Bachelor thesis

Human Factors

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Learning complex motor procedures in minimally invasive surgery

Supervisor: Dr. Martin Schmettow (Cognitive Psychology and Ergonomics, UT)

Dr. Marleen Groenier (Technische Geneeskunde)

Purpose

The study explores individual differences in how quick and well people learn the complex motor procedures of minimally invasive surgery. We wish to understand whether a person’s talent to master MIS can validly be assessed by MIS simulators, as well as everyday “dexterity” tasks, such as tying a knot or other games of skill.

Background

Learning to perform minimally invasive surgical procedures (‘keyhole’ surgery) differs from learning to perform regular, open surgical procedures. During the first procedures that a surgeon performs the risk of complications is increased compared to open surgical procedures. Surgical errors are related to ergonomic factors, such as the technology used, human (cognitive) factors and the amount of training a surgeon has received.

Minimally invasive surgery

Minimally invasive surgery places high demands on human spatial orientation and motor dexterity. Many studies attempted to predict MIS skills by classic experimental tests, but most failed. In collaboration with the department for Technical Medicine at the UT, we attempt a reboot. Our assumption is that development of complex skills may be better viewed holistically, rather than thinking in cognitive components. If that is true, a person’s ability to learn a complex task is best predicted by … letting the person learn a complex task. This principle is reminiscent of what is done in usability testing of software or assessment centers for recruitment.

The thesis project

In your thesis you will

  • Write a brief literature overview on performance in minimally invasive surgery.
  • Read about the estimation of learning curves in Psychology.
  • Design a games of skill, such as tying a sailor knot
  • Recruit a sample of participants via SONA and your own social network
  • Run a two part experiment: first, you observe the learning of participants when doing dexterity tasks, then you observe them while learning several MIS procedures in the simulator.
  • Interpret the estimated learning curves and assess correlations between different tasks.
  • Discuss your findings and shed new light on the issue of MIS performance.

Notes

This is a group assignment for 2 students.

References

  • Groenier, M., Schraagen, J. M. C., Miedema, H. A. T., & Broeders, I. A. J. M. (2014). The role of cognitive abilities in laparoscopic simulator training. Advances in Health Sciences Education : Theory and Practice, 19(2), 203–17. doi:10.1007/s10459-013-9455-7
  • Heathcote, a, Brown, S., & Mewhort, D. J. (2000). The power law repealed: the case for an exponential law of practice. Psychonomic Bulletin & Review, 7(2), 185–207. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10909131
Range anxiety of prospective electric vehicles users

Supervisor: Suzanne Vosslamber

Nowadays, different strategies are developed to decrease CO2 emissions and reduce dependence on fossil fuels. Using an electric vehicle (EV) instead of a vehicle with a combustion engine (VCE) seems a promising strategy. Different European governments, for instance the Dutch government, approved new laws in order to establish tax advantages for citizens that are buying and using new EVs in place of a VCE (Rijksoverheid, 2011). The goal of the Dutch government is to reach one million EVs in the Netherlands in 2025 at the latest.

In order to reach this goal, though, users have to adopt EVs. One major barrier for adopting an EV is the fear of not reaching your destination (i.e. range anxiety). One way to reduce range anxiety might be an improved user-interface design in an EV, which for example helps with providing knowledge to reduce uncertainty or with accurate tracking of the range safety buffer, which in turn might improve user experience when driving an EV (Rauh, Franke, & Krems, 2014). However, research also showed there are individual differences in what people experience as comfortable range, i.e. a user’s preferred range safety buffer (Franke, Günther, Trantow, Rauh & Krems, 2015). Franke and Krems (2013) found that the average user felt comfortable when using 75-80% of the available range resources of their EV. They however pointed out that the average user in their sample is an early adopter, and therefore the number of 75-80% average comfortable range utilization may be the upper limit and not representative for the whole population of car buyers.

The points mentioned above raise questions such as: what is the comfortable range for users that aren’t early adopters? Can we distinguish different types of (prospective) EV users based on their perception of (comfortable) range? How should a display be designed for these users? And should displays be designed differently for different types of users based on their comfortable range? In your thesis you will focus on one or more of these questions by doing qualitative research. You can think of carrying out a user requirements study or creating personas.

References

  • Franke, T., & Krems, J. F. (2013). Interacting with limited mobility resources: Psychological range levels in
  • electric vehicle use. Transportation Research Part A: Policy and Practice, 48, 109-122.
  • doi:10.1016/j.tra.2012.10.010
  • Franke, T., Günther, M., Trantow, M., Rauh, N., & Krems, J. F. (2015). The range comfort zone of electric
  • vehicle users – concept and assessment. IET Intelligent Transport Systems, 9(7), 740-745.
  • doi:10.1049/iet-its.2014.0169 
  • Rauh, N., Franke, T., & Krems, J. F. (2014). User experience with electric vehicles while driving in a
  • critical range situation – A qualitative approach. IET Intelligent Transport Systems, 9(7), 734-739.
  • doi:10.1049/iet-its.2014.0214
  • Rijksoverheid (2011). Plan van aanpak elektrisch vervoer. Retrived from
  • www.rijksoverheid.nl/bestanden/documenten-en-publicaties/richtlijnen/2011/10/03/ bijlage-2-
  • plan-van-aanpak-elektrisch-vervoer-elektrisch-rijden-in-de-versnelling/bijlage-2-plan-van-aanpak-elektrisch-vervoer-elektrisch-rijden-in-de-versnelling.pdf
The broad transfer hypothesis on chess experts

Supervisor: dr. R.H.J. van der Lubbe

The broad transfer hypothesis proposes that expert skills acquired in one domain transfer to better performance in other domains. For example, chess training has been argued to lead to scholastic improvements, which may be ascribed to improved functioning of spatial working memory or other more general attentional skill. However, it has been argued that this relation is not that clear (e.g., see Bart, 2014). In this research project, psychophysical methods will be employed to provide a more accurate index of visual working memory, visual attention, and visual perception to further detail their relation with chess expertise.

The broad transfer hypothesis on gaming experts

Supervisor: dr. R.H.J. van der Lubbe

The broad transfer hypothesis proposes that expert skills acquired in one domain transfer to better performance in other domains. Experts on action video games may for example perform better on standard measures of visual attention (e.g., see Green & Bavelier, 2003, 2006). Nevertheless, the question may be raised whether these effects are really due to acquired expertise, as they may also be due to self-selection (see Boot et al., 2008; Van Ravenzwaaij et al., 2014). In this research project, psychophysical methods will be employed to provide a more accurate index of visual attention, visual working memory, and visual perception to further detail their relation with gaming expertise.

The smell of wine

Supervisor: dr. R.H.J. van der Lubbe

It is commonly known that tasting actually occurs through the nose. In this project, we want to examine how well people can actually discriminate between different wines by just smelling them. Several psychophysical methods like signal detection theory (SDT) and scaling will be employed to examine how well people actually can distinguish between various types of wines on the basis of smell.

The taste of wine

Supervisor: dr. R.H.J. van der Lubbe

Some people think that wines from bottles taste better than wines from plastic packs, some people believe that Chile wines taste the best, other people only drink French wines, and some other people actually think that South African wines are the best. However, are people really that well in distinguishing between various types of wines? In this project, several psychophysical methods like signal detection theory (SDT) and scaling will be employed to examine how well people actually can distinguish between various types of wines on the basis of taste.

The vigilant brain

Supervisor: dr. R.H.J. van der Lubbe

The vigilant state of an individual is known to vary over time. The electroencephalogram (EEG) is generally considered to be one of the best online measures of the state of the brain. For example, drowsiness is easy visible due to an increase in lower frequencies like theta and delta activity. However, changes in attentional processes over time may be more subtle and less easy to detect. A new task, the sustained lateral attention task will be employed that allows to extract a new direct attentional index based on wavelet analyses of the EEG (e.g., see Van der Lubbe & Utzerath, 2013). A few participants will be instructed to attend during a series of trials to either the left or the right of a screen, while sometimes a to be detected target will occur. The validity of the new attentional index will be examined by examining its predictive power for attentional lapses.

What self-reported experience of people is the best predictor of fluctuations in heart rate and skin conductance in daily life?

Wearable technology is becoming available, for example the Apple Watch, that allow consumers to measure their own physiological signals (e.g. heart rate) and potentially share them with others. In many cases, fluctuations in these physiological signals are operationalized by the wearable technology to some kind of human experience: The device is measuring your heart rate, but it would indicate your stress levels on its screen or a coupled smartphone. This operationalization might seem logical, but empirical studies and modern theories on human emotion and physiological computing show and indicate that these kind of substitutions are problematic (Barrett & Simmons, 2015; Evers et al., 2014; Fairclough, 2009).

In this study you will examine what fluctuations in self-reported experience of people best predicts the physiological signals that are captured by a wearable bio-sensor. This watch-like bio sensor (although it doesn’t tell you the time) can measure both heart rate and skin conductance (Fletcher, Poh, & Eydgahi, 2010). These two signals capture the activity of different parts of the human nervous system to a different degree. Skin conductance, for example, is supposed to mostly capture the activity of the sympathetic part of the autonomic nervous system (Boucsein, 2012). This part of the nervous system is very often associated with the arousal dimension of emotion within dimensional theories of emotion (Russell, 2009). However, a limited amount of studies have systematically tested whether such an operationalization makes sense in people’s daily lives (where people use these types of sensors, but see for example (Kidd, Carvalho, & Steptoe, 2014)). In the present study you will test different types of operationalization with an experience sampling design (Conner & Barrett, 2012) in which people are asked to answer a limited set of questions per day while they are also wearing the biosensor. Different people will be answering different questions. Some will express their experiences with a concept such as stress, while others would use the concept of arousal.

Depending on the number of student researchers that work on this project the number of concepts we can include might differ. Probably all participants will answer the questions for different time scales (e.g. ‘how stressed are you now?’, but also ‘how stressed were you the past 3 hours?’) since this is supposed to be an important predictor of differences in the coherence between self-reported and physiological signals (Robinson & Clore, 2002). Finally, it seems obvious that many individual differences exist in how people map their experiences in daily life on fuzzy concepts such as stress or arousal. Within individuals this mapping might also fluctuate from situation to situation. This means this project depends on appropriate statistical modelling of the individual time courses (Bolger & Laurenceau, 2013).

SUPERVISOR FOR THIS PROJECT

Dr. Matthijs Noordzij, https://www.utwente.nl/bms/cpe/employees/noordzij/.

NUMBER OF STUDENTS THAT CAN APPLY

Given the many research questions that fit in the aims of this project, at present there are no restrictions on the number of students that can work on this project. Students that have affinity with doing psychological research in daily life and are interested in (exploratory) data analysis are especially invited to apply for this project. You might want to work on the very accessible and informative one-hour tutorials (two in total) on linear (mixed) models by Bodo Winter (http://bodowinter.com/tutorials.html, (Winter, 2013)). If you enjoy that, then this project is probably something for you.

REFERENCES

  • Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews Neuroscience, 16(7), 1–11. http://doi.org/10.1038/nrn3950
  • Bolger, N., & Laurenceau, J. P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. (T. D. Little, Ed.)Methodology in the Social Sciences Series. New York: Guilford Press.
  • Boucsein, W. (2012). Electrodermal Activity (2nd ed.). New York, NY, USA: Springer.
  • Conner, T. S., & Barrett, L. F. (2012). Trends in Ambulatory Self-Report. Psychosomatic Medicine, 74(4), 327–337. http://doi.org/10.1097/PSY.0b013e3182546f18
  • Evers, C., Hopp, H., Gross, J. J., Fischer, A. H., Manstead, A. S. R., & Mauss, I. B. (2014). Emotion response coherence: A dual-process perspective. Biological Psychology, 98(1), 43–49. http://doi.org/10.1016/j.biopsycho.2013.11.003
  • Fairclough, S. H. (2009). Fundamentals of physiological computing. Interacting with Computers, 21(1–2), 133–145. http://doi.org/10.1016/j.intcom.2008.10.011
  • Fletcher, R. R., Poh, M. Z., & Eydgahi, H. (2010). Wearable sensors: Opportunities and challenges for low-cost health care. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, 1763–1766. http://doi.org/10.1109/IEMBS.2010.5626734
  • Kidd, T., Carvalho, L. a., & Steptoe, A. (2014). The relationship between cortisol responses to laboratory stress and cortisol profiles in daily life. Biological Psychology, 99(1), 34–40. http://doi.org/10.1016/j.biopsycho.2014.02.010
  • Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility model of emotional self-report. Psychological Bulletin, 128(6), 934–960. http://doi.org/10.1037/0033-2909.128.6.934
  • Russell, J. A. (2009). Emotion, core affect, and psychological construction. Cognition and Emotion, 23(7), 1259–1283. http://doi.org/10.1080/02699930902809375
  • Winter, B. (2013). Linear models and linear mixed effects models in R with linguistic applications. arXiv, (1308.5499), 1–22.