Criteria of machine learning applied to animal learning
The machine learning literature provides concrete criteria for
identifying if an algorithm has learned (Thrun & Pratt 1998).
Specifically, given (1) a task , (2) training experience ,
and (3) a performance measure , if performance at the task
improves with experience, the algorithm is said to have learned. This is
a useful framework for interpreting observational animal movement data.
For example, for the sheep and moose in Jesmer et al. (2018) thetask was maximizing energy intake and the training
experience was several years of moving around the landscape. Theperformance measure was the question: Did the animals adopt a
migratory movement strategy to track variability in energy availability
across space and time? Because of an increase in the proportion of
migrants in the population over time (and, thereby, an increase in the
proportion of individuals with increased energy intake), the animals
likely had “learned”.
A major challenge to applying machine learning criteria to moving
animals involves identifying the task and performance
measure in meaningful ways, given the animals’ spatial context and
scale of movement. Survival and reproduction are the ultimate tasks, but
foraging, resting, finding a mate, and avoiding predation are all
proximal tasks. Nonetheless, the framework helpfully and unambiguously
associates movement in the environment with training experience .
Table 2 cross-references a machine-learning example with field studies
that provided experimental evidence of learning.
Cognitive ecologists typically have more stringent criteria for
identifying learning. For example, experimentation plus control
conditions sufficient to rule out alternate explanations are fundamental
to confirming the existence of social learning (Reader & Biro 2010). In
this framework, experimentation could involve manipulation of physical
aspects of the environment, individual animals via translocations or
similar means, or the routes governing social transmission of
information.
A particularly difficult challenge involves applying the statistical
learning criteria to problems where the learning involves updating
the world model (as described above) in a familiar landscape rather
than learning about a fundamentally novel landscape. For example, in the
foraging models of Bracis et al. (2015, 2018), the task is
maximization of instantaneous energy intake, the trainingexperience is the movement (together with the acquisition of
information for updating the cognitive map), and the performance
measure is the amount of forage obtained. However, because the modeled
movement process is stationary (in the stochastic sense, meaning that
the underlying movement parameters do not change over time), there is no
measurable improvement over the long term, merely a constant updating.
Nevertheless, without learning (i.e., without the ability to update the
cognitive map), the forager performs much worse. Exploring these
theoretical discoveries in field systems appears quite challenging
because it is difficult to experimentally manipulate the map updating.
This contrasts with the heightened feasibility of experimental studies
in which learning can be assessed as improvement in animals’ performance
as a function of time (see below; Fig. 3).