A Model of Individual Differences in Gaze Control During Reading

Abstract

We develop a statistical model of saccadic eye movements during reading of isolated sentences. The model is focused on representing individual differences between readers and supports the inference of the most likely reader for a novel set of eye movement patterns. We empirically study the model for biometric reader identification using eye-tracking data collected from 20 individuals and observe that the model distinguishes between 20 readers with an accuracy of up to 98%.

A Semiparametric Model for Bayesian Reader Identification

“…We observe that Semiparametric outperforms Landwehr et al, reducing the error by more than a factor of three. Consistent with results reported in Landwehr et al (2014), Holland & K. (unweighted) is less accurate than Landwehr et al, but more accurate than the simplified variants. We next study how the amount of data available at test time-that is, the amount of time we can observe a reader before having to make a decision-influences accuracy.…”

A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements

“…We collected eye-tracking data of 62 readers who read 12 scientific texts and answered comprehension questions for each text. We can conclude that the inclusion of lexical features leads to a significant improvement compared to the original generative model [7], and that a discriminative model using a Fisher kernel gives an additional considerable improvement over the generative model. We conclude that this model significantly outperforms the semiparametric model of [8] in some cases, which, to the best of our knowledge, is the best published biometric model that is based on eye movements.…”

“…The aim was to i) predict the readers' identity, and ii) their level of text comprehension. To this end, we built on the work of [7] and developed a generative graphical model of scanpaths that takes into account lexical features of the fixated word, derived a Fisher representation of scanpaths from this model, and subsequently used this Fisher kernel to classify the data using an SVM. We collected eye-tracking data of 62 readers who read 12 scientific texts and answered comprehension questions for each text.…”

“…The next baseline is the Fisher SVM without lexical features-that is, an SVM with the Fisher kernel derived from the generative model described in Section 4.1. We compare this discriminative model to the full generative model (Landwehr et al, 2014) [7] without lexical features and without the simplification introduced in Section 4.1.…”

“…The difference between this simplified variant and the original model [7] is that the original model truncates the gamma distributions in order to fit within the limits of the text interval defined by the saccade type; for instance, to the currently fixated word for refixations. Since this truncation causes unsteadiness of the Fisher scores, we instead let the amplitudes be governed by regular gamma distributions with scale parameter α u or γ u and shape parameter β u or δ u .…”