Predict the individual reasoner: A new approach

Abstract

Reasoning is a core ability of humans being explored across disciplines during the last millenia. Investigations focused, however, often on identifying general principles of human reasoning or correct reasoning, but less on predicting conclusions for an individual reasoner. It is a desideratum to have artificial agents that can adapt to the individual human reasoner. We present an approach which successfully predicts individual performance across reasoning domains for reasoning about quantified or conditional statements using collaborative filtering techniques. Our proposed models are simple but efficient: they take some answers from a subject, and then build pair-wise similarities and predict missing answers based on what similar reasoners concluded. Our approach has a high accuracy in different data sets, and maintains this accuracy even when more than half of the data is missing. These features suggest that our approach is able to generalize and account for realistic scenarios, making it an adequate tool for artificial reasoning systems for predicting human inferences.