A New Data-Mining Method to Search for Behavioral Properties That Induce Alignment and Their Involvement in Social Learning in Medaka Fish (Oryzias Latipes)

BACKGROUND: Coordinated movement in social animal groups via social learning facilitates foraging activity. Few studies have examined the behavioral cause-and-effect between group members that mediates this social learning. METHODOLOGY/PRINCIPAL FINDINGS: We first established a behavioral paradigm for visual food learning using medaka fish and demonstrated that a single fish can learn to associate a visual cue with a food reward. Grouped medaka fish (6 fish) learn to respond to the visual cue more rapidly than a single fish, indicating that medaka fish undergo social learning. We then established a data-mining method based on Kullback-Leibler divergence (KLD) to search for candidate behaviors that induce alignment and found that high-speed movement of a focal fish tended to induce alignment of the other members locally and transiently under free-swimming conditions without presentation of a visual cue. The high-speed movement of the informed and trained fish during visual cue presentation appeared to facilitate the alignment of naïve fish in response to some visual cues, thereby mediating social learning. Compared with naïve fish, the informed fish had a higher tendency to induce alignment of other naïve fish under free-swimming conditions without visual cue presentation, suggesting the involvement of individual recognition in social learning. CONCLUSIONS/SIGNIFICANCE: Behavioral cause-and-effect studies of the high-speed movement between fish group members will contribute to our understanding of the dynamics of social behaviors. The data-mining method used in the present study is a powerful method to search for candidates factors associated with inter-individual interactions using a dataset for time-series coordinate data of individuals.