Spontaneous emergence of distinct movement patterns from local variance-based feedback

This study proposes a stochastic movement model based on correlated random walks in which the turning-angle parameter σ is adapted according to differences in recent local angle deviations. The threshold governing these updates is dynamically adjusted. Without external switching, the model spontaneously generates various diffusive properties under identical parameter values: normal diffusion and superdiffusive movement. Superdiffusive trajectories exhibit power-law-like tails. These results demonstrate that diverse movement patterns, including power-law-like dynamics, can emerge from simple local variance-based feedback alone.