The Role of the Availability Heuristic in Multiple-Choice Answering Behaviour

TL;DR

This study investigates how the availability heuristic influences multiple-choice answering, showing that correct answers are more cognitively available and that selecting the most available option improves guessing accuracy significantly.

Contribution

It introduces a computational method to assess option availability using large corpora and demonstrates its predictive power across multiple question sets.

Findings

Correct answers are more available than incorrect options.

Choosing the most available option improves accuracy by 13.5% to 32.9%.

LLM-generated options show similar availability patterns to expert-created options.

Abstract

When students are unsure of the correct answer to a multiple-choice question (MCQ), guessing is common practice. The availability heuristic, proposed by A. Tversky and D. Kahneman in 1973, suggests that the ease with which relevant instances come to mind, typically operationalised by the mere frequency of exposure, can offer a mental shortcut for problems in which the test-taker does not know the exact answer. Is simply choosing the option that comes most readily to mind a good strategy for answering MCQs? We propose a computational method of assessing the cognitive availability of MCQ options operationalised by concepts' prevalence in large corpora. The key finding, across three large question sets, is that correct answers, independently of the question stem, are significantly more available than incorrect MCQ options. Specifically, using Wikipedia as the retrieval corpus, we find that always selecting the most available option leads to scores 13.5% to 32.9% above the random-guess baseline. We further find that LLM-generated MCQ options show similar patterns of availability compared to expert-created options, despite the LLMs' frequentist nature and their training on large collections of textual data. Our findings suggest that availability should be considered in current and future work when computationally modelling student behaviour.