Angiosperms display a combination of olfactory and visual cues to attract or deter pollinators. For example, in terms of visual spatial cues, bee-flies prefer large, and dissected flower models (Johnson and Dafni, 1998), while beetles have a preference for large and circular “bowl-shaped” flowers (Dafni and Kevan, 1997). Different species of small bees prefer to visit small flowers presenting broken outlines while larger bee species preferentially visit large circular flowers (Dafni and Kevan, 1997). The specific spatial preferences of pollinators may thus drive the evolution of angiosperms and the phenotypes of their flowers and in turn, the coevolution of plants and pollinators (Giurfa et al., 1999a; Fenster et al., 2004, 2006; Lázaro and Totland, 2014; Gómez et al., 2016).

Flowers pollinated by bees often share a similar, centrally symmetric, star-like configuration (Dafni et al., 1997). Howard et al. (2019e, 2021) suggested that shape may be a cue used by bees when attracted to images of unknown insect-pollinated flowers, as opposed to bird-pollinated flowers when bees are confronted with a binary choice between these options. A preference for star-like flower configurations has been demonstrated in both eusocial (Lehrer et al., 1995; Dafni et al., 1997; Howard et al., 2019e) and non-eusocial bees (Howard et al., 2021).

Honeybees can rapidly learn to discriminate between flower signals and/or cues including scent, colour, shape, size, and symmetry (scent: Deisig et al., 2001; Vergoz et al., 2007; Giurfa and Sandoz, 2012; colour: Dyer and Chittka, 2004; Giurfa, 2004; Dyer and Neumeyer, 2005; Dyer et al., 2008; Dyer and Murphy, 2009; Dyer, 2012b; Avarguès-Weber and Giurfa, 2014; Sommerlandt et al., 2016; shape: Lehrer et al., 1995; de Ibarra and Giurfa, 2003; Morawetz et al., 2013; size: Avarguès-Weber et al., 2014; Howard et al., 2017a,b; symmetry: Giurfa et al., 1996). This learning fits their foraging lifestyle when considering that foraging bees must discriminate between flowers to make a choice on where to collect nectar and/or pollen. Furthermore, efficient decision making promotes optimal nutrition collection to help enable colony survival (Burns and Dyer, 2008).

Previously, we have reported initial evidence that experienced honeybees show significantly improved learning of configured flowers over scrambled versions (Dyer et al., 2013). Bees are highly sensitive to spatial configuration, including prioritising global configurations over local features when memorising stimuli (Avarguès-Weber et al., 2015). Bees are also able to use spatial configurations to categorise stimuli (Avarguès-Weber et al., 2010), and can use holistic processing, an advanced form of configural processing where the predictive power of certain features is enhanced by the way such features are arranged (Maurer et al., 2002; Avarguès-Weber et al., 2018). For example, considering human vision and face processing, if features like noses, eyes, and mouths are scrambled to different positions within a stimulus, then we are less accurate at processing the face, even though the same basic featural information is available (Tanaka and Farah, 1993; Collishaw and Hole, 2000; Maurer et al., 2002). Importantly, relying on spatial configurations to identify visual objects is thought to be advantageous, as spatial arrangements of features are robust to changes in luminosity, viewpoint and partial occlusion, and holistic processing in humans facilitates subtle discrimination between highly similar objects such as human faces (Tanaka and Farah, 1993; Maurer et al., 2002).

Interestingly, bees learn normal contrast flower pictures more quickly compared to highly contrasted pictures (black and white) (Dyer et al., 2013). This high contrast picture modification was consistent with frequent forms of parameterised stimulus presentation in experiments involving honey bee vision (Lehrer et al., 1995; Dafni and Kevan, 1997; Dafni et al., 1997; Horridge, 1997; Avarguès-Weber et al., 2011). However, recordings of contrast sensitivity functions for orientation sensitive neurons in honeybees show that neural responses increase for a low-contrast range, but saturate beyond intermediate contrast values. This result suggests that lower and intermediate contrast images may already induce efficient discrimination due to maximal activation of feature detectors in the visual brain (Yang and Maddess, 1997). The question of how completely flower-naïve bees may learn and process flower configurations is crucial to start unravelling the mechanisms of flower learning and discrimination by experienced foragers.

High-level performance in bees when processing and discriminating between flower stimuli could be explained by top-down processing due to intensive experience. Top-down processing is a form of information processing where prior learnt knowledge or experience is used to inform behaviour (Sarter et al., 2001). Interestingly, honeybees have demonstrated evidence of using top-down processing (Zhang and Srinivasan, 1994; Chittka and Niven, 2009). In a classic experiment by Zhang and Srinivasan (1994), honeybees initially failed to learn to discriminate three-dimensional camouflaged shapes against a similar patterned background; but when bees were provided with prior experience with salient three-dimensional shapes they could subsequently solve the more complex camouflaged problem. Thus, bees demonstrated a capacity to use acquired information in the brain to improve subsequent discrimination performance. This is opposed to automatic and unidirectional reactions to stimuli inputs without using other knowledge acquired by the brain, which is described as a bottom-up way of processing. Bottom-up processing could potentially explain flower discrimination if bees possess specific cue detectors or filters in the early stage of neuronal visual processing that respond preferentially to key flower cues using a matched filtering type process (Wehner, 1987). In this case, bees should also be efficient in discriminating normally configured flowers within a short number of learning events. For example, honeybees perform well when learning salient colours within 3–7 visits to only a target colour (Menzel, 1967). Matched filter mechanisms can be easily exploited by deceptive species (Warrant, 2016), and thus animals may also require a capacity to learn to be flexible. In this scenario, a choice strategy driven by matched filters, which may underlie initial preferences by naïve animals, could easily been overridden by experience, which may lead to different choices. Considering colour processing in honeybees, fine colour differences require differential conditioning to both target and distractor stimuli for greater than 15 choices (Giurfa, 2004; Reser et al., 2012; Sommerlandt et al., 2016) to learn how to avoid deceptive options, and such learning promotes changes in the higher levels of visual processing in a bee brain and the development of long-term memories (Dyer and Chittka, 2004; Dyer and Garcia, 2014; Sommerlandt et al., 2016). Thus, simple bottom-up or more advanced top-down type processes can be approached by testing stimuli that require differential conditioning for multiple trials (Dyer, 2012a). In such circumstances, animals are expected to assign a higher weight to the role of experience than to innate preferences (if any). The latter would manifest in the very first choices, yet they could be rapidly overshadowed by information acquired through individual experience.

Honeybees show evidence of learning spatial stimuli in a complex and dynamic way depending upon factors including stimulus salience; type of conditioning; and length of training. For example, considering simple stimuli composed of a low number of salient elements, honeybees and bumblebees have been observed to scan each element, which appears to be an innate initial learning behaviour (Lehrer et al., 1985; Dafni et al., 1997; MaBouDi et al., 2020) in a fashion somewhat analogous to how human vision moves eyes to initially scan salient elemental features in a scene when learning a new face, for instance (Henderson et al., 2005). This initial scanning behaviour can permit elemental learning based on spatial frequencies which could be explained by a matched filtering type process and subsequently allow a simple stimulus to be discriminated from alternative dissimilar stimuli (Srinivasan and Lehrer, 1988). One form of such learning can be described as absolute conditioning, where with a low number of learning events, sometimes within less than 10 trials, fast but relatively coarse discrimination is possible (Horridge and Zhang, 1995; Giurfa et al., 1999b). An alternative type of conditioning is termed differential conditioning, where rewarded target stimuli are learnt in the presence of non-rewarded and perceptually similar distractor stimuli. Differential conditioning enables finer levels of discrimination (Giurfa et al., 1999b; Stach et al., 2004). Learning with differential conditioning is dependent on the length of training. For instance, Stach and Giurfa (2005) reported that long training of 42 learning events (trials) led to significantly improved learning outcomes, and a fundamentally different type of visual processing compared to short training of 21 learning events (trials). This difference based upon experience explains why honeybees show evidence of moving from a simple scanning behaviour to a more complex, “cortical-like” way of processing spatial information (Srinivasan et al., 1993). Subsequent work has confirmed the dynamic nature of honeybee vision, and the dependence of training time required to enable different types of visual processing on task complexity (Avarguès-Weber et al., 2020). Interestingly, the human visual system also shows some similar patterns. For instance, in early stages of learning, eye movements are required to initiate learning (Henderson et al., 2005). Yet, later recognition of complex stimuli, such as faces, develops into robust holistic-type processing (Tanaka and Farah, 1993; Maurer et al., 2002) and can occur in the absence of scanning (Thorpe et al., 1996). Thus, visual learning and recognition appear to be enabled by dynamic processes in brains, although our understanding of these processes in insects is still emerging.

Here, we tested the performance of both flower-naïve and experienced bees learning to discriminate between pictures of either normally configured or scrambled flowers. In parallel, we tested the learning abilities of bees with high contrast pictures of normally configured flowers. We decided to employ a differential conditioning procedure with enough stimuli exposure to allow the bees to develop a complete representation of the stimuli.

We aimed to determine if the bee brain is inherently tuned to process flower cues, or if this type of specialisation emerges from foraging experience. To explore this question, we tested both flower-experienced and flower-naïve honeybee foragers with respect to their ability to learn rewarding vs. non-rewarding images depending on how the flower information was presented. We trained bees to discriminate between two flowers when flower spatial information was normally configured, symmetrically scrambled (experienced foragers only), asymmetrically scrambled, or high-contrasted. Experienced foragers were tested on all four stimulus comparisons, while naïve bees were tested on three of the comparisons. Our results suggest that flower shape recognition and memorisation is influenced by both hard-wired neural circuitry and experience-dependent factors.

Honeybees learnt to discriminate between normally configured pictures of flowers more efficiently than scrambled configurations or high-contrasted versions of the unscrambled pictures, even though these picture manipulations contained a similar level of featural information. Such superior performance for processing normal flower pictures was found both with experienced foragers and flower-naïve bees, providing evidence of an innate bias toward stimuli displaying flower-like configurations. Interestingly, bees which were completely flower naïve showed a higher rate of success for the modified pictures than experienced bees, which we discuss below.

Having an innate template and ability to process flowers would be beneficial to honeybees in a complex natural environment. Such a template would enable naïve foragers to efficiently detect and recognise rewarding flowers whilst avoiding deceptive alternatives like mimics that are known to exist in nature (Dyer and Chittka, 2004; Garcia et al., 2020). Our results (Figures 4–6) suggest that there is a component of innate preference for configured flowers – much like a flower template which is most likely “hard-wired” in the synaptic structure of the brain. Our findings also indicate that with experience, bees may improve their flower recognition and discrimination. When emerging from the hive, naïve honeybees may consequently employ a flower template, as previously shown by Lehrer et al. (1995) for star shaped stimuli, that permits efficient learning of similar star shaped flowers. The neural basis for such a template could be the presence of “matched filters” in the nervous system (Wehner, 1987), that is, visual detectors that specialise in extracting and responding to specific relevant features or configurations in the external world. Whilst initially thought to operate at an early stage of visual processing, such filters show some evidence of operating at the more central levels of sensory processing (Warrant, 2016). The presence of such filters predicts rapid responding to biologically relevant stimuli (Warrant, 2016), although this is not fully consistent with our accumulated evidence that both naïve and experienced foragers take about 30 learning trials with differential conditioning to reliably discriminate between the similar flower images. Detectors in early stages of visual processing could thus only contribute to enhancing discrimination learning abilities by increasing stimuli saliency and by mobilising attentional processes. Indeed, extended learning, which involves some form of top-down use of information, is essential to how bees acquire a capacity to identify target or deceptive flower species. In mammalian systems it is now well appreciated that learning of fine perceptual tasks may involve both higher level neural representations, and also feedback mechanisms that potentially tune responses of feature detectors in the earlier stages of visual processing (Ahissar and Hochstein, 2004; Ahissar et al., 2009). Whilst such phenomenon have proved difficult to access in bees because learning of more complex tasks requires free flying test conditions (Dyer, 2012a; Dyer and Griffiths, 2012), this possibility for information processing remains an interesting area for future work to understand how a miniature brain can enable fine discrimination tasks. Neural elements specialised and tuned via experience in responding to radially organised stimuli could thus promote efficiency to enable avoidance of deceptive stimuli, maximising nutrition collection and promoting colony survival.

There are two potential evolutionary processes by which bees may have acquired specialisation for configured, star-shaped flowers (Howard et al., 2019e, 2021). The first process suggests that the flowers of angiosperms would have evolved to exploit the preferences of insects, such as honeybees, for certain flower morphologies (Lehrer et al., 1995; Gegear et al., 2017). This theory is supported by the knowledge that Hymenoptera, including bees, arose during the Permian time period, millions of years before angiosperms arose during the Late Triassic (van der Kooi and Ollerton, 2020). In the other process, honeybees have evolved a preference for the star-like shapes of flowers pollinated by insects due to the nectar and pollen being generally more accessible in this morphology than in other morphological types (Brown et al., 2011).

The flower naïve test group very efficiently learnt to discriminate between the rewarding and non-rewarding real flower images that we used as stimuli, and this learning was significantly better than for the asymmetrically scrambled flowers that would disrupt an innate flower shape template (Figure 6). Interestingly, whilst not a specifically designed research question of the current study, the naïve bees appeared to learn to discriminate between the holistic flower stimuli to a higher level than the experienced bee group (Figure 6), although both groups demonstrated significant learning after 30 trials. One possible explanation for the potentially better learning in flower naïve honeybees is that the experienced bees had very likely visited a range of different flowers in the university garden prior to participating in the experiment. Previous research on naïve honeybee innate preferences for colour stimuli shows that an initial preference (Menzel, 1967) is overwritten by experience with other rewarding colours (Giurfa et al., 1995). A similar phenomenon has also been observed in free-flying bumblebees (Gumbert, 2000) where bees revert to innate preferences if conditions require. Thus, experience with the shapes of real flowers may lead to overwriting any initial innate preferences to some extent. However, a confound to this conclusion is that the naïve bees also showed the capacity to learn to recognise features in the asymmetric flower stimuli, whilst the experienced bees did not (Figures 5, 6). Therefore, the naïve bees could potentially just have been better learners by chance, and/or the experienced bees had developed a more holistic processing strategy. The latter has been observed to emerge in honeybees through experience (Avarguès-Weber et al., 2020). In humans, holistic processing is known to reduce some featural processing capacity (Maurer et al., 2002). Dissecting how experience may influence innate preferences for flowers in bees appears to be a fruitful avenue for future research.

Our current study complements previous research establishing honeybee’s preference for specific flower-like geometric patterns (star-shaped stimuli and stimuli with elements which radiate outward) (Lehrer et al., 1995; Dafni et al., 1997). The importance of flower configuration for bees may explain their tendency to preferentially approach unfamiliar insect-pollinated flowers, originating from a different continent, since insect-pollinated flowers tend to share star-like shapes and radiating elements by opposition to other flowers. The flower configuration not only attracts bees but also induces improved discrimination and learning performance probably due to a combination of bottom-up (feature detectors) and top-down (attentional bias and refinement of the template with experience) processing.

Brains have evolved to be tuned to important stimuli which are species specific. For example, humans and other primates are tuned to detect, process, recognise, and discriminate between the faces of conspecifics (Kanwisher et al., 1997; Pascalis et al., 2002; Wilmer et al., 2010; Young and Burton, 2018). Non-human primates have a network of cortical face-selective “patches” distributed across the inferior temporal and frontal cortex (Hung et al., 2015; Schaeffer et al., 2020), in which neuronal tuning to faces develops from a scaffolding that is already present at birth (Livingstone et al., 2017). Moreover, human infants prefer human face-like stimuli to other non-face-like stimuli (Goren et al., 1975; Valenza et al., 1996; Mondloch et al., 1999; Pascalis et al., 2002), although experience also plays a decisive role in tuning our capacity to discriminate and memorise faces (Pascalis et al., 2002; Feng et al., 2011). Within insects, paper wasps, capable of individual recognition in a hierarchical context, also possess enhanced capacities to learn conspecific faces by opposition to scrambled faces, prey or high-contrast geometric shapes (Sheehan and Tibbetts, 2011). The paper wasp’s specialisation for processing conspecific faces is composed of an innate and acquired component (Tibbetts et al., 2019a,b).

Previous research shows that paper wasps (Sheehan and Tibbetts, 2011) learn to differentiate natural stimuli more efficiently than simple high contrast patterns. Our observations with naïve and experienced honeybee foragers are consistent with the previous observations for wasps, suggesting insects have visual systems tuned to solve the most biologically relevant tasks in their environment. Such a result might not be expected, as the majority of research on honeybees has used simple high contrast stimuli (e.g., Horridge, 1997), however, measurement of neural responses from feature detectors present in the primary visual centres of honeybees are saturated beyond a contrast of 35–40% (Yang and Maddess, 1997). This result then is not surprising when considering that the brain should likely have evolved to process natural rather than parameterised stimuli (Field, 1987). While high-contrast elemental stimuli may help in certain categorisation tasks, natural stimuli providing more complex information may be favoured to allow fine discrimination between similar stimuli. The current study suggests that this is the case at least for processing flowers, which are ecologically relevant stimuli for bees. Neural recordings in other insects like flies and hoverflies suggest optimal processing may indeed be tuned for more natural type scenes (Maddess and Laughlin, 1985; Straw et al., 2008). Future work should explore whether such a bias in processing natural stimuli presenting realistic contrast levels and coherent spatial arrangement of features stands also for any kind of visual object, thus suggesting a general property of the insect visual system.

Honeybees are a model invertebrate species for studying visual learning and rule acquisition such as size rules (Avarguès-Weber et al., 2014; Howard et al., 2017a,b), oddity rules (Giurfa et al., 2001), above vs. below rules (Avarguès-Weber et al., 2011), numerical rules (Gross et al., 2009; Howard et al., 2018, 2019b,c,d), and maze navigation (Collett et al., 1993; Zhang et al., 1996, 2000), among other tasks. Furthermore, honeybees are also excellent learners of patterns and complex images. In the current study, bees were able to learn the configuration of certain flower types within 30 trials, a timeframe which is longer than might be expected if bees were only using a simple matched filtering type process, but is within the timeframe that bees start to develop more complex configural type representations of spatial stimuli (Stach and Giurfa, 2005). Previous studies have demonstrated similar results, showing that bees can learn to recognise and discriminate complex stimuli which may or may not be biologically relevant. For example, honeybees can learn to recognise images of human faces and discriminate between these faces (Dyer et al., 2005; Avarguès-Weber et al., 2010, 2018; Chittka and Dyer, 2012), learn complex pattern discrimination (Srinivasan et al., 1993; Zhang and Srinivasan, 1994; Giger and Srinivasan, 1996; Horridge, 1997; Giurfa et al., 1999b; Efler and Ronacher, 2000; Deisig et al., 2001; Stach et al., 2004; Dyer and Griffiths, 2012), associate abstract characters or colours with concepts (Howard et al., 2019a,b,d), and categorise complex images (Giurfa et al., 1996; Zhang et al., 2004; Benard et al., 2006; Avarguès-Weber et al., 2010). These studies suggest that bees have adapted to processing patterns and images, like those of flowers, and that plasticity is important to survival for a generalist forager. Our study further demonstrates that bees are efficient visual learners and may be innately primed for specific pattern learning. Performance improvement beyond the initial bias toward preferred flower configurations as a result of experience is a potential mechanism for maximising resource acquisition in the specific environments visited by foraging honeybees.

The datasets supporting this article have been uploaded as part of the Supplementary Material.

SRH, AGD, MG, DR, MGPR, and AA-W developed the original conceptual framework of the study. SRH and AGD collected the behavioural data. SRH and JEG analysed and modelled the data. SRH, AGD, JEG, and AA-W wrote the initial draft. All authors contributed to reviewing the manuscript and checking domain specific facts.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.