EEG & Eye Tracking User Experiments for Spatial Memory Task on Maps

The aim of this research is to evaluate the use of ET and EEG for studying the cognitive processes of expert and novice map users and to explore these processes by comparing two types of spatial memory experiments through cognitive load measurements. The first experiment consisted of single trials and participants were instructed to study a map stimulus without any time constraints in order to draw a sketch map afterwards. According to the ET metrics (i.e., average fixation duration and the number of fixations per second), no statistically significant differences emerged between experts and novices. A similar result was also obtained with EEG Frontal Alpha Asymmetry calculations. On the contrary, in terms of alpha power across all electrodes, novices exhibited significantly lower alpha power, indicating a higher cognitive load. In the second experiment, a larger number of stimuli were used to study the effect of task difficulty. The same ET metrics used in the first experiment indicated that the difference between these user groups was not statistically significant. The cognitive load was also extracted using EEG event-related spectral power changes at alpha and theta frequency bands. Preliminary data exploration mostly suggested an increase in theta power and a decrease in alpha power.

[1]  R. Davidson Affective Style and Affective Disorders: Perspectives from Affective Neuroscience , 1998 .

[2]  Max Eckert On The Nature Of Maps And Map Logic , 1977 .

[3]  Amy L. Griffin Cartography, visual perception and cognitive psychology , 2017 .

[4]  Xuwei Chen,et al.  A comparison of usefulness of 2D and 3D representations of urban planning , 2015 .

[5]  H. Merckelbach,et al.  The validity of individual frontal alpha asymmetry EEG neurofeedback. , 2016, Social cognitive and affective neuroscience.

[6]  Cees van Leeuwen,et al.  Combining EEG and eye movement recording in free viewing: Pitfalls and possibilities , 2016, Brain and Cognition.

[7]  Michael E. Smith,et al.  Neurophysiological measures of cognitive workload during human-computer interaction , 2003 .

[8]  Laurynas Gedminas,et al.  Evaluating hurricane advisories using eye-tracking and biometric data , 2011 .

[9]  Björn N.S. Vlaskamp,et al.  Crowding degrades saccadic search performance , 2005 .

[10]  Frank Dickmann,et al.  Grids in Topographic Maps Reduce Distortions in the Recall of Learned Object Locations , 2014, PloS one.

[11]  Jessica Witvoet,et al.  Does cognitive load influence performance in a game-based learning task? , 2013 .

[12]  A. Jon Kimerling,et al.  Map Use: Reading and Analysis , 2009 .

[13]  A. Gevins,et al.  Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style. , 2000, Cerebral cortex.

[14]  Richard J. Davidson,et al.  Affect, cognition, and hemispheric specialization. , 1985 .

[15]  Kristien Ooms Maps, how do users see them?: an in depth investigation of the map users' cognitive processes , 2012 .

[16]  Kristien Ooms,et al.  Education in cartography: what is the status of young people’s map-reading skills? , 2016 .

[17]  Arzu Çöltekin,et al.  Exploring the efficiency of users' visual analytics strategies based on sequence analysis of eye movement recordings , 2010, Int. J. Geogr. Inf. Sci..

[18]  Richard C. Atkinson,et al.  Human Memory: A Proposed System and its Control Processes , 1968, Psychology of Learning and Motivation.

[19]  M. Benedek,et al.  Alpha power increases in right parietal cortex reflects focused internal attention , 2014, Neuropsychologia.

[20]  Brandi Lee Drisdelle,et al.  Electrophysiological impact of multiple concussions in asymptomatic athletes: A re-analysis based on alpha activity during a visual-spatial attention task , 2018, Neuropsychologia.

[21]  Irma Kveladze,et al.  The Usability of a GeoVisual Analytics Environment for the Exploration and Analysis of Different Datasets , 2017 .

[22]  Daphne N. Yu,et al.  High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. , 1997, Cerebral cortex.

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

[24]  H. Togami,et al.  Affects on visual search performance of individual differences in fixation time and number of fixations , 1984 .

[25]  Mathias Benedek,et al.  Divergent thinking training is related to frontal electroencephalogram alpha synchronization , 2006, The European journal of neuroscience.

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

[27]  Robert J. K. Jacob,et al.  Eye tracking in human-computer interaction and usability research : Ready to deliver the promises , 2002 .

[28]  Veerle Fack,et al.  Listen to the Map User: Cognition, Memory, and Expertise , 2015 .

[29]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[30]  A. Neubauer,et al.  Intelligence and neural efficiency , 2009, Neuroscience & Biobehavioral Reviews.

[31]  Patrick E. McKnight,et al.  A capability model of individual differences in frontal EEG asymmetry , 2006, Biological Psychology.

[32]  R. E. Wheeler,et al.  Individual differences in anterior brain asymmetry and fundamental dimensions of emotion. , 1992, Journal of personality and social psychology.

[33]  Manuel Schabus,et al.  Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. , 2005, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[34]  A. Jacobs,et al.  Coregistration of eye movements and EEG in natural reading: analyses and review. , 2011, Journal of experimental psychology. General.

[35]  Enrique Bigné,et al.  Elapsed time on first buying triggers brand choices within a category: A virtual reality-based study , 2016 .

[36]  Jürgen Margraf,et al.  The influence of frontal alpha-asymmetry on the processing of approach- and withdrawal-related stimuli-A multichannel psychophysiology study. , 2017, Psychophysiology.

[37]  Sudhir Gupta,et al.  Case Studies , 2013, Journal of Clinical Immunology.

[38]  Kristien Ooms,et al.  Digital sketch maps and eye tracking statistics as instruments to obtain insights into spatial cognition , 2018, Journal of eye movement research.

[39]  Sara Maggi Depicting movement data with animations for embodied and real-time decision-making: a user study with air traffic control displays and real-time movement data , 2017 .

[40]  Alan M. MacEachren,et al.  How Maps Work - Representation, Visualization, and Design , 1995 .

[41]  W. Klimesch EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[42]  Chun-Hsiang Chuang,et al.  Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving , 2018, Front. Neurosci..

[43]  Naveen Kumar,et al.  Measurement of Cognitive Load in HCI Systems Using EEG Power Spectrum: An Experimental Study , 2015, IHCI.

[44]  John J. B. Allen,et al.  Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry. , 2017, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[45]  E. Harmon-Jones Early Career Award. Clarifying the emotive functions of asymmetrical frontal cortical activity. , 2003, Psychophysiology.