Abstract
Asking questions is a pervasive human activity, but little is understood about what makes them difficult to answer. An analysis of a pair of large databases, New York Times crosswords and questions from the quiz-show Jeopardy, establishes two orthogonal dimensions of question difficulty: obscurity (the rarity of the answer) and opacity (the indirectness of question cues, operationalized with word2vec). The importance of opacity, and the role of synergistic information in resolving it, suggests that accounts of difficulty in terms of prior expectations captures only a part of the question-asking process. A further regression analysis shows the presence of additional dimensions to question-asking: question complexity, the answer's local network density, cue intersection, and the presence of signal words. Our work shows how question-askers can help their interlocutors by using contextual cues, or, conversely, how a particular kind of unfamiliarity with the domain in question can make it harder for individuals to learn from others. Taken together, these results suggest how Bayesian models of question difficulty can be supplemented by process models and accounts of the heuristics individuals use to navigate conceptual spaces.
Highlights
We learn from more than just isolated exploration of our environment; we seek information from others by asking questions
In the questions we consider in this work, answers are drawn from a structured semantic space
Simple Bayesian models can capture this dimension as a process of overcoming low priors (Oaksford & Chater, 2007), or as a search problem made harder when the question-answerer must explore less-encountered conceptual spaces
Summary
We learn from more than just isolated exploration of our environment; we seek information from others by asking questions The aim of these questions may be to gather novel information, check our understanding, or to compare our understanding with others. A second property of the questions we consider is that they involve retrieval: the answers are words or concepts previously encountered (Bourgin et al, 2014), which contrasts with questions, such as mathematics problems, whose answers require novel construction. These two properties, semantic structure and retrieval, help delineate, but do not fully define, the set of questions we consider.
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