Automata Modeling for Cognitive Interference in Users' Relevance Judgment

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