Temporal dynamics of eye‐tracking and EEG during reading and relevance decisions

Assessment of text relevance is an important aspect of human–information interaction. For many search sessions it is essential to achieving the task goal. This work investigates text relevance decision dynamics in a question‐answering task by direct measurement of eye movement using eye‐tracking and brain activity using electroencephalography EEG. The EEG measurements are correlated with the user's goal‐directed attention allocation revealed by their eye movements. In a within‐subject lab experiment (N = 24), participants read short news stories of varied relevance. Eye movement and EEG features were calculated in three epochs of reading each news story (early, middle, final) and for periods where relevant words were read. Perceived relevance classification models were learned for each epoch. The results show reading epochs where relevant words were processed could be distinguished from other epochs. The classification models show increasing divergence in processing relevant vs. irrelevant documents after the initial epoch. This suggests differences in cognitive processes used to assess texts of varied relevance levels and provides evidence for the potential to detect these differences in information search sessions using eye tracking and EEG.

[1]  G. McArthur,et al.  Validation of the Emotiv EPOC® EEG gaming system for measuring research quality auditory ERPs , 2013, PeerJ.

[2]  Lynne M. Reder,et al.  Moses Illusion: Implication for Human Cognition , 2004 .

[3]  Samuel Kaski,et al.  Predicting term-relevance from brain signals , 2014, SIGIR.

[4]  Pinar Heggernes,et al.  Minimal triangulations of graphs: A survey , 2006, Discret. Math..

[5]  Andreas Dengel,et al.  Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond , 2012, TIIS.

[6]  Arthur R. Taylor,et al.  User relevance criteria choices and the information search process , 2012, Inf. Process. Manag..

[7]  Mohammad Soleymani,et al.  Human behavior sensing for tag relevance assessment , 2013, MM '13.

[8]  Tomasz Waller,et al.  Familial or Sporadic Idiopathic Scoliosis – classification based on artificial neural network and GAPDH and ACTB transcription profile , 2013, BioMedical Engineering OnLine.

[9]  W W Abbott,et al.  Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain–machine interfaces , 2012, Journal of neural engineering.

[10]  Minho Kim,et al.  Quantitative Evaluation of a Low-Cost Noninvasive Hybrid Interface Based on EEG and Eye Movement , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Keith Rayner,et al.  Parafoveal processing in reading , 2011, Attention, Perception, & Psychophysics.

[12]  Katarzyna Blinowska,et al.  ELECTROENCEPHALOGRAPHY (EEG) , 2006 .

[13]  Jonathan L. Herlocker,et al.  Click data as implicit relevance feedback in web search , 2007, Inf. Process. Manag..

[14]  Dean Cvetkovic,et al.  EEG inter/intra-hemispheric coherence and asymmetric responses to visual stimulations , 2009, Medical & Biological Engineering & Computing.

[15]  Frank E. Pollick,et al.  Understanding Relevance: An fMRI Study , 2013, ECIR.

[16]  ZhangXiangmin,et al.  Inferring user knowledge level from eye movement patterns , 2013 .

[17]  W. Levelt,et al.  Pupillary dilation as a measure of attention: a quantitative system analysis , 1993 .

[18]  Josef P. Rauschecker,et al.  Wernicke’s area revisited: Parallel streams and word processing , 2013, Brain and Language.

[19]  Ryen W. White,et al.  User see, user point: gaze and cursor alignment in web search , 2012, CHI.

[20]  Carol L. Barry User-Defined Relevance Criteria: An Exploratory Study , 1994, J. Am. Soc. Inf. Sci..

[21]  Michael A. Shepherd,et al.  Effect of task on time spent reading as an implicit measure of interest , 2005, ASIST.

[22]  Deirdre Wilson,et al.  Relevance theory: A tutorial , 2002 .

[23]  Mark D. Smucker,et al.  Mouse movement during relevance judging: implications for determining user attention , 2014, SIGIR.

[24]  Sergio I. Giraldo,et al.  Musical neurofeedback for treating depression in elderly people , 2015, Front. Neurosci..

[25]  Tefko Saracevic Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance , 2007 .

[26]  L. Garey Brodmann's localisation in the cerebral cortex , 1999 .

[27]  Nicholas J. Belkin,et al.  Personalizing information retrieval for multi-session tasks: the roles of task stage and task type , 2010, SIGIR '10.

[28]  Ciya Liao,et al.  A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine , 2010, WSDM '10.

[29]  Ian Ruthven,et al.  Relevance behaviour in TREC , 2014, J. Documentation.

[30]  Birger Hjørland,et al.  The foundation of the concept of relevance , 2010, J. Assoc. Inf. Sci. Technol..

[31]  A. Frolov,et al.  Brain-Computer Interface Based on Generation of Visual Images , 2011, PloS one.

[32]  Erik D. Reichle,et al.  The E-Z Reader model of eye-movement control in reading: Comparisons to other models , 2003, Behavioral and Brain Sciences.

[33]  Dagobert Soergel,et al.  Relevance: An improved framework for explicating the notion , 2013, J. Assoc. Inf. Sci. Technol..

[34]  Ian Ruthven,et al.  An eye-tracking approach to the analysis of relevance judgments on the Web: The case of Google search engine , 2012, J. Assoc. Inf. Sci. Technol..

[35]  Eugene Agichtein,et al.  Towards predicting web searcher gaze position from mouse movements , 2010, CHI Extended Abstracts.

[36]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[37]  Nicholas J. Belkin,et al.  Predicting Search Task Difficulty at Different Search Stages , 2014, CIKM.

[38]  L. Cooke,et al.  Is the Mouse a “ Poor Man ’ s Eye Tracker ” ? , 2006 .

[39]  Alan F. Smeaton,et al.  Eye fixation related potentials in a target search task , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  Tefko Saracevic,et al.  RELEVANCE: A review of and a framework for the thinking on the notion in information science , 1997, J. Am. Soc. Inf. Sci..

[41]  Kerry Rodden,et al.  Eye-mouse coordination patterns on web search results pages , 2008, CHI Extended Abstracts.

[42]  Nicholas J. Belkin,et al.  Display time as implicit feedback: understanding task effects , 2004, SIGIR '04.

[43]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Jacek Gwizdka,et al.  Inferring user knowledge level from eye movement patterns , 2013, Inf. Process. Manag..

[45]  Ryen W. White,et al.  Text selections as implicit relevance feedback , 2012, SIGIR '12.

[46]  K Rayner,et al.  Parafoveal identification during a fixation in reading. , 1975, Acta psychologica.

[47]  Boris Reuderink,et al.  Distinguishing between target and nontarget fixations in a visual search task using fixation-related potentials. , 2013, Journal of vision.

[48]  Fang Song Cui,et al.  A Dictionary Storage Technique for LZW Compression Algorithm , 2013 .

[49]  Ellen M. Voorhees,et al.  Variations in relevance judgments and the measurement of retrieval effectiveness , 1998, SIGIR '98.

[50]  Ilpo Kojo,et al.  Using hidden Markov model to uncover processing states from eye movements in information search tasks , 2008, Cognitive Systems Research.

[51]  Thierry Dutoit,et al.  Performance of the Emotiv Epoc headset for P300-based applications , 2013, Biomedical engineering online.

[52]  Daniel M. Russell,et al.  Discriminating the relevance of web search results with measures of pupil size , 2009, CHI.

[53]  Yang Wang,et al.  Measuring Cognitive Workload with Low-Cost Electroencephalograph , 2011, INTERACT.

[54]  J. S. Barlow,et al.  Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking. , 1990, Electroencephalography and clinical neurophysiology.

[55]  Frank E. Pollick,et al.  When Relevance Judgement is Happening?: An EEG-based Study , 2015, SIGIR.

[56]  Thierry Baccino,et al.  Decision-making in information seeking on texts: an eye-fixation-related potentials investigation , 2013, Front. Syst. Neurosci..

[57]  LarrañagaPedro,et al.  A review of feature selection techniques in bioinformatics , 2007 .

[58]  Stefan M. Wierda,et al.  Pupil dilation deconvolution reveals the dynamics of attention at high temporal resolution , 2012, Proceedings of the National Academy of Sciences.

[59]  Ryen W. White,et al.  The Use of Implicit Evidence for Relevance Feedback in Web Retrieval , 2002, ECIR.

[60]  B. Hjorth EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[61]  Michael E. Lesk,et al.  Relevance assessments and retrieval system evaluation , 1968, Inf. Storage Retr..

[62]  John Shawe-Taylor,et al.  Can eyes reveal interest? Implicit queries from gaze patterns , 2009, User Modeling and User-Adapted Interaction.

[63]  Nicholas J. Belkin,et al.  Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback , 2001, Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.

[64]  Jacek Gwizdka,et al.  Using Wireless EEG Signals to Assess Memory Workload in the $n$-Back Task , 2016, IEEE Transactions on Human-Machine Systems.

[65]  JungSeikyung,et al.  Click data as implicit relevance feedback in web search , 2007 .

[66]  M. Just,et al.  The psychology of reading and language comprehension , 1986 .

[67]  Jacek Gwizdka,et al.  Characterizing relevance with eye-tracking measures , 2014, IIiX.

[68]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.

[69]  Chad Galloway,et al.  Relevance judging, evaluation, and decision making in virtual libraries: A descriptive study , 2001, J. Assoc. Inf. Sci. Technol..

[70]  Laurence R. Horn,et al.  The handbook of pragmatics , 2004 .

[71]  H. E. Krugman,et al.  Some Applications of Pupil Measurement , 1964 .

[72]  Cláudio T. Silva,et al.  A User Study of Visualization Effectiveness Using EEG and Cognitive Load , 2011, Comput. Graph. Forum.

[73]  T. Ferrée,et al.  Fluctuation Analysis of Human Electroencephalogram , 2001, physics/0105029.

[74]  Jacek Gwizdka,et al.  Differences in Eye-Tracking Measures Between Visits and Revisits to Relevant and Irrelevant Web Pages , 2015, SIGIR.

[75]  Ryen W. White,et al.  A study on the effects of personalization and task information on implicit feedback performance , 2006, CIKM '06.

[76]  Charles Oppenheim,et al.  A model of cognitive load for IR: implications for user relevance feedback interaction , 2001 .

[77]  Pia Borlund,et al.  The concept of relevance in IR , 2003, J. Assoc. Inf. Sci. Technol..

[78]  Gamini Dissanayake,et al.  Choice modeling and the brain: A study on the Electroencephalogram (EEG) of preferences , 2012, Expert Syst. Appl..

[79]  Mari-Carmen Marcos,et al.  Effect of Snippets on User Experience in Web Search , 2015, Interacción.

[80]  Martin Halvey,et al.  Is relevance hard work?: evaluating the effort of making relevant assessments , 2013, SIGIR.

[81]  S. Weber,et al.  Educational paper , 2012, European Journal of Pediatrics.

[82]  A. Mognon,et al.  ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.

[83]  Jacek Gwizdka,et al.  Task and user effects on reading patterns in information search , 2011, Interact. Comput..