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Attentional Characteristics of Anomaly Detection in Conceptual Modeling

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Presentation at NeuroIS Retreat 2018. Authors : Boutin, Léger, Davis, Hevner, Labonté-LeMoyne.

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Attentional Characteristics of Anomaly Detection in Conceptual Modeling

  1. 1. Attentional Characteristics of Anomaly Detection in Conceptual Modeling Karl-David Boutin Pierre-Majorique Léger, Ph.D. Élise Labonté-LeMoyne, Ph.D. HEC Montréal Christopher J. Davis, Ph.D. Alan H. Hevner, Ph.D. University of South Florida NeuroIS Retreat 2018, Vienna (Austria), June 19th - 21st
  2. 2. © Copyright Léger et al. 2018 Conceptual Modeling ▪▪ Facilitate communication about business domains and their processes ▪▪ Primary medium used in design activities ▪▪ Support the investigation of problems and improvement of processes
  3. 3. © Copyright Léger et al. 2018 Research goals Explore the differences in the attentional characteristics between successful and unsuccessful diagnostics in a detection task.
  4. 4. © Copyright Léger et al. 2018 Literature review BUSINESS PROCESS MODELING NOTATION (BPMN) ▪▪ Easily learned for simple use ▪▪ Composed of visual syntax and visual semantics ▪▪ Compare both the semantic and syntactic error identification process
  5. 5. © Copyright Léger et al. 2018 Literature review VISUAL ATTENTION Paradigms Findings Studies Error detection tasks Errors arefixated more often and longer than irrelevant information Van Waes & al.(2009) ; Henderson and Hollingworth (1999) ; Holmqvist& al.(2011) High number of fixations on the stimulus is correlated with an ineffective search Holmqvist& al.(2011) ; Goldberg and Kotval (1999) Expert-novice differences Experts spend less timelooking at stimuli beforefixating relevant areas or anomalies Gegenfurtner & al.(2011) ; Krupinski (2000) ; Reingold and Sheridan (2011) ; Sheridan and Reingold (2014) Experts also tend to have fewer fixations and fixation durations Gegenfurtner & al.(2011) ; Yusuf & al.(2007) ; Sheridan and Reingold (2014)
  6. 6. © Copyright Léger et al. 2018 Hypotheses H1 Successful error detections in conceptual modeling will require less time spent looking at the stimulus than unsuccessful error detections. H2 Successful error detections in conceptual modeling will require, in total, fewer fixations than unsuccessful error detections, but with a higher proportion of fixations on the error. H3 Successful error detections in conceptual modeling will require, on average, shorter fixation duration than unsuccessful error detections, but with longer fixation duration on the error.
  7. 7. © Copyright Léger et al. 2018 Research method Fast food Banking Air travel Medical appointment Online shopping Stimuli No error Semantic error Syntactic error
  8. 8. © Copyright Léger et al. 2018 Stimuli
  9. 9. © Copyright Léger et al. 2018 Experimental design
  10. 10. © Copyright Léger et al. 2018 Instruments and Measures MEASURES ▪▪ Number and duration of fixations ▪▪ Time before the first fixation in an AOI ▪▪ Total view time INSTRUMENTS SMI RED250 Eye Tracking Device
  11. 11. © Copyright Léger et al. 2018 Participants ▪▪ 18 participants (7 males, 11 females) ▪▪ Aged between 21 and 53 years (average 32.05 ; std. Dev. 10.24) ▪▪ 7 students and 11 professionals from 11 different organizations ▪▪ Compensation of 20$ at the university’s bookstore
  12. 12. © Copyright Léger et al. 2018 Results Significant links between successfully detecting an error and: ▪▪ Smaller amount of time spent on a stimulus (H1) ▪▪ Fewer fixations and a greater proportion of fixations in the areas of interest (H2) ▪▪ Shorter fixation duration (H3) Semantic AOIs Measure Estimate (β) P-Value Fixationcount (H2) -0.04956 0.0897* Fixationduration (H3) 0.1654 0.0061** Fixationproportion(H2) 0.5448 < 0.0001**** Time to first fixation -0.3333 0.0027** *=P<0.1 ; **= P<0.01 ; ***=P<0.001 ; ****= P<0.0001 Syntactic AOIs Measure Estimate (β) P-Value Fixationcount (H2) 0.3654 < 0.0001**** Fixationduration (H3) 0.4436 < 0.0001**** Fixationproportion(H2) 0.9379 < 0.0001**** *=P<0.1 ; **= P<0.01 ; ***=P<0.001 ; ****= P<0.0001 All models Measure Estimate (β) P-Value Fixationcount (H2) -0.4402 < 0.0001**** Fixationduration (H3) -0.373 < 0.0001**** Total view time (H1) -0.3934 < 0.0001**** *=P<0.1 ; **= P<0.01 ; ***=P<0.001 ; ****= P<0.0001 Effect on visual attention for correct answers
  13. 13. Contribution ▪▪ Evolution of BPMN and other notations ▪▪ Recommendations for curriculum development and training methods. ▪▪ Comparaison of semantic and syntactic errors
  14. 14. © Copyright Léger et al. 2018 Next step ▪▪ Replicate the study in order to have a bigger sample ▪▪ Study the effect of expertise ▪▪ Change the complexity of the stimuli ▪▪ EEG and other apparatus and measures
  15. 15. Thank you! QUESTIONS?

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