Understanding the role of age and fluid intelligence in information search

In this study, we explore the role of age and fluid intelligence on the behavior of people looking for information in a real-world search space. Analyses of mouse moves, clicks, and eye movements provide a window into possible differences in both task strategy and performance, and allow us to begin to separate the influence of age from the correlated but isolable influence of cognitive ability. We found little evidence of differences in strategy between younger and older participants matched on fluid intelligence. Both performance and strategy differences were found between older participants having higher versus lower fluid intelligence, however, suggesting that cognitive factors, rather than age per se, exert the dominant influence. This underscores the importance of measuring and controlling for cognitive abilities in studies involving older adults.

[1]  Wai-Tat Fu,et al.  Interactive effects of age and interface differences on search strategies and performance , 2010, CHI.

[2]  T. Braver,et al.  Cognitive control, goal maintenance, and prefrontal function in healthy aging. , 2008, Cerebral cortex.

[3]  Julie Chen,et al.  The bloodhound project: automating discovery of web usability issues using the InfoScentπ simulator , 2003, CHI '03.

[4]  R. Cattell,et al.  Refinement and test of the theory of fluid and crystallized general intelligences. , 1966, Journal of educational psychology.

[5]  T. Salthouse,et al.  Differential age-related influences on memory for verbal-symbolic information and visual-spatial information? , 1995, The journals of gerontology. Series B, Psychological sciences and social sciences.

[6]  Peter G. Fairweather.  How older and younger adults differ in their approach to problem solving on a complex website , 2008, Assets '08.

[7]  Michael I. Posner,et al.  Slow But Sure: A Chronometric Analysis of the Process of Aging. , 1975 .

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

[9]  Marilyn Hughes Blackmon,et al.  Tool for accurately predicting website navigation problems, non-problems, problem severity, and effectiveness of repairs , 2005, CHI.

[10]  Tim Halverson,et al.  Cleaning up systematic error in eye-tracking data by using required fixation locations , 2002, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

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

[12]  Jeffrey T. Steedle,et al.  Working memory, fluid intelligence, and science learning , 2006 .

[13]  Vicki L. Hanson.  Influencing technology adoption by older adults , 2010, Interact. Comput..

[14]  Simeon Keates,et al.  Effect of age and Parkinson's disease on cursor positioning using a mouse , 2005, Assets '05.

[15]  Bonnie E. John,et al.  Exploration of Costs and Benefits of Predictive Human Performance Modeling for Design , 2010 .

[16]  Wai-Tat Fu,et al.  Adaptive information search: age-dependent interactions between cognitive profiles and strategies , 2009, CHI.

[17]  Peter Gregor,et al.  Older web users' eye movements: experience counts , 2011, CHI.

[18]  David Sloan,et al.  Age-specific predictive models of human performance , 2012, CHI EA '12.

[19]  Joseph Sharit,et al.  Aging, Motor Control, and the Performance of Computer Mouse Tasks , 1999, Hum. Factors.

[20]  Mario A. Hernández,et al.  Variability among older adults in Internet health information-seeking performance , 2010 .

[21]  Joseph Sharit,et al.  Investigating the Roles of Knowledge and Cognitive Abilities in Older Adult Information Seeking on the Web , 2008, TCHI.

[22]  C. Riviere,et al.  Effects of age and disability on tracking tasks with a computer mouse: accuracy and linearity. , 1996, Journal of rehabilitation research and development.

[23]  Neil Charness,et al.  The Model Human Processor and the older adult: parameter estimation and validation within a mobile phone task. , 2007, Journal of experimental psychology. Applied.

[24]  Ruth B. Ekstrom,et al.  Manual for kit of factor-referenced cognitive tests , 1976 .

[25]  Joseph Sharit,et al.  Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). , 2006, Psychology and aging.

[26]  Bonnie E. John,et al.  CogTool-Explorer: a model of goal-directed user exploration that considers information layout , 2012, CHI.