I am interested in understanding adaptive human behavior. How do cognitive characteristics, task characteristics, and context shape human behavior and performance? And how well can people optimize their performance given these constraints? Of particular interest are human behavior in mulitasking settings, driver distraction, and when interacting with automated system such as autonomous cars.
In the multitasking domain, I study how well people can optimize the way they interleave their attention between tasks given the task characteristics and the priorities that they set (or the rewards that they receive).
In general, I study aspects of (reinforcement) learning & adaptation, performance and resource trade-offs, and the influence of cognitive and environmental constraints on behavior. Recently I have also started to look into individual differences in performance and their underlying causes.
In my research I use a combination of empirical studies and formal modeling. Observations that are made in studies are formalized in theories as developed in computational cognitive models. I have also used these models to develop adaptive "user models", which can dynamically adapt the state of a system to the performance and characteristics of individual users. In my work I combine insights from, and publish in, the fields of cognitive science, experimental psychology, vision, human factors, human-computer interaction, and computer science.
Below are listed some specific projects that I am or was involved in. If you are a student, there are almost always opportunities to do a research project related to each of these themes.
Understanding human behavior in (semi-)autonomous vehicles (link)
Understanding Strategic Adaptation in Dual-Task Situations as Cognitively Bounded Rational Behavior (initiated during my PhD research, UCL)(link)
Understanding how conversations while driving can be made safer (With colleagues at Microsoft Research)(link)
Adaptive visual strategies in healthy adults and people with AMD (initiated as post-doc research, Smith-Kettlewell Eye Research Institute) (link)
When, what, and how much to reward in reinforcement learning models of cognition (initiated as MSc research, RPI) (link)
User modeling for training recommendation in a depression prevention game (initiated as RA at Groningen) (link)
Personalization of a Virtual Museum Tour using Eye-gaze (initiated as BSc research, Groningen) (link)
Technological development of autonomous vehicles is in full swing. Although the technology is promising, less is known about human behavior. Automation research suggests that human behavior changes when automation is introduced. We try to understand how human behavior changes when the car is automated. Several projects in my lab investigate aspects of human behavior in automated settings. This includes the PhD research or Remo van der Heiden (of whom I am primary supervisor) and my own Marie Curie supported research. I also have various international collaborations on this topic on various themes (see publications).
Janssen, C.P., Iqbal, S.T., Kun, A.L., and Donker, S.F. (2019) Interrupted by my car? Implications of interruption and interleaving research for automated vehicles. International Journal of Human-Computer Studies, 130. 221-233. DOI: https://doi.org/10.1016/j.ijhcs.2019.07.004 [Publisher copy (open access)]
Janssen, C.P., Donker, S.F., Brumby, D.P., Kun, A.L. (2019 online first) History and Future of Human-Automation Interaction. International Journal of Human-Computer Studies. DOI: https://doi.org/10.1016/j.ijhcs.2019.05.006 [Publisher copy (open access)]
Janssen, C.P., Boyle, L, Kun, A.L., Ju, W., & Chuang, L. (2019) A Hidden Markov Framework to Capture Human-Machine Interaction in Automated Vehicles. International Journal of Human Computer Interaction, 35(11), 947-955. DOI: 10.1080/10447318.2018.1561789 [Author URL, Publisher copy (Open Access)]
Kun, A.L., van der Meulen, H., & Janssen, C.P. (in press) Calling while Driving using Augmented Reality: Blessing or Curse? Accepted for publication in Presence: Teleoperators and Virtual Environments.[Author version]
Van der Heiden, R.M.A., Janssen, C.P., Donker, S.F., Merkx, C. (2018, online first) Visual In-car Warnings: How Fast Do Drivers Respond? Transportation Research Part F: Psychology and Behaviour. Doi: 10.1016/j.trf.2018.02.024 [Publisher copy (open access), Video explaining the research]
You can also check out my papers on related projects regarding multitasking and driver distraction (here and (here).
Janssen, C.P., Van der Heiden, R.M.A., Donker, S.F., & Kenemans, J.L. (2019) Measuing Susceptibility to Alerts while Encountering Mental Workload. Extended Abstracts of the SIGCH Conference on Automotive User Interfaces and Invehicular Applications (Auto-UI). Utrecht, The Netherlands. [Link to paper to follow]
Janssen, C.P., Kun, A.L., Brewster, S, Boyle, L., Brumby, D.P., & Chuang, L.L. (2019) Exploring the Concept of the (Future) Mobile Office. Extended Abstracts of the SIGCH Conference on Automotive User Interfaces and Invehicular Applications (Auto-UI). Utrecht, The Netherlands. [Link to paper and Video to follow]
Chuang, L., Donker, S.F., Kun, A.L., Janssen, C.P. (2018)Workshop on The Mobile Office. In Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '18). ACM, New York, NY, USA, 10-16. DOI: https://doi.org/10.1145/3239092.3239094 [Author version, Associated Blog]
Kun, A., Van der Meulen, H, & Janssen, C.P. (2017) Calling while driving: an initial experiment with hololens. Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. [Author URL]
Janssen, C.P. & Kenemans, J.L. (2015) Multitasking in Autonomous Vehicles: Ready to Go? Proceedings of the 3rd Workshop on User Experience of Autonomous Vehicles at AutoUI í15. Nottingham, UK. (position paper) [Publisher URL]
Video clips describing (part of) this work
On my personal Youtube channel, I post video clips describing my research. Here are clips about my work on autonomous driving:
A grant of the European Unionís Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 705010, 'Detect and React', awarded to CP Janssen, 2016-2018).
Dutch Ministry of Transportation (Rijkswaterstaat) (2016-2019)
I successfully defended my thesis in April 2012. I received the 2014 Briggs Award for the Best Doctoral Dissertation in Applied Experimental/Engineering Psychology, awarded by the APA 21 division (site)
This is the abstract of my thesis:
"In this thesis I explored when people interleave attention in dual-task settings. The hypothesis is that people try to perform in a cognitively bounded rational way. Performance is limited by constraints that come from the task environment and cognition. If, given these constraints, multiple strategies for interleaving tasks are available, then people will interleave tasks in a way that aligns with their local priority objective (Chapter 3), or which maximizes the value of an objective payoff function that evaluates performance (Chapter 4). This hypothesis was tested using a combination of experimental studies and computational cognitive models. Across a series of studies, the interplay between different constraints was investigated. In Chapters 5 and 6, I developed mathematical models to study what task combinations in general allowed for "ideal payoff manipulations" to study task interleaving. The work contributed to the existing literature in four ways: (1) it provided an overarching theory of skilled human dual-task performance and tested this in relatively applied settings, (2) the theory was formalized in computational cognitive models that can predict performance of unobserved strategies and that can bracket the (optimal) performance space, (3) linear and logarithmic tasks were identified as an ideal combination for achieving ideal payoff manipulations, and (4) results demonstrated that in multitasking situations attention is not necessarily interleaved solely at chunk boundaries and other "natural breakpoints", but that this depends on a person's priorities. The work has implications for driver distraction research, in that it helps in systematically understanding the performance trade-offs that people face when multitasking. Moreover, the modeling framework could be used for model-based evaluation of new mobile interfaces. Finally, the demonstration that priorities can strongly influence multitasking performance highlights the importance of public safety campaigns that emphasize awareness of driver safety. Limitations and further implications are discussed. "
Janssen, C.P. (2012)Understanding Strategic Adaptation in Dual-Task Situations as Cognitively Bounded Rational Behavior. Phd Thesis [PDF version]
Supervisors: Dr. Duncan P Brumby, Dr. John Dowell, Prof. Nick Chater (U. Warwick)
Collaborator: Prof. Andrew Howes (U. Birmingham)
Examiners: Prof. David Shanks (UCL) and Prof. Stephen Payne (U. Bath)
Janssen, C.P., Everaert, E., Hendriksen, H., Mensing, G., Tigchelaar, L., & Nunner, H. (2019). The influence of rewards on (sub-)optimal interleaving. PLoS ONE 14(3): e0214027. https://doi.org/10.1371/journal.pone.0214027. [Publisher copy (open access)]
Janssen, C.P., Gould, S.J.J., Li, S.Y.W., Brumby, D.P., & Cox, A.L. (2015). Integrating Knowledge of Multitasking and Interruptions Across Different Perspectives and Research Methods. International Journal of Human-Computer Studies, 79, 1-5.doi:10.1016/j.ijhcs.2015.03.002 [Author URL, Publisher URL ]
Talking while you are driving can be distracting. However, not all conversations are equally distracting. In this work we try to understand what makes conversations distracting. We then try to use these insights to try and make in-car conversations safer. In the study described below participants drive in a driving simulator. While they drive, they also perform a conversation task with a remote partner. Unknown to the driver, we share sounds from the driver's context (e.g., car honks, sirens) with the remote caller. We found that sharing these sounds changes the remote caller's perception of how busy the driver is. However, this only had a modest effect on the conversation and on driving performance. This highlights a need for better training or information of how to then act on this information to make conversations less distracting to the driver.
(of course, if you find yourself in a situation like this, the safest thing is to hang up the phone and post-pone the call to a later, safer time!)
You can watch a video of this setup by clicking on the following link (to video).
Age-related macular degeneration (AMD) is one of the leading causes of reduced visual function that cannot be corrected optically. In the United States alone, it affects 6.5% of the population over the age of 40 (Klein, Chou, Klein, Zhang, Meuer, & Saaddine, 2011). However, it is a condition that becomes more pervasive with age, and by the age of 80, around a third of all adults has some form of AMD (Friedman et al., 2004). In AMD, a scotoma (or blindspot) develops, typically around the fovea. Due to the scotoma, people have to change their visual strategies for locating information in the world. No longer can they use their high-resolution fovea, which they used for decades and to which the visual and cognitive system was adapted.
In the absence of foveal vision, most individuals learn to use an eccentric pseudo fovea, called the preferred retinal locus (PRL), which is typically located just outside the scotoma. But many of these individuals have difficulty directing the PRL to effectively scan items of interest, and have difficulty reading (e.g., Nilsson, Frennesson, & Nilsson, 2003; Seiple, Grant, & Szlyk, 2011). Developing a better understanding of how and why these adaptive strategies work for some and not others, has the potential to inform eye movement training methods for all people with AMD, such that their sight can improve.
To work towards this larger goal, I examine strategies for moving the eyes efficiently both in healthy adults and adults with AMD. The research uses a combination of empirical studies (with healthy adults and a patient population) and computational cognitive modeling.
Janssen, C.P. & Verghese, P. (2016) Training eye movements for visual search in individuals with macular degeneration. Journal of Vision, 16(15):29, 1-20, doi:10.1167/16.15.29 [Publisher URL]
Janssen, C.P. & Verghese, P. (2015). Stop before you saccade: Looking into an artificial peripheral scotoma. Journal of Vision, 15(5) article 7:1-19 doi:10.1167/15.5.7.[Publisher URL]
Verghese, P., & Janssen, C.P. (2015) Scotoma Awareness and Eye Movement Training in Age-Related Macular Degeneration. Accepted for presentation at ARVO.
Janssen, C.P. & Verghese, P. (2014) Stop & think: Looking into a scotoma. Accepted for oral presentation at the annual meeting of the Vision Science Society [abstract]
Janssen, C. P. & Verghese, P. (2013) Eye-Movement Strategies in Multiple Target Search for Target Location and Uncertainty Reduction. Poster to be presented at the Annual Meeting of the Cognitive Science Society, Berlin, Germany.
Janssen, C. P. & Verghese, P. (2013) Towards a Better Understanding of Eye-Movement Strategies in Multiple Target Search. Poster to be presented at the Annual Conference of the Vision Science Society, Naples, Florida. [poster]
Horizon (Pacific Vision Foundation Newsletter) (2014). "Not seeing what is really there - PVF funded research seeks scotoma solutions". This short article describes my research with elderly people who have macular degeneration. The work is done together with Dr. Preeti Verghese. You can read the article here: [PDF]
Rachel C. Atkinson Fellowship (October 2012 - October 2014). My personal fellowship to support my work at Smith-Kettlewell.
Pacific Vision Foundation (September 2013 - September 2014). Funding for Verghese & Janssen. Testing A Novel Method for Teaching Scotoma Awareness.
Janssen, C.P., & Gray, W.D.. When, What, and How Much to Reward in Reinforcement Learning based Models of Cognition. Cognitive Science, 36(2), 333-358. DOI: 10.1111/j.1551-6709.2011.01222.x [fully formatted ; preprint ]
I worked as a junior researcher of the Cognitive modeling research group of the Artificial Intelligence Department of the University of Groningen, and developed a user model for a serious game. Within this game players learned to cope with depression. My model used principles from cognitive science and AI to capture the learned social skills of the user automatically.
As a result, each game can adapt to characteristics of the individual user. This individual approach is very important, as depression is characterized by several characteristics, of which a depressed person most of the time only has a subset. The intensity of those characteristics differs for each person.
For my BSc project, I developed a system that tracked people's fixations while they were watching digital paintings. Based on the fixations, a "virtual tour guide" would play audio snipets that explained aspects of the art work - especially those that the user fixated most on. Overall goal was to find a way in which a tour through a digital museum could be personalized.