Trajectory directionality and active sensing for random and static experiments.
Arenas on the top row (mean displacement vector – see color scale between panels b and e) correspond to the ones immediately below them (hole checking spatial distribution); the red “A” label marks the target (food site), which is pointed by the food (target) vector (purple arrow). Top row (A,B,E,F): the color and arrows indicate the most probable route taken (red=more probable; only p<0.001 displacements shown; pink arrow=inferred target position, or TEV; shaded pink sector=S.D. of TEV; see Methods, and Fig. S7). Bottom row (C,D,G,H): spatial distribution of hole-checks; size and color of circles=normalized frequency that a hole was checked (larger pink circles=higher frequency); Black ellipse (x=mean): covariance of spatial distribution. Green ellipse (+=mean): covariance of spatial distribution restricted to ≤20cm of the target. Random entrance experiments (N=8; panels A,C: trial 1; B,D: trial 14): regardless of training stage, no significant preferred routes and the TEV does not point to target (A,B); hole checks are randomly distributed throughout the arena, and shift from the walls (c) to near the center (d) after learning. Static entrance experiments (N=8; panels E,G: trial 1; F,H: trial 14): after learning (f) the TEV and significant displacements go straight to the target (although individual trajectories are variable); and hole checks align along the start-target path (h). Panel I: deviation between the TEV (pink arrow) and the target vector (purple arrow) illustrated in top panels. Directionality is quickly learned (static case). Panels J,K: hole-check area density corresponding to the spatial profiles in bottom panels. Density after learning is larger near the target (static case), supporting the path integration hypothesis. Asterisks/star: p<0.05 (paired t-test). Note the presence of more significant displacements in late learning for static entrances only, and the associated alignment of the TEV and food vector.