Adaptive Visualization Interface That Manages User's Cognitive Load Based on Interaction Characteristics

Efficiency of visualization interfaces in terms of their users' decisions speed and accuracy in safety-critical areas is extremely important as late or wrong reaction on displayed information may cause at least financial losses (not to mention the damage to human health and/or environment). Users of such systems can be overloaded with the displayed information and therefore it can take them more time to make a decision. In this paper, a novel visualization interface is presented, that can detect its user's cognitive overload and adapt the amount of information to be displayed and its visualization according to user's current cognitive capabilities. Results provided by the conducted user study have demonstrated that such adaptation technique benefits visualization interface efficiency.

[1]  Gerald L. Lohse,et al.  The role of working memory on graphical information processing , 1997, Behav. Inf. Technol..

[2]  D. Leutner,et al.  Direct Measurement of Cognitive Load in Multimedia Learning , 2003 .

[3]  David R. Flatla,et al.  SSMRecolor: improving recoloring tools with situation-specific models of color differentiation , 2012, CHI.

[4]  Rex B. Kline,et al.  Usability measurement and metrics: A consolidated model , 2006, Software Quality Journal.

[5]  Mario Mišić SECURITY INFORMATION AND EVENT MANAGEMENT SYSTEMS , 2013 .

[6]  Brian Shackel,et al.  Usability - Context, framework, definition, design and evaluation , 1991, Interact. Comput..

[7]  A. Jameson Adaptive interfaces and agents , 2002 .

[8]  F. Paas,et al.  Instructional control of cognitive load in the training of complex cognitive tasks , 1994 .

[9]  Allen Newell,et al.  The psychology of human-computer interaction , 1983 .

[10]  Sharon L. Oviatt,et al.  When do we interact multimodally?: cognitive load and multimodal communication patterns , 2004, ICMI '04.

[11]  B. Schneirdeman,et al.  Designing the User Interface: Strategies for Effective Human-Computer Interaction , 1998 .

[12]  D. Leutner,et al.  Assessment of Cognitive Load in Multimedia Learning with Dual-Task Methodology: Auditory Load and Modality Effects , 2004 .

[13]  W. Buxton Human-Computer Interaction , 1988, Springer Berlin Heidelberg.

[14]  Maarten A. S. Boksem,et al.  Effects of mental fatigue on attention: an ERP study. , 2005, Brain research. Cognitive brain research.

[15]  Larry L. Constantine,et al.  Software for Use - A Practical Guide to the Models and Methods of Usage-Centered Design , 1999 .

[16]  Atsuo Murata,et al.  An Attempt to Evaluate Mental Workload Using Wavelet Transform of EEG , 2005, Hum. Factors.

[17]  Katharina Scheiter,et al.  Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? , 2009, Comput. Hum. Behav..

[18]  Pamela M Allen,et al.  The Effect of Cognitive Load on Decision Making with Graphically Displayed Uncertainty Information , 2014, Risk analysis : an official publication of the Society for Risk Analysis.

[19]  David E. Kieras Using the Keystroke-Level Model to Estimate Execution Times , 2003 .

[20]  K Smith Needs Analysis: Or, How Do You Capture, Represent and Validate User Requirements in a Formal Manner/Notation before Design , 2011 .

[21]  Zhen Wen,et al.  Behavior-driven visualization recommendation , 2009, IUI.

[22]  Ben Shneiderman,et al.  Designing the User Interface: Strategies for Effective Human-Computer Interaction , 1998 .

[23]  F. Paas,et al.  Cognitive Architecture and Instructional Design , 1998 .

[24]  Anne Marsden,et al.  International Organization for Standardization , 2014 .

[25]  V. Dorokhov [Somnology and occupational safety]. , 2013, Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova.

[26]  N. Cowan The magical number 4 in short-term memory: A reconsideration of mental storage capacity , 2001, Behavioral and Brain Sciences.

[27]  Jakob Nielsen,et al.  Usability engineering , 1997, The Computer Science and Engineering Handbook.

[28]  Krzysztof Z. Gajos,et al.  Automatically generating personalized user interfaces with Supple , 2010, Artif. Intell..

[29]  Colin Potts,et al.  Design of Everyday Things , 1988 .

[30]  Christopher D Wickens,et al.  Processing Resources in Attention, Dual Task Performance, and Workload Assessment. , 1981 .

[31]  Brian D. Fisher,et al.  Impact of personality factors on interface interaction and the development of user profiles: Next steps in the personal equation of interaction , 2012, Inf. Vis..

[32]  Krista E. DeLeeuw,et al.  A Comparison of Three Measures of Cognitive Load: Evidence for Separable Measures of Intrinsic, Extraneous, and Germane Load , 2008 .

[33]  F. Donders On the speed of mental processes. , 1969, Acta psychologica.

[34]  Cristina Conati,et al.  User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities , 2013, IUI '13.

[35]  P. Chandler,et al.  Cognitive Load Theory and the Format of Instruction , 1991 .

[36]  T. Jong Cognitive load theory, educational research, and instructional design: some food for thought , 2010 .

[37]  F. Paas,et al.  Cognitive Load Measurement as a Means to Advance Cognitive Load Theory , 2003 .

[38]  Ergonomic requirements for office work with visual display terminals ( VDTs ) — Part 11 : Guidance on usability , 1998 .

[39]  W. Verwey,et al.  Detecting short periods of elevated workload: A comparison of nine workload assessment techniques , 1996 .

[40]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[41]  F. Mathy,et al.  What’s magic about magic numbers? Chunking and data compression in short-term memory , 2012, Cognition.

[42]  Joachim Funke,et al.  Complex problem solving: a case for complex cognition? , 2010, Cognitive Processing.

[43]  Marilyn Tremaine,et al.  Understanding visualization through spatial ability differences , 2005, VIS 05. IEEE Visualization, 2005..

[44]  Edward E. Smith,et al.  Working Memory: A View from Neuroimaging , 1997, Cognitive Psychology.

[45]  Cristina Conati,et al.  Individual user characteristics and information visualization: connecting the dots through eye tracking , 2013, CHI.

[46]  Dennis Gamayunov,et al.  Adaptive Security Event Visualization for Continuous Monitoring , 2013, UMAP Workshops.

[47]  Ben Shneiderman,et al.  Designing the user interface (2nd ed.): strategies for effective human-computer interaction , 1992 .

[48]  K. R. Ridderinkhof,et al.  Impaired cognitive control and reduced cingulate activity during mental fatigue. , 2005, Brain research. Cognitive brain research.

[49]  Miles MacLeod,et al.  The MUSiC performance measurement method , 1997, Behav. Inf. Technol..

[50]  Kelly M. Kavanagh,et al.  Magic Quadrant for Security Information and Event Management , 2011 .

[51]  H. Endo,et al.  Mental fatigue and impaired response processes: event-related brain potentials in a Go/NoGo task. , 2009, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[52]  Ann Williamson,et al.  The link between fatigue and safety. , 2011, Accident; analysis and prevention.

[53]  Dennis Gamayunov,et al.  Visualization of complex attacks and state of attacked network , 2009, 2009 6th International Workshop on Visualization for Cyber Security.

[54]  R. Mayer,et al.  Nine Ways to Reduce Cognitive Load in Multimedia Learning , 2003 .

[55]  Weidong Huang,et al.  Measuring Effectiveness of Graph Visualizations: A Cognitive Load Perspective , 2009, Inf. Vis..

[56]  Peter Brusilovsky,et al.  Adaptive Knowledge-Based Visualization for Accessing Educational Examples , 2006, Tenth International Conference on Information Visualisation (IV'06).