Explicit and implicit mentalization of patients with first-episode schizophrenia: a study of self-referential gaze perception with eye movement analysis using hidden Markov models

Chan, Sherry Kit Wa; Hsiao, Janet H.; Wong, Audrey On Yui; Liao, Yingqi; Suen, Yi Nam; Yan, Eric Wai Ching; Poon, Lap-tak; Siu, Man Wah; Hui, Christy Lai Ming; Chang, Wing Chung; H, Lee; Chen, Eric

doi:10.1007/s00406-022-01383-y

Cited by 9 publications

(10 citation statements)

References 44 publications

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“…Then, for each individual HMM, we defined AB scale as (A -B)/(|A| + |B|), where A and B referred to the model's data log-likelihood of patterns A and B, respectively. Each model's similarity along the contrast between patterns A and B was quantified using AB scale 4,5,[34][35][36][37][38][39][40][41][42][43] .…”

Section: Computational Modellingmentioning

confidence: 99%

“…To match the modelling procedure (which also matched previous human participant studies 3,27,36,62 ), we used the first three fixations in each trial to train the HMM. Then, following previous studies 4,5,[34][35][36][37][38][39][40][41][42][43] , we clustered all HMMs into two representative patterns, pattern A and B, and calculated the log-likelihood of each individual's eye movements being generated by the two pattern HMMs. We then calculate AB scale as (A -B)/(|A| + |B|), where A and B referred to the model's data log-likelihood of patterns A and B, respectively.…”

Section: Eye Movement Analysis With Hidden Markov Models (Emhmm)mentioning

confidence: 99%

“…Differences among individual HMMs can be assessed quantitatively using data likelihoods, which reflect similarities among individual patterns. Thus, this method is particularly suitable for examining the relationship between eye movements and other measures [2][3][4][5][34][35][36][37][38][39][40][41][42][43] .…”

Section: Introductionmentioning

confidence: 99%

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Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models

Hsiao

Hui

et al. 2022

npj Sci. Learn.

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Greater eyes-focused eye movement pattern during face recognition is associated with better performance in adults but not in children. We test the hypothesis that higher eye movement consistency across trials, instead of a greater eyes-focused pattern, predicts better performance in children since it reflects capacity in developing visual routines. We first simulated visual routine development through combining deep neural network and hidden Markov model that jointly learn perceptual representations and eye movement strategies for face recognition. The model accounted for the advantage of eyes-focused pattern in adults, and predicted that in children (partially trained models) consistency but not pattern of eye movements predicted recognition performance. This result was then verified with data from typically developing children. In addition, lower eye movement consistency in children was associated with autism diagnosis, particularly autistic traits in social skills. Thus, children’s face recognition involves visual routine development through social exposure, indexed by eye movement consistency.

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Section: Computational Modellingmentioning

confidence: 99%

Section: Eye Movement Analysis With Hidden Markov Models (Emhmm)mentioning

confidence: 99%

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Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models

Hsiao

Hui

et al. 2022

npj Sci. Learn.

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“…Following previous studies 8 , 11 – 16 , we clustered individual HMMs into two representative patterns: Pattern 1 and Pattern 2. The similarity of a participant’s eye movement data to a suggestive pattern could be quantified using the log-likelihood of the participant’s eye movement data being generated by the representative model.…”

Section: Methodsmentioning

confidence: 99%

“…The similarity of a participant’s eye movement data to a suggestive pattern could be quantified using the log-likelihood of the participant’s eye movement data being generated by the representative model. We then assessed each participant’s eye movement pattern using a 1–2 scale, defined as (L1 − L2)/(|L1| +|L2|), where L1 and L2 stand for the log-likelihoods of the participant’s eye movement data being generated by Pattern 1 and Pattern 2 respectively 13 – 16 . Finally, eye movement consistency was assessed using the HMM’s overall entropy, where entropy was a measure of regularity or predictability of eye movements 17 .…”

Section: Methodsmentioning

confidence: 99%

Eye movement analysis of children’s attention for midline diastema

Cho

Hsiao

Chan

et al. 2022

Sci Rep

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No previous studies have investigated eye-movement patterns to show children’s information processing while viewing clinical images. Therefore, this study aimed to explore children and their educators’ perception of a midline diastema by applying eye-movement analysis using the hidden Markov models (EMHMM). A total of 155 children between 2.5 and 5.5 years of age and their educators (n = 34) viewed pictures with and without a midline diastema while Tobii Pro Nano eye-tracker followed their eye movements. Fixation data were analysed using data-driven, and fixed regions of interest (ROIs) approaches with EMHMM. Two different eye-movement patterns were identified: explorative pattern (76%), where the children’s ROIs were predominantly around the nose and mouth, and focused pattern (26%), where children’s ROIs were precise, locating on the teeth with and without a diastema, and fixations transited among the ROIs with similar frequencies. Females had a significantly higher eye-movement preference for without diastema image than males. Comparisons between the different age groups showed a statistically significant difference for overall entropies. The 3.6–4.5y age groups exhibited higher entropies, indicating lower eye-movement consistency. In addition, children and their educators exhibited two specific eye-movement patterns. Children in the explorative pattern saw the midline diastema more often while their educators focussed on the image without diastema. Thus, EMHMMs are valuable in analysing eye-movement patterns in children and adults.

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Mentalizing impairments and hypermentalizing bias in individuals with first-episode schizophrenia-spectrum disorder and at-risk mental state: the differential roles of neurocognition and social anxiety

Tsui,

Liao,

Hsiao

et al. 2024

Eur Arch Psychiatry Clin Neurosci

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Mentalizing, or theory of mind (ToM), impairments and self-referential hypermentalizing bias are well-evident in schizophrenia. However, findings compared to individuals with at-risk mental states (ARMS) are inconsistent, and investigations into the relationship between social cognitive impairments and social anxiety in the two populations are scarce. This study aimed to examine and compare these deficits in first-episode schizophrenia-spectrum disorder (FES) and ARMS, and to explore potential specific associations with neurocognition and symptomatology. Forty patients with FES, 40 individuals with ARMS, and 40 healthy controls (HC) completed clinical assessments, a battery of neurocognitive tasks, and three social cognitive tasks. The comic strip and hinting tasks were used to measure non-verbal and verbal mentalizing abilities, and the gaze perception task was employed to assess self-referential hypermentalizing bias. FES and ARMS showed comparable mentalizing impairments and self-referential hypermentalizing bias compared to HC. However, only ambiguous self-referential gaze perception (SRGP) bias remained significantly different between three groups after controlling for covariates. Findings suggested that self-referential hypermentalizing bias could be a specific deficit and may be considered a potential behavioral indicator in early-stage and prodromal psychosis. Moreover, working memory and social anxiety were related to the social cognitive impairments in ARMS, whereas higher-order executive functions and positive symptoms were associated with the impairments in FES. The current study indicates the presence of stage-specific mechanisms of mentalizing impairments and self-referential hypermentalizing bias, providing insights into the importance of personalized interventions to improve specific neurocognitive domains, social cognition, and clinical outcomes for FES and ARMS.

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Explicit and implicit mentalization of patients with first-episode schizophrenia: a study of self-referential gaze perception with eye movement analysis using hidden Markov models

Cited by 9 publications

References 44 publications

Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models

Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models

Eye movement analysis of children’s attention for midline diastema

Mentalizing impairments and hypermentalizing bias in individuals with first-episode schizophrenia-spectrum disorder and at-risk mental state: the differential roles of neurocognition and social anxiety

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