One does not Simply RSVP: Mental Workload to Select Speed Reading Parameters using Electroencephalography

Abstract

Rapid Serial Visual Presentation (RSVP) has gained popularity as a method for presenting text on wearable devices with limited screen space. Nonetheless, it remains unclear how to calibrate RSVP display parameters, such as spatial alignments or presentation rates, to suit the reader’s information processing ability at high presentation speeds. Existing methods rely on comprehension and subjective workload scores, which are influenced by the user’s knowledge base and subjective perception. Here, we use electroencephalography (EEG) to directly determine how individual information processing varies with changes in RSVP display parameters. Eighteen participants read text excerpts with RSVP in a repeated-measures design that manipulated the Text Alignmentand Presentation Speed of text representation. We evaluated how predictive EEG metrics were of gains in reading speed, subjective workload, and text comprehension. We found significant correlations between EEG and increasing Presentation Speeds and propose how EEG can be used for dynamic selection of RSVP parameters. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. CHI ’20, April 25–30, 2020, Honolulu, HI, USA © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-6708-0/20/04. . . 15.00 DOI: https://doi.org/10.1145/3313831.3376766

Publication
In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems