Modeling frameworks for exploring how learning operates
Dynamical systems models are often used to investigate learning and animal movement in a purely theoretical context. The most common purpose is to investigate possible emergent patterns, which arise from the inclusion of learning in movement models. Here spatial location and spatial memory are given by variables that change in time and space, and dynamical rules postulate how these variables could change through the interplay of movement and learning. The actual form of the dynamical systems ranges from difference equations used to analyze home ranges (Van Moorter et al. 2009), to partial differential equations used to analyse searching ability (Berbert & Lewis 2018) stochastic processes used to investigate patrolling ability (Schlägel & Lewis 2014). Agent-based simulations have also been used to track the development of complex spatial movement behaviours via learning (Tang & Bennett 2010; Avgar et al. 2013). Theoretical studies can investigate relationships or feedbacks between movement and learning that generate patterns similar those seen in nature. They can also be used to explore the features of environments where the ability to learn, access and adapt spatial memory might confer benefits. Theoretical explorations are particularly useful for studying the updating the world model type of learning, where it is more difficult to make a clear distinction between precipitating events of experiences and movement outcomes.
Machine learning and artificial intelligence are emerging as a powerful paradigm for the analysis of many biological systems. In the context of learning and animal movement, these approaches can map environmental conditions to movement behaviour outcomes without necessarily investigating the learning process itself. An example of such a link is given by Muelleret al. (2011), who employed neural nets to link canonical classes of spatial movement behaviours in ungulates (e.g., nomadism, migration, range residency) to classes of environmental conditions (spatially constant versus variable and temporally constant versus variable). Furthermore, as described earlier, machine learning and artificial intelligence can serve as prototype models for the process of animal learning itself.