Sage Journals: Discover world-class research

Abstract

Objective

The objective of this study was to develop and evaluate an adaptive user interface that could detect states of operator information overload and calibrate the amount of information on the screen.

Background

Machine learning can detect changes in operating context and trigger adaptive user interfaces (AUIs) to accommodate those changes. Operator attentional state represents a promising aspect of operating context for triggering AUIs. Behavioral rather than physiological indices can be used to infer operator attentional state.

Method

In Experiment 1, a network analysis task sought to induce states of information overload relative to a baseline. Streams of interaction data were taken from these two states and used to train machine learning classifiers. We implemented these classifiers in Experiment 2 to drive an AUI that automatically calibrated the amount of information displayed to operators.

Results

Experiment 1 successfully induced information overload in participants, resulting in lower accuracy, slower completion time, and higher workload. A series of machine learning classifiers detected states of information overload significantly above chance level. Experiment 2 identified four clusters of users who responded significantly differently to the AUIs. The AUIs benefited performance, completion time, and workload in three clusters.

Conclusion

Behavioral indices can successfully detect states of information overload and be used to effectively drive an AUI for some user groups. The success of AUIs may be contingent on characteristics of the user group.

Application

This research applies to domains seeking real-time assessments of user attentional or psychological state.

Keywords

attentional processes adaptive automation information overload passive data monitoring machine learning

Get full access to this article

View all access options for this article.

References

Amazon . (2018). Worker web site FAQs. Retrieved March 3, 2020, from https://www.mturk.com/worker/help#what_is_master_worker

Angelini

Santucci

. (2017). Cyber situational awareness: From geographical alerts to high-level management. Journal of Visualization, 20, 453–459.doi:10.1007/s12650-016-0377-3

Aoidh

E. M.

Bertolotto

Wilson

D. C

. (2012). Towards dynamic behavior-based profiling for reducing spatial information overload in map browsing activity. GeoInformatica, 16, 409–434.doi:10.1007/s10707-011-0137-4

Bennett

K. B.

Bryant

Sushereba

. (2018). Ecological interface design for computer network defense. Human Factors, 60, 610–625.doi:10.1177/0018720818769233

Benselin

J. C.

Ragsdell

. (2016). Information overload: The differences that age makes. Journal of Librarianship and Information Science, 48, 284–297.doi:10.1177/0961000614566341

Carroll

L. N.

A. P.

Detwiler

L. T.

T.-C.

Painter

I. S.

Abernethy

N. F

. (2014). Visualization and analytics tools for infectious disease epidemiology: A systematic review. Journal of Biomedical Informatics, 51, 287–298.doi:10.1016/j.jbi.2014.04.006

Chan

T. C. Y.

Cho

J. A.

Novati

D. C

. (2012). Quantifying the contribution of NHL player types to team performance. Interfaces, 42, 131–145.doi:10.1287/inte.1110.0612

Christ

Braun

Neuffer

Kempa-Liehr

A. W

. (2018). Time series feature extraction on basis of scalable hypothesis tests (tsfresh – a python package). Neurocomputing, 307, 72–77.doi:10.1016/j.neucom.2018.03.067

Cockburn

Karlson

Bederson

B. B

. (2009). A review of overview+detail, zooming, and focus+context interfaces. ACM Computing Surveys, 41, 1–31.doi:10.1145/1456650.1456652

10.

Dadashi

Golightly

Sharples

. (2017). Seeing the woods for the trees: The problem of information inefficiency and information overload on operator performance. Cognition, Technology & Work, 19, 561–570.doi:10.1007/s10111-017-0451-1

11.

Eppler

M. J

. (2015). Information quality and information overload: The promises and perils of the information age. In Cantoni

Danowski

J. A.

(Eds.), Communication and Technology (5th ed.,pp. 215–231). Walter de Gruyter GmbH.

12.

Eppler

M. J.

Mengis

. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society, 20, 325–344.doi:10.1080/01972240490507974

13.

Feigh

K. M.

Dorneich

M. C.

Hayes

C. C

. (2012). Toward a characterization of adaptive systems: A framework for researchers and system designers. Human Factors, 54, 1008–1024.doi:10.1177/0018720812443983

14.

Fisher

C. W.

Kingma

B. R

. (2001). Criticality of data quality as exemplified in two disasters. Information & Management, 39, 109–116.doi:10.1016/S0378-7206(01)00083-0

15.

Friedrich

M. B.

Ritter

F. E

. (2020). Understanding strategy differences in a fault-finding task. Cognitive Systems Research, 59, 133–150.doi:10.1016/j.cogsys.2019.09.017

16.

Furnas

G. W

. (1986). Generalized fisheye views. In Human Factors in Computing Systems CHI (pp. 16–23). https://doi.org/10.1145/22339.22342.

17.

Goodall

J. R.

Lutters

W. G.

Komlodi

. (2009). Developing expertise for network intrusion detection. Information Technology & People, 22, 92–108.doi:10.1108/09593840910962186

18.

Haase

R. F.

Jome

L. M.

Ferreira

J. A.

Santos

E. J. R.

Connacher

C. C.

Sendrowitz

. (2014). Individual differences in capacity for tolerating information overload are related to differences in culture and temperament. Journal of Cross-Cultural Psychology, 45, 728–751.doi:10.1177/0022022113519852

19.

Hall

Walton

. (2004). Information overload within the health care system: A literature review. Health Information & Libraries Journal, 21, 102–108.doi:10.1111/j.1471-1842.2004.00506.x

20.

Hart

S. G.

Staveland

L. E

. (1988). Developent of a multi-dimensional workload rating scale: Results of empirical and theoretical research. In Hancock

P. A.

Meshkati

(Eds.), Human Mental Workload (pp. 139–183). Elsevier.

21.

Hauser

D. J.

Schwarz

. (2016). Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behavior Research Methods, 48, 400–407.doi:10.3758/s13428-015-0578-z

22.

Hochreiter

Schmidhuber

. (1997). Long short-term memory. Neural Computation, 9, 1735–1780.doi:10.1162/neco.1997.9.8.1735

23.

Ishihara

. (1937). The series of plates designed as tests for color blindness. Kanehara & Co.

24.

Key Facts . (2019). Facts and figures. Retrieved August 9, 2020, from https://web.archive.org/web/20070529041317/http://www.tfl.gov.uk/corporate/modesoftransport/londonunderground/1608.aspx

25.

Kortschot

S. W.

Jamieson

G. A

. (2020). Classification of attentional tunneling through behavioral indices. Human Factors, 62, 973–986.doi:10.1177/0018720819857266

26.

Kortschot

S. W.

Sovilj

Jamieson

G. A.

Sanner

Carrasco

Soh

. (2018). Measuring and mitigating the costs of attentional switches in active network monitoring for cybersecurity. Human Factors, 60, 962–977.doi:10.1177/0018720818784107

27.

Lance

G. N.

Williams

W. T

. (1966). A generalized sorting strategy for computer classifications. Nature, 212, 218.doi:10.1038/212218a0

28.

Lipton

Z. C.

Berkowitz

Elkan

. (2015). A critical review of recurrent neural networks for sequence learning. Retrieved from https://arxiv.org/abs/1506.00019

29.

Lipton

Z. C.

Kale

D. C.

Elkan

Wetzel

. (2016). Learning to diagnose with LSTM recurrent neural networks. In ICLR (pp. 1–18).

30.

Lopes

C. T.

Franz

Kazi

Donaldson

S. L.

Morris

Bader

G. D

. (2010). Cytoscape web: An interactive web-based network browser. Bioinformatics, 26, 2347–2348.doi:10.1093/bioinformatics/btq430

31.

Mangos

P. M.

Hulse

N. A

. (2017). Advances in machine learning applications for scenario intelligence: Deep learning. Theoretical Issues in Ergonomics Science, 18, 184–198.

32.

McDonald

A. D.

Lee

J. D.

Schwarz

Brown

T. L

. (2014). Steering in a random forest: Ensemble learning for detecting drowsiness-related lane departures. Human Factors, 56, 986–998.doi:10.1177/0018720813515272

33.

Michel

Helmick

Mayron

. (2011). Cognitive cyber situational awareness using virtual worlds. In 2011 IEEE International mutli-disciplinary conference on cognitive methods in situation awareness and decision support, CogSIMA (pp. 179–182).

34.

Milgram

. (1970). The experience of living in cities. Science, 167, 1461–1468.doi:10.1126/science.167.3924.1461

35.

Misra

Stokols

. (2012). Psychological and health outcomes of perceived information overload. Environment and Behavior, 44, 737–759.doi:10.1177/0013916511404408

36.

Mitropoulos

Patsos

Douligeris

. (2006). On incident handling and response: A state-of-the-art approach. Computers & Security, 25, 351–370.doi:10.1016/j.cose.2005.09.006

37.

Nowak

Rüger

. (2010). How reliable are annotations via crowdsourcing? In Multimedia information retrieval (pp. 557–566).

38.

Palmius

Tsanas

Saunders

K. E. A.

Bilderbeck

A. C.

Geddes

J. R.

Goodwin

G. M.

De Vos

. (2017). Detecting bipolar depression from geographic location data. IEEE Transactions on Biomedical Engineering, 64, 1761–1771.doi:10.1109/TBME.2016.2611862

39.

Pedregosa

Varoquaux

Gramfort

Michel

Thirion

Grisel

Duchesnay

. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research: JMLR, 12, 2825–2830.

40.

Sarkar

Brown

M. H

. (1992). Graphical fisheye views of graphs. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI) (pp. 83-91), Association for Computing Machinery.

41.

Siegler

R. S

. (1987). The perils of averaging data over strategies: An example from children’s addition. Journal of Experimental Psychology: General, 116, 250–264.doi:10.1037/0096-3445.116.3.250

42.

Swain

Haka

. (2000). Effects of information load on capital budgeting decisions. Behavioral Research in Accounting, 12, 171–198.

43.

Vrigkas

Nikou

Kakadiaris

I. A

. (2015). A review of human activity recognition methods. Frontiers in Robotics and AI, 2, 1–28.doi:10.3389/frobt.2015.00028

44.

Zhu

Cao

. (2016). Influence of information overload on operator’s user experience of human–machine interface in LED manufacturing systems. Cognition, Technology & Work, 18, 161–173.doi:10.1007/s10111-015-0352-0

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

0.03 MB

0.17 MB