Understanding the collinear masking effect in visual search through eye tracking

Hsiao, Janet H.; Chan, Antoni B.; An, Jeehye; Yeh, Su‐Ling; Jingling, Li

doi:10.3758/s13423-021-01944-7

Cited by 18 publications

(22 citation statements)

References 38 publications

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“…Entropy is a measure of predictability; higher entropy indicates lower consistency (Cover & Thomas, 2006 ). In EMHMM, entropy of HMMs has been used to quantify participants’ eye movement consistency during visual tasks (Hsiao et al, 2021b ). Previous studies on face recognition have suggested that the first 2–3 fixations in a trial play a more important role in accounting for recognition performance than later fixations (Chuk et al, 2017b ; Hsiao & Cottrell, 2008 ).…”

Section: Methodsmentioning

confidence: 99%

“…Recent research has reported substantial individual differences in eye movement patterns during face recognition that can indicate differences in recognition performance and cognitive abilities (e.g., Chan et al, 2018 ; Chuk et al, 2017b ; Hsiao et al, 2021a ; Peterson & Eckstein, 2013 ; Peterson et al, 2016 ). To take individual differences in both temporal and spatial dimensions of eye movements into account in data analysis, Chuk et al ( 2014 ) developed a machine learning-based approach, eye movement analysis with hidden Markov models (EMHMM; hidden Markov model, or HMM, is a type of time-series statistical model in machine learning), which provides quantitative measures of eye movement pattern and consistency (Chan et al, 2018 ; Hsiao et al, 2021b ). In this approach, an individual’s eye movement pattern in a visual task is first summarized using an HMM, including person-specific regions of interest (ROIs) and transition probabilities among the ROIs.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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We examined how mask use affects performance and eye movements in face recognition and whether strategy change reflected in eye movements is associated with performance change. Eighty-eight participants performed face recognition with masked faces either during learning only, during recognition only, or during both learning and recognition. As compared with the baseline condition where faces were unmasked during both learning and recognition, participants had impaired performance in all three scenarios, with larger impairment when mask conditions during learning and recognition did not match. When recognizing unmasked faces, whether the faces were learned with or without a mask on did not change eye movement behavior. Nevertheless, when recognizing unmasked faces that were learned with a mask on, participants who adopted more eyes-focused patterns had less performance impairment as compared with the baseline condition. When recognizing masked faces, participants had more eyes-focused patterns and more consistent gaze transition behavior than recognizing unmasked faces regardless of whether the faces were learned with or without a mask on. Nevertheless, when recognizing masked faces that were learned without a mask, participants whose gaze transition behavior was more consistent had less performance impairment as compared with the baseline condition. Thus, although eye movements during recognition were mainly driven by the mask condition during recognition but not that during learning, those who adjusted their strategy according to the mask condition difference between learning and recognition had better performance. This finding has important implications for identifying populations vulnerable to the impact of mask use and potential remedial strategies.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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].…”

Section: Discussionmentioning

confidence: 99%

“…Here, we termed the two representative eye movement patterns distributed and centralized patterns hereafter in the present study based on their characteristics (cf. [26,42,43,45]). The mean-log-likelihood (MLL) of each participant's eye movement data given the representative HMMs of the distributed and centralized patterns of eye movements was calculated.…”

Section: Discussionmentioning

confidence: 99%

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

Lee

Chen

Yeh

et al. 2021

Sensors

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Mind-wandering has been shown to largely influence our learning efficiency, especially in the digital and distracting era nowadays. Detecting mind-wandering thus becomes imperative in educational scenarios. Here, we used a wearable eye-tracker to record eye movements during the sustained attention to response task. Eye movement analysis with hidden Markov models (EMHMM), which takes both spatial and temporal eye-movement information into account, was used to examine if participants’ eye movement patterns can differentiate between the states of focused attention and mind-wandering. Two representative eye movement patterns were discovered through clustering using EMHMM: centralized and distributed patterns. Results showed that participants with the centralized pattern had better performance on detecting targets and rated themselves as more focused than those with the distributed pattern. This study indicates that distinct eye movement patterns are associated with different attentional states (focused attention vs. mind-wandering) and demonstrates a novel approach in using EMHMM to study attention. Moreover, this study provides a potential approach to capture the mind-wandering state in the classroom without interrupting the ongoing learning behavior.

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