A Novel Technique to Develop Cognitive Models for Ambiguous Image Identification Using Eye Tracker

Human behavior can be analyzed using Eye tracker. Thus, it is used for revealing the cognitive processes for object identification. Cognitive process is the mental ability for identification of what our eyes see. Vision with 20/20 sometimes may not reveal the purpose. In this study, ambiguous images are taken to observe the cognitive process in participants. During the perception of an object, a participant uses goal-directed search for identifying various objects. Dense gaze coordinates provide the region of interests and are considered as the target regions for object identification in ambiguous images. These data are used to develop cognitive models for identification of ambiguous images. Features such as, eye fixation, pupil diameter, fixation durations, moments of inertia, and polar moments are used for developing the cognitive model. Three different feature selection methods along with six different classifiers are used for the task of classification. The selection of a subset of features using hypothesis testing performed well, compared to principal component analysis based dimensionality reduction method. This study could be used in detecting whether a participants is lying or not while perceiving an ambiguous image.

[1]  Kyle E Mathewson,et al.  Simultaneous Perception of Both Interpretations of Ambiguous Figures , 2011, Perception.

[2]  S. Kosslyn,et al.  Visual mental images can be ambiguous: insights from individual differences in spatial transformation abilities , 2002, Cognition.

[3]  P Suppes,et al.  Eye-movement models for arithmetic and reading performance. , 1990, Reviews of oculomotor research.

[4]  Steven J. Landry,et al.  An Eye Movement Analysis Algorithm for a Multielement Target Tracking Task: Maximum Transition-Based Agglomerative Hierarchical Clustering , 2015, IEEE Transactions on Human-Machine Systems.

[5]  Oleg V. Komogortsev,et al.  Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors , 2010, IEEE Transactions on Biomedical Engineering.

[6]  D. Levitin.  The foundations of cognitive psychology: Core readings , 2005, History & Philosophy of Psychology.

[7]  Carlos Fonseca,et al.  Classification success of six machine learning algorithms in radar ornithology , 2016 .

[8]  Daniel Reisberg,et al.  Can mental images be ambiguous , 1985 .

[9]  Steven J. Landry,et al.  Using Scanpaths as a Learning Method for a Conflict Detection Task of Multiple Target Tracking , 2014, Hum. Factors.

[10]  Qiang Ji,et al.  A robust 3D eye gaze tracking system using noise reduction , 2008, ETRA.

[11]  小羽 俊士,et al.  Wisconsin card sorting test , 2013 .

[12]  Cheng Li,et al.  Fisher Linear Discriminant Analysis , 2014 .

[13]  Ralf Engbert,et al.  Length, frequency, and predictability effects of words on eye movements in reading , 2004 .

[14]  Catherine Jones.  Wisconsin card sorting test , 2013 .

[15]  © Tan,et al.  Data Mining Classification : Basic Concepts , Decision Trees , and Model Evaluation , 2004 .

[16]  Leland Wilkinson,et al.  The History of the Cluster Heat Map , 2009 .

[17]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[18]  Ahmed Hamza Osman,et al.  A Novel Feature Selection Based on One-Way ANOVA F-Test for E-Mail Spam Classification , 2014 .

[19]  Joseph Jastrow,et al.  Fact and Fable in Psychology , 1901, Nature.

[20]  Fernando Berzal Galiano,et al.  On the quest for easy-to-understand splitting rules , 2003, Data Knowl. Eng..

[21]  C. Blundo,et al.  Perception of ambiguous figures after focal brain lesions , 1990, Neuropsychologia.

[22]  D. Powers.  Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation , 2008 .

[23]  Simone Benedetto,et al.  Driver workload and eye blink duration , 2011 .

[24]  Marcus Nyström,et al.  An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data , 2010, Behavior research methods.

[25]  Changxu Wu,et al.  A computational cognition model of perception, memory, and judgment , 2014, Science China Information Sciences.

[26]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[27]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[28]  Thomas C. Toppino,et al.  Enduring interest in perceptual ambiguity: alternating views of reversible figures. , 2004, Psychological bulletin.

[29]  Pilar Orero,et al.  Aggregate gaze visualization with real-time heatmaps , 2012, ETRA.

[30]  Hong Liu,et al.  Coarse to fine K nearest neighbor classifier , 2013, Pattern Recognit. Lett..

[31]  Stephan Diehl,et al.  Comparing the Readability of Graph Layouts using Eyetracking and Task-oriented Analysis , 2009, CAe.

[32]  Christine Fernandez-Maloigne,et al.  On the comparison of visual discomfort generated by S3D and 2D content based on eye-tracking features , 2014, Electronic Imaging.

[33]  David Swapp,et al.  Where do we look when we steer and does it matter , 2010 .

[34]  Steven B. Smith,et al.  Digital Signal Processing: A Practical Guide for Engineers and Scientists , 2002 .

[35]  Ralf Wieland,et al.  Classification in conservation biology: A comparison of five machine-learning methods , 2010, Ecol. Informatics.

[36]  M Raab,et al.  Moving your Eyes to Solution: Effects of Movements on the Perception of a Problem-Solving Task , 2014, Quarterly journal of experimental psychology.

[37]  John R. Anderson,et al.  Tracing Eye Movement Protocols with Cognitive Process Models , 1998 .

[38]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[39]  John R. Anderson,et al.  Integrating analogical mapping and general problem solving: the path-mapping theory , 2001, Cogn. Sci..

[40]  Mark S. Gilzenrat,et al.  Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function , 2010, Cognitive, affective & behavioral neuroscience.

[41]  Virginio Cantoni,et al.  Image Segmentation Scrutiny by Eye Tracking , 2014, 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems.

[42]  Nanying Yin,et al.  Analysis of the Leaky Bucket Algorithm for ON-OFF Data Sources , 1993, J. High Speed Networks.

[43]  Duncan Mortimer,et al.  Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments , 2015, BMC Medical Informatics and Decision Making.

[44]  M. Peterson,et al.  Mental images can be ambiguous: Reconstruals and reference-frame reversals , 1992, Memory & cognition.

[45]  D. Muresanu,et al.  Reading beyond the glance: eye tracking in neurosciences , 2015, Neurological Sciences.

[46]  E. Boring.  A new ambiguous figure. , 1930 .

[47]  Maya R. Gupta,et al.  Bayesian Quadratic Discriminant Analysis , 2007, J. Mach. Learn. Res..

[48]  A. Glaros,et al.  Understanding the accuracy of tests with cutting scores: the sensitivity, specificity, and predictive value model. , 1988, Journal of clinical psychology.

[49]  M. Tyers,et al.  BoxPlotR: a web tool for generation of box plots , 2014, Nature Methods.

[50]  L. Cohen,et al.  Perception of reversible figures after brain injury. , 1959, A.M.A. archives of neurology and psychiatry.

[51]  T. Åkerstedt,et al.  Subjective sleepiness, simulated driving performance and blink duration: examining individual differences , 2006, Journal of sleep research.

[52]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[53]  Andrew T. Duchowski,et al.  Eye Tracking Methodology: Theory and Practice , 2003, Springer London.

[54]  B Dhillon,et al.  Gaze and pupil changes during navigation in age-related macular degeneration , 2014, British Journal of Ophthalmology.

[55]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[56]  Talma Hendler,et al.  Vase or face? A neural correlate of shape-selective grouping processes in the human brain , 2001, NeuroImage.

[57]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[58]  D. Coomans,et al.  Alternative k-nearest neighbour rules in supervised pattern recognition : Part 1. k-Nearest neighbour classification by using alternative voting rules , 1982 .

[59]  Mihai Dupac,et al.  Advanced Dynamics: Analytical and Numerical Calculations with MATLAB , 2012 .

[60]  A PETRIE,et al.  A comparison of the psychological effects of different types of operations on the frontal lobes. , 1952, The Journal of mental science.