The SEEV model of visual scanning offers a quick and easy way of evaluating the attentional demands of various tasks and displays. A SEEV model can be developed without relying on complicated mathematical software or background, making the conceptual model highly accessible. Implementation of SEEV modeling can further be improved by easing the process of running simulations and providing actionable information. In this paper, we showcase the SEEV Modeler, a GUI-based prototype of the computational SEEV model that lowers the technical barriers for human factors practitioners. Furthermore, the prototype’s ability to predict eye movements in dynamic driving scenarios was tested, with an emphasis on the impacts of the attention shifting effort and inhibition of return (IOR) on the model’s prediction performance. The SEEV Modeler produced model fits comparable to those of previous mathematical modeling approaches but also revealed limitations and practical issues to be addressed in the final version.
Get full access to this article
View all access options for this article.
References
1.
CarbonnellJ. F.WardJ. L.SendersJ. W. (1968). A queuing model of visual sampling: Experimental validation. IEEE Transactions on Man Machine Systems, MMS-9, 82–87.
2.
EriksenC. W.JamesJ. D.S. (1986). Visual attention within and around the field of focal attention: A zoom lens model. Perception & Psychophysics, 40(4), 225–240.
3.
FolkC. L.RemingtonR. W.JohnstonJ.C. (1992). Involuntary covert orienting is contingent on attentional control settings. Journal of Exp. Psych.: Human Perception and Performance, 18(4), 1030–1044.
4.
GrundgeigerT.WurmbT.HappelO. (2020). Statistical Modeling of Visual Attention of Junior and Senior Anesthesiologists During the Induction of General Anesthesia in Real and Simulated Cases. IEEE Transactions on Human-Machine Systems, 50(4), 317–326.
5.
HarelJ.KochC.PeronaP. (2007). Graph-based visual saliency. Advances in neural information processing systems 19 (pp. 545–552). MIT Press.
6.
HorreyW. J.WickensC. D.ConsalusK. P. (2006). Modeling drivers’ visual attention allocation while interacting with in-vehicle technologies. Journal of Exp. Psych.: Applied, 12(2), 67–78.
7.
International Commission on Illumination (CIE). (2004). Colorimetry (3rd ed.). Bureau Central de la CIE.
8.
IttiL.KochC. (2000). A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40(10-12), 1489–1506.
9.
KleinR. M. (2000). Inhibition of return. Trends in Cognitive Sciences, 4(4), 138–147.
10.
LemonnierS.BremondR.BaccinoT. (2015). Gaze behavior when approaching an intersection: Dwell time distribution and comparison with a quantitative prediction. Transportation Research Part F: Traffic Psychology and Behaviour, 35, 60–74.
11.
PalazziA.AbatiD.SoleraF.CucchiaraR. (2018). Predicting the Driver’s Focus of Attention: the DR (eye) VE Project. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(7), 1720–1733.
12.
PiechullaW.MayserC.GehrkeH.KonigW. (2003). Reducing drivers ‘ mental workload by means of an adaptive man- machine interface. Traffic Psychology and Behaviour, 6(4), 233–248.
SarterN. (2008). Investigating mode errors on automated flight decks: Illustrating the problem-driven, cumulative, and interdisciplinary nature of human factors research. Human Factors, 50(3), 506–510.
15.
SteelmanK. S.McCarleyJ. S.WickensC. D. (2011). Modeling the control of attention in visual workspaces. Human Factors, 53(2), 142–153.
16.
SteelmanK. S.McCarleyJ. S.WickensC. D. (2013). Great expectations: Top-down attention modulates the costs of clutter and eccentricity. Journal of Exp. Psych.: Applied, 19(4), 403–419.
17.
SteelmanK. S.McCarleyJ. S.WickensC. D. (2017). Theory-based models of attention in visual workspaces. International Journal of HumanComputer Interaction, 33(I), 35–43.
18.
Steelman-AllenK. S.McCarleyJ. S.WickensC.SebokA.BzostekJ. (2009). N-SEEV: A computational model of attention and noticing. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 53, No. 12, pp. 774–778). SAGE Publications.
19.
TipperS. P.DriverJ.WeaverB. (1991). Object-centred inhibition of return of visual attention. The Quart. J. of Exp. Psych., 43(2), 289–298.
20.
WickensC. D. (1984). Processing resources in attention. In ParasuramanR.DaviesD.R. (Eds.), Varieties of attention (pp. 63–102). Academic Press.
21.
WickensC. D. (1993). Cognitive factors in di splay design. Journal of the Washington Academy of Sciences, 83(4), 179–201.
22.
WickensC. D. (2002). Multiple resources and performance prediction. Theoretical Issues in Ergonomic Science, 3, 159–177.
23.
WickensC. D. (2015). Noticing events in the visual workplace: The SEEV and NSEEV models. In HoffmanR. R.HancockP. A.ScerboM. W.ParasuramanR.SzalmaJ. L. (Eds.), The Cambridge handbook of applied perception research (Vol. 2, pp. 749–768). Cambridge University Press.
24.
WickensC.McCarleyJ.Steelman-AllenK. (2009). NT-SEEV: A model of attention capture and noticing on the flight deck. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 53, No. 12, pp. 769–773). SAGE Publications.
25.
WolfeJ.M. (2007). Guided search 4.0: Current Progress with a model of visual search. In GrayW. (Ed.), Integrated models of cognitive systems (pp. 99–119). Oxford University Press.
26.
WolfeJ.M.O’NeillP.BennettS. C. (1998). Why are there eccentricity effects in visual search? Visual and attentional hypotheses. Perception & Psychophysics, 60(1), 140–156
