Understanding Individual Differences in Eye Movement Patterns

Title: Culture Reveals a Flexible System for Face Processing

Author: Roberto Caldara

Affiliation: Eye and Brain Mapping Laboratory, Department of Psychology, University of Fribourg, Fribourg, Switzerland.


The human face transmits a wealth of signals that readily provide crucial information for social interactions, such as facial identity and emotional expression. Nonetheless, a fundamental question remains debated: is face processing governed by universal perceptual processes? Historically, it has long been presumed that this is the case. However, over the past decade our work has questioned this widely-held assumption. We have investigated the eye movements of Western and Eastern observers across various face processing tasks to determine the effect of culture on perceptual processing. Commonalities aside, we found that Westerners distribute local fixations across the eye and mouth regions, whereas Easterners preferentially deploy central –  global – fixations during face recognition. Moreover, during the recognition of facial expressions of emotion, Westerners sample relatively more the mouth to discriminate across expressions, while Easterners the eye region. These observations demonstrate that the face system relies on different strategies to achieve a range of socially-relevant face processing tasks with comparable levels of efficiency. Overall, these cultural perceptual biases challenge the view of universal processes dedicated to face processing, favoring instead the existence of idiosyncratic flexible strategies. The way humans perceive the world and process faces is determined by experience and environmental factors.



Title: Subclusters of Autistic Traits: Links with Looking at the Eyes, and Face Identity Recognition Ability

Author: Romina Palermo1, Joshua Davis2, Marc Zirnsak3, Tirin Moore3, Richard O’Kearney2, Deborah Apthorp2, Elinor McKone4

Affiliation: 1ARC Centre of Excellence in Cognition and its Disorders, and School of Psychology, University of Western Australia, Perth, WA, 6009, Australia./2 Research School of Psychology, The Australian National University, Canberra, ACT, 2011, Australia./3 Department of Neurobiology, and Howard Hughes Medical Institute Stanford University School of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA./
4Research School of Psychology, and ARC Centre of Excellence in Cognition and its Disorders, The Australian National University, Canberra, ACT, 2011, Australia


Autistic traits, as measured with the Autism Quotient (AQ) (Baron-Cohen et al., 2001) vary across the general population, and can be split into subclusters - social aspects (AQ-Social) and non-social aspects (AQ-Attention). These subclusters of autistic traits may have opposite-effects on the amount of looking at the eyes of faces, which may differentially affect face recognition ability. We used eye-tracking to measure looking time to the eyes of faces, and used regression and mediation to link these with AQ subclusters and face recognition ability. The social and non-social aspects were differentially associated with looking at eyes of faces: AQ-Social was linked with a tendency to reduced looking at eyes, whereas AQ-Attention was associated with increased looking at eyes. Moreover, higher AQ-Attention was then indirectly related to improved face recognition, mediated by increased number of fixations to the eyes during face learning. In contrast, higher levels of AQ-Social were related with poorer recognition of faces. This study highlights the value of distinguishing between different sub clusters of traits when attempting to understand the complex links between autistic traits and person perception in the general population, and suggests that clinical studies might similarly benefit from considering symptom sub-clusters.



Title: Understanding Eye Movement Patterns in Face Recognition Using Hidden Markov Models

Author: Janet Hsiao

Affiliation: Department of Psychology, University of Hong Kong, Hong Kong.


Recent research has reported substantial individual differences in eye movement patterns in visual tasks. Here we present a hidden Markov model (HMM) based approach for eye movement data analysis that takes individual differences into account. In this approach, each individual’s eye movements are modeled with an HMM, including both person-specific regions of interests (ROIs) and transitions among the ROIs. Individual HMMs can be clustered to discover common patterns, and similarities between individual patterns can be quantitatively assessed. Through this approach, we discovered two common patterns for viewing faces: holistic (looking mostly at the face center) and analytic (looking mostly at the two eyes). Most participants used holistic patterns for face learning and analytic patterns for face recognition. Participants who used the same or different patterns during learning and recognition did not differ in recognition performance, in contrast to the scan path theory. Interestingly, analytic patterns were associated with better face recognition performance and higher activation in brain regions important for top-down control of visual attention, whereas holistic patterns were associated with ageing and lower cognitive status in older adults. This result suggests the possibility of using eye movements as an easily deployable screening assessment for cognitive decline or deficits.



Title: Classifying Eye Gaze Patterns and Inferring Individual Preferences Using Hidden Markov Models

Author: Antoni B. Chan1, Antoine Coutrot2

Affiliation: 1 Department of Computer Science, City University of Hong Kong, Hong Kong./2 Institute of Behavioural Neuroscience, University College London, United Kingdom.


Eye movements can be used to infer characteristics of the observers and what is being observed.  However most of the literature relies on limited gaze descriptors unable to capture the wealth of information contained in these highly dynamic signals.  In this talk, we present two methods for analyzing eye gaze patterns using hidden Markov models (HMMs).  The first method is for classifying individual's scanpaths.  An individual's dynamic gaze behavior is modeled with an HMM. HMM parameters are used to train classifiers to capture systematic gaze patterns diagnostic of the task, the observer's gender, or the presence of soundtrack while watching videos. The second method, called switching HMM (SHMM), extends the HMM to model changes in gaze patterns due to switches in high-level behavior. The SHMM is applied to eye gaze patterns from a preference decision making task, where it discovers two high-level behaviors: exploration and decision-making.  Through clustering individual's characteristic SHMMs, we automatically discovered two groups of participants with different decision making behavior. The SHMMs were also able to infer participants’ preference choice on each trial with high accuracy.  Our approaches make it possible to reveal individual differences in task behavior, and discover individual preferences from eye movement data.

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 Important Dates

Call for abstracts:
Nov 15,2016

Symposium submission deadline:
Feb 28, 2017

Abstract submission deadline:
Mar 31, 2017 Apr 17, 2017

Early registration deadline:
Mar 31, 2017 Apr 30, 2017

All deadlines are midnight latest time zone on earth.