Vision is one of the most important sensory modalities for the perception of biologically relevant stimuli. It is one of the major senses of insects like honeybees, and there is abundant evidence for the honeybee’s ability to quickly learn colours, shapes and patterns (von Frisch 1914, 1967; Zhang et al. 1995; Srinivasan 2010). However, simple visual cues rarely exist in nature: during their daily foraging trips, honeybees have to rely on a variety of complex visual cues from their environment in order to navigate, such as constellations of landmarks, multifaceted landscapes, and flowering trees (Collett 1996; Collett and Collett 2002; Collett et al. 2003; Steffan-Dewenter and Kuhn 2003; Dyer et al. 2008). This requires sophisticated visual processing and learning abilities. Indeed, bees have been shown to discriminate complex forest scenes (Dyer et al. 2008), be capable of categorizing images from natural scenes such as different flower shapes (Zhang et al. 2004), and most surprisingly, human faces (Dyer et al. 2005; Dyer and Vuong 2008; Avarguès-Weber et al. 2010). Furthermore, bees have been shown to display numerical processing abilities, solve delayed-matching-to-sample tasks, learn abstract rules and concepts, and transfer these to novel stimuli and tasks, even to different sensory modalities (Srinivasan et al. 1998; Giurfa et al. 2001; Giurfa 2007; Gross et al. 2009; Avarguès-Weber et al. 2011, 2012). These are remarkable capabilities for an insect, comparable to those of vertebrates. In spite of their small brain, honeybees have the capacity to process and learn complex visual information, which in turn facilitates efficient navigation and assists foraging in their ever-changing visual environment.

Although numerous studies have demonstrated that bees can learn much more than just simple patterns, colours and shapes, the cues that honeybees use to solve complex visual tasks are still a matter of debate. Some models assume that bee vision and visual learning is determined by mechanistic hardwired circuits, and that bees rely only on low-level feature detectors and elemental cues (Horridge 2000, 2005, 2009a, b). In this scenario bees learn combinations of coinciding elemental cues as retinotopic label for a particular image and generalize between images containing similar cues. This simple elemental processing, however, cannot explain how bees use previously acquired information to solve novel tasks, categorize novel stimuli that significantly differ in low-level cues, and transfer abstract concepts to novel domains. Therefore, other models suggest that honeybee vision and visual learning is a plastic system based on multiple mechanisms (Dyer and Griffiths 2012). Depending on the visual task at hand, honeybees may rely on simple, elemental processing if sufficient, however, with increasing complexity of the task and continued visual experience, honeybees can learn to move to non-elemental processing, such as configural type processing and rule-learning, and can access top-down information to solve novel tasks (Giurfa et al. 2003; Stach et al. 2004; Stach and Giurfa 2005; Giurfa 2007; Avarguès-Weber et al. 2010; Dyer 2012).

To further investigate the cues and mechanisms underlying honeybee visual learning, we asked whether the honeybee’s ability to discriminate between complex stimuli could be extended to the discrimination of paintings, which humans distinguish on the basis of artistic style—that is, Claude Monet paintings from the Impressionist period and Pablo Picasso paintings from the Cubist period. Previous work with birds has already demonstrated that the capacity for discrimination of artistic style is not limited to humans: Pigeons can learn to distinguish Monet from Picasso paintings, generalize to novel paintings by the same artist and even to paintings by other artists from the same period (Watanabe 2001; Watanabe et al. 1995). If honeybees were similarly able to distinguish multiple paintings by Monet and Picasso and then transfer this discrimination to novel paintings by the same artists, it would suggest that they are sensitive to the visual characteristics that are common to each style. With each painting being unique and differing in countless visual details from others even by the same artist, honeybees are unlikely to achieve generalization to novel paintings, if they rely only on simple elemental processing and retinotopic image matching.

Here, we investigate for the first time whether discrimination of paintings and generalization of artistic styles can be achieved by an insect that has a brain the size of a grass seed containing less than one million neurons. In a series of experiments, we tested whether honeybees could discriminate Monet from Picasso paintings at all; whether bees could learn to discriminate more than one painting pair at the same time; and whether bees could generalize their discrimination to novel paintings.