No abstract

Attention allows animals to respond selectively to competing stimuli, enabling some stimuli to evoke a behavioral response while others are ignored. How the brain does this remains mysterious, although it is increasingly evident that even animals with the smallest brains display this capacity. For example, insects respond selectively to salient visual stimuli, but it is unknown where such selectivity occurs in the insect brain, or whether neural correlates of attention might predict the visual choices made by an insect. Here, we investigate neural correlates of visual attention in behaving honeybees (Apis mellifera). Using a closed-loop paradigm that allows tethered, walking bees to actively control visual objects in a virtual reality arena, we show that behavioral fixation increases neuronal responses to flickering, frequency-tagged stimuli. Attention-like effects were reduced in the optic lobes during replay of the same visual sequences, when bees were not able to control the visual displays. When bees were presented with competing frequency-tagged visual stimuli, selectivity in the medulla (an optic ganglion) preceded behavioral selection of a stimulus, suggesting that modulation of early visual processing centers precedes eventual behavioral choices made by these insects.invertebrate | vision | electrophysiology | local field potential A ttention allows animals to respond selectively to competing stimuli (1, 2). Stimulus-selective responses in the human brain can be endogenously driven, and this volitional form of attention has been referred to as a "top-down" process, to distinguish it from salience-driven or "bottom-up" attention (3). Although even insects display bottom-up attention (4-10), it is unclear whether attention-like selection in the insect brain might also precede or predict behavioral choices. The case for top-down attention is especially compelling for honeybees, which have welldemonstrated visual discrimination and cognitive capabilities (11)(12)(13)(14). To effectively relate attention processes to behavior, however, requires sophisticated behavioral tracking or recording brain activity from behaving insects selecting distinct objects (15). Previous psychophysical studies in insects have measured whole body movements using tethered, closed-loop flight paradigms (4-8, 15). However, most studies of visual perception and memory in the bee have involved free flight (11, 13, 14; but see ref. 16). To address the neural mechanisms subserving these behaviors, researchers have traditionally recorded brain activity from immobilized bees performing elemental associative learning (e.g., refs. 17 and 18). Animal immobilization, however, is not ideal for gaining a better understanding of the relationship between the complex cognitive behaviors seen in freely moving bees and the underlying neural activity (13,14). To this end, we developed a closed-loop paradigm for walking honey bees (19), allowing them to select and fixate visual cues by rotating an air-supported ball. To simultaneously examine attentio...

When using virtual-reality paradigms to study animal behaviour, careful attention must be paid to how the animal's actions are detected. This is particularly relevant in closed-loop experiments where the animal interacts with a stimulus. Many different sensor types have been used to measure aspects of behaviour, and although some sensors may be more accurate than others, few studies have examined whether, and how, such differences affect an animal's behaviour in a closed-loop experiment. To investigate this issue, we conducted experiments with tethered honeybees walking on an airsupported trackball and fixating a visual object in closed-loop. Bees walked faster and along straighter paths when the motion of the trackball was measured in the classical fashion -using optical motion sensors repurposed from computer mice -than when measured more accurately using a computer vision algorithm called 'FicTrac'. When computer mouse sensors were used to measure bees' behaviour, the bees modified their behaviour and achieved improved control of the stimulus. This behavioural change appears to be a response to a systematic error in the computer mouse sensor that reduces the sensitivity of this sensor system under certain conditions. Although the large perceived inertia and mass of the trackball relative to the honeybee is a limitation of tethered walking paradigms, observing differences depending on the sensor system used to measure bee behaviour was not expected. This study suggests that bees are capable of fine-tuning their motor control to improve the outcome of the task they are performing. Further, our findings show that caution is required when designing virtual-reality experiments, as animals can potentially respond to the artificial scenario in unexpected and unintended ways.