Modulation of mood on eye movement and face recognition performance.

Abstract

In face recognition, looking at the eyes has been associated with engagement of local attention, as well as better recognition performance. As recent research has suggested negative mood facilitates local attention while positive mood facilitates global attention, negative mood changes may lead to more eyes-focused eye movement patterns and consequently enhance recognition performance. Here we test this hypothesis using mood induction. Through eye movement analysis with hidden Markov models, we discovered eyes-focused and nose-focused eye movement strategies in the participants, and the eyes-focused strategy was associated with better recognition performance. During the recognition phase, participants with a negative mood change had increased eye movement pattern similarity to the eyes-focused strategy, and participants’ mood change was correlated with eye movement pattern similarity change. Nevertheless, mood change did not significantly change participants’ eye movement strategy classification despite changes in eye movement pattern similarity, and the eye movement pattern similarity change did not modulate recognition performance. These results suggest that mood changes through mood induction lead to slight changes in eye movement pattern that may not be sufficient to modulate recognition performance. Thus, individuals may have preferred eye movement strategies in face recognition impervious to transitory mood changes. This finding is consistent with a recent speculation on limited plasticity in adult face recognition and suggests that eye movements in face recognition may provide reliable information about an individual’s cognitive abilities.

Impact of mask use on face recognition: an eye-tracking study

“…Using EMHMM on eye movement data during face recognition, two representative eye movement patterns, i.e., eyes-focused and nose-focused patterns, have been consistently reported in adult face recognition, and the eyes-focused pattern is shown to be associated with better recognition performance (An & Hsiao, 2021 ; Chuk et al, 2017a , 2017b ; Hsiao et al, 2021a ). These eye movement patterns for face recognition observed in adults have been shown to be consistent over time within an individual and impervious to the influence of transitory mood changes (An & Hsiao, 2021 ; Hsiao et al, 2021a ; Peterson & Eckstein, 2013 ; Peterson et al, 2016 ). This phenomenon may be related to adults’ abundant experience in face recognition, which leads to a well-developed, consistent visual routine for recognizing faces that is inflexible to change.…”

When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models

“…We then cluster the individuals' HMMs into groups and form representative HMMs for each group, which summarize each group's eye movements. The number of clusters was predetermined, which followed previous EMHMM studies where participants' eye movement patterns could be quantified along the dimension between two contrasting patterns [25,27,28,[40][41][42][43][44][45]. To cluster the HMMs into two groups, so as to reveal common patterns among individuals, we used the variational hierarchical expectation-maximization (VHEM) algorithm [46], which clustered HMMs into groups in a bottom-up way based on their similarities and further produced the representative HMMs for each group We then cluster the individuals' HMMs into groups and form representative HMMs for each group, which summarize each group's eye movements.…”

“…To cluster the HMMs into two groups, so as to reveal common patterns among individuals, we used the variational hierarchical expectation-maximization (VHEM) algorithm [46], which clustered HMMs into groups in a bottom-up way based on their similarities and further produced the representative HMMs for each group We then cluster the individuals' HMMs into groups and form representative HMMs for each group, which summarize each group's eye movements. The number of clusters was predetermined, which followed previous EMHMM studies where participants' eye movement patterns could be quantified along the dimension between two contrasting patterns [25,27,28,[40][41][42][43][44][45]. To cluster the HMMs into two groups, so as to reveal common patterns among individuals, we used the variational hierarchical expectation-maximization (VHEM) algorithm [46], which clustered HMMs into groups in a bottom-up way based on their similarities and further produced the representative HMMs for each group to describe the ROIs and transitional information in the cluster [26].…”

How does music reading experience modulate eye movement pattern in sentence reading in bilinguals?

“…Each individual model with a different preset number of ROIs was trained for 100 times, and the model with the highest data loglikelihood was used. Following previous studies using EMHMM [33][34][35][36][37][38][39][40][41][42][43][44][45] , we clustered individual HMMs into two clusters to discover two representative patterns, so that each individual's eye movement pattern could be quanti ed (using data log-likelihoods) along the dimension contrasting the two representative patterns. The number of ROIs for creating representative HMMs of the clusters was set to the median number of ROIs in the individual models.…”

“…The two patterns were signi cantly different, as the data log-likelihoods of the dispersed patterns given the representative dispersed HMM were signi cantly higher than those given the representative sequential HMM, t(110) = 15.947, p < .001, d = 1.514, and vice versa for the sequential patterns, t(146) = 12.146, p < .001, d = 1.002 28 . To quantify participants' eye movement pattern along the dispersed-sequential pattern dimension, following previous studies 34,39,41 , we de ned D-S scale as (D -S)/(|D| + |S|), where D refers to the log-likelihood of the eye movement data being generated by the representative dispersed pattern HMM, and S for the representative sequential pattern HMM. A more positive value indicated higher similarity to the dispersed pattern.…”