High-Speed Videography Reveals How Honeybees Can Turn a Spatial Concept Learning Task Into a Simple Discrimination Task by Stereotyped Flight Movements and Sequential Inspection of Pattern Elements

Abstract

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.

Meat ants cut more trail shortcuts when facing long detours

“…Interestingly, no single heuristic could reliably predict performance on group level, because individual ants chose different heuristics. This finding reflects the discussion of chapters 4 & 5 that measures of group performance often do not well represent individual behaviour and highlights the potential of studies investigating the individual behavioural strategies used to solve tasks in social insects (Guiraud et al 2018).…”

“…However, there has been extensive debate regarding how honeybees solve those complex tasks and whether they require top-down control by the animal (Cope et al 2018;Guiraud et al 2018;Peng and Chittka 2017;Roper et al 2017;Vasas and Chittka 2019). It has been argued that honeybees' success in same/different tasks could be mediated by appetitive or aversive modulation of their innate tendency to revisit similar flowers (Collett 2005, but see Brown and Sayde 2013) or by sensory accommodation, i.e.…”

“…A recent experiment closely investigating the strategies deployed by bees while learning an above/below concept found that they can use sequential inspection of the presented items to succeed in the task, without the need for a spatial concept (Guiraud et al 2018). Similarly, tasks that require animals to discriminate numerosities could be achieved by a small subset of neurons Yet, prerequisites assumed to be crucial for concept learning are met by other Hymenoptera, such as ants, as well (Avarguès-Weber and Giurfa 2013).…”

“…In other words, if false-positives (actions which lead to an error) only induce minor costs or if the foraging context is highly variable, animals might resort to heuristics instead of learning(Arkes 1991;Haselton et al 2015). Heuristics can pre-equip animals to solve complex problems such as nest size estimation by scouting ants using the frequency of own trails crossings (Buffon's needle;Mallon and Franks 2000), best-of-N rule in nest-searching honeybee swarms(Seeley and Buhrman 2001) or prey interception in dragonflies (research on concept learning(Avarguès-Weber et al 2011;Brown and Sayde 2013;Giurfa et al 2001;Guiraud et al 2018;Howard et al 2018) and other complex cognitive abilities of honey bees such as metacognition (Perry and Barron 2013) are now being supplemented by studies on potential underlying neural mechanisms(MaBouDi et al 2017;Peng and Chittka 2017;Seilheimer et al 2014; Vasas and Chittka 2019), studies on complex cognition in insects other than honeybees remain very scarce(Brown and Sayde 2013;Tibbetts et al 2019).…”

Bumblebees Learn a Relational Rule but Switch to a Win-Stay/Lose-Switch Heuristic After Extensive Training

“…However, in some of these cases, it may be that bees use a variety of different strategies to solve the tasks they are confronted with Cope et al ( 2018 ) and Skorupski et al ( 2018 ). One recent study showed that bees can solve a spatial concept learning task using a simple visual discrimination strategy through sequential scanning of stimuli rather than needing to compare stimuli based on an abstract rule, though some individuals may well follow such a rule (Guiraud et al, 2018 ). In numerical cognition tasks, honeybees may also use alternative cues that correlate with a number, but are not in themselves numerical (Vasas and Chittka, 2019 ; MaBouDi et al, 2020 ).…”