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Markov and quantum information processing models are compared with respect to their capability of explaining two different puzzling findings from empirical research on human inference and decision making. Both findings involve a task that requires making an inference about one of two possible uncertain states, followed by decision about two possible courses of action. Two conditions are compared: under one condition, the decisions are obtained after discovering or measuring the uncertain state; under another condition, choices are obtained before resolving the uncertainty so that the state remains unknown or unmeasured. Systematic departures from the Markov model are observed, and these deviations are explained as interference effects using the quantum model.

Quantum computing and information theory (Neilsen & Chuang, 2000) provides exciting new possibilities for computer science. But what importance does this new theory have for cognitive science? This is a question that is beginning to be asked by an increasing number researchers from a variety of fields including language (Gabora & Aerts, 2002), decision making (Bordley, 1998; Haven, 2006; LaMura, 2006; Mogiliansky, Zamir, & Zwirn, 2004) game theory (Eisert, Wilkens, & Lewenstein, 1999; Piotrowski, & Sladkowski, 2003) and neural nets (Gupta & Zia, 2001; Pribram, 1993).