Understanding visual attention to face emotions in social anxiety using hidden Markov models

Abstract

Theoretical models propose that attentional biases might account for the maintenance of social anxiety symptoms. However, previous eye-tracking studies have yielded mixed results. One explanation is that existing studies quantify eye-movements using arbitrary, experimenter-defined criteria such as time segments and regions of interests that do not capture the dynamic nature of overt visual attention. The current study adopted the Eye Movement analysis with Hidden Markov Models (EMHMM) approach for eye-movement analysis, a machine-learning, data-driven approach that can cluster people’s eye-movements into different strategy groups. Sixty participants high and low in self-reported social anxiety symptoms viewed angry and neutral faces in a free-viewing task while their eye-movements were recorded. EMHMM analyses revealed novel associations between eye-movement patterns and social anxiety symptoms that were not evident with standard analytical approaches. Participants who adopted the same face-viewing strategy when viewing both angry and neutral faces showed higher social anxiety symptoms than those who transitioned between strategies when viewing angry versus neutral faces. EMHMM can offer novel insights into psychopathology-related attention processes.