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

[1]  J. Hsiao,et al.  Differential audiovisual information processing in emotion recognition: An eye-tracking study. , 2022, Emotion.

[2]  J. Hsiao,et al.  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 , 2022, European Archives of Psychiatry and Clinical Neuroscience.

[3]  S. Yeh,et al.  When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models , 2021, Sensors.

[4]  J. Hsiao,et al.  Does adding video and subtitles to an audio lesson facilitate its comprehension? , 2021, Learning and Instruction.

[5]  Antoni B. Chan,et al.  The effects of attentional and interpretation biases on later pain outcomes among younger and older adults: A prospective study , 2021, European journal of pain.

[6]  Antoni B. Chan,et al.  Understanding the collinear masking effect in visual search through eye tracking , 2021, Psychonomic Bulletin & Review.

[7]  Antoni B. Chan,et al.  Eye movement analysis with hidden Markov models (EMHMM) with co-clustering , 2021, Behavior Research Methods.

[8]  Antoni B. Chan,et al.  Do portrait artists have enhanced face processing abilities? Evidence from hidden Markov modeling of eye movements , 2021, Cognition.

[9]  Antoni B. Chan,et al.  Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach , 2021, Chinese Geographical Science.

[10]  Naoyuki Kubota,et al.  Attention mechanism-based CNN for facial expression recognition , 2020, Neurocomputing.

[11]  Antoni B. Chan,et al.  The interrelation between interpretation biases, threat expectancies and pain‐related attentional processing , 2020, European journal of pain.

[12]  Antoni B. Chan,et al.  Understanding visual attention to face emotions in social anxiety using hidden Markov models , 2020, Cognition & emotion.

[13]  Antoni B. Chan,et al.  Interpretation biases and visual attention in the processing of ambiguous information in chronic pain , 2020, European journal of pain.

[14]  J. Hsiao,et al.  Modulation of mood on eye movement and face recognition performance. , 2020, Emotion.

[15]  H. Ling,et al.  Attention-based convolutional neural network for deep face recognition , 2019, Multimedia Tools and Applications.

[16]  Tim Chuk,et al.  Eye movement analysis with switching hidden Markov models , 2019, Behavior Research Methods.

[17]  David B. Leake,et al.  CBR Confidence as a Basis for Confidence in Black Box Systems , 2019, ICCBR.

[18]  Mark T. Keane,et al.  How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems , 2019, ICCBR.

[19]  Katarzyna Chawarska,et al.  The role of limited salience of speech in selective attention to faces in toddlers with autism spectrum disorders. , 2019, Journal of child psychology and psychiatry, and allied disciplines.

[20]  Connor J. Parde,et al.  Deep convolutional neural networks in the face of caricature , 2018, Nature Machine Intelligence.

[21]  Antoni B. Chan,et al.  Individuals with insomnia misrecognize angry faces as fearful faces while missing the eyes: an eye-tracking study , 2018, Sleep.

[22]  W. Sommer,et al.  Configural face perception in childhood and adolescence: An individual differences approach. , 2018, Acta psychologica.

[23]  Tommi S. Jaakkola,et al.  Towards Robust Interpretability with Self-Explaining Neural Networks , 2018, NeurIPS.

[24]  William J. Clancey,et al.  Explaining Explanation, Part 4: A Deep Dive on Deep Nets , 2018, IEEE Intelligent Systems.

[25]  Mei Wang,et al.  Deep Face Recognition: A Survey , 2018, Neurocomputing.

[26]  A. Klin.  Biomarkers in Autism Spectrum Disorder: Challenges, Advances, and the Need for Biomarkers of Relevance to Public Health. , 2018, Focus.

[27]  Antoni B. Chan,et al.  Eye-movement patterns in face recognition are associated with cognitive decline in older adults , 2018, Psychonomic bulletin & review.

[28]  Tim Chuk,et al.  Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures , 2017, Cognition.

[29]  J. Wilmer.  Individual Differences in Face Recognition: A Decade of Discovery , 2017 .

[30]  Tim Chuk,et al.  Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling , 2017, Vision Research.

[31]  Jianfei Cai,et al.  Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[32]  Ben Poole,et al.  Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.

[33]  Zhen Li,et al.  Towards Better Analysis of Deep Convolutional Neural Networks , 2016, IEEE Transactions on Visualization and Computer Graphics.

[34]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[35]  Garrison W. Cottrell,et al.  Humans have idiosyncratic and task-specific scanpaths for judging faces , 2015, Vision Research.

[36]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[37]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[38]  Tim Chuk,et al.  Understanding eye movements in face recognition using hidden Markov models. , 2014, Journal of vision.

[39]  D. Ballard,et al.  Modeling Task Control of Eye Movements , 2014, Current Biology.

[40]  Alex Graves,et al.  Recurrent Models of Visual Attention , 2014, NIPS.

[41]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[42]  F. Shic,et al.  Decreased Spontaneous Attention to Social Scenes in 6-Month-Old Infants Later Diagnosed with Autism Spectrum Disorders , 2013, Biological Psychiatry.

[43]  Matthew F. Peterson,et al.  Individual Differences in Eye Movements During Face Identification Reflect Observer-Specific Optimal Points of Fixation , 2013, Psychological science.

[44]  Reinhold Kliegl,et al.  The generation of secondary saccades without postsaccadic visual feedback. , 2013, Journal of vision.

[45]  Antoni B. Chan,et al.  Clustering hidden Markov models with variational HEM , 2012, J. Mach. Learn. Res..

[46]  Shigeru Kitazawa,et al.  How Children With Specific Language Impairment View Social Situations: An Eye Tracking Study , 2012, Pediatrics.

[47]  Jon Brock,et al.  Visual Scan Paths and Recognition of Facial Identity in Autism Spectrum Disorder and Typical Development , 2012, PloS one.

[48]  R. Schultz,et al.  The social motivation theory of autism , 2012, Trends in Cognitive Sciences.

[49]  B. Scassellati,et al.  Limited activity monitoring in toddlers with autism spectrum disorder , 2011, Brain Research.

[50]  Nobumasa Kato,et al.  Atypical gaze patterns in children and adults with autism spectrum disorders dissociated from developmental changes in gaze behaviour , 2010, Proceedings of the Royal Society B: Biological Sciences.

[51]  S. Jaeggi,et al.  The concurrent validity of the N-back task as a working memory measure , 2010, Memory.

[52]  Viola Macchi Cassia,et al.  Holistic processing for faces and cars in preschool-aged children and adults: evidence from the composite effect. , 2009, Developmental science.

[53]  Janet Hui-wen Hsiao,et al.  NIMBLE: a kernel density model of saccade-based visual memory. , 2008, Journal of vision.

[54]  G. Cottrell,et al.  Two Fixations Suffice in Face Recognition , 2008, Psychological science.

[55]  Peter J. B. Hancock,et al.  Viewing it differently: Social scene perception in Williams syndrome and Autism , 2008, Neuropsychologia.

[56]  S. Baron-Cohen,et al.  The Autism Spectrum Quotient: Children’s Version (AQ-Child) , 2008, Journal of autism and developmental disorders.

[57]  Benjamin W Tatler,et al.  The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. , 2007, Journal of vision.

[58]  J. H. Steiger,et al.  Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. , 2004, Psychological methods.

[59]  Frédéric Gosselin,et al.  Spatio-temporal dynamics of face recognition in a flash: it's in the eyes , 2004, Cogn. Sci..

[60]  F. Volkmar,et al.  Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. , 2002, Archives of general psychiatry.

[61]  I. Craw,et al.  Effects of high-pass and low-pass spatial filtering on face identification , 1996, Perception & psychophysics.

[62]  R. Reitan.  Validity of the Trail Making Test as an Indicator of Organic Brain Damage , 1958 .

[63]  S. Kesavan.  Measure , 2019, Texts and Readings in Mathematics.

[64]  Howard Riley,et al.  Are Portrait Artists Superior Face Recognizers? Limited Impact of Adult Experience on Face Recognition Ability , 2017, Journal of experimental psychology. Human perception and performance.

[65]  J. O'Regan,et al.  Some results on translation invariance in the human visual system. , 1990, Spatial vision.

[66]  M. Kerslake.  Across cultures. , 1988, The New Zealand nursing journal. Kai tiaki.

[67]  C. Eriksen,et al.  Effects of noise letters upon the identification of a target letter in a nonsearch task , 1974 .

[68]  J. Raven,et al.  Manual for Raven's progressive matrices and vocabulary scales , 1962 .