Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2637002.2637022acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiiixConference Proceedingsconference-collections
research-article

The effect of cognitive abilities on information search for tasks of varying levels of complexity

Published: 26 August 2014 Publication History

Abstract

Although web search engines are designed as one-size-fits-all tools, people do not come in one size, but instead vary across many different attributes. One such attribute is cognitive ability. Because information search is primarily a cognitive activity, understanding the extent to which variations in cognitive abilities impact search behaviors and outcomes is especially important. We describe a study in which we explore how people's cognitive abilities affect their search behaviors and perceptions of workload while conducting search tasks with different levels of complexity. Twenty-one adults from the general public completed this study. We assessed participants' associative memory, perceptual speed, and visualization abilities and also measured workload. To evaluate the relationship between cognitive ability, task complexity and workload, we conducted three separate mixed factor ANOVAs corresponding to each of the abilities. Our results suggest three important trends: (1) associative memory ability had no significant effect on search behavior and workload, (2) visualization ability had a significant effect on search behavior, but not workload, and (3) perceptual speed had a significant effect on search behavior and workload. Specifically, participants with high perceptual speed ability engaged in more search activity in less time and experienced less workload. While the interactions were not significant, the differences were more pronounced for more complex tasks. We also found a significant relationship between task complexity and workload, and task complexity and search behaviors, which corroborates previous research.

References

[1]
Al-Maskari, A. and Sanderson, M., 2011. The effect of user characteristics on search effectiveness in information retrieval. IP&M, 47, 5, 719--729.
[2]
Allen, B., 1994. Perceptual speed, learning and information retrieval performance. In Proc. SIGIR, 71--80.
[3]
Anderson, L. W., Krathwohl, D. R., and Bloom, B. S., 2001. A taxonomy for learning, teaching, and assessing. Allyn & Bacon, New York.
[4]
Arguello, J., 2014. Predicting search task difficulty. In Proc. ECIR, 88--99.
[5]
Arguello, J., Wu, W.-C., Kelly, D., and Edwards, A., 2012. Task complexity, vertical display and user interaction in aggregated search. In Proc. SIGIR, 435--444.
[6]
Baddeley, A. D., 2003. Working memory: Looking back and looking forward. Neuroscience, 4, 829--839.
[7]
Bates, M. J., 1993. The design of browsing and berrypicking techniques for the online search interface. Online Information Review, 13, 5, 407--424.
[8]
Belkin, N. J., 1980. Anomalous states of knowledge as a basis for information retrieval. Can J Inf Sci, 5, 133--134.
[9]
Bell, D. J. and Ruthven, I., 2004. Searcher assessments of task complexity for Web searching. In Advances in Information Retrieval, S. McDonald and J. Tait Eds. Springer, 57--71.
[10]
Borgman, C. L., 1989. All users of information retrieval systems are not created equal: An exploration into individual differences. IP&M, 25, 3, 237--251.
[11]
Borgman, C. L., 1996. Why are online catalogs still hard to use? JASIS, 47, 7, 493--503.
[12]
Bystrom, K. and Jarvelin, K., 1995. Task complexity affects information seeking and use. IP&M, 31, 191--213.
[13]
Campagnoni, F. R. and Erlich, K., 1989. Information retrieval using a hypertext-based help system. In Proc. SIGIR, 212--220.
[14]
Campbell, D. J., 1988. Task Complexity: A Review and Analysis. Acad Manage Rev, 13, 1, 40--52.
[15]
Carroll, J. B., 1993. Human cognitive abilties: A survey of factor-analytic studies. Cambridge University Press, NY.
[16]
Debowski, S., Wood, R. E., and Bandura, A., 2001. Impact of guided exploration & enactive exploration on self-regulatory mechanisms & information acquisition through electronic search. J Appl Psychol, 86, 6, 1129--1141.
[17]
Di Stasi, L. L., Antolí, A., Gea, M., and Cañas, J. J., 2011. A neuroergonomic approach to evaluating mental workload in hypermedia interactions. Int J Ind Ergonom, 41, 3, 298--304.
[18]
Downing, R. E., Moore, J. L., and Brown, S. W., 2005. The effects and interaction of spatial visualization and domain expertise on information seeking. Comput Hum Behav, 21, 2, 195--209.
[19]
Ekstrom, R. B., 1973. Cognitive factors: Some recent literature. Office of Naval Research, Report No. PR-73-30; TR-2.
[20]
Ekstrom, R. B., French, J. W., Harman, H. H., and Dermen, D., 1976. Kit of factor-referenced cognitive tests. Educational Testing Service, Princeton, NJ.
[21]
Ekstrom, R. B., French, J. W., Harman, H. H., and Dermen, D., 1976. Manual for kit of factor-referenced cognitive tests. Educational Testing Service.
[22]
Ford, N., Miller, D., and Moss, N., 2001. The Role of Individual Differences in Internet Searching: An Empirical Study. JASIST, 52, 12, 1049--1066.
[23]
Gao, Q., 2011. Empirical study of tagging for personal information organization: Performance, workload, memory, consistency. Int J HCI, 27, 9, 821--863.
[24]
Gwizdka, J., 2009. Assessing cognitive load on web search tasks. Ergonom Open J, 2, 114--123.
[25]
Gwizdka, J., 2009. What a difference a tag cloud makes: effects of tasks and cognitive abilities on search results interface use. Info Res, 14, 4, paper 14.
[26]
Gwizdka, J., 2010. Distribution of cognitive load in Web search. JASIST, 61, 11, 2167--2187.
[27]
Haapalainen, E., Kim, S. J., Forlizzi, J. F., and Dey, A. K., 2010. Psycho-physiological measures for assessing cognitive load. In Proc. UbiComp, ACM, 301--310.
[28]
Hart, S. G., 2006. Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proc. Hum Fac Ergonom Soc, 50, 9, 904--908.
[29]
Hart, S. G. and Staveland, L. E., 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Human Mental Workload, P. A. Hancock and N. Meshkati Eds. NH Press, Amsterdam.
[30]
Ingwersen, P., 1996. Cognitive perspectives of information retrieval interaction: Elements of a cognitive IR theory. J Doc, 52, 11, 3--50.
[31]
Jansen, B. J., Booth, D., and Smith, B., 2009. Using the taxonomy of cognitive learning to model online searching. IP&M, 45, 6, 643--663.
[32]
Jex, H., 1988. Measuring mental workload: Problems, progress, promises. In Human Mental Workload, P. Hancock and N. Meshkati Eds. Elsevier, 5--39.
[33]
Kammerer, Y., Nairn, R., Pirolli, P., and Chi, E. H., 2009. Signpost from the masses: learning effects in an exploratory social tag search browser. In Human Compu, ACM, Boston, MA, 625--634.
[34]
Kelton, A. S. and Pennington, R. R., 2012. Internet financial reporting: The effects of information presentation format and content differences on investor decision making. Comput Hum Behav, 28, 4, 1178--1185.
[35]
Kim, K. and Allen, B., 2002. Cognitive & task influences on web searching behavior. JASIST, 53, 2, 109--119.
[36]
MacFarlane, A., Al-Wabil, A., Marshall, C., Albrair, A., Jones, S. A., and Zaphiris, P., 2010. The effect of dyslexia on information retrieval: A pilot study. J Doc, 66, 3, 307--326.
[37]
MacFarlane, A., Albrair, A., Marshall, C., and Buchanan, G., 2012. Phonological working memory impacts on information searching: An investigation of dyslexia. In Proc. IIiX 2012, 27--34.
[38]
Marchionini, G., 1995. Information seeking in electronic environments. Cambridge University Press, New York.
[39]
Megaw, T., 2005. The definition and measurement of mental workload. In Evaluation of human work, E. N. Corlett and J. R. Wilson Eds. Taylor & Francis, Boca Raton, FL, 525--551.
[40]
Niu, X. and Kelly, D., 2014. The use of query suggestions during information search. IP&M, 50, 1, 218--234.
[41]
Pak, R., Rogers, W. A., and Fisk, A. D., 2006. Spatial Ability Subfactors and Their Influences on a Computer-Based Information Search Task. Proc. Hum Fac Ergonom Soc, 48, 1, 154--165.
[42]
Palmquist, R. A. and Kyung-Sun, K., 2000. Cognitive style & online database search experience as predictors of web search performance. ASIS, 51, 6, 558--566.
[43]
Pirolli, P. and Card, S. K., 1999. Information Foraging. Psycho Rev, 106, 4.
[44]
Santos, E., Nguyen, H., Zhao, Q., and Pukinskis, E., 2003. Empirical Evaluation of Adaptive User Modeling in a Medical Information Retrieval Application. In User Modeling 2003, P. Brusilovsky, A. Corbett and F. De Rosis Eds. Springer Berlin/Heidelberg, 148--148.
[45]
Schmutz, P., Heinz, S., Metrailler, Y., and Opwis, K., 2009. Cognitive load in eCommerce applications: Measurement and effects on user satisfaction. Adv HCI.
[46]
Speier, C. and Morris, M. G., 2003. The influence of query interface design on decision-making performance. MIS Quart, 27, 3, 397--423.
[47]
Swan, R. C. and Allan, J., 1998. Aspect windows, 3-D visualizations, and indirect comparisons of information retrieval systems. In Proc. SIGIR, ACM, 173--181.
[48]
Toms, E. G., O' Brien, H. L., Mackenzie, T., Jordan, C., Freund, L., Toze, S., Dawe, E., and Macnutt, A., 2007. Task effects on interactive search: The query factor. In Proc. INEX 2007, 359--372.
[49]
Wechsler, D., 1997. Wechsler Adult Intelligence Test. The Psychological Corporation, San Antonio, TX.
[50]
Westerman, S. J., Davies, D. R., Glendon, A. I., Stammers, R. B., and Matthews, G., 1995. Age and cognitive ability as predictors of computerized information retrieval. Behav Inform Technol, 14, 5, 313--326.
[51]
Wood, R. E., 1986. Task complexity: Definition of the construct. Organ Behav Hum Dec, 37, 1, 60--82.
[52]
Wothke, W., Curran, L. T., Augustin, J. W., Guerrero, C., Jr., Bock, R. D., Fairbank, B. A., and Gillet, A. H., 1991. Factor Analytic Examination of the ASVAB and Kit of Factor-Referenced Tests, A. F. Systems Ed., Brooks Air Force Base, Texas, 76.
[53]
Wu, W.-C., Kelly, D., Edwards, A., and Arguello, J., 2012. Grannies, tanning beds, tattoos and NASCAR: Evaluation of search tasks with varying levels of cognitive complexity. In Proc. IIiX 2012.
[54]
Xie, B. and Salvendy, G., 2000. Prediction of mental workload in single and multiple task environments. Int J Cog Ergonom, 4, 3, 213--242.

Cited By

View all
  • (2024)Exploring the Impact of Verbal-Imagery Cognitive Style on Web Search Behaviour and Mental WorkloadProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638313(303-316)Online publication date: 10-Mar-2024
  • (2024)Impact of information accessibility and diagnosticity on eye movements of children searching for informationThe Electronic Library10.1108/EL-10-2023-025542:4(617-642)Online publication date: 31-May-2024
  • (2023)A Systematic Review of Cost, Effort, and Load Research in Information Search and Retrieval, 1972–2020ACM Transactions on Information Systems10.1145/358306942:1(1-39)Online publication date: 18-Aug-2023
  • Show More Cited By

Index Terms

  1. The effect of cognitive abilities on information search for tasks of varying levels of complexity

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    IIiX '14: Proceedings of the 5th Information Interaction in Context Symposium
    August 2014
    368 pages
    ISBN:9781450329767
    DOI:10.1145/2637002
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Sponsors

    • University of Regensburg: University of Regensburg

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 August 2014

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. cognitive abilities
    2. individual differences
    3. information search
    4. search behavior
    5. user study
    6. workload

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    IIiX '14
    Sponsor:
    • University of Regensburg

    Acceptance Rates

    IIiX '14 Paper Acceptance Rate 21 of 45 submissions, 47%;
    Overall Acceptance Rate 21 of 45 submissions, 47%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)60
    • Downloads (Last 6 weeks)4
    Reflects downloads up to 23 Dec 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Exploring the Impact of Verbal-Imagery Cognitive Style on Web Search Behaviour and Mental WorkloadProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638313(303-316)Online publication date: 10-Mar-2024
    • (2024)Impact of information accessibility and diagnosticity on eye movements of children searching for informationThe Electronic Library10.1108/EL-10-2023-025542:4(617-642)Online publication date: 31-May-2024
    • (2023)A Systematic Review of Cost, Effort, and Load Research in Information Search and Retrieval, 1972–2020ACM Transactions on Information Systems10.1145/358306942:1(1-39)Online publication date: 18-Aug-2023
    • (2023)How do Human and Contextual Factors Affect the Way People Formulate Queries?Proceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578336(499-503)Online publication date: 19-Mar-2023
    • (2023)Driven to Distraction: Examining the Influence of Distractors on Search Behaviours, Performance and ExperienceProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578298(83-94)Online publication date: 19-Mar-2023
    • (2023)The Influences of a Knowledge Representation Tool on Searchers with Varying Cognitive AbilitiesACM Transactions on Information Systems10.1145/352766141:1(1-35)Online publication date: 25-Feb-2023
    • (2022)Understanding the “Pathway” Towards a Searcher’s Learning ObjectiveACM Transactions on Information Systems10.1145/349522240:4(1-43)Online publication date: 11-Jan-2022
    • (2021)Human-centred Persona Driven Personalization in Business Data AnalyticsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3462241(175-180)Online publication date: 21-Jun-2021
    • (2021)Investigating the Influence of Ads on User Search Performance, Behaviour, and Experience during Information SeekingProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446024(107-117)Online publication date: 14-Mar-2021
    • (2021)In quest of goldilocks ranges in searching for information on the webJournal of Documentation10.1108/JD-01-2021-001978:2(264-283)Online publication date: 25-May-2021
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media