ABSTRACT
This chapter reviews work in which a formal characterization of human decision making allows a person and a machine to work together as components of an algorithm used to investigate a person's beliefs. It also reviews evidence of the effectiveness of Markov chain Monte Carlo with people (MCMCP) and compares it to another well-known method for eliciting beliefs about complex naturalistic stimuli, reverse correlation. The chapter discusses three ways in which MCMCP has been extended namely taking advantage of a manifold structure, working with discrete stimuli, and focusing trials in areas of interest to the experimenter. The computer metaphor provides much inspiration for psychology. The recent close links between computer science and cognitive psychology leads to more productive developments. The chapter examines the consequences of treating people as components in an algorithm usually executed on digital computers.
