Predicting perceptual speed from search behaviour

Olivia Foulds, Alessandro Suglia, Leif A. Azzopardi, Martin Halvey

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Perceptual Speed (PS) is a cognitive ability that is known to affect multiple factors in Information Retrieval (IR) such as a user's search performance and subjective experience. However PS tests are difficult to administer which limits the design of user-adaptive systems that can automatically infer PS to appropriately accommodate low PS users. Consequently, this paper evaluated whether PS can be automatically classified from search behaviour using several machine learning models trained on features extracted from TREC Common Core search task logs. Our results are encouraging: given a user's interactions from one query, a Decision Tree was able to predict a user's PS as low or high with 86% accuracy. Additionally, we identified different behavioural components for specific PS tests, implying that each PS test measures different aspects of a person's cognitive ability. These findings motivate further work for how best to design search systems that can adapt to individual differences.
Original languageEnglish
Title of host publicationSIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450380164
Publication statusPublished - 25 Jul 2020


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