Gauging the utility of ambient displays by measuring cognitive load

Abstract

Ambient Displays, a sub-class of ubiquitous computing, aim to present non-critical information using peripheral visualisation with minimal distraction. The utility of Ambient Displays relies on providing useful, well-designed information in a way that does not increase the users cognitive load. Assessing the cognitive load of an Ambient Display is thus an important part of the development process. In this paper we review the key design dimensions of Ambient Displays and consider how they impact on cognitive load. We then examine various approaches for measuring cognitive load before describing a study that investigates a novel use of a dual-task measure to evaluate the cognitive load of a specific Ambient Display. A between-subjects design with 40 participants was used, with the Ambient Display active for half of these participants. All participants completed three different primary tasks (n-back, visual digit span, and auditory digit span) alongside the secondary, Detection Response Task. The results show that the n-back task is the most appropriate for manipulating primary task load when evaluating such displays and that the dual-task paradigm can be used to provide an objective measure of workload. Analysis of the participants primary and secondary task performance indicates that the evaluated Ambient Display imposed no additional cognitive load.

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References

  1. Albers M (2011) Tapping as a measure of cognitive load and website usability. In: Proceedings of the 29th ACM international conference on Design of communication (SIGDOC'11), Pisa, Italy, October 2011

  2. Ames M, Dey A (2002) Description of Design Dimensions and Evaluation for Ambient Displays. Computer Science Division, University of California. https://www2.eecs.berkeley.edu/Pubs/TechRpts/2002/6193.html. Accessed 29 May 2020

  3. Anmarkrud Ø, Andresen A, Bråten I (2019) Cognitive load and working memory in multimedia learning: Conceptual and measurement issues. Educ Psychol 54:61–83. https://doi.org/10.1080/00461520.2018.1554484

    Article  Google Scholar 

  4. Appel T, Sevcenko N, Wortha F, Tsarava K, Ninaus M, Kasneci E, Gerjets P (2019) Predicting Cognitive Load in an Emergency Simulation Based on Behavioral and Physiological Measures. In: Proceedings of 2019 International Conference on Multimodal Interaction (ICMI '19), Suzhou, China, October 2019

  5. Ayaz H, Shewokis P, Bunce S, Izzetoglu K, Willems B, Onaral B (2012) Optical brain monitoring for operator training and mental workload assessment. Neuroimage. https://doi.org/10.1016/j.neuroimage.2011.06.023

    Article  Google Scholar 

  6. Bartram L (2015) Design challenges and opportunities for eco-feedback in the home. IEEE Comput Graph 35(4):52–62. https://doi.org/10.1109/MCG.2015.69

    Article  Google Scholar 

  7. Bella M, Hanington B (2012) Universal Methods of Design: 100 Ways To Research Complex Problems, Develop Innovative Ideas. Rockport Publishers, Massachusetts, And Design Effective Solutions

    Google Scholar 

  8. Binder J, Frost J, Hammeke T, Cox R, Rao S, Prieto T (1997) Human brain language areas identified by functional magnetic resonance imaging. J Neurosci. https://doi.org/10.1523/JNEUROSCI.17-01-00353.1997

    Article  Google Scholar 

  9. Brünken R, Steinbacher S, Plass J, Leutner D (2002) Assessment of cognitive load in multimedia learning using dual-task methodology. Exp Psychol 49(2):109‐119. https://psycnet.apa.org/doi/10.1027/1618-3169.49.2.109

  10. Bunce S, Izzetoglu M, Izzetoglu K, Onaral B, Pourrezaei K (2006) Functional near-infrared spectroscopy. IEEE Eng Med Biol 26(4):38–46. https://doi.org/10.1109/MEMB.2006.1657788

    Article  Google Scholar 

  11. Cegarra J, Chevalier A (2008) The use of Tholos software for combining measures of mental workload: Toward theoretical and methodological improvements. Behav Res Methods 40:988–1000. https://doi.org/10.3758/BRM.40.4.988

    Article  Google Scholar 

  12. Chen S, Epps J (2014) Using task-induced pupil diameter and blink rate to infer cognitive load. Human-Computer Interaction 29:390–413. https://doi.org/10.1080/07370024.2014.892428

    Article  Google Scholar 

  13. Chen S, Epps J, Ruiz N, Chen F (2011) Eye activity as a measure of human mental effort in HCI. In: Proceedings of the 16th international conference on Intelligent user interfaces (IUI '11), Palo Alto, USA, February 2011

  14. Chen J, Zhang Q, Cheng L, Xudong G, Ding L (2019) A Cognitive Load Assessment Method Considering Individual Differences in Eye Movement Data. In: IEEE 15th International Conference on Control and Automation (ICCA), Edinburgh, United Kingdom, 16–19 July 2019

  15. Chewar C, McCrickard D, Sutcliffe A (2004) Unpacking Critical Parameters for Interface Design: Evaluating Notification Systems with the IRC Framework. In: Proceedings of the 5th conference on Designing interactive systems: processes, practices, methods, and techniques (DIS '04), Cambridge, August 2004

  16. Cinaz B (2013) Monitoring of cognitive load and cognitive performance using wearable sensing. Dissertation, ETH Zürich

  17. Chuang H, Liu H (2012) Effects of different multimedia presentations on viewers’ information-processing activities measured by eye-tracking technology. J Sci Educ Technol 21:276–286. https://doi.org/10.1007/s10956-011-9316-1

    Article  Google Scholar 

  18. Clinch S, Alexander J, Gehring S (2016) A survey of pervasive displays for information presentation. IEEE Pervas Comput 15:14–22. https://doi.org/10.1109/MPRV.2016.55

    Article  Google Scholar 

  19. Conti A, Dlugosch C, Vilimek R, Keinath A, Bengler K (2012) An assessment of cognitive workload using detection response tasks. In: Proceedings of the 4th International Conference on Applied Human Factors and Ergonomics (AHFE 2012), San Francisco, USA, 21–25 July 2012

  20. Cooper G, Harper Jr R (1969) The use of pilot rating in the evaluation of aircraft handling qualities. NASA Ames Technical Report NASA TN‐D‐5153. Moffett Field, CA: NASA Ames Research Center. Washington DC https://ntrs.nasa.gov/search.jsp?R=19690013177 Accessed 20 May 2020

  21. Cranford K, Tiettmeyer J, Chuprinko B, Jordan S, Grove N (2014) Measuring Load on Working Memory: The Use of Heart Rate as a Means of Measuring Chemistry Students’ Cognitive Load. J Chem Educ 91:641–647. https://doi.org/10.1021/ed400576n

    Article  Google Scholar 

  22. de Greef T, Lafeber H, van Oostendorp H, Lindenberg J (2009) Eye Movement as Indicators of Mental Workload to Trigger Adaptive Automation. In: Schmorrow D, Estabrooke V, Grootjen M. (eds) Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience. FAC 2009. Lecture Notes in Computer Science, vol 5638. Springer, Berlin, Heidelberg

  23. Debue N, Van De Leemput C (2014) What does germane load mean? An empirical contribution to the cognitive load theory. Front Psychol

  24. Di Stasi L, Antolí A, Gea M, Cañas J (2011) A neuroergonomic approach to evaluating mental workload in hypermedia interactions. Int J Ind Ergon 41:298–304. https://doi.org/10.1016/j.ergon.2011.02.008

    Article  Google Scholar 

  25. Ferscha A. (2007) Informative Art Display Metaphors. In: Stephanidis C. (eds) Universal Access in Human-Computer Interaction. Ambient Interaction. UAHCI 2007. Lecture Notes in Computer Science, vol 4555. Springer, Berlin, Heidelberg

  26. Govender A, Wagner A, King S (2019) Using pupil dilation to measure cognitive load when listening to text-to-speech in quiet and in noise. In: Proceedings of Interspeech 2019, Austria, 15–19 September 2019

  27. Harbluk J, Burns P, Tam J, Glazduri V (2013) Detection response tasks: Using remote, headmounted and Tactile signals to assess cognitive demand while driving. In: Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, New York, USA, 17–20 June 2013

  28. Hart S (2006) NASA-task load index (NASA-TLX); 20 years later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50:904–908. https://doi.org/10.1177/154193120605000909

    Article  Google Scholar 

  29. Hervás R, Nava S.W, Chavira G, Villarreal V, Bravo J (2009) PIViTa: Taxonomy for Displaying Information in Pervasive and Collaborative Environments. In: Corchado M, Tapia I, Bravo J (eds) 3rd Symposium of Ubiquitous Computing and Ambient Intelligence 2008. Advances in Soft Computing, vol 51. Springer, Berlin, Heidelberg

  30. Hohl M (2009) Beyond the screen: visualizing visits to a website as an experience in physical space. Vis Commun 8:273–284. https://doi.org/10.1177/1470357209106469

    Article  Google Scholar 

  31. Holland M, Tarlow G (1972) Blinking and Mental Load. Psychol Rep 31:119–127. https://doi.org/10.2466/pr0.1972.31.1.119

    Article  Google Scholar 

  32. Hossain D, Salimullah S, Chowdhury A, Hasan S, Kabir E, Mahmudi R, Islam M (2019) Measurement of cognitive load for writing tasks using galvanic skin response. In: Proceedings of the 6th International Conference on Networking, Systems and Security (NSysS '19), Dhaka Bangladesh, December 2019

  33. Huang E, Mynatt E (2003) Semi-public displays for small, co-located groups. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Ft. Lauderdale, Florida, April 2003

  34. Hussain S, Chen S, Calvo R, Chen F (2011) Classification of Cognitive Load from Task Performance & Multichannel Physiology during Affective Changes. In: 13th International Conference on Multimodal Interaction (ICMI 2011), Alicante, Spain, November 2011

  35. Hyönä J (2010) The use of eye movements in the study of multimedia learning. Learn Instr 20:172–176. https://doi.org/10.1016/j.learninstruc.2009.02.013

  36. Iqbal S, Zheng X, Bailey B (2004) Task-evoked pupillary response to mental workload in human-computer interaction. In: Extended Abstracts on Human Factors in Computing Systems, Vienna, Austria, April 2004

  37. ISO 2016 International Organization for Standardization. (2016) Road vehicles. Transport information and control systems. Detection-response task (DRT) for assessing attentional effects of Cognitive Load in driving (ISO Standard No. 17488:2016). https://www.iso.org/standard/59887.html. Accessed 20 May 2020

  38. Izzetoglu K, Bunce S, Izzetoglu M, Onaral B, Pourrezaei K (2003) fNIR spectroscopy as a measure of cognitive task load. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico, 17–21 September 2003

  39. Jaeggi S, Studer-Luethi B, Buschkuehl M, Su Y, Jonides J, Perrig W (2010) The relationship between n-back performance and matrix reasoning-implications for training and transfer. Intelligence 38:625–635. https://doi.org/10.1016/j.intell.2010.09.001

    Article  Google Scholar 

  40. JASP Team (2018) JASP (Version 0.9)[Computer software]. https://jasp-stats.org/. Accessed May 29 2020

  41. Kahneman D (1973) Attention and effort. Prentice-Hall, Englewood Cliffs, New Jersey

    Google Scholar 

  42. Khut G (2016) Designing Biofeedback Artworks for Relaxation. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, San Jose, USA, May 2016

  43. Kim T, Hong H, Magerko B (2010) Design requirements for ambient display that supports sustainable lifestyle. In: Proceedings of the 8th ACM Conference on Designing Interactive Systems, Aarhus, Denmark, August 2010

  44. Klingner J, Kumar R, Hanrahan P (2008) Measuring the task-evoked pupillary response with a remote eye tracker. In: Proceedings of the 2008 symposium on Eye tracking research & applications, Savannah, Georgia, March 2008

  45. Krell M (2017) Evaluating an instrument to measure mental load and mental effort considering different sources of validity evidence. Cogent Educ 4:1280256. https://doi.org/10.1080/2331186X.2017.1280256

    Article  Google Scholar 

  46. Lau A, Moere A (2007) Towards a Model of Information Aesthetics in Information Visualization. In: 11th International Conference on Information Visualization, Zurich, Switzerland, 4–6 July 2007

  47. Leonardi C, Zancanaro M (2011) Exploring limits and opportunities for public displays in dementia care centers. In: Proceedings of the 9th ACM SIGCHI Italian Chapter International Conference on Computer-Human Interaction: Facing Complexity, Alghero, Italy, September 2011

  48. Löcken A, Heuten W, Boll S (2016) Enlightening Drivers: A Survey on In-Vehicle Light Displays. In: Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ann Arbor MI, USA, October 2016

  49. Matthews T, Dey A, Mankoff J, Carter S, Rattenbury T (2004) A toolkit for managing user attention in peripheral displays. In: Proceedings of the 17th annual ACM symposium on User interface software and technology, Santa Fe NM, USA, October 2004

  50. MacLean K (2009) Putting haptics into the ambience. IEEE T Haptics 2:123–135. https://doi.org/10.1109/TOH.2009.33

    Article  Google Scholar 

  51. Mankoff J, Dey A, Hsieh G, Kientz J, Lederer S, Ames M (2003) Heuristic evaluation of ambient displays. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Florida, USA, April 2003

  52. Marshall S (2007) Identifying cognitive state from eye metrics. Aviat Space Environ Med 78(5 Suppl):B165–B175

    Google Scholar 

  53. Matthews T, Rattenbury T, Carter S (2007) Defining, designing, and evaluating peripheral displays: An analysis using activity theory. Hum Comput Interact 22:221–261. https://doi.org/10.1080/07370020701307997

    Article  Google Scholar 

  54. McDuff D, Gontarek S, Picard R (2014) Remote measurement of cognitive stress via heart rate variability. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 26–30 August 2014

  55. Mehler B, Reimer B, Coughlin J, Dusek J (2009) Impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers. Transp Res Rec 2138:6–12. https://doi.org/10.3141/2138-02

    Article  Google Scholar 

  56. Millisecond. (2019), Inquisit (Version 5) [Computer software]. https://www.millisecond.com/products/inquisit5/laboverview.aspx. Accessed 26 May 2020

  57. Moere A.V (2007) Aesthetic Data Visualization as a Resource for Educating Creative Design. In: Dong A, Moere A.V, Gero J.S (eds) Computer-Aided Architectural Design Futures (CAADFutures) 2007. Springer, Dordrecht

  58. Moere A (2008) Beyond the tyranny of the pixel: Exploring the physicality of information visualization. In: 12th International Conference on Information Visualisation, London, 9–11 July 2008

  59. Moere A, Offenhuber D (2009) Beyond ambient display: a contextual taxonomy of alternative information display. Int J Amb Comp Intell (IJACI) 1:39–46. https://doi.org/10.4018/jaci.2009040105

    Article  Google Scholar 

  60. Müller C, Großmann-Hutter B, Jameson A, Rummer R, Wittig F. (2001) Recognizing Time Pressure and Cognitive Load on the Basis of Speech: An Experimental Study. In: Bauer M, Gmytrasiewicz P.J, Vassileva J (eds) User Modeling 2001. UM 2001. Lecture Notes in Computer Science, vol 2109. Springer, Berlin, Heidelberg

  61. Mulvenna M, Carswell W, McCullagh P, Augusto JC, Zheng H (2011) Visualization of data for ambient assisted living services. IEEE Commun Mag 49:110–117. https://doi.org/10.1109/MCOM.2011.5681023

    Article  Google Scholar 

  62. Nourbakhsh N, Wang Y, Chen F, Calvo R (2012) Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks. In: Proceedings of the 24th Australian Computer-Human Interaction Conference, Melbourne, Australia, November 2012

  63. Noyes J, Bruneau D (2007) A self-analysis of the NASA-TLX workload measure. Ergonomics 50:514–519. https://doi.org/10.1080/00140130701235232

    Article  Google Scholar 

  64. Otjacques B, Feltz F (2007) Redesign of classic information visualization techniques in an artistic computing perspective. In: Proceedings of the 4th symposium on Applied perception in graphics and visualization, Tubingen, Germany, July 2007

  65. Paas F, Tuovinen J, Tabbers H, Van Gerven P (2003) Cognitive load measurement as a means to advance cognitive load theory. Educ Psychol 38:63–71. https://doi.org/10.1207/S15326985EP3801_8

    Article  Google Scholar 

  66. Paas F, Van Merriënboer J (1994) Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. J Educ Psychol 86:122. https://doi.org/10.1037/0022-0663.86.1.122

    Article  Google Scholar 

  67. Pousman Z, Stasko J (2006) A taxonomy of ambient information systems: four patterns of design. In: Proceedings of the working conference on Advanced visual interfaces, Venezia, Italy, May 2006

  68. Pousman Z, Stasko J, Mateas M (2007) Casual information visualization: Depictions of data in everyday life. IEEE T Vis Comput Gr 13:1145–1152. https://doi.org/10.1109/TVCG.2007.70541

    Article  Google Scholar 

  69. Redström J, Skog T, Hallnäs L (2000) Informative art: using amplified artworks as information displays. In: Proceedings of DARE 2000 on Designing augmented reality environments, Elsinore, Denmark, April 2000

  70. Reid G, Nygren T (1988) The subjective workload assessment technique: A scaling procedure for measuring mental workload. Adv Psychol 52:185–218. https://doi.org/10.1016/S0166-4115(08)62387-0

    Article  Google Scholar 

  71. Reid G, Potter S, Bressler J (1989) Subjective workload assessment technique (swat): A user’s guide. Wright Patterson Air Force Base, OH: Harry G. Armstrong Aerospace Medical Research Laboratory. https://apps.dtic.mil/dtic/tr/fulltext/u2/a215405.pdf. Accessed 24 May 2020

  72. Rodríguez M, García-Vázquez J, Andrade Á (2011) Design dimensions of ambient information systems to facilitate the development of AAL environments. In: Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments, Heraklion, Greece, May 2011

  73. Russell D, Streitz N, Winograd T (2005) Building disappearing computers. Commun ACM 48:42–48. https://doi.org/10.1145/1047671.1047702

    Article  Google Scholar 

  74. Shami N.S, Leshed G, Klein D (2005) Context of Use Evaluation of Peripheral Displays (CUEPD). In: Costabile M.F, Paternò F (eds) Human-Computer Interaction - INTERACT 2005. Lecture Notes in Computer Science, vol 3585. Springer, Berlin, Heidelberg

  75. Shelton B, Nesbitt K (2016) The aesthetic awareness display: a new design pattern for ambient information systems. In: Proceedings of the Australasian Computer Science Week Multiconference (ACSW '16), Canberra, Australia, February 2016

  76. Shelton B, Nesbitt K (2017) Evaluating WaveWatch: An Ambient Display of Web Traffic. In: Proceedings of the Australasian Computer Science Week Multiconference (ACSW '17), Geelong, Australia, January 2017

  77. Shelton, B, Nesbitt, K (2019) Categorised Ambient Displays published between 1996 and 2016 and identified in a systematic review. Mendeley Data. https://doi.org/10.17632/m8zwv4jdwr.1. Accessed 10 January 2020

  78. Sheng Q, Shakshuki E, Ma J (2014) User-centric ambient information systems and applications. Pers Ubiquit Comput 18:819–820. https://doi.org/10.1007/s00779-013-0693-8

    Article  Google Scholar 

  79. Shi Y, Ruiz N, Taib R, Choi E, Chen F (2007) Galvanic skin response (GSR) as an index of cognitive load. In: Extended Abstracts on Human Factors in Computing Systems, San Jose, USA, April 2007

  80. Siegle G, Ichikawa N, Steinhauer S (2008) Blink before and after you think: blinks occur prior to and following cognitive load indexed by pupillary responses. Psychophysiology 45:679–687. https://doi.org/10.1111/j.1469-8986.2008.00681.x

    Article  Google Scholar 

  81. Stojmenova K, Sodnik J (2018) Detection-Response Task-Uses and Limitations Sensors 18(2):594. https://doi.org/10.3390/s18020594

    Article  Google Scholar 

  82. Strayer DL, Johnston WA (2001) Driven to distraction: Dual-task studies of simulated driving and conversing on a cellular phone. Psychol Sci 12(6):462–466. https://doi.org/10.1111/1467-9280.00386

    Article  Google Scholar 

  83. Strayer DL, Cooper JM, Goethe RM, McCarty MM, Getty DJ, Biondi F (2019). Assessing the visual and cognitive demands of in-vehicle information systems. Cogn Res Princ Implic 4(1). https://doi.org/10.1186/s41235-019-0166-3

  84. Strayer D, Drews F, Albert R, Johnston W (2001) Cell phone induced perceptual impairments during simulated driving. In: McGehee D, Lee J, Rizzo M (eds) Driving Assessment 2001: International Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Aspen, Colorado, August 2001

  85. Streitz N, Rocker C, Prante T, van Alphen D, Stenzel R, Magerkurth C (2005) Designing smart artifacts for smart environments. Comput 38:41–49. https://doi.org/10.1109/MC.2005.92

    Article  Google Scholar 

  86. Suriya-Prakash M, John-Preetham G, Sharma R (2015) Is heart rate variability related to cognitive performance in visuospatial working memory? PeerJ PrePrints. https://doi.org/10.7287/peerj.preprints.1377v1

    Article  Google Scholar 

  87. Sweller J (1988) Cognitive load during problem solving: Effects on learning. Cogn Sci 12:257–285. https://doi.org/10.1016/0364-0213(88)90023-7

    Article  Google Scholar 

  88. Sweller J, Ayres P, Kalyuga S (2011) Measuring Cognitive Load. In: Cognitive Load Theory. Explorations in the Learning Sciences, Instructional Systems and Performance Technologies, vol 1. Springer, New York, NY

  89. Thorpe A, Nesbitt K, Eidels A (2019) Assessing Game Interface Workload and Usability: A Cognitive Science Perspective. In: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2019), Sydney, Australia, January 2019

  90. Tomitsch M, Kappel K, Lehner A, Grechenig T (2007) Towards a taxonomy for ambient information systems. In: Proceedings of the 1st International Workshop on Ambient Information Systems, Toronto, Canada, May 2007

  91. Townsend JT, Eidels A (2011) Workload capacity spaces: A unified methodology for response time measures of efficiency as workload is varied. Psychon Bull Rev 18(4):659–681. https://doi.org/10.3758/s13423-011-0106-9

    Article  Google Scholar 

  92. Van Dillen L, Heslenfeld D, Koole S (2009) Tuning down the emotional brain: an fMRI study of the effects of cognitive load on the processing of affective images. Neuroimage 45:1212–1219. https://doi.org/10.1016/j.neuroimage.2009.01.016

    Article  Google Scholar 

  93. van Gog T, Jarodzka H (2013) Eye Tracking as a Tool to Study and Enhance Cognitive and Metacognitive Processes in Computer-Based Learning Environments. In: Azevedo R, Aleven V (eds) International Handbook of Metacognition and Learning Technologies. Springer International Handbooks of Education, vol 28. Springer, New York, NY

  94. Vanderhaegen F (2017) Towards increased systems resilience: New challenges based on dissonance control for human reliability. Cyber-Physical&Human Systems Annu Rev Control 44:316–322. https://doi.org/10.1016/j.arcontrol.2017.09.008

    Article  Google Scholar 

  95. Vanderhaegen F, Wolff M, Mollard R, 2019. Synchronization of stimuli with heart rate: a new challenge to control attentional dissonances. In Automation Challenges of Socio-technical Systems: Paradoxes and Conflicts, Vanderhaegen F, Maaoui C, Sallak M, Berdjag D (Eds), Wiley, pp. 3–28

  96. Vatavu R (2013) On designing interactivity awareness for ambient displays. Multimed Tools Appl 66:59–80. https://doi.org/10.1007/s11042-012-1140-y

    Article  Google Scholar 

  97. Vogel D, Balakrishnan R (2004) Interactive public ambient displays: transitioning from implicit to explicit, public to personal, interaction with multiple users. In: Proceedings of the 17th annual ACM symposium on User interface software and technology, Santa Fe NM, USA, October 2004

  98. Weiser M (1991) The Computer for the 21st Century. Sci Am 265(3):94–104

    Article  Google Scholar 

  99. Weiser M, Brown J (1997) The Coming Age of Calm Technology. Beyond Calculation. https://doi.org/10.1007/978-1-4612-0685-9_6

    Article  Google Scholar 

  100. Whelan R (2007) Neuroimaging of cognitive load in instructional multimedia. Educ Res Rev 2:1–12. https://doi.org/10.1016/j.edurev.2006.11.001

    Article  Google Scholar 

  101. Wickens C (2008) Multiple resources and mental workload. Hum Factors 50:449–455. https://doi.org/10.1518/001872008X288394

    Article  Google Scholar 

  102. Woods D, Kishiyama M, Herron LE, Edwards B, Poliva O, Hink RF, Reed B, (2011) Improving digit span assessment of short-term verbal memory. J Clin Exp NeuroPsyc 33:101–111. https://doi.org/10.1080/13803395.2010.493149

    Article  Google Scholar 

  103. Yin B, Chen F (2007) Towards Automatic Cognitive Load Measurement from Speech Analysis. In: Jacko J (eds) Human-Computer Interaction. Interaction Design and Usability. HCI 2007. Lecture Notes in Computer Science, vol 4550. Springer, Berlin, Heidelberg

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Ben Shelton: Conceptualization, Methodology, Software, Writing—Original draft preparation, Data curation. Keith Nesbitt: Conceptualization, Methodology, Writing- Original draft preparation, Writing- Reviewing and Editing, Supervision. Alexander Thorpe: Methodology, Writing- Reviewing and Editing. Ami Eidels: Methodology, Writing- Reviewing and Editing, Supervision.

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Shelton, B., Nesbitt, K., Thorpe, A. et al. Gauging the utility of ambient displays by measuring cognitive load. Cogn Tech Work (2020). https://doi.org/10.1007/s10111-020-00639-8

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Keywords

  • Cognitive load
  • Dual task
  • Detection response task
  • Ambient display
  • Peripheral display
  • Ubiquitous computing