Bayes and Bias
Johan Lauwereyns · The MIT Press eBooks · 2010
This chapter develops the concept of bias based on notions from Bayesian probability, signal detection theory, and current neural models of decision making. The term “bias” relates to inconsistent treatment given to different information gathered from various sources based on preferences, prejudices, or other forms of selective processing. The aim is to interpret the computational properties of bias into easily identifiable neural signatures. Neural signatures are presented by means of concrete examples from the experimental literature. The chapter also analyses the types of experimental paradigms that provide relevant data.