Opacity, Obscurity, and the Geometry of Question-Asking

TL;DR

This paper investigates the factors influencing question difficulty by analyzing large datasets, identifying key dimensions like obscurity and opacity, and highlighting the role of contextual cues and heuristics in question-asking processes.

Contribution

It introduces a novel analysis of question difficulty using large datasets, revealing multiple dimensions beyond prior expectations, including opacity, complexity, and network density.

Findings

Opacity significantly affects question difficulty.

Contextual cues can aid in question understanding.

Multiple dimensions influence question-asking beyond prior models.

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, of 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.