Do portrait artists have enhanced face processing abilities? Evidence from hidden Markov modeling of eye movements

Hsiao, Janet H.; An, Jeehye; Zheng, Yueyuan; Chan, Antoni B.

doi:10.1016/j.cognition.2021.104616

Cited by 35 publications

(45 citation statements)

References 77 publications

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“…We also found the analytic eyemovement (eyes-nose) pattern during recognition was associated with better recognition performance as compared with the holistic (nose-focused) pattern (Fig. 1), suggesting that retrieval of diagnostic information (i.e., the eyes) is a better predictor for performance (e.g., Chuk et al, 2017b;Chuk et al, 2017a;Hsiao et al, 2021;Chan et al, 2018). In another study, we found that local attention priming using hierarchical letter stimuli made participants' eye movements more analytic and increased their recognition performance, as compared with no priming or global attention priming conditions (Cheng et al, 2015).…”

Section: Introductionmentioning

confidence: 80%

Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

et al. 2021

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The eye movement analysis with hidden Markov models (EMHMM) method provides quantitative measures of individual differences in eye-movement pattern. However, it is limited to tasks where stimuli have the same feature layout (e.g., faces). Here we proposed to combine EMHMM with the data mining technique co-clustering to discover participant groups with consistent eye-movement patterns across stimuli for tasks involving stimuli with different feature layouts. Through applying this method to eye movements in scene perception, we discovered explorative (switching between the foreground and background information or different regions of interest) and focused (mainly looking at the foreground with less switching) eye-movement patterns among Asian participants. Higher similarity to the explorative pattern predicted better foreground object recognition performance, whereas higher similarity to the focused pattern was associated with better feature integration in the flanker task. These results have important implications for using eye tracking as a window into individual differences in cognitive abilities and styles. Thus, EMHMM with co-clustering provides quantitative assessments on eye-movement patterns across stimuli and tasks. It can be applied to many other real-life visual tasks, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.

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Section: Introductionmentioning

confidence: 80%

Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

et al. 2021

Self Cite

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“…Also, the task used, passive viewing with a follow-up musical segment recognition task, differed from musicians' usual sight-reading experiences with music notations, which may have obscured their expertise. Indeed, eye movements in visual tasks are shown to be task-speci c 40,49 . Similarly, previous study showed that music expertise modulated visual span for the identi cation of English letters but not music notes, and they speculated that this phenomenon may be because the stimuli used, random notes, differed from musicians' usual reading experience 2 .…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Methodsmentioning

confidence: 99%

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

Liao

Hsiao

2022

Preprint

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Here we tested the hypothesis that in Chinese-English bilinguals, music reading experience may modulate eye movement planning in reading English but not Chinese sentences due to the similarity in perceptual demands on processing sequential symbol strings separated by spaces between music notation and English sentence reading. Chinese-English bilingual musicians and non-musicians read legal, semantically incorrect, and syntactically (and semantically) incorrect sentences in both English and Chinese. In English reading, musicians showed more dispersed eye movement patterns in reading syntactically incorrect sentences than legal sentences, whereas non-musicians did not. This effect was not observed in Chinese reading. Musicians also had shorter saccade lengths when viewing syntactically incorrect than correct musical notations and sentences in an unfamiliar alphabetic language (Tibetan), whereas non-musicians did not. Thus, musicians’ eye movement planning was disturbed by syntactic violations in both music and English reading but not in Chinese reading, and this effect was generalized to an unfamiliar alphabetic language. These results suggested that music reading experience may modulate perceptual processes in reading differentially in bilinguals’ two languages, depending on their processing similarities.

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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%

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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Do portrait artists have enhanced face processing abilities? Evidence from hidden Markov modeling of eye movements

Cited by 35 publications

References 77 publications

Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

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

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

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