In recent decades we have seen a string of remarkable discoveries detailing the impressive cognitive abilities of bees (social learning, concept learning and even counting). But should these discoveries be regarded as spectacular because bees manage to achieve human-like computations of visual image analysis and reasoning? Here I offer a radically different explanation. Using theoretical bee brain models and detailed flight analysis of bees undergoing behavioural experiments I counter the widespread view that complex visual recognition and classification requires animals to not only store representations of images, but also perform advanced computations on them. Using a bottom-up approach I created theoretical models inspired by the known anatomical structures and neuronal responses within the bee brain and assessed how much neural complexity is required to accomplish behaviourally relevant tasks. Model simulations of just eight large-field orientation-sensitive neurons from the optic ganglia and a single layer of simple neuronal connectivity within the mushroom bodies (learning centres) generated performances remarkably similar to the empirical result of real bees during both discrimination and generalisation orientation pattern experiments. My models also hypothesised that complex 'above and below' conceptual learning, often used to exemplify how 'clever' bees are, could instead be accomplished by very simple inspection of the target patterns. Analysis of the bees' flight paths during training on this task found bees utilised an even simpler mechanism than anticipated, demonstrating how the insects use unique and elegant solutions to deal with complex visual challenges. The true impact of my research is therefore not merely showing a model that can solve a particular set of generalisation experiments, but in providing a fundamental shift in how we should perceive visual recognition problems. Across animals, equally simple neuronal architectures may well underlie the cognitive affordances that we currently assume to be required for more complex conceptual and discrimination tasks.

Honigbienen (Apis mellifera L.) sind in der Lage mit ihren Komplexaugen visuelle Muster wahrzunehmen und die Musterinformation im Zentralen Nervensystem zu speichern und für Ähnlichkeitsbewertungen wieder abzurufen. Die vorliegende Arbeit zeigt klare Evidenz gegen eine ausschließliche Bewertung von Schwarzweißmustern mit Hilfe von Template-Matching-Mechanismen. Mit systematisch abgewandelten Dressurparadigmen trainierte Bienen bewerteten Muster unabhängig von der erfolgten Dressur stets bevorzugt gemäß eher grober Mustereigenschaften, wie zum Beispiel die Parameter "schwarzer Musterzentralbereich" und "Musterzerstreutheit". Veränderte man in einem weiteren Versuchansatz die Musterinformation der Schwarzweißmuster zudem gezielt durch geeignete Fourier-Filterung, zeigte sich, dass Bienen zur Musterdiskriminierung bereits die Frequenzinformation von 2 - 8 Schwingungen/Bildbreite genügte. Diese Unschärfe der bewerteten Bildinformation ließ sich nicht ausschließlich aus den optischen Eigenschaften des visuellen Apparates der Bienen ableiten. Videodokumentationen und Einzelbildanalyse des Flugverhaltens der Bienen vor den Mustern ergaben zudem keinerlei Hinweise für eine Nutzung des Flugverhaltens als Bewertungsgrundlage zur Musterdiskriminierung. Die erhaltenen Ergebnisse zur Musterdiskriminierung wurden vor dem Hintergrund eines ökonomischen Entscheidungsmodells für menschliches Verhalten, den Frugalheuristiken, diskutiert und Hinweise auf eine ökonomische Bewertungsstrategie der Bienen entsprechend einer Take-The-Best-Heuristik gefunden. / Honeybees (Apis mellifera L.) are able to perceive visual patterns through their compound eyes and store the visual information in the central nervous system for subsequent use in pattern discrimination tasks. This thesis provides clear evidence against the assumption that pattern discrimination relies exclusively on template matching mechanisms. Bees discriminated pairs of patterns preferential using extracted pattern parameters. Within this thesis the preferred parameters of the bees following the training paradigms were coarse parameters such as "black centre" and "pattern disruption". In experiments with Fourier filtered patterns the frequency information of the patterns were additionally reduced. The results showed that bees could discriminate patterns using only 2 - 8 cycles/pattern-width of the frequency information. The fuzziness of the exploited visual information could not be assigned to restrictions of the visual system of bees. Additional documentation and single picture analysis of the videotaped flight behaviour in front of the patterns provided no evidence for bees using their flight behaviour in order to enhance the pattern discrimination ability. Application of economic human decision models (frugal heuristics) to the behavioural results showed clues that bees'' decisions could be explained with the help of the Take-The-Best-heuristic.