Scanning and active sampling behaviours emerge from conserved insect neural circuits

Navigating insects often pause and rotate to sample their surroundings, behaviours termed scanning. These and other active sampling behaviours embody navigational uncertainty, and are key for spatial learning, yet their neural basis remains unclear and existing models impose scanning behaviours rather than explaining its emergence. Here, we show that scanning can emerge spontaneously from the same conserved neural circuits used for goal-directed navigation, without requiring a specialized scanning module. We built a biologically grounded model combining central complex (CX) steering, lateral accessory lobe (LAL) oscillators, and stochastic inhibition of forward speed. This minimal system produced diverse, realistic scan dynamics; saccades, fixations and reversals, whose features were qualitatively compared to high-speed video recordings of Melophorus bagoti scanning. Detailed analysis of these natural scans confirmed model predictions, including how scan structure depends on oscillator phase, goal-heading deviation, and navigational uncertainty. Furthermore, the model reveals that simple modulation of forward speed unifies a broad range of behaviors across ant species, from dashes to smooth oscillatory trajectories to pirouettes and voltes. Crucially, it establishes a general distributed control principle; where forward speed acts as a single adjustable parameter regulating the balance between goal-driven exploitation and information-seeking exploration, without requiring centralized decision-making processes.