Augmented cognition is a form of human-systems interaction in which a tight coupling between user and computer is achieved via physiological and neurophysiological sensing of a user's cognitive state. This interactive paradigm seeks to revolutionize the manner in which humans engage with computers by leveraging this knowledge of cognitive state to precisely adapt user-system interaction in real time. This review provides an overview of contemporary works in the field of augmented cognition and details regarding the three main components of an augmented cognition system: cognitive state sensors, adaptation strategies, and control systems. The review provides a perspective on the field as well as insights into the many challenges that lie ahead for those who endeavor to realize the full potential of augmented cognition.
Get full access to this article
View all access options for this article.
References
1.
AasmanJ.MulderG.MulderL. J. M. (1987). Operator effort and the measurement of heart rate variability. Human Factors, 29, 161–170.
2.
ArroyoE.SelkerT.StouffsA. (2002). Interruptions as multimodal outputs: Which are the less disruptive? In Proceedings of the IEEE 4th International Conference on Multimodal Interfaces (ICMI ′02, pp. 479–483). Los Alamitos, CA: IEEE Computer Society.
3.
BaccinoT.ManuntaY. (2005). Eye-fixation-related potentials: Insight into parafoveal processing. Journal of Psychophysiology, 19, 204–215.
4.
BarkerR. A.EdwardsR. E.O'NeillK. R.TollarJ. R. (2004). DARPA improving warfighter information intake under stress—Augmented cognition concept validation experiment (CVE) analysis report for the Boeing team (Contract NBCH030031). Arlington, VA: Defense Advanced Research Projects Agency.
5.
BaronS. (2002). Problem or challenge? Serving library customers that technology left behind. In Sarkodie-MensahK. (Ed.), Helping difficult library patrons: New approaches to examining and resolving longstanding and ongoing problems (pp. 129–147). Binghamton, NY: Reference Librarian.
6.
BaronS.KruserD. S.MessickB. (1990). Quantitative modeling of human performance in complex, dynamic systems. Washington, DC: National Academy Press.
7.
BarsalouL. W.SimmonsW. K.BarbeyA. K.WilsonC. D. (2003). Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences, 7, 84–91.
8.
BergerH. (1929). Uber das Elektrenkephalogramm des Menschen [On theelectroencephalogram of man]. Archiv für Psychiatrie und Nervenkrankheiten, 87, 527–570.
9.
BerkaC.LevendowskiD. J.CvetinovicM. M.PetrovicM. M.DavisG.LumicaoM. N., et al. (2004). Realtime analysis of EEG Indexes of alertness, cognition, and memory acquired with a wireless EEG headset. International Journal of Human-Computer Interaction, 17, 151–170.
10.
BerntsonG. G.BiggerJ. T.EckbergD. L.GrossmanP.KaufmanP. G.MalikM., et al. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34, 623–648.
11.
BoucseinW.BacksR. W. (2000). Engineering psychophysiology as a discipline: Historical and theoretical aspects. In BoucseinW.BacksR. W. (Eds.), Engineering psychophysiology: Issues and applications (pp. 3–29). London: Erlbaum.
12.
BradleyM. M.MoulderB.LangP. J. (2005). When good things go bad: The reflex physiology of defense. Psychological Science, 16, 468–473.
13.
BruneauD.SasseM. A.McCarthyJ. D. (2002). The eyes never lie: The use of eye tracking data in HCI research. Presentation at J. Allanson & G. M. Wilson (organizers), Physiological Computing Workshop, CHI ′02 Extended Abstracts on Human Factors in Computing Systems (pp. 912–913). New York: ACM Press. Retrieved June 30, 2009, from http://hornbeam.cs.ucl.ac.uk/hcs/people/documents/Angela%20Publications/2002/.
14.
BrunsA.KincsesW. E. (2004). DARPA improving warfighter information intake under stress—Augmented cognition concept validation experiment (CVE) analysis report for DaimlerChrysler team (Tech. Rep. submitted to DARPA/IPTO). Arlington, VA: Defense Advanced Research Projects Agency.
15.
BunceS. C.IzzetogluM.IzzetogluK.OnaralB.PourrezaeiK. (2006). Functional near-infrared spectroscopy: An emerging neuroimaging modality. IEEE Engineering in Medicine and Biology, 17(2), 211–231.
16.
CacioppoJ. T.BerntsonG. G.SheridanJ. F.McClintockM. K. (2000). Multilevel integrative analyses of human behavior: Social neuroscience and the complementing nature of social and biological approaches. Psychological Bulletin, 126, 829–843.
17.
CaldwellJ. A.WilsonG. F.CetingucM.GaillardA. W. K.GundelA.LagardeD., et al. (1994). Psychophysiological assessment methods (AGARD-AR-324). Neuilly-sur-Seine, France: Advisory Group for Aerospace Research and Development.
18.
CritchleyH. D. (2002). Electrodermal responses: What happens in the brain. Neuroscientist, 8(2), 132–142.
19.
DicksonB. T. (2005). Closed-loop systems—Stability and predictability. In SchmorrowD. D. (Ed.), Foundations of augmented cognition (pp. 617–620). Mahwah, NJ: Erlbaum.
20.
DietheT. (2005). The future of augmentation managers. In SchmorrowD. D. (Ed.), Foundations of augmented cognition (pp. 631–640). Mahwah, NJ: Erlbaum.
21.
DietheT.DicksonB. T.SchmorrowD.RaleyC. (2004). Toward an augmented cockpit. In VicenziD. A.MoulouaM.HancockP. A. (Eds.), Human performance, situation awareness and automation: Current research and trends (Vol. 2, pp. 65–69). Mahwah, NJ: Erlbaum.
22.
DingesD. F.MallisM.MaislinG.PowellJ. W. (1998). Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management (Final report for the U.S. Department of Transportation, Rep. No. DOT HS 808 762). Washington, DC: National Highway Traffic Safety Administration.
23.
DorneichM. C.WhitlowS. D.MathanS.CarciofiniJ.VerversP. M. (2005). The communications scheduler: A task scheduling mitigation for a closed loop adaptive system. In SchmorrowD. D. (Ed.), Foundations of augmented cognition (pp. 132–141). Mahwah, NJ: Erlbaum.
24.
DorneichM. C.VerversP. M.MathanS.WhitlowS. D. (2006). Evaluation of a tactile navigation cueing system triggered by a real-time assessment of cognitive state. In Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting (pp. 2600–2604). Santa Monica, CA: SAGE Publications.
25.
DorneichM. C.WhitlowS. D.VerversP. M.MathanS.RajA.MuthE., et al. (2004). DARPA improving warfighter information intake under stress—Augmented cognition concept validation experiment (CVE) analysis report for the Honeywell team (Contract No. DAAD16–03-C-0054). Arlington, VA: Defense Advanced Research Projects Agency.
26.
DuRousseauD. R. (2004). Multimodal cognitive assessment system (Final Tech. Rep. DAAH01–03-C-R232). Arlington, VA: Defense Advanced Research Projects Agency.
27.
DutaM.AlfordC.WilonS.TarassenkoL. (2004). Neural network analysis of the mastoid EEG for the assessment of vigilance. International Journal of Human-Computer Interaction, 17(2), 171–195.
28.
EgelundN. (1982). Spectral analysis of heart rate variability as an indicator of driver fatigue. Ergonomics, 25, 663–672.
29.
FarwellL. A.DonchinE. (1988). Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70, 510–523.
30.
FreemanF. G.MikulkaP. J.PrinzelL. J.ScerboM. W. (1999). Evaluation of an adaptive automation system using three EEG indices with a visual tracking system. Biological Psychology, 50, 61–76.
31.
FuchsS.HaleK. S.BerkaC.LevendowskiD.JuhnkeJ. (2006). Physiological sensors cannot effectively drive system mitigation alone. In SchmorrowD. D.StanneyK. M.ReevesL. M. (Eds.), Foundations of augmented cognition (2nd ed., pp. 193–200). Arlington, VA: Strategic Analysis.
32.
FuchsS.HaleK. S.StanneyK. M.JuhnkeJ.SchmorrowD. (2007). Enhancing mitigation in augmented cognition. Journal of Cognitive Engineering and Decision Making, 1, 309–326.
33.
FuchsS.JonesD. L.HaleK. S. (2007, October). Approaches for bias mitigation during imagery analysis. Paper presented at Augmented Cognition International 2007, Baltimore, MD.
34.
GevinsA.SmithM. E.LeongH.McEvoyL.WhitfieldS.DuR., et al. (1998) Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors, 40, 79–91.
35.
GoldbergH. J.WichanskyA. M. (2003). Eye tracking in usability evaluation: A practitioner's guide. In HyönäJ.RadachR.DeubelH. (Eds.), The mind's eye: Cognitive and applied aspects of eye movement research (pp. 493–516). Amsterdam: Elsevier.
36.
GopherD.ArmonyL.GreenspanY. (2000). Switching tasks and attention policies. Journal of Experimental Psychology: General, 129, 308–339.
37.
GranholmE.AsarnowR. F.SarkinA. J.DykesK. L. (1996). Pupillary responses Index cognitive resource limitations. Psychophysiology, 3, 457–461.
38.
GrattonG.KramerA. F.FabianiM. (2008). Brain sensors and measures. In SchmorrowD. D.StanneyK. M. (Eds.), Augmented cognition: A practitioner's guide (pp. 1–26). Santa Monica, CA: SAGE Publications.
39.
HadleyG. A.PrinzelL. J.FreemanF. G.MikulkaP. J. (1999). Behavioral, subjective and psychophysiological correlates of various schedules of short-cycle automation. In ScerboM. W.MoulouaM. (Eds.), Automation technology and human performance (pp. 139–143). Mahwah, NJ: Erlbaum.
40.
HaleK. S.FuchsS.AxelssonP.BaskinA.JonesD. (2007). Determining gaze parameters to guide EEG/ERP evaluation and imagery analysis. In SchmorrowD. D.NicholsonD. M.DrexlerJ. M.ReevesL. M. (Eds.), Foundations of augmented cognition (4th ed., pp. 33–40). Arlington, VA: Strategic Analysis, Inc.
41.
HaleK.ReevesL.SammanS.AxelssonP.StanneyK. (2005). Validation of predictive workload component of the Multimodal Information Design Support (MIDS) system. In Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting (pp. 1162–1166). Santa Monica, CA: SAGE Publications.
42.
HealeyJ. A. (2000). Wearable and automotive system for affect recognition from physiology. Unpublished Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA.
43.
HooverA.MuthE. (2004). A real-time Index of vagal activity. International Journal of Human-Computer Interaction, 17(2), 197–210.
44.
InhoffA. W.RadachR. (1998). Definition and computation of oculomotor measures in the study of cognitive processes. In UnderwoodG. (Ed.), Eye guidance in reading, driving and scene perception (pp. 29–53). New York: Elsevier.
45.
IqbalS. T.AdamczykP. D.ZhengX. S.BaileyB. P. (2005). Towards an Index of opportunity: Understanding changes in mental workload during task execution. In Proceedings of CHI 2005 (pp. 311–320). New York: ACM Press.
46.
IqbalS. T.BaileyB. P. (2006). Leveraging characteristics of task structure to predict the cost of interruption. In Proceedings of CHI 2006 (pp. 741–750). New York: ACM Press.
47.
IqbalS. T.ZhengX. S.BaileyB. P. (2004). Task-evoked pupillary response to mental workload in human-computer interaction. In Dykstra-EricksonE.Manfred TscheligiK. (Eds.), CHI ′04 Extended Abstracts on Human Factors in Computing Systems (pp. 1477–1480). New York: ACM Press.
48.
IzzetogluK.BunceS.OnaralB.PourrezaeiK.ChanceB. (2003). Functional optical brain imaging using near-infrared during cognitive tasks. International Journal of Human-Computer Interaction, 17(2), 211–227.
49.
IzzetogluM.BunceS.IzzetogluK.OnaralB.PourrezaeiK. (2007). Functional brain imaging using near infrared technology for cognitive activity assessment. IEEE Engineering in Medicine and Biology, 26(4), 38–46.
50.
IzzetogluM.IzzetogluK.BunceS.AyazH.DevarajA.OnaralB., et al. (2005). Functional near-infrared neuroimaging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13, 153–159.
51.
JacobR. J. K.KarnK. S. (2003). Eye tracking in human-computer interaction and usability research: Ready to deliver the promises (Section Commentary). In HyonaJ.RadachR.DeubelH., (Eds.), The mind's eye: Cognitive and applied aspects of eye movement research (pp. 573–605). Amsterdam: Elsevier Science.
52.
JangD. P.KimI. Y.NamS. W.WiederholdB. K.WiederholdM. D.KimS. I. (2002). Analysis of physiological response to two virtual environments: Driving and flying simulation. Cyberpsychology & Behaviour, 5(1), 11–18.
53.
JosephsonS.HolmesM. E. (2002). Visual attention to repeated Internet images: Testing the scanpath theory on the World Wide Web. In Proceedings of the Eye Tracking Research and Applications Symposium (pp. 43–49). New York: ACM Press.
54.
JungT.MakeigS.StensmoM.SejnowskiT. (1997). Estimating alertness from the EEG power spectrum. IEEE Transactions on Biomedical Engineering, 44, 60–69.
55.
JustM.CarpenterP. A. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8, 441–480.
56.
JustM.CarpenterP. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87, 329–354.
57.
KaberD. B.PerryC. M.SegallN.Sheik-NainarM. A. (2007). Workload state classification with automation during simulated air traffic control. International of Aviation Psychology, 17, 371–390.
58.
KaberD. B.PrinzelL. J.III. (2006). Adaptive and adaptable automation design: A critical review of the literature and recommendations for future research (Tech. Rep. NASA/TM-2006–214504). Hampton, VA: NASA Langley Research Center.
59.
KaberD. B.RileyJ. M. (1999). Adaptive automation of a dynamic control task based on secondary task workload measurement. International Journal of Cognitive Ergonomics, 3, 169–187.
60.
KaberD. B.RileyJ. M.TanK.-W.EndsleyM. R. (2001). On the design of adaptive automation for complex systems. International Journal of Cognitive Ergonomics, 5, 37–57.
61.
KirshD. (2000). A few thoughts on cognitive overload. Intellectica, 30, 19–51.
62.
KniplingR. R.WierwilleW. W. (1994, April). Vehicle-based drowsy driver detection: Current status and future prospects. Paper presented at the IVHS America Fourth Annual Meeting, Atlanta, GA.
63.
KramerA. F. (1991). Physiological metrics of mental workload: A Review of recent progress. In DamosD. L. (Ed.), Multiple task performance (pp. 279–328). Washington, DC: Taylor & Francis.
64.
KroftP.WickensC. D. (2001). The display of multiple geographical data bases: Implications of visual attention (Rep. No. ARL-01–2/NASA-01–2). Savoy, IL: University of Illinois, Aviation Research Laboratory. Retrieved May 6, 2009, from http://www.humanfactors.uiuc.edu/ReportsapersPDFs/TechReport/01-02.pdf.
LeeC. K.YooS.ParkY. J.KimN.JeongK.LeeB. (2005). Using neural network to recognize human emotions from heart rate variability and skin resistance. In Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 5523–5525). Los Alamitos, CA: IEEE Computer Society.
67.
LiuY.FeyenR.TsimhoniO. (2006). Queuing Network-Model Human Processor (QN-MHP): A computational architecture for multitask performance in human-machine systems. ACM Transactions on Computer-Human Interaction, 13(1), 37–70.
68.
Lockheed Martin ATL. (2005). DARPA improving warfighter information intake under stress—Augmented cognition Phase 3 concept validation experiment (CVE) analysis report for the Lockheed Martin AT L team (Tech. Rep. submitted to DARPA/IPTO). Arlington, VA: Defense Advanced Research Projects Agency.
69.
MamykinaL.MynattE.TerryM. A. (2001). Time aura: Interfaces for pacing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 144–151). New York: ACM Press.
70.
MandrykR. L. (2005). Modeling user emotion in interactive play environments: A fuzzy physiological approach. Unpublished Ph.D. thesis, Simon Fraser University, Burnaby, Canada.
71.
MandrykR. L.InkpenK. (2004). Physiological indicators for the evaluation of co-located collaborative play. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW ′04) (pp. 102–111). New York: ACM Press.
72.
MathanS.ErdogmusD.HuangY.PavelM.VerversP.CarciofiniJ., et al. (2008). Rapid image analysis using neural signals. In Conference on Human Factors in Computing Systems (pp. 3309–3314). New York: ACM Press.
73.
MathanS.VerversP.DorneichM.WhitlowS.CarciofiniJ.ErdogmusD., et al. (2006). Neurotechnology for image analysis: Searching for needles in haystacks efficiently. In SchmorrowD. D.StanneyK. M.ReevesL. M. (Eds.), Foundations of augmented cognition (2nd ed., pp. 3–11). Arlington, VA: Strategic Analysis.
74.
MatthewsR.McDonaldN. J.TrejoL. J. (2005). Psycho-physiological sensor techniques: An overview. In SchmorrowD. D. (Ed.), Foundations of augmented cognition (pp. 263–272)). Mahwah, NJ: Erlbaum.
75.
McFarlaneD. C. (2002). Comparison of four primary methods for coordinating the interruption of people in human-computer interaction, Human-Computer Interaction, 17, 63–139.
76.
MillerC.DorneichM. C. (2006). From associate systems to augmented cognition: 25 Years of user adaptation in high criticality systems. In SchmorrowD. D.StanneyK. M.ReevesL. M. (Eds.), Foundations of augmented cognition (2nd ed., pp. 344–353). Arlington, VA: Strategic Analysis.
77.
MooreM. M.DuaU. (2004). A galvanic skin response interface for people with severe motor disabilities. In Assets ′04: Proceedings of the 6th International ACM SIGACCESS Conference on Computers and Accessibility (pp. 48–54). New York: ACM Press.
78.
MorrisonJ. G.KobusD. A.BrownC. M. (2006). Volume I: DARPA improving warfighter information intake under stress—Augmented cognition. Phase II concept validation (Tech. Rep. 1940). San Diego, CA: Pacific Science and Engineering Group. Retrieved June 30, 2009, from http://handle.dtic.mil/100.2/ADA457526.
79.
NakayamaM.TakahashiK.ShimizuY. (2002). The act of task difficulty and eye-movement frequency for the “oculo-motor indices.” In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 37–42). New York: ACM Press.
80.
NewellA. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
81.
NickelP.NachreinerF. (2003). Sensitivity and diagnosticity of the 0.1 Hz component of heart rate variability as an indicator of mental workload. Human Factors, 45, 575–590.
82.
NishimuraE. M.StautzenbergerJ.RobinsonW.DownsT. H.DownsJ. H.III. (2007). A new approach to fNIR: OTIS. IEEE Engineering in Medicine and Biology, 26(4), 25–29.
83.
OgataK. (2002). Modern control engineering (4th ed.) Englewood Cliffs, NJ: Prentice-Hall.
84.
ParasuramanR. (1993). Effects of adaptive function allocation on human performance. In GarlandD. J.WiseJ. A. (Eds.), Human factors and advanced aviation technologies (pp. 147–158). Daytona Beach, FL: Embry-Riddle Aeronautical University Press.
85.
ParraL. C.SpenceC. D.GersonA. D.SajdaP. (2005). Recipes for the linear analysis of EEG. Neuroimage, 28, 326–341.
86.
PecherD.ZeelenbergR.BarsalouL. W. (2003). Verifying different-modality properties for concepts produces switching costs. Psychological Science, 14, 119–124.
87.
PelzJ. B.CanosaR.BabcockJ. (2000, November). Extended tasks elicit complex eye movement patterns. Paper presented at the Symposium of Eye Tracking Research and Applications, Palm Beach Gardens, FL.
88.
PhillipsC. L.ParrJ. M. (1999). Signals, systems, and transforms (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall.
89.
Pleydell-PearceC. W.WhitecrossS. E.DicksonB. T. (2003). Multivariate analysis of EEG: Predicting cognition on the basis of frequency decomposition, inter-electrode correlation, coherence, cross phase, and cross power. In Abstract Proceedings of the 36th Annual Hawaii International Conference on System Sciences (Track 5, CD-ROM). Los Alamitos, CA: IEEE Computer Society.
90.
PolichJ. (1999). P300 in clinical applications. In NiedermeyerE.da SilvaF. Lopes (Eds.), Electroencephalography: Basic principles, clinical applications and related fields (4th ed., pp. 1073–1091). Baltimore: Urban & Schwarzenberg.
91.
PolichJ. (2004). Neuropsychology of P3a and P3b: A theoretical overview. In MooreN. C.ArikanK. (Eds.), Brainwaves and mind: Recent developments (pp. 15–29). Wheaton, IL: Kjellberg.
92.
PolichJ.KokA. (1995). Cognitive and biological determinants of P300: An integrative Review. Biological Psychology, 41, 103–146.
93.
PomplunM.SunkaraS. (2003). Pupil dilation as an indicator of cognitive workload in human-computer interaction. In Proceedings of HCI International 2003 (Vol. 3, pp. 542–546). Mahwah, NJ: Erlbaum.
94.
PooleA.BallL. J. (2006). Eye tracking in HCI and usability research. In GhaouiC. (Ed.), Encyclopedia of human computer interaction (pp. 211–219). Hershey, PA: Idea Group.
95.
PopeA. T.BogartE. H.BartolomeD. S. (1995). Biocybernetic system validates Index of operator engagement in automated task. Biological Psychology, 40, 187–195.
96.
PosnerM. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3–25.
97.
PosnerM. I.SnyderC. R. R.DavidsonB. J. (1980). Attention and the detection of signals. Journal of Experimental Psychology: General, 109, 160–174.
98.
PrinzelL. J.IIIFreemanF. C.ScerboM. W.MikulkaP. J.PopeA. T. (2000). A closed-loop system for examining psychophysiological measures for adaptive task allocation. International Journal of Aviation Psychology, 10, 393–410.
99.
PrinzelL. J.IIIParasuramanR.FreemanF. G.ScerboM. W.MikulkaP. J.PopeA. T. (2003). Three experiments examining the use of electroencephalogram, event-related potentials, and heart rate variability for real-time human-centered adaptive automation design (Tech. Rep. NASA/TP-2003–212442). Hanover, MD: NASA Scientific and Technical Information Program Office.
100.
RapoportE. D.NishimuraE. M.RaleyC.DownsT. H.DownsJ. H.III. (2008). Functional near infrared (fNIR) sensors. In SchmorrowD. D.StanneyK. M. (Eds.), Augmented cognition: A practitioner's guide (pp. 27–40). Santa Monica, CA: SAGE Publications.
101.
RobertsR. J.WeertsT. C. (1982), Cardiovascular responding during anger and fear imagery. Psychological Reports., 50, 219–230.
102.
RouseW. B. (1988). Adaptive aiding for human/computer control. Human Factors, 30, 431–443.
103.
RussellC. A. (2005). Operator state estimation for adaptive aiding in uninhabited combat air vehicles. Unpublished Ph.D. thesis (AFIT/DS/ENG/05–01), Air Force Institute of Technology, Wright-Patterson Air Force Base, OH. Retrieved June 30, 2009, from http://aerade.cranfield.ac.uk/subject-listing/rpv.html.
104.
SajdaP.GersonA. D.PhiliastidesM. G.ParraL. C. (2007). Single-trial analysis of EEG during rapid visual discrimination: Enabling cortically-coupled computer vision. In DornhegeG.MillanJ. R.HinterbergerT.McFarlandD. J.MullerK. R. (Eds.), Towards brain-computer interfacing (pp. 423–44). Cambridge, MA: MIT Press.
105.
ScallenS. F.HancockP. A. (2001). Implementing adaptive function allocation. International Journal of Aviation Psychology, 11, 197–221.
106.
ScerboM. W. (1996). Theoretical perspectives on adaptive automation. In ParasuramanR.MoulouaM. (Eds.), Automation and human performance: Theory and applications (pp. 37–64). Mahwah, NJ: Erlbaum.
107.
ScerboM. W. (2001). Adaptive automation. In KarwowskiW. (Ed.), International encyclopedia of ergonomics and human factors (pp. 1077–1079). London: Taylor & Francis.
108.
ScerboM. W.FreemanF. G.MikulkaP. J.ParasuramanR.NoceroF. D.PrinzelL. J.III. (2001). The efficacy of psychophysiological measures for implementing adaptive technology (Tech. Rep. NASA/TP-2001–211018). Hanover, MD: NASA Scientific and Technical Information Program Office.
109.
SchnellT.KellerM.MacudaT. (2007). Application of the cognitive avionics tool set (CATS) in airborne operator state classification. In SchmorrowD. D.NicholsonD. M.DrexlerJ. M.ReevesL. M. (Eds.), Foundations of augmented cognition (4th ed., pp. 3–12). Arlington, VA: Strategic Analysis.
110.
SchnellT.KellerM.MacudaT. (2008). Sensor integration to characterize operator state. In SchmorrowD. D.StanneyK. M. (Eds.), Augmented cognition: A practitioner's guide (pp. 41–74). Santa Monica, CA: SAGE Publications.
111.
SeyleH. (1956). The stress of life. New York: McGraw-Hill.
112.
SmithM. E.GevinsA.BrownH.KarnikA.DuR. (2001). Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction. Human Factors, 43, 366–380.
113.
JohnM.St.KobusD. A.MorrisonJ. G.SchmorrowD. (2005). Overview of the DARPA augmented cognition technical integration experiment. In SchmorrowD. (Ed.), Foundations for augmented cognition (pp. 446–452). Mahwah, NJ: Erlbaum.
114.
JohnM.St.ManesD. I.SmallmanH. S.FeherB. A.MorrisonJ. G. (2004). Heuristic automation for decluttering tactical displays. In Proceedings of the Human Factors and Ergonomics Society 48th Annual Meeting (pp. 416–420). Santa Monica, CA: SAGE Publications.
115.
SukaviriyaP.FoleyJ. D. (1990). Coupling a UI-framework with automatic generation of context-sensitive animated help. In Proceedings of the 3rd Annual ACM SIGGRAPH Symposium on User Interface Software and Technology (pp. 152–166). New York: ACM Press.
116.
TarvainenM. P.KarjalainenP. A.KoistinenA. S.Valkonen-KorhonenM. V. (2000). Principal component analysis of galvanic skin responses. In Proceedings of the 22nd Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (Vol. 4, pp. 3011–3014). Los Alamitos, CA: IEEE Computer Society.
TollarJ. T. (2005). Statistical process control as a triggering mechanism for augmented cognition mitigations. In SchmorrowD. D. (Ed.), Foundations of augmented cognition (pp. 414–420). Mahwah, NJ: Erlbaum.
119.
TremouletP.BartonP.CravenP.CorradoC.MayerG.StiblerK., et al. (2005). DARPA improving warfighter information intake under stress—Augmented cognition Phase 3 concept validation experiment (CVE) analysis report for the Lockheed-Martin ATL team (Prepared under Contract No. NBCH030032). Arlington, VA: Defense Advanced Research Projects Agency/Information Processing Techniques Office.
120.
TsengH. M.MacleodH. A.WrightP. (1997). Computer anxiety and measurement of mood change. Computers in Human Behavior, 13, 305–316.
121.
VeltmanJ. A.GaillardA. W. K. (1998). Physiological workload reactions to increasing levels of task difficulty. Ergonomics, 41, 656–669.
122.
VinayagamoorthyV.BrogniA.GilliesM.SlaterM.SteedA. (2004). An investigation of presence response across variations in visual realism. In Presence 2004: 7th Annual International Presence Workshop (pp. 148–155). Retrieved June 30, 2009, from http://www.temple.edu/ispr/prev_conferences/proceedings/2004/Index.html.
123.
WickensC. D. (2002). Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science, 3, 159–177.
124.
WickensC. D.ConejoR.GemplerK. (1999). Unreliable automated attention cueing for air-ground targeting and traffic maneuvering. In Proceedings of the Human Factors and Ergonomics Society 43rd Annual Meeting (pp. 21–25). Santa Monica, CA: SAGE Publications.
125.
WickensC. D.LiuY. (1988). Codes and modalities in multiple resources: A success and a qualification. Human Factors, 30, 599–616.
126.
WientjesC. J. E. (1992). Respiration in psychophysiology: Methods and applications. Biological Psychology, 34, 179–204.
127.
WilsonC. R. (1992). Applied use of cardiac and respiration measures: Practical considerations and precautions. Biological Psychology, 34, 163–178.
128.
WilsonG. F.FisherF. (1995). Cognitive task classification based upon topographic EEG data. Biological Psychology, 40, 239–250.
129.
WilsonG. F.LambertJ. D.RussellC. A. (2000). Performance enhancement with real-time physiologically controlled adaptive aiding. In Proceedings of the Human Factors and Ergonomics Society 44th Annual Meeting (pp. 361–364). Santa Monica, CA: SAGE Publications.
130.
WilsonG. F.RussellC. A. (2003). Operator functional state classification using multiple psychophysiological features in an air traffic control task. Human Factors, 45, 381–389.
131.
WilsonG. F.RussellC. A. (2006). Psychophysiologically versus task determined adaptive aiding accomplishment. In SchmorrowD. D.StanneyK. M.ReevesL. M. (Eds.), Foundations of augmented cognition (2nd ed., pp. 201–207). Arlington, VA: Strategic Analysis.
132.
WilsonG. F.SchlegelR. E. (2004). Operator functional state assessment (NATO RTO Publication RTO-TR-HFM-104). Neuilly sur Seine, France: NATO Research and Technology Organization.
133.
WinterH.ChampigneuxG.ReisingJ.StrohalM. (1997). Intelligent decision aids for human operators. AGARD Conference Proceedings, 600-Vol 2 -Future Aerospace Technology in the Service of the Alliance, B3. Neuilly sur Seine, France: NATO Research and Technology Organisation. Available at http://www.rta.nato.int/Pubs/RDP.asp?RDP=AGARD-CP-600-Vol-2.
134.
YamadaM.FukudaT. (1986). Quantitative evaluation of eye movements as judged by sight-line displacements. Society of Motion Picture and Television Engineers (SMPTE) Journal, 95, 1230–1241.
135.
YehM.WickensC. D. (2001). Attentional filtering in the design of electronic map displays: A comparison of color coding, intensity coding, and decluttering techniques. Human Factors, 43, 543–562.
136.
YossR. E.MoyerN. J.HollenhorstR. W. (1970). Pupil size and spontaneous papillary waves associated with alertness, drowsiness, and sleep. Neurology, 20, 545–554.
137.
YoungP. M. (2001). Structured singular value approach for systems with parametric uncertainty. International Journal of Robust and Nonlinear Control, 11, 653–680.
138.
YoungP.M.CleggB.SmithC. (2004). Dynamic models of augmented cognition. International Journal of Human-Computer Interaction, 17, 259–273.
