A wearable cognitive assistant (WCA) is a computer-based application that guides a user through a task with input from wearable devices with the aid of computational resources in nearby locations (cloudlets). Psychological science informs development of WCAs and is encountering new issues for research. We discuss three relevant research areas: response time, action segmentation, and task comprehension.
AndersonJ. R.CorbettA. T.KoedingerK.PelletierR. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4, 167–207.
2.
AnnettJ. (2003). Hierarchical task analysis. In HolnagelE. (Ed.), Handbook of cognitive task design (pp. 17–36). Routledge and CRC Press.
3.
BeltramaA.PapafragouA. (2021). We are what we say: Pragmatic violations have social costs. Proceedings of the Annual Meeting of the Cognitive Science Society, 43, 1423–1429. https://escholarship.org/uc/item/24g6n4f9
4.
BesslerD.PorzelR.PomarlanM.BeetzM. (2023). Foundational models for manipulation activity parsing. In JungT.tom DieckM. C.Correia LoureiroS. M. (Eds.), Extended reality and Metaverse: Immersive technology in times of crisis. Springer. https://doi.org/10.1007/978-3-031-25390-4_10
5.
BrassM.LiefoogheB.BraemS.De HouwerJ. (2017). Following new task instructions: Evidence for a dissociation between knowing and doing. Neuroscience and Biobehavioral Reviews, 81(Pt. A), 16–28. https://doi.org/10.1016/j.neubiorev.2017.02.012
6.
CaiB.HeH.WangA.ZhangM. (2022). Levels of neuroticism can predict attentional performance during cross-modal nonspatial repetition inhibition. Attention, Perception, & Psychophysics, 84, 2552–2561. https://doi.org/10.3758/s13414-022-02583-3
7.
CharneyD. H.RederL. M.WellsG. W. (1988). Studies of elaboration in instructional texts. In Doheny-FarinaS. (Ed.), Effective documentation: What we have learned from research (pp. 47–72). MIT Press.
8.
ChenZ.HuW.WangJ.ZhaoS.AmosB.WuG.HaK.ElgazzarK.PillaiP.KlatzkyR.SiewiorekD.SatyanarayananM. (2017). An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance. In ZhangJ. (Chair), Proceedings of the Second ACM/IEEE Symposium on Edge Computing (Article No. 14). https://doi.org/10.1145/3132211.3134458
9.
ClarkH. H.BlyB. B. (1995). Pragmatics and discourse. In MillerJ. L.EimasP. D (Eds.), Handbook of perception and cognition, Vol. 11: Speech, language and communication (pp. 371–410). Academic Press.
10.
DabrowskiJ.MunsonE. V. (2011). 40 years of searching for the best computer system response time. Interacting with Computers, 23, 555–564.
11.
FlanaganJ. R.BowmanM. C.JohanssonR. S. (2006). Control strategies in object manipulation tasks. Current Opinion in Neurobiology, 16, 650–659.
12.
GraftonS. T.de HamiltonA. F. D. C. (2007). Evidence for a distributed hierarchy of action representation in the brain. Human Movement Science, 26, 590–616.
13.
HaK.ChenZ.HuW.RichterW.PadmanabhanP.SatyanarayananM. (2014). Towards wearable cognitive assistance. In CampbellA.KotzD. (Chairs), Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services (pp. 68–81). https://doi.org/10.1145/2594368.2594383
14.
HirshJ. B.MorisanoD.PetersonJ. B. (2008). Delay discounting: Interactions between personality and cognitive ability. Journal of Research in Personality, 42, 1646–1650. https://doi.org/10.1016/j.jrp.2008.07.005
15.
HockleyW. E. (1984). Analysis of response time distributions in the study of cognitive processes. Journal of Experimental Psychology: Learning, Memory, & Cognition, 10, 598–615.
16.
HollanJ. D.HutchinsE. L.WeitzmanL. (1984). STEAMER: An interactive inspectable simulation-based training system. AI Magazine, 5, 15–27.
17.
HommelB. (2019). Theory of event coding (TEC) V2.0: Representing and controlling perception and action. Attention, Perception, & Psychophysics, 81, 2139–2154. https://doi.org/10.3758/s13414-019-01779-4
18.
IrrazabalN.SauxG.BurinD. (2016). Procedural multimedia presentations: The effects of working memory and task complexity on instruction time and assembly accuracy. Applied Cognitive Psychology, 30(6), 1052–1060.
19.
IyengarR. (2023). Scaling up wearable cognitive assistance for assembly tasks [Unpublished PhD thesis]. Computer Science Department, Carnegie Mellon University.
20.
JohnO. P.SrivastraS. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In PervinL. A.JohnO. P. (Eds.), Handbook of personality: Theory and research (pp. 102–138). Guilford Press.
21.
KarshN.EitamB. (2015). I control therefore I do: Judgments of agency influence action selection. Cognition, 138, 122–131.
22.
KeeleS. W.SummersJ. J. (1976). The structure of motor programs. In StelmachG. E. (Ed.), Motor control: Issues and trends (pp. 109–142). Academic Press.
23.
KimJ.NguyenP. T.WeirS.GuoP. J.MillerR. C.GajosK. Z. (2014). Crowdsourcing step-by-step information extraction to enhance existing how-to videos. In M. Jones & P. Palanque (Chairs), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April (pp. 4017–4026). https://doi.org/10.1145/2556288.2556986
24.
LongoM. R.HaggardP. (2009). Sense of agency primes manual motor responses. Perception, 38, 69–78.
25.
MacLellanC. J.KoedingerK. R. (2022). Domain-general tutor authoring with apprentice learner models. International Journal of Artificial Intelligence in Education, 32, 76–117. https://doi.org/10.1007/s40593-020-00214-2
26.
MuraK.PetersenN.MarkusH.TandraG. (2013). IBES: A tool for creating instructions based on event segmentation. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00994
27.
NaterF.GrabnerH.Van GoolL. (2011). Unsupervised workflow discovery in industrial environments. In WS13: 2nd IEEE Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (pp. 1912–1919). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCVW.2011.6130482
28.
NielsenJ. (1994). Usability engineering. Morgan Kaufmann.
Olguín MuñozM.KlatzkyR.WangJ.PillaiP.SatyanarayananM.GrossJ. (2021). Impact of delayed response on wearable cognitive assistance. PLOS ONE, 16(3), Article e0248690. https://doi.org/10.1371/journal.pone.0248690
31.
PhamT. A.WangJ.XiaoY.PadmanabhanP.IyengarR.KlatzkyR.SatyanarayananM. (2021). Ajalon: Simplifying the authoring of wearable cognitive assistants. Software: Practice and Experience, 51, 1773–1797.
32.
RatcliffR.McKoonG. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20, 873–922.
33.
RohdeM.van DamL. C. J.ErnstM. O. (2014). Predictability is necessary for closed-loop visual feedback delay adaptation. Journal of Vision, 14(3), 4, 1–23.
34.
RosenbaumD. A.KennyS. B.DerrM. A. (1983). Hierarchical control of rapid movement sequences. Journal of Experimental Psychology: Human Perception & Performance, 9, 86–102.
35.
SaletJ. M.KruijneW.van RijnH. (2021). Implicit learning of temporal behavior in complex dynamic environments. Psychonomic Bulletin and Review, 28, 1270–1280. https://doi.org/10.3758/s13423-020-01873-x
36.
SatyanarayananM.BahlP.CaceresR.DaviesN. (2009). The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing, 8, 14–23. https://doi.org/10.1109/MPRV.2009.82
37.
SatyanarayananM.BeckmannN.LewisG. A.LuciaB. (2021). The role of edge offload for hardware-accelerated mobile devices. In M. Musolesi (Chair), Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications (pp. 22–29). Association for Computing Machinery. https://doi.org/10.1145/3446382.3448360
38.
SeowS. C. (2008). Designing and engineering time: The psychology of time perception in software. Addison-Wesley.
39.
ShneidermanB. (1987). Designing the user interface: Strategies for effective human-computer interaction. Addison-Wesley.
40.
SteinbornM. B.RolkeB.BratzkeD.UlrichR. (2009). Dynamic adjustment of temporal preparation: Shifting warning signal modality attenuates the sequential foreperiod effect. Acta Psychologica, 132, 40–47. https://doi.org/10.1016/j.actpsy.2009.06.002
41.
TanakaT.WatanabeK.TankaK. (2021). Immediate action effects motivate actions based on the stimulus–response relationship. Experimental Brain Research, 239, 67–78. https://doi.org/10.1007/s00221-020-05955-z
42.
WangJ.FengZ.GeorgeS.IyengarR.PadmanabhanP.SatyanarayananM. (2019). Towards scalable edge-native applications. In ChenS.OnishiR. (Eds.), Proceedings of the 4th ACM/IEEE Symposium on Edge Computing (pp. 152–165). Association for Computing Machinery. https://doi.org/10.1145/3318216.3363308
43.
WitmerB. G.SingerM. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence, 7, 225–240. https://doi.org/10.1162/105474698565686
44.
XiongA.ProctorR. (2018). The role of task space in action control: Evidence from research on instructions. In FedermeierK. D. (Ed.), Psychology of learning and motivation (Vol. 69, pp. 325–364). Academic Press.
45.
ZhaoS.WangJ.LengH.LiuY.LiuH.SiewiorekD.SatyanarayananM.KlatzkyR. (2020). Edge-based wearable systems for cognitive assistance: Design challenges, solution framework, and application to emergency healthcare (Technical Report CMU-CS-20-102). School of Computer Science, Carnegie Mellon University. https://www.cs.cmu.edu/~satya/docdir/CMU-CS-20-102.pdf