Adaptive Sampling Policies Imply Biased Beliefs: A Generalization of the
  Hot Stove Effect
Jerker Denrell
The Hot Stove Effect is a negativity bias resulting from the adaptive
character of learning. The mechanism is that learning algorithms that pursue
alternatives with positive estimated values, but avoid alternatives with
negative estimated values, will correct errors of overestimation but fail to
correct errors of underestimation. Here, we generalize the theory behind the
Hot Stove Effect to settings in which negative estimates do not necessarily
lead to avoidance but to a smaller sample size (i.e., a learner selects fewer
of alternative B if B is believed to be inferior but does not entirely avoid
B). We formally demonstrate that the negativity bias remains in this set-up. We
also show there is a negativity bias for Bayesian learners in the sense that
most such learners underestimate the expected value of an alternative.