Peripheral Light Cues for In-vehicle Task Resumption

Shadan Sadeghian BorojeniAbdallah El AliWilko HeutenSusanne Boll

1NordiCHI 2016, 27th Oct - Gothenburg, Sweden

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10 percent of fatal crashes, 18 percent of injury crashes, and 16 percent of all police-reported motor vehicle traffic crashes in 2014 were reported as distraction-affected crashes (NHTSA)

Motivation

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In-vehicle Interruption

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In-vehicle Interruption

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phone ring [tertiary]

driving

navigation input [secondary]

Using Cues to Support Task Resumption

Associative learning mechanisms claim that the formation of a link between a goal and a cue is by their co-occurrence.

Cues must be obvious enough during both the interval: (a) when the goal is stored (e.g., insert route)(b) and when it is retrieved (where was I?)

This helps create a link between them.6

Scenario

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Begin Secondary

Task

Alert for Tertiary Task

Begin Tertiary Task

End Tertiary Task

Resume Secondary

Task

Resumption LagInterruption Lag

Trafton et al. 2003

phone ring answering ending call navigation inputnavigation input

Peripheral Light Cues

◦ In-Vehicle Information Systems (IVIS) tasks are always of lower priority in comparison with driving.

◦ Peripheral displays provide users with information while they are attending to their primary tasks.

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Resumption Cues

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Begin Secondary

Task

Alert for Tertiary Task

Begin Tertiary Task

End Tertiary Task

Resume Secondary

Task

Resumption LagInterruption Lag

Trafton et al. 2003

Resumption Cues

[intention creation] [intention retrieval]

Research Question

Can peripheral light cues be detected and associated with the tasks for retrieving information at resumption, independent of the strengths of the association?

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Hypotheses

H1: The resumption time is reduced in the cued condition in comparison with the non-cued condition.

H2: The number of errors at resumption are reduced in the cued condition in comparison with the non-cued condition.

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Method

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[primary]

[secondary]

[tertiary]

Apparatus

◦ Fixed right-hand traffic driving simulator with a field of vision of 150°

◦ Simulation created using SILAB software

◦ Tablet PC used to show the navigation system

◦ RGB LED strip

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Apparatus

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Pilot Study

◦ 10 participants (M = 29.1, SD = 10.04) ◦ Cued and non-cued conditions

Results◦ Average time needed for switching from navigation to phone call =

7 s◦ Resumption time varied between 2-25 s

Therefore, we chose to:◦ Present cues during interruption lag for 7 s◦ Present cues during resumption lag until the correct dialog box button is tapped on

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Participants

◦ 28 (10 female) participants (M = 25.9, SD = 4.17) ◦ 2-12 years driving experience

Training◦ 10-15 min. (four trials) of driving, navigation

task, phone call

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Study Design

Cuing Condition (IV):• No cue • Light cue

Dependent measures (DVs):• Resumption time• Number of errors

+ Questionnaire and Interview

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Resumption Time

Wilcoxon Signed-rank test showed that presence of cues had a significant effect on task resumption time (W = 61.5, Z = 3.22, p <0.01, r = 0.60).

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Resumption Errors

Wilcoxon Signed-rank test shows that there is a significant effect of cue (W = 42.5, Z = 2.93, p <0.05, r = 0.55) on the number of errors.

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Questionnaire

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MD IQR

I felt confident using the system. 4 1

The light cue helps me remember the interrupted task faster.

4 1

I make less errors when having the light cue. 4 1

The light cue makes it easier for me to remember the task.

4 0.25

5-point Likert scale (Cronbach’s 𝛼 = 0.78)

Further results

◦ In 95% of the trials, the cues were successfully detected.

◦ “Having cues prepared me for upcoming interruptions, and after the call, the cue was present again, and I knew what task was due”

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Discussion

◦ Presence of peripheral light cues in interruption and resumption lag creates a link between intention and retrieval of prospective memory tasks.

◦ Association was made between the cues and the interrupted task regardless of presentation color or strength of association.

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Thank you

Summary:

◦ Peripheral light displays can be useful for managing attention and interruptions, especially in safety critical contexts

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References1. Erik M. Altmann and J. Gregory Trafton. 2002. Memory for Goals: An Activation-based Model. Cognitive Science: A Multidisciplinary Journal 26: 39–83. 2. John R. Anderson, Dan Bothell, Christian Lebiere, and Michael Matessa. 1998. An Integrated Theory of List Memory. Journal of Memory and Language 38, 4: 341 – 380. 3. Yujia Cao, Frans Van Der Sluis, Mariët Theune, Anton Nijholt, and others. 2010. Evaluating informative auditory and tactile cues for in-vehicle information systems. In Proc. AutoUI ’10, 102–109. 4. R.K. Dismukes and J.L. Nowinski. 2006. Prospective Memory, Concurrent Task Management, and Pilot Error. Attention: From theory to practice: 225–236. 5. Rahul M Dodhia and Robert K Dismukes. 2009. Interruptions create prospective memory tasks. Applied Cognitive Psychology 23, 1: 73–89. 6. Shamsi T. Iqbal, Yun-Cheng Ju, and Eric Horvitz. 2010. Cars, Calls and Cognition: Investigating Driving and Divided Attention. In Proc. CHI ’10. 7. Tara Matthews, Tye Rattenbury, and Scott Carter. 2007. Defining, designing, and evaluating peripheral displays: An analysis using activity theory. Human–Computer Interaction 22, 1-2: 221–261. 8. D C. McFarlane and K A. Latorella. 2002. The Scope and Importance of Human Interruption in Human-computer Interaction Design. Hum.-Comput. Interact. 17, 1: 1–61. 9. Dario D. Salvucci, Niels A. Taatgen, and Jelmer P. Borst. 2009. Toward a Unified Theory of the Multitasking Continuum: From Concurrent Performance to Task Switching, Interruption, and Resumption. In Proc. CHI ’09, 1819–1828. 10. J. Gregory Trafton, Erik M. Altmann, Derek P. Brock, and Farilee Mintz. 2003. Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal. Int. J. Hum.-Comput. Stud. 58: 583–603.

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