning strategies and selective attention to learned regions. These findings highlight active vision as a crucial aspect of bumblebees' visual processing, where selective scanning behaviours during flight enhance discrimination accuracy and enable efficient environmental analysis and visual encoding.

Transmission between hosts is crucial to the growth, development and reproduction of many parasites. As a consequence, parasites are under selection to maximise transmission success and exhibit many behavioural and morphological adaptations that allow detection of, and movement between, hosts. However, transmission success is not determined by parasites alone, but is also shaped by host behaviours. Often, host behaviours function to minimise the risk of exposure to parasites; in some cases, however, host behaviours may be manipulated by parasites to increase transmission success. In this study, we investigated transmission of the parasitic mite Riccardoella oudemansi between slug ( Limacus maculatus ) hosts, considering the role of both host and parasite behaviour in determining transmission success. Host-host transmission occurred when slugs were in physical contact, but mites were also capable of moving across the substrate to locate new hosts, a process facilitated by mucus trails. We found no strong evidence that slugs avoid parasitised conspecifics, or that mites manipulate slug behaviour to increase transmission. Finally, mites showed a preference for the mucus of parasitised slugs, but did not discriminate between mucus from their own host and another parasitised slug. A general preference for mucus from parasitised slugs is likely to be important in encouraging mites to remain in close contact with their host and may also facilitate host-switching and outbreeding. We encourage further study of parasitism by Riccardoella in limacid slugs, where cross-species variation in host social behaviour may drive differences in the rate and success of parasite transmission across slug species.

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.

Automated sorption balances are widely used for characterizing the interaction of water vapor with hygroscopic materials. These instruments provide an efficient way to collect sorption isotherm data and kinetic data. A typical method for defining equilibrium after a step change in relative humidity (RH) is using a particular threshold value for the rate of change in mass with time. Recent studies indicate that commonly used threshold values yield substantial errors and that further measurements are needed at extended hold times as a basis to assess the accuracy of abbreviated equilibration criteria. However, the mass measurement accuracy at extended times depends on the operational stability of the instrument. Published data on the stability of automated sorption balances are rare. An interlaboratory study was undertaken to investigate equilibration criteria for automated sorption balances. This paper focuses on the mass, temperature, and RH stability and includes data from 25 laboratories throughout the world. An initial target for instrument mass stability was met on the first attempt in many cases, but several instruments were found to have unexpectedly large instabilities. The sources of these instabilities were investigated and greatly reduced. This paper highlights the importance of verifying operational mass stability of automated sorption balances, gives a method to perform stability checks, and provides guidance on identifying and correcting common sources of mass instability.