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.