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Information Systems and Neuroscience pp 57–63Cite as

Attentional Characteristics of Anomaly Detection in Conceptual Modeling

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Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 29))

Abstract

We use eye tracking to better understand the attentional characteristics specific to successful error detection in conceptual models. This phase of our multi-step research project describes the visual comportments associated with successful semantic and syntactic error identification and diagnosis. We test our predictions, based on prior studies on visual attention in an error detection task, or studies comparing experts and non-experts in diverse tasks, in a controlled experiment where participants are tasked with detecting and diagnosing errors in 75 BPMN® models. The results suggest that successful error diagnostics are linked with shorter total view time and shorter fixation duration, with a significant difference between semantic and syntactic errors. By identifying the visual attention differences and tendencies associated with successful detection tasks and the investigation of semantic and syntactic errors, we highlight the non-polarity of the ‘scale’ of expertise and allow clear recommendations for curriculum development and training methods.

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Author information

Authors and Affiliations

  1. HEC Montréal, Montréal, Québec, Canada

    Karl-David Boutin, Pierre-Majorique Léger & Élise Labonté-LeMoyne

  2. University of South Florida, Saint Petersburg, FL, USA

    Christopher J. Davis

  3. University of South Florida, Tampa, FL, USA

    Alan R. Hevner

Authors
  1. Karl-David Boutin
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  2. Pierre-Majorique Léger
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  3. Christopher J. Davis
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  4. Alan R. Hevner
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  5. Élise Labonté-LeMoyne
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Corresponding author

Correspondence to Karl-David Boutin .

Editor information

Editors and Affiliations

  1. Information Systems and Quantitative Sciences (ISQS), Texas Tech University, Lubbock, TX, USA

    Fred D. Davis

  2. University of Applied Sciences Upper Austria, Steyr, Oberösterreich, Austria

    René Riedl

  3. Department of Information Systems, University of Liechtenstein, Vaduz, Liechtenstein

    Jan vom Brocke

  4. Department of Information Technology, HEC Montréal, Montreal, QC, Canada

    Pierre-Majorique Léger

  5. Department of Information Systems, Kennesaw State University, Kennesaw, GA, USA

    Adriane B. Randolph

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Boutin, KD., Léger, PM., Davis, C.J., Hevner, A.R., Labonté-LeMoyne, É. (2019). Attentional Characteristics of Anomaly Detection in Conceptual Modeling. In: Davis, F., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A. (eds) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-01087-4_7

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