Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures

Human attention guided explainable artificial intelligence for computer vision models

Explainable artificial intelligence (XAI) has been increasingly investigated to enhance the transparency of black-box artificial intelligence models, promoting better user understanding and trust. Developing an XAI that is faithful to models and plausible to users is both a necessity and a challenge. This work examines whether embedding human attention knowledge into saliency-based XAI methods for computer vision models could enhance their plausibility and faithfulness. Two novel XAI methods for object detection models, namely FullGrad-CAM and FullGrad-CAM++, were first developed to generate object-specific explanations by extending the current gradient-based XAI methods for image classification models. Using human attention as the objective plausibility measure, these methods achieve higher explanation plausibility. Interestingly, all current XAI methods when applied to object detection models generally produce saliency maps that are less faithful to the model than human attention maps from the same object detection task. Accordingly, human attention-guided XAI (HAG-XAI) was proposed to learn from human attention how to best combine explanatory information from the models to enhance explanation plausibility by using trainable activation functions and smoothing kernels to maximize the similarity between XAI saliency map and human attention map. The proposed XAI methods were evaluated on widely used BDD-100K, MS-COCO, and ImageNet datasets and compared with typical gradient-based and perturbation-based XAI methods. Results suggest that HAG-XAI enhanced explanation plausibility and user trust at the expense of faithfulness for image classification models, and it enhanced plausibility, faithfulness, and user trust simultaneously and outperformed existing state-of-the-art XAI methods for object detection models.

Developing an eyeball positioning method in the eye orbit for craniofacial identification in Korean population

We analysed the skulls and faces of Korean subjects using anthropometric methods to understand the anatomical characteristics of the eyeball and eye orbit region of Korean population and to determine the correlations between the hard and soft tissues around the eyeball and eye orbit region. In total, 82 sections in the region were measured to determine the correlations; among them, 34 showed significant differences by sex, and 6 showed significant differences by age. As the distance from the centre of the eye lens to the eye orbit is calculated as a ratio, we determined that the centre of the eye lens is located relatively on the lateral and superior position in each eye orbit in front view. Fourteen sections that could be used for craniofacial reconstruction/approximation in men and women were selected. Regression equations were derived according to the correlation of each section, and their reliabilities were verified by out of sample validation tests. Therefore, our results increase the accuracy of eyeball position determination, which would be useful for more efficient craniofacial reconstruction/approximation of the Korean population and should improve the efficiency of facial recognition.

Social episodic memory in severe alcohol use disorder: Positive encoding bias and negative bias in accessibility of interpersonal information

BACKGROUND

Alterations in higher-order social cognition are well documented in individuals with severe alcohol use disorder (SAUD). However, the basic mechanisms underpinning them are not well understood. This knowledge gap hampers the development of targeted therapeutic interventions. Here, we investigated whether individuals with SAUD show abnormalities in social episodic memory processes, which may represent relevant candidate mechanisms for alterations in social cognition.

METHODS

Recently detoxified patients with SAUD and matched healthy controls (HCs) completed two experimental tasks. We first used a Social Recognition Task in 40 SAUD patients and 40 HCs to measure the participants' ability to implicitly memorize the facial identity and emotion of novel interpersonal cues (i.e., dynamic facial expressions of anger and happiness). We then used a Social Memory Accessibility Task in 29 SAUD patients and 30 HCs) to measure participants' access to and fluency for already existing social memories by asking them to retrieve as many specific positive and negative interpersonal events as possible within equal time limits.

RESULTS

In the Social Recognition Task, we found that, compared to HCs, patients with SAUD had a globally lower recognition performance for the facial identities of novel social stimuli, but a preserved bias toward positive information. Conversely, in the social memory accessibility task, patients showed greater access to and fluency for negative interpersonal memories than controls (no group differences were observed for positive ones), resulting in a negative accessibility bias.

CONCLUSIONS

This exploration of episodic social memory in individuals with SAUD showed (1) a preserved bias for the encoding of positive versus negative novel social information, and (2) greater access to negative than positive interpersonal memories. These results enhance our understanding of socio-affective processing in individuals with SAUD and identify social memory alterations that may contribute to social cognition and interpersonal difficulties.

Understanding Human Cognition Through Computational Modeling

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

Explanation strategies in humans versus current explainable artificial intelligence: Insights from image classification

Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. Here, we examined human participants' attention strategies when classifying images and when explaining how they classified the images through eye-tracking and compared their attention strategies with saliency-based explanations from current XAI methods. We found that humans adopted more explorative attention strategies for the explanation task than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations, which contained more specific information for inferring class labels, whereas the other involved explorative scanning with more visual explanations, which were rated higher in effectiveness for early category learning. Interestingly, XAI saliency map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans use both visual and conceptual information during explanation, which serve different purposes, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.

A review of machine learning in scanpath analysis for passive gaze-based interaction

The scanpath is an important concept in eye tracking. It refers to a person's eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.e., interfaces that implicitly observe and interpret human eye movements, with the goal of improving the interaction. This literature review investigates research on machine learning applications in scanpath analysis for passive gaze-based interaction between 2012 and 2022, starting from 2,425 publications and focussing on 77 publications. We provide insights on research domains and common learning tasks in passive gaze-based interaction and present common machine learning practices from data collection and preparation to model selection and evaluation. We discuss commonly followed practices and identify gaps and challenges, especially concerning emerging machine learning topics, to guide future research in the field.

Differences in eye movements for face recognition between Canadian and Chinese participants are not modulated by social orientation

Face recognition strategies do not generalize across individuals. Many studies have reported robust cultural differences between West Europeans/North Americans and East Asians in eye movement strategies during face recognition. The social orientation hypothesis posits that individualistic vs. collectivistic (IND/COL) value systems, respectively defining West European/North American and East Asian societies, would be at the root of many cultural differences in visual perception. Whether social orientation is also responsible for such cultural contrast in face recognition remains to be clarified. To this aim, we conducted two experiments with West European/North American and Chinese observers. In Experiment 1, we probed the existence of a link between IND/COL social values and eye movements during face recognition, by using an IND/COL priming paradigm. In Experiment 2, we dissected the latter relationship in greater depth, by using two IND/COL questionnaires, including subdimensions to those concepts. In both studies, cultural differences in fixation patterns were revealed between West European/North American and East Asian observers. Priming IND/COL values did not modulate eye movement visual sampling strategies, and only specific subdimensions of the IND/COL questionnaires were associated with distinct eye-movement patterns. Altogether, we show that the typical contrast between IND/COL cannot fully account for cultural differences in eye movement strategies for face recognition. Cultural differences in eye movements for faces might originate from mechanisms distinct from social orientation.

Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM

The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters ( K ) and the number of hidden states ( S ) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers K and S are automatically determined in two ways. First, we place a prior on the pair (K,S) and approximate their posterior probabilities, from which the values with the maximum posterior are selected. Second, some clusters and states are pruned out implicitly when no data samples are assigned to them, thereby leading to automatic selection of the model complexity. Experiments on synthetic and real data demonstrate that our algorithm performs better than using model selection techniques with maximum likelihood estimation.

Development of Emotion Recognition from Facial Expressions with Different Eye and Mouth Cues in Japanese People

Research has reported that Japanese people are more likely to focus on and look longer at eyes when reading emotions from facial expressions than their western counterparts. However, how these tendencies develop and whether there is a relationship between the two tendencies (to focus on the eyes and to look longer at the eyes) is unclear. The present study examined emotion recognition and gaze patterns in Japanese preschool children (n = 51) and university students (n = 57), using facial expressions with different eye and mouth cues. The results showed developmental changes in emotion recognition, with adults being more sensitive to negative emotions, whereas gaze patterns showed no developmental changes. Furthermore, there was no relationship between emotion recognition and gaze patterns. This suggests that the implicit and explicit processing of emotion recognition develops at different times, and that there is no direct relationship between the two processes.

Do they 'look' different(ly)? Dynamic face recognition in Malaysians: Chinese, Malays and Indians compared

Previous cross-cultural eye-tracking studies examining face recognition discovered differences in the eye movement strategies that observers employ when perceiving faces. However, it is unclear (1) the degree to which this effect is fundamentally related to culture and (2) to what extent facial physiognomy can account for the differences in looking strategies when scanning own- and other-race faces. In the current study, Malay, Chinese and Indian young adults who live in the same multiracial country performed a modified yes/no recognition task. Participants' recognition accuracy and eye movements were recorded while viewing muted face videos of own- and other-race individuals. Behavioural results revealed a clear own-race advantage in recognition memory, and eye-tracking results showed that the three ethnic race groups adopted dissimilar fixation patterns when perceiving faces. Chinese participants preferentially attended more to the eyes than Indian participants did, while Indian participants made more and longer fixations on the nose than Malay participants did. In addition, we detected statistically significant, though subtle, differences in fixation patterns between the faces of the three races. These findings suggest that the racial differences in face-scanning patterns may be attributed both to culture and to variations in facial physiognomy between races.

How does face mask in COVID-19 pandemic disrupt face learning and recognition in adults with autism spectrum disorder?

Use of face masks is one of the measures adopted by the general community to stop the transmission of disease during this ongoing COVID-19 pandemic. This wide use of face masks has indeed been shown to disrupt day-to-day face recognition. People with autism spectrum disorder (ASD) often have predisposed impairment in face recognition and are expected to be more vulnerable to this disruption in face recognition. Here, we recruited typically developing adult participants and those with ASD, and we measured their non-verbal intelligence, autism spectrum quotient, empathy quotient, and recognition performances of faces with and without a face mask covering the lower halves of the face. When faces were initially learned unobstructed, we showed that participants had a general reduced face recognition performance for masked faces. In contrast, when masked faces were first learned, typically developing adults benefit with an overall advantage in recognizing both masked and unmasked faces; while adults with ASD recognized unmasked faces with a significantly more reduced level of performance than masked faces—this face recognition discrepancy is predicted by a higher level of autistic traits. This paper also discusses how autistic traits influence processing of faces with and without face masks.

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

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.

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

Mentalizing impairment is one of the core features of schizophrenia, and bias judgement of others' gaze as self-directing is common to schizophrenia patients. In this case-control study, 30 patients with first-episode schizophrenia (FES) and 30 matched healthy controls were assigned gaze perception tasks with variable stimulus presentation times (300 ms and no time limit) to determine the presence of self-referential gaze perception (SRGP) bias. The eye movement pattern during the task were tracked and data were analysed using hidden Markov models (HMMs). The SRGP involves reporting of others' gaze intent and was used as a measurement of explicit mentalizing process. Eye movement measurement represents automated visual attention pattern and was considered as a measurement of implicit mentalizing process. The patients with FES had significantly more SRGP bias than the controls in the 300 ms condition but not in the no-time-limit condition. Social cognitive function was related to SRGP bias in the patient group. Two distinct eye movement patterns were identified: eye-focused and nose-focused. Significant group differences in eye movement patterns in the 300 ms condition were found with more controls had eye-focused pattern. Social anxiety symptoms were related to the nose-focused pattern, positive psychotic symptoms were related to the eye-focused pattern, and depressive symptoms were related to less consistent eye movement patterns. No significant relationship was found between SRGP bias and eye movement patterns. The dissociation between explicit and implicit mentalizing processes with different cognitive and symptom dimensions associated with the two processes suggests the presence of different mechanisms.

Neural sensitivity to faces is increased by immersion into a novel ethnic environment: Evidence from ERPs

Previous reports suggest that East-Asians may show larger face-elicited N170 components in the ERP as compared to Caucasian participants. Since the N170 can be modulated by perceptual expertise, such group differences may be accounted for by differential experience, for example, with logographic versus alphabetic scripts (script system hypothesis) or by exposure to abundant novel faces during the immersion into a new social and/or ethnic environment (social immersion hypothesis). We conducted experiments in Hong Kong and Berlin, recording ERPs in a series of one-back tasks, using same- and other-ethnicity face stimuli in upright and inverted orientation and doodle stimuli. In Hong Kong we tested local Chinese residents and foreign guest students who could not read the logographic script; in Berlin we tested German residents who could not read the logographic script and foreign Chinese visitors. In both experiments, we found significantly larger N170 amplitudes to faces, regardless of ethnicity, in the foreign than in the local groups. Moreover, this effect did not depend on stimulus orientation, suggesting that the N170 group differences do not reflect differences in configural visual processing. A group of short-term German residents in Berlin did not differ in N170 amplitude from long-term residents. Together, these findings indicate that the extensive confrontation with novel other-ethnicity faces during immersion in a foreign culture may enhance the neural response to faces, reflecting the short-term plasticity of the underlying neural system.

Music reading experience modulates eye movement pattern in English reading but not in Chinese reading

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.

Non-monotonic developmental trend of holistic processing in visual expertise: the case of Chinese character recognition

Holistic processing has been identified as an expertise marker of face and object recognition. By contrast, reduced holistic processing is purportedly an expertise marker in recognising orthographic characters in Chinese. Does holistic processing increase or decrease in expertise development? Is orthographic recognition a domain-specific exception to all other kinds of recognition (e.g. face and objects)? In two studies, we examined the developmental trend of holistic processing in Chinese character recognition in Chinese and non-Chinese children, and its relationship with literacy abilities: Chinese first graders—with emergent Chinese literacy acquired in kindergarten—showed increased holistic processing perhaps as an inchoate expertise marker when compared with kindergartners and non-Chinese first graders; however, the holistic processing effect was reduced in higher-grade Chinese children. These results suggest a non-monotonic inverted U-shape trend of holistic processing in visual expertise development: An increase in holistic processing due to initial reading experience followed by a decrease in holistic processing due to literacy enhancement. This result marks the development of holistic and analytic processing skills, both of which can be essential for mastering visual recognition. This study is the first to investigate the developmental trend of holistic processing in Chinese character recognition using the composite paradigm.

Preference at First Sight: Effects of Shape and Font Qualities on Evaluation of Object-Word Pairs

Subjective preferences for visual qualities of shapes and fonts have been separately reported. Such preferences are often similarly attributed to factors such as aesthetic impressions, attributed meaning from the visual properties, or processing fluency. Because shapes and fonts were rarely studied together, we investigated whether these qualities had a similar impact on preference judgment of object-word pairs. Each pair consisted of an abstract object with either preferred or disliked shape qualities and a pseudoword with either preferred or disliked font qualities. We found that only shape qualities, but not font qualities, influenced preference ratings of the object-word pairs, with higher preferences for pairs with preferred than disliked shapes. Moreover, eye movement results indicated that while participants fixated the word before the object, their prolonged fixation on the object when first attending to it might have contributed to the preference ratings. Nonetheless, other measures, including response times, total fixation numbers, and total dwell time, showed different patterns for shape and font qualities, revealing that participants attended more to objects with preferred than disliked shapes, and to words with disliked than preferred fonts. Taken together, these results suggest that shape and font qualities have differential influences on preferences and processing of objects and words.

Understanding children’s attention to dental caries through eye-tracking

Visual attention is a significant gateway to a child's mind, and looking is one of the first behaviors young children develop. Untreated caries and the resulting poor dental aesthetics can have adverse emotional and social impacts on children's oral health-related quality of life due to its detrimental effects on self-esteem and self-concept. Therefore, we explored preschool children's eye movement patterns and visual attention to images with and without dental caries via eye movement analysis using hidden Markov models (EMHMM). We calibrated a convenience sample of 157 preschool children to the eye-tracker (Tobii Nano Pro) to ensure standardization. Consequently, each participant viewed the same standardized pictures with and without dental caries while an eye-tracking device tracked their eye movements. Subsequently, based on the sequence of viewed regions of interest (ROIs), a transition matrix was developed where the participants' previously viewed ROI informed their subsequently considered ROI. Hence, an individual's HMM was estimated from their eye movement data using a variational Bayesian approach to determine the optimal number of ROIs automatically. Consequently, this data-driven approach generated the visual task participants' most representative eye movement patterns. Preschool children exhibited two different eye movement patterns, distributed (78%) and selective (21%), which was statistically significant. Children switched between images with more similar probabilities in the distributed pattern while children remained looking at the same ROI than switching to the other ROI in the selective pattern. Nevertheless, all children exhibited an equal starting fixation on the right or left image and noticed teeth. The study findings reveal that most preschool children did not have an attentional bias to images with and without dental caries. Furthermore, only a few children selectively fixated on images with dental caries. Therefore, selective eye-movement patterns may strongly predict preschool children's sustained visual attention to dental caries. Nevertheless, future studies are essential to fully understand the developmental origins of differences in visual attention to common oral health presentations in children. Finally, EMHMM is appropriate for assessing inter-individual differences in children's visual attention.

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

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.

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

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.

Music-reading expertise associates with face but not Chinese character processing ability

A growing number of behavioural and neuroimaging studies have investigated the cognitive mechanisms and neural substrates underlying various forms of visual expertise, such as face and word processing. However, it remains poorly understood whether and to what extent the acquisition of one form of expertise would be associated with that of another. The current study examined the relationship between music-reading expertise and face and Chinese character processing abilities. In a series of experiments, music experts and novices performed discrimination and recognition tasks of musical notations, faces, and words. Results consistently showed that musical experts responded more accurately to musical notations and faces, but not to words, than did musical novices. More intriguingly, the music expert's age of training onset could well predict their face but not word processing performance: the earlier musical experts began musical notation reading, the better their face processing performance. Taken together, our findings provide preliminary and converging evidence that music-reading expertise links with face, but not word, processing, and lend support to the notion that the development of different types of visual expertise may not be independent, but rather interact with each other during their acquisition.

The effect of implicit racial bias on recognition of other-race faces

Previous research has established a possible link between recognition performance, individuation experience, and implicit racial bias of other-race faces. However, it remains unclear how implicit racial bias might influence other-race face processing in observers with relatively extensive experience with the other race. Here we examined how recognition of other-race faces might be modulated by observers’ implicit racial bias, in addition to the effects of experience and face recognition ability. Caucasian participants in a culturally diverse city completed a memory task for Asian and Caucasian faces, an implicit association test, a questionnaire assessing experience with Asians and Caucasians, and a face recognition ability test. As expected, recognition performance for Asian faces was positively predicted by increased face recognition ability, and experience with Asians. More importantly, it was also negatively predicted by increased positive bias towards Asians, which was modulated by an interaction between face recognition ability and implicit bias, with the effect of implicit bias observed predominantly in observers with high face recognition ability. Moreover, the positions of the first two fixations when participants learned the other-race faces were affected by different factors, with the first fixation modulated by the effect of experience and the second fixation modulated by the interaction between implicit bias and face recognition ability. Taken together, these findings suggest the complexity in understanding the perceptual and socio-cognitive influences on the other-race effect, and that observers with high face recognition ability may more likely evaluate racial features involuntarily when recognizing other-race faces.

Oxytocin Modulates Neural Individuation/Categorization Processing of Faces in Early Face-Selective Areas

Oxytocin (OT) is known as a neuropeptide that promotes social adaptation. Individuating racial in-group members and viewing racial out-groups in categories is an adaptive strategy that evolved to aid effective social interaction. Nevertheless, whether OT modulates the neural individuation/categorization processing of racial in-group and out-group faces remain unknown. After intranasal OT or placebo administration, 46 male participants (OT: 24, placebo: 22) were presented with face pairs with the same or different identities or races in rapid succession. The neural repetition suppression (RS) effects to identity and race were measured using functional magnetic resonance imaging (fMRI) as indices of individuation/categorization face-processing. The results showed that while OT increased the RS effect to race, it decreased the RS effect to identity in the right fusiform face area. As for the left occipital face area, OT enlarged the differential RS effects to identities of in-group and out-group faces. Additionally, OT modulated the association of interdependence self-construal and the RS effects on identity and race. These findings bring to light preliminary evidence that OT can regulate neuronal specificity of identity and race in early face-selective regions and benefit adaptive individuation/categorization face-processing.

Average faces: How does the averaging process change faces physically and perceptually?

Average faces have been used frequently in face recognition studies, either as a theoretical concept (e.g., face norm) or as a tool to manipulate facial attributes (e.g., modifying identity strength). Nonetheless, how the face averaging process- the creation of average faces using an increasing number of faces -changes the resulting averaged faces and our ability to differentiate between them remains to be elucidated. Here we addressed these questions by combining 3D-face averaging, eye-movement tracking, and the computation of image-based face similarity. Participants judged whether two average faces showed the same person while we systematically increased their average level (i.e., number of faces being averaged). Our results showed, with increasing averaging, both a nonlinear increase of the computational similarity between the resulting average faces and a nonlinear decrease of face discrimination performance. Participants' performance dropped from near-ceiling level when two different faces had been averaged together to chance level when 80 faces were mixed. We also found a nonlinear relationship between face similarity and face discrimination performance, which was fitted nicely with an exponential function. Furthermore, when the comparison task became more challenging, participants performed more fixations onto the faces. Nonetheless, the distribution of fixations across facial features (eyes, nose, mouth, and the center area of a face) remained unchanged. These results not only set new constraints on the theoretical characterization of the average face and its role in establishing face norms but also offer practical guidance for creating approximated face norms to manipulate face identity.

Understanding the collinear masking effect in visual search through eye tracking

Recent research has reported that, while both orientation contrast and collinearity increase target salience in visual search, a combination of the two counterintuitively masks a local target. Through eye-tracking and eye-movement analysis with hidden Markov models (EMHMM), here we showed that this collinear masking effect was associated with reduced eye-fixation consistency (as measured in entropy) at the central fixation cross prior to the search display presentation. As a decreased precision of saccade landing position is shown to be related to attention shift away from the saccadic target, our result suggested that the collinear masking effect may be related to attention shift to a non-saccadic-goal location in expectation of the search display before saccading to the central fixation cross. This attention shift may consequently interfere with attention capture by the collinear distractor containing the target, resulting in the masking effect. In contrast, although older adults had longer response times, more dispersed eye-movement pattern, and lower eye-movement consistency than young adults during visual search, the two age groups did not differ in the masking effect, suggesting limited contribution from ageing-related cognitive decline. Thus, participants' pre-saccadic attention shift prior to search may be an important factor influencing their search behavior.

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

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.

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

Recent research has suggested the importance of part-based information in face recognition in addition to global, whole-face information. Nevertheless, face drawing experience was reported to enhance selective attention to the eyes but did not improve face recognition performance, leading to speculations about limited plasticity in adult face recognition. Here we examined the mechanism underlying the limited advantage of face drawing experience in face recognition through the Eye Movement analysis with Hidden Markov Models (EMHMM) approach. We found that portrait artists showed more eyes-focused eye movement patterns and outperformed novices in face matching, and participants' drawing rating was correlated with both eye movement pattern and performance. In contrast, portrait artists did not outperform novices and did not differ from novices in eye movement pattern in either the face recognition or part-whole tasks, although the eyes-focused pattern was associated with better recognition performance and longer response times in the whole condition relative to the part condition. Interestingly, in contrast to the face recognition and part-whole tasks, participants' performance in face matching was predicted by their drawing rating but not eye movement pattern. These results suggested that artists' advantage in face processing is specific to tasks similar to their drawing experience such as face matching, and may be related to their better ability in extracting identity-invariant information between two faces rather than more eyes-focused eye movement patterns.

A hidden Markov model for analyzing eye-tracking of moving objects : Case study in a sustained attention paradigm

Eye-tracking provides an opportunity to generate and analyze high-density data relevant to understanding cognition. However, while events in the real world are often dynamic, eye-tracking paradigms are typically limited to assessing gaze toward static objects. In this study, we propose a generative framework, based on a hidden Markov model (HMM), for using eye-tracking data to analyze behavior in the context of multiple moving objects of interest. We apply this framework to analyze data from a recent visual object tracking task paradigm, TrackIt, for studying selective sustained attention in children. Within this paradigm, we present two validation experiments to show that the HMM provides a viable approach to studying eye-tracking data with moving stimuli, and to illustrate the benefits of the HMM approach over some more naive possible approaches. The first experiment utilizes a novel 'supervised' variant of TrackIt, while the second compares directly with judgments made by human coders using data from the original TrackIt task. Our results suggest that the HMM-based method provides a robust analysis of eye-tracking data with moving stimuli, both for adults and for children as young as 3.5-6 years old.

Interpretation biases and visual attention in the processing of ambiguous information in chronic pain

BACKGROUND

Theories propose that interpretation biases and attentional biases might account for the maintenance of chronic pain symptoms, but the interactions between these two forms of biases in the context of chronic pain are understudied.

METHODS

To fill this gap, 63 participants (40 females) with and without chronic pain completed an interpretation bias task that measures participants' interpretation styles in ambiguous scenarios and a novel eye-tracking task where participants freely viewed neutral faces that were given ambiguous pain/health-related labels (i.e. 'doctor', 'patient' and 'healthy people'). Eye movements were analysed with the Hidden Markov Models (EMHMM) approach, a machine-learning data-driven method that clusters people's eye movements into different strategy subgroups.

RESULTS

Adults with chronic pain endorsed more negative interpretations for scenarios related to immediate bodily injury and long-term illness than healthy controls, but they did not differ significantly in terms of their eye movements on ambiguous faces. Across groups, people who interpreted illness-related scenarios in a more negative way also focused more on the nose region and less on the eye region when looking at patients' and healthy people's faces and, to a lesser extent, doctors' faces. This association between interpretive and attentional processing was particularly apparent in participants with chronic pain.

CONCLUSIONS

In summary, the present study provided evidence for the interplay between multiple forms of cognitive biases. Future studies should investigate whether this interaction might influence subsequent functioning in people with chronic pain.

Holistic but with reduced right-hemisphere involvement: The case of dyslexia in Chinese character recognition

Recent research on visual object recognition has suggested that the right hemisphere can engage either holistic or part-based processing depending on whether the recognition relies on configural (exact distances among features) or featural information, respectively. Consistent with this finding, expert Chinese reading has been marked by a left-side bias (an indication of right-hemisphere lateralization) with decreased holistic processing (as assessed using the composite paradigm) due to its reliance on featural information. Here we examine two common perceptual expertise phenomena in object recognition - holistic processing and left-side bias - of Chinese characters in adolescents with developmental dyslexia and matched controls. We found that those with dyslexia showed stronger holistic processing, a weaker left-side bias, and worse performance in Chinese character dictation than controls. This was in contrast to Limited writers (proficient readers with limited writing experience) reported in Tso, Au, and Hsiao (Psychological Science, 25, 1757-1767, 2014), who showed stronger holistic processing and worse dictation performance, but the same level of left-side bias as controls. This result demonstrated two different perceptual mechanisms underlying holistic processing: Limited writers' holistic processing may be due to difficulties in de-emphasizing configural information unimportant to Chinese characters, whereas readers with dyslexia may have deficits selectively attending to character components to form appropriate part-based representations in the right hemisphere.

Cognitive strategies revealed by clustering eye movement transitions

In cognitive tasks, solvers can adopt different strategies to process information which may lead to different response behavior. These strategies might elicit different eye movement patterns which can thus provide substantial information about the strategy a person uses. However, these strategies are usually hidden and need to be inferred from the data. After an overview of existing techniques which use eye movement data for the identification of latent cognitive strategies, we present a relatively easy to apply unsuper-vised method to cluster eye movement recordings to detect groups of different solution processes that are applied in solving the task. We test the method's performance using simulations and demonstrate its use on two examples of empirical data. Our analyses are in line with presence of different solving strategies in a Mastermind game, and suggest new insights to strategic patterns in solving Progressive matrices tasks.

Eye movement analysis with switching hidden Markov models

Here we propose the eye movement analysis with switching hidden Markov model (EMSHMM) approach to analyzing eye movement data in cognitive tasks involving cognitive state changes. We used a switching hidden Markov model (SHMM) to capture a participant's cognitive state transitions during the task, with eye movement patterns during each cognitive state being summarized using a regular HMM. We applied EMSHMM to a face preference decision-making task with two pre-assumed cognitive states-exploration and preference-biased periods-and we discovered two common eye movement patterns through clustering the cognitive state transitions. One pattern showed both a later transition from the exploration to the preference-biased cognitive state and a stronger tendency to look at the preferred stimulus at the end, and was associated with higher decision inference accuracy at the end; the other pattern entered the preference-biased cognitive state earlier, leading to earlier above-chance inference accuracy in a trial but lower inference accuracy at the end. This finding was not revealed by any other method. As compared with our previous HMM method, which assumes no cognitive state change (i.e., EMHMM), EMSHMM captured eye movement behavior in the task better, resulting in higher decision inference accuracy. Thus, EMSHMM reveals and provides quantitative measures of individual differences in cognitive behavior/style, making a significant impact on the use of eyetracking to study cognitive behavior across disciplines.

Gender-based eye movement differences in passive indoor picture viewing: An eye-tracking study

Male and female brains have different structures, which can make genders produce various eye- movement patterns. This study presents the results of an eye tracking experiment in which we analyzed the eye movements of 25 male and 20 female participants during passive indoor picture viewing. We examined eye-movement parameters, namely fixation duration, scan path length, number of saccades, spatial density, saccade amplitude, and the ratio of total fixation duration to total saccade duration so as to investigate gender differences in eye-movement patterns while indoor picture viewing. We found significant differences in eye-movement patterns between genders. The results of eye-movement analysis also indicated that females showed more explorative gaze behavior, indicated by larger saccade amplitudes, and by longer scan paths. Furthermore, owing to shorter ratio of fixation durations to saccade duration in females as compared to male, we speculate that females inspect the images faster than males. In addition, we classified the genders into two subgroups-males and females-based on their eye-movement parameters by using a support vector machine classifier achieving an accuracy of 70%. We have come to the result males and females - with same culture - see the environment differently. Our findings have profound implications for researches employing gaze-based models.

Eye Movement Dynamics Differ between Encoding and Recognition of Faces

Facial recognition is widely thought to involve a holistic perceptual process, and optimal recognition performance can be rapidly achieved within two fixations. However, is facial identity encoding likewise holistic and rapid, and how do gaze dynamics during encoding relate to recognition? While having eye movements tracked, participants completed an encoding ("study") phase and subsequent recognition ("test") phase, each divided into blocks of one- or five-second stimulus presentation time conditions to distinguish the influences of experimental phase (encoding/recognition) and stimulus presentation time (short/long). Within the first two fixations, several differences between encoding and recognition were evident in the temporal and spatial dynamics of the eye-movements. Most importantly, in behavior, the long study phase presentation time alone caused improved recognition performance (i.e., longer time at recognition did not improve performance), revealing that encoding is not as rapid as recognition, since longer sequences of eye-movements are functionally required to achieve optimal encoding than to achieve optimal recognition. Together, these results are inconsistent with a scan path replay hypothesis. Rather, feature information seems to have been gradually integrated over many fixations during encoding, enabling recognition that could subsequently occur rapidly and holistically within a small number of fixations.

Eye-movement patterns in face recognition are associated with cognitive decline in older adults

The Hidden Markov Modeling approach for eye-movement data analysis is able to quantitatively assess differences and similarities among individual patterns. Here we applied this approach to examine the relationships between eye-movement patterns in face recognition and age-related cognitive decline. We found that significantly more older than young adults adopted "holistic" patterns, in which most eye fixations landed around the face center, as opposed to "analytic" patterns, in which eye movements switched among the two eyes and the face center. Participants showing analytic patterns had better performance than those with holistic patterns regardless of age. Interestingly, older adults with lower cognitive status (as assessed by the Montreal Cognitive Assessment), particularly in executive and visual attention functioning (as assessed by Tower of London and Trail Making Tests) were associated with a higher likelihood of holistic patterns. This result suggests the possibility of using eye movements as an easily deployable screening assessment for cognitive decline in older adults.

Investigation of eye movement pattern parameters of individuals with different fluid intelligence

Eye movement studies are subject of interest in human cognition. Cortical activity and cognitive load impress eye movement influentially. Here, we investigated whether fluid intelligence (FI) has any effect on eye movement pattern in a comparative visual search (CVS) task. FI of individuals was measured using the Cattell test, and participants were divided into three groups: low FI, middle FI, and high FI. Eye movements of individuals were then recorded during the CVS task. Eye movement patterns were extracted and compared statistically among the three groups. Our experiment demonstrated that eye movement patterns were significantly different among the three groups. Pearson correlation coefficients between FI and eye movement parameters were also calculated to assess which of the eye movement parameters were most affected by FI. Our findings illustrate that saccade peak velocity had the greatest positive correlation with FI score and the ratio of total fixation duration to total saccade duration had the greatest negative correlation with FI. Next, we extracted 24 features from eye movement patterns and designed: (1) a classifier to categorize individuals and (2) a regression analysis to predict the FI score of individuals. In the best case examined, the classifier categorized subjects with 68.3% accuracy, and the regression predicted FI of individuals with a 0.54 correlation between observed FI and predicted FI. In our investigation, the results have emphasized that imposed loads on low FI individuals is greater than that of high FI individuals in the cognitive load tasks.