Understanding how neighbors influence each other in space and time is essential for explaining how collective behavior emerges. In this work, we present an information-theoretic approach that uses time-delayed mutual information to extract the optimal temporal influence delay and distance constrained transfer entropy to extract the spatial influence range. We first validate the approach with synthetic data generated by an agent based model with different interaction delays, spatial cutoffs, and noise levels, and we show that the method recovers the true temporal delay and spatial influence range. We then apply the approach to experiments in which a zebrafish follows a virtual fish performing controlled perturbations. The analysis reveals an interaction delay of about 600 milliseconds and a spatial interaction range between 3 and 6 body lengths, depending on how much information flows from the leader to the follower. These results demonstrate that information-theoretic tools can quantify the sensorimotor delays and spatial interaction ranges that shape social responses in animal collectives.