  The framework of cognitively bounded rationality treats problem solving as
fundamentally rational, but emphasises that it is constrained by cognitive
architecture and the task environment. This paper investigates a simple
decision making heuristic, Take The Best (TTB), within that framework. We
formulate TTB as a likelihood-based probabilistic model, where the decision
strategy arises by probabilistic inference based on the training data and the
model constraints. The strengths of the probabilistic formulation, in addition
to providing a bounded rational account of the learning of the heuristic,
include natural extensibility with additional cognitively plausible constraints
and prior information, and the possibility to embed the heuristic as a subpart
of a larger probabilistic model. We extend the model to learn cue
discrimination thresholds for continuous-valued cues and experiment with using
the model to account for biased preference feedback from a boundedly rational
agent in a simulated interactive machine learning task.
