Do graph readers prefer the graph type most suited to a given task? Insights from eye tracking

Research on graph comprehension suggests that point differences are easier to read in bar graphs, while trends are easier to read in line graphs. But are graph readers able to detect and use the most suited graph type for a given task? In this study, we applied a dual repre-sentation paradigm and eye tracking methodology to determine graph readers’ preferential processing of bar and line graphs while solving both point difference and trend tasks. Data were analyzed using linear mixed-effects models. Results show that participants shifted their graph preference depending on the task type and refined their preference over the course of the graph task. Implications for future research are discussed.

[1]  Alan F. Blackwell,et al.  Introduction: Thinking with Diagrams , 2004, Artificial Intelligence Review.

[2]  A. Baddeley.  The magical number seven: still magic after all these years? , 1994, Psychological review.

[3]  M A Just,et al.  A theory of reading: from eye fixations to comprehension. , 1980, Psychological review.

[4]  Frances R. Curcio,et al.  Comprehension of Mathematical Relationships Expressed in Graphs. , 1987 .

[5]  Izak Benbasat,et al.  Processing of graphical information: a decomposition taxonomy to match data extraction tasks and gra , 1990 .

[6]  M. Wertheimer.  Laws of organization in perceptual forms. , 1938 .

[7]  Steven Pinker,et al.  A theory of graph comprehension. , 1990 .

[8]  V. Carey,et al.  Mixed-Effects Models in S and S-Plus , 2001 .

[9]  P. Shah,et al.  Review of Graph Comprehension Research: Implications for Instruction , 2002 .

[10]  Kenneth R. Koedinger,et al.  Toward a Model of Learning Data Representations , 2001 .

[11]  Jeffrey P. Dorman.  The effect of clustering on statistical tests: an illustration using classroom environment data , 2008 .

[12]  T. Haladyna.  Developing and Validating Multiple-Choice Test Items , 1994 .

[13]  Peter C.-H. Cheng,et al.  Modeling the Effect of Task and Graphical Representation on Response Latency in a Graph Reading Task , 2003, Hum. Factors.

[14]  David Peebles,et al.  The Effect of Gestalt Laws of Perceptual Organization on the Comprehension of Three-Variable Bar and Line Graphs , 2013, Hum. Factors.

[15]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[16]  Susan Bell Trickett,et al.  Toward a Comprehensive Model of Graph Comprehension: Making the Case for Spatial Cognition , 2006, Diagrams.

[17]  Tengfei Wang,et al.  A learning-based account of fluid intelligence from the perspective of the position effect , 2014 .

[18]  Carmel M. Diezmann,et al.  Solving Graphics Problems: Student Performance in Junior Grades , 2007 .

[19]  Benjamin D. Wright,et al.  Validity and Reliability of True-False Tests , 1985 .

[20]  Johannes Hartig,et al.  A multilevel item response model for item position effects and individual persistence , 2012 .

[21]  K. A. Ericsson,et al.  Verbal reports as data. , 1980 .

[22]  Alan F. Blackwell,et al.  Goal recognition through goal graph analysis , 2001 .

[23]  Marlit Annalena Lindner,et al.  Tracking the Decision-Making Process in Multiple-Choice Assessment: Evidence from Eye Movements , 2014 .

[24]  S. Kosslyn.  Understanding charts and graphs , 1989 .

[25]  Martin Meißner,et al.  Eye-Tracking Information Processing in Choice-Based Conjoint Analysis , 2010 .

[26]  Wolfgang Schnotz,et al.  Commentary: Towards an Integrated View of Learning from Text and Visual Displays , 2002 .

[27]  Helen C. Purchase,et al.  Twelve years of diagrams research , 2014, J. Vis. Lang. Comput..

[28]  Joseph H. Goldberg,et al.  Eye tracking for visualization evaluation: Reading values on linear versus radial graphs , 2011, Inf. Vis..

[29]  H. Bergh,et al.  Examples of Mixed-Effects Modeling with Crossed Random Effects and with Binomial Data. , 2008 .

[30]  Thomas S. Tullis,et al.  Online Viewing and Aesthetic Preferences of Generation Y and the Baby Boom Generation: Testing User Web Site Experience Through Eye Tracking , 2011, Int. J. Electron. Commer..

[31]  M. D’Esposito.  Working memory. , 2008, Handbook of clinical neurology.

[32]  Patrick Olivier,et al.  Diagrammatic Representation and Reasoning , 2001 .

[33]  Howard Wainer,et al.  Understanding Graphs and Tables , 1992 .

[34]  Alan Bell,et al.  The Interpretation of Graphs Representing Situations. , 1981 .

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

[36]  William Winn,et al.  A theoretical framework for research on learning from graphics , 1990 .

[37]  Herbert A. Simon,et al.  Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987, Cogn. Sci..

[38]  Linda J. Lombardino,et al.  Comparing graphs and text: Effects of complexity and task , 2015 .

[39]  Fred Paas,et al.  Uncovering cognitive processes: Different techniques that can contribute to cognitive load research and instruction , 2009, Comput. Hum. Behav..

[40]  D. Barr.  Analyzing ‘visual world’ eyetracking data using multilevel logistic regression , 2008 .

[41]  Roel Bosker,et al.  Multilevel analysis : an introduction to basic and advanced multilevel modeling , 1999 .

[42]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[43]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[44]  Jeffery. M. Zacks,et al.  Bars and lines: A study of graphic communication , 1999, Memory & cognition.

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

[46]  D. Barr,et al.  Random effects structure for confirmatory hypothesis testing: Keep it maximal. , 2013, Journal of memory and language.

[47]  David K. Simkin,et al.  An Information-Processing Analysis of Graph Perception , 1987 .

[48]  Priti Shah,et al.  A Model of the Cognitive and Perceptual Processes in Graphical Display Comprehension , 1997 .

[49]  Jacques Bertin,et al.  Semiology of Graphics - Diagrams, Networks, Maps , 2010 .

[50]  Richard E. Mayer,et al.  Multimedia Learning , 2001, Visible Learning Guide to Student Achievement.

[51]  Lesa Hoffman,et al.  Multilevel models for the experimental psychologist: Foundations and illustrative examples , 2007, Behavior research methods.

[52]  Nirit Glazer,et al.  Challenges with graph interpretation: a review of the literature , 2011 .

[53]  M. Hegarty,et al.  Graphs as aids to knowledge construction: Signaling techniques for guiding the process of graph comprehension. , 1999 .

[54]  Neff Walker,et al.  A classification of visual representations , 1994, CACM.

[55]  J. Bain,et al.  PSYCHOLOGICAL SCIENCE Research Article How Many Variables Can Humans Process? , 2022 .

[56]  Priti Shah,et al.  Graph Comprehension: The Role of Format, Content and Individual Differences , 2002, Diagrammatic Representation and Reasoning.

[57]  P. Carpenter,et al.  Conceptual limitations in comprehending line graphs. , 1995 .

[58]  Jacques D. Fleuriot,et al.  Diagrammatically-Driven Formal Verification of Web-Services Composition , 2012, Diagrams.

[59]  Kenneth Holmqvist,et al.  Eye tracking: a comprehensive guide to methods and measures , 2011 .