Automata modeling for cognitive interference in users' relevance judgment

Quantum theory has recently been employed to further advance the theory of information retrieval (IR). A challenging research topic is to investigate the so called quantum-like interference in users' relevance judgment process, where users are involved to judge the relevance degree of each document with respect to a given query. In this process, users' relevance judgment for the current document is often interfered by the judgment for previous documents, due to the interference on users' cognitive status. Research from cognitive science has demonstrated some initial evidence of quantum-like cognitive interference in human decision making, which underpins the user's relevance judgment process. This motivates us to model such cognitive interference in the relevance judgment process, which in our belief will lead to a better modeling and explanation of user behaviors in relevance judgement process for IR and eventually lead to more user-centric IR models. In this paper, we propose to use probabilistic automaton (PA) and quantum finite automaton (QFA), which are suitable to represent the transition of user judgment states, to dynamically model the cognitive interference when the user is judging a list of documents.