See Bachelor thesis

Cognitive Psychology

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BCP1 - Is motor learning better with backward than with forward chaining?


Backward chaining is a method to improve the effect of practice. It entails practicing first later parts of a serial movement pattern and then the earlier parts are added. Forward chaining involves practicing first the earlier part and only then the later part is included in practice.

The two projects will test the hypothesis that backward chaining improves learning more than forward chaining, and that this holds especially with limited practice while this learning difference will be reduced with extended practice. These hypotheses will be tested in two related bachelor projects in BMS lab. Each involves an experiment in which participants will develop automaticity by practicing two fixed movement patterns while reaction times and error are measured. The results will contribute to guidelines how the development of sequential motor skills can be optimized.


Abrahamse, E. L., Ruitenberg, M. F. L., De Kleine, E., & Verwey, W. B. (2013). Control of automated behaviour: Insights from the Discrete Sequence Production task. Frontiers in Human Neuroscience, 7(82), 1-16.

Fontana, F. E., Mazzardo, O., Furtado Jr, O., & Gallagher, J. D. (2009). Whole and part practice: A meta-analysis. Perceptual and Motor Skills, 109(2), 517-530.

Wightman, D. C., & Lintern, G. (1985). Part-task training for tracking and manual control. Human Factors, 27(3), 179-209.

BCP2 - Is learning movement sequences influenced by eye fixation location?


When people are developing serial movement skills, they are assumed to develop memory representations of those sequences. These representations eliminate the need to select the individual movement in the series. Little is known about the content of these representations. This project entails an experiment testing the hypothesis that one of these movement representations involves spatial coordinates relative to the eye fixation location. In the experiment, participants will develop motor automaticity by practicing two fixed 6-element key pressing sequences.  While practicing they will focus a particular screen location. This may mean that the participants learn the location of the key-specific stimuli in terms of their relative location to the fixation location. In the ensuing test phase, participants will execute the practiced and new sequences with the same and with a different fixation location. This study will be carried out in the cubicles of the BMS lab.


Abrahamse, E. L., Ruitenberg, M. F. L., De Kleine, E., & Verwey, W. B. (2013). Control of automated behaviour: Insights from the Discrete Sequence Production task. Frontiers in Human Neuroscience, 7(82), 1-16.

BCP3 - Artificial intelligence conversational agents: a measure of satisfaction in use

SUPERVISOR: DR. SIMONE BORSCI (4 students)             


Conversational agents, such as chatbots and voice interfaces, can be used for multiple purposes e.g., support customer experience with services etc. These new tools are growing and more and more integrated into systems such as websites, social networks, cars. Smart and AI-based conversational agents are shaping the future of human-computer interaction however little is known about how to assess people reaction and satisfaction after the use of these systems.


You will build upon previous work done on a new scale to assess satisfaction with chatbots. Your experimental work will focus on the evaluation of conversational agents to further streamline the reliability and validity of the scale. You will perform remote usability testing on 10 different chatbots (of your choice) by using Qualtrics (a template will be provided) to collect data.

The target is to involve at least a large number of participants working (potentially) in a team and perform a confirmatory factorial analysis.

You can also potentially contribute to the ongoing development of the scale by adding new languages i.e., the scale was currently tested in English and Dutch. Moreover, you can propose to add research questions of your interest to the current evaluation protocol.


You should be aware of the statistical methods regarding factorial analysis and be able to use SPSS or R for such purposes.

BCP4 - Do biological faces trigger the Uncanny Valley effect?



Social robots and other artificial agents should be designed as likeable as possible. A common assumption is that emotional acceptance can be improved by mimicking human appearance and behavior as close as technically possible. However, research in the field of Human-Robot Interaction has revealed a chilling fact:  The emotional responses towards a robot increases only up to a certain point of human-likeness. When a robot face reaches closer resemblance with a human face, the observer experiences the exact opposite: a spine-tingling feeling. This sudden drop in emotional response is called the Uncanny Valley.

In previous studies, participants were asked to rate artificial faces ranging from mechanical to human-like. We could show, that the bizarre Uncanny Valley effect is universal in that everyone seems to experience it. This suggests, that it is deeply rooted in the human mind, which raises the question of its original evolutionary function. Certainly, it is not there to protect us from falling in love with androids.

In your study, you will explore if and how the UV effect arises for

·       biological faces (apes, ancestors of Homo Sapiens Sap)
·       modified faces, e.g. cosmetic surgery or photo-shopped

In your introduction, you create a literature overview on related ideas or findings and make a connection with the literature on evolution of face recognition. For your experiment you will create a new collection of stimuli and determine their human-likeness score. You test for the Uncanny Valley on a sample of participants.

Finally, you discuss the implications of your findings with respect to:

-        theories on the effect
-        societal implications of the effect (e.g. racism)

This is a group project for four students.

Mathur, M. B., & Reichling, D. B. (2016). Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley. Cognition, 146, 22–32.

Keeris, D., & Schmettow, M. (2016). Replicating the uncanny valley across conditions using morphed and robotic faces. University of Twente.

Schmettow, M., Koopman, Robbin (2019) The Uncanny Valley as a universal experience : a replication study using multilevel modelling.

Schmettow, M. (2020). New Statistics for design researchers. Chapters 4.6 and 5.4.

BCP5 - What causes the “sharp end” effect in recall of disaster reports?



Safety science distinguishes between ‘sharp end’ and ‘blunt end’ causes of accidents. The former are the most proximal causes, both in time and in place, to the actual incident, for instance an airline pilot being blamed for having ‘inadequate situation awareness’ or a surgeon for ignoring the input of a nurse or making a medication error. The latter are the more distal causes, further removed from the accident in time and place, for instance, the safety culture of an organization, managerial pressure emphasizing production over safety, or government regulations stimulating innovation to the neglect of safety.

It has often been stated that the sharp end bears most of the blame, and that people, when talking about accidents frequently remember sharp end causes rather than blunt end causes (e.g., Besnard & Hollnagel, 2014). In the past, this might have been due to biases by accident investigation boards towards reporting sharp end causes, but nowadays, models of accident causation have become more sophisticated and accident investigation boards report on both sharp and blunt end causes.

Previous research (Moning, 2014) has demonstrated a memory effect under controlled laboratory conditions in the recall of disaster-related information. With articles containing both sharp end and blunt end information on two disasters (Tenerife and Challenger), participants recalled significantly more sharp end information than blunt end information. This was regardless of whether a ‘story grammar’ was present or not, in other words whether the article was structured according to a standard sequence (setting-theme-plot-resolution). Therefore, the presence of a story grammar cannot explain the “sharp end” effect in recall of disaster-related information.

A similar sharp end effect, this time with different and relatively unknown disasters, was found in a recent experiment by Berkemeier (in progress). She also manipulated the presence or absence of sharp ends in the texts, as well as the presence or absence of concrete blunt end blaming at the end of the text. With concrete blaming at the end, both the percentage of blunt ends and sharp ends recalled decreased to the same extent. Contrary to our expectations, blunt ends were blamed more than sharp ends, although this effect was moderated by the presence of sharp ends in the texts: when sharp ends were present, there was less blunt end blaming and more sharp end blaming. Therefore, it seems that blaming and recall are two different processes, each affected by different factors. However, there were some methodological issues with Berkemeier’s research that prevent us from drawing firm conclusions.

Your assignment

You will carry out two experiments (one by each student) building upon the work carried out by Berkemeier. In one experiment, the responsibility questions will be presented one at a time rather than all at once. The other experiment will add concrete sharp end blaming at the end. The students will re-use all stimulus materials and follow the same procedure as Berkemeier did.


Besnard, D., & Hollnagel, E. (2014). I want to believe: Some myths about the management of industrial safety. Cognition, Technology, & Work, 16, 13-23.

Moning, I. (2014). How do people recall articles about disaster? Effects of story grammar on recall of sharp end and blunt end causes. Bachelor thesis, University of Twente.

BCP6 - Behavioural development of motor learning expertise


Motor learning is an important aspect of daily life.  The development of expertise in motor learning however is still  an overlooked and automated process meaning that  many factors are largely unknown (Verwey, 1999; Verwey & Abrahamse, 2012; Verwey et al., 2015).  The goal of this experiment is to understand motor learning from an in-depth behavioural perspective by segregating different kinds of learners.  A secondary goal is to understand the differences between easy, moderate and harder sequences amongst these learners.  Participants will be practicing three fixed sequences in the form of the Discrete Sequence Production Task till automaticity.  As a bachelor student, the requirement is for you to help with recruitment of participants and run analysis on the behavioural data using mixed-effects models.  You will have a chance to work closely with the main supervisor to apply R and build statistical models to investigate these differences.  You will also have the opportunity learn and assist with EEG neuro data collection.

Requirement: 1 student with statistical understanding.  Interested to learn R and/or Python.  Interested to learn EEG.

Primary supervisor: Dr. Russell Chan

Secondary supervisor:  Prof. Willem Verwey


Verwey, W. B. (1999). Evidence for a multistage model of practice in a sequential movement task. Journal of Experimental Psychology: Human Perception and Performance, 25(6), 1693-1708.

Verwey, W. B., & Abrahamse, E. L. (2012, Jul). Distinct modes of executing movement sequences: reacting, associating, and chunking. Acta Psychol (Amst), 140(3), 274-282.

Verwey, W. B., Shea, C. H., & Wright, D. L. (2015, Feb). A cognitive framework for explaining serial processing

BCP7 - Motor learning in the form of a step-task


Motor sequence learning research has largely been focused on using keyboard-based tasks to understand learning processes.  This has produced strong theory although its application in whole body/ foot-based sequence learning is largely invalidated.  Previous research has shown that it could be performed (Dotan Ben-Soussan et al., 2013; Du & Clark, 2018), but not specific to the Discrete Sequence Production Task (Verwey & Abrahamse, 2012).  The goal of this experiment is to program the DSP task into a foot-based stepping task and pilot the approach with the use of 3D Xsens sensors.  You will have the opportunity to unravel reaction time and Centre of Mass changes to analyse motor learning. 

Requirement: 1 student with interest in 3D motion capture, willing to learn how to program psychological experiments in E-Prime and interested to learn R and/or Python.

Primary supervisor: Dr. Russell Chan

Secondary supervisor (possible): Dr. Martin Schmettow


Dotan Ben-Soussan, T., Glicksohn, J., Goldstein, A., Berkovich-Ohana, A., & Donchin, O. (2013). Into the Square and out of the Box: The effects of Quadrato Motor Training on Creativity and Alpha Coherence. PLoS One, 8(1), e55023. doi:10.1371/journal.pone.0055023

Du, Y., & Clark, J. E. (2018, 2018/05/03/). The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task. JoVE(135), e56483.      

Verwey, W. B., & Abrahamse, E. L. (2012). Distinct modes of executing movement sequences: reacting, associating, and chunking. Acta Psychol (Amst), 140(3), 274-282.

BCP8 - Short-Term Memory for Color, Form or Orientation


Recent ideas on working memory or short-term memory (STM) propose that STM may be better conceptualized as a limited resource that is flexibly distributed among items to be maintained in memory rather than holding a fixed number of elements active. Many aspects of STM are still unknown. In this project, we will especially focus on memory for colors/form/orientation. How precise are our memories for color/form/orientation and how does this preciseness depend on the number of presented objects?

BCP9 - Testing the Motor-Cognitive Model of Motor Imagery


A dominant view in the field of motor imagery is the functional equivalence model (Jeannerod, 2006). This model implies that the processes activated during motor imagery are the same as the processes activated during motor execution, except for the execution itself. Recently, it has been proposed that in case of complex new movements additional processes are activated as motor imagery requires more control and possibly inhibition: the motor-cognitive model. Goal of the project is to examine at what level of response complexity the motor-cognitive model provides a better explanation for performance than the functional equivalence model. This will be done by comparing the durations of physical and mental execution of motor tasks that vary in complexity.

BCP10 - Conceptual Learning



Concepts and their relations play a crucial role in human cognition. In particular, they are the building blocks of our semantic cognition, with which we understanding our environment. Concepts can vary from concrete, as given by the concept "dog", to abstract, such as the concept "honesty". Learning concepts can be based on learning perceptual classifications, such as learning the concept "dog" from classifying individual dogs, or by classifying or recognizing actions as performed by certain agents. But concepts can also be learned by combining other concepts and their relations. So, the concept "animal" could be learned from understanding the similarities between concepts such as "dogs" and "cats" and their differences with other concepts like "chair" or "house". In this way, we also learn relations between concepts, for example that a dog is an animal, but not every animal is a dog. Because concepts (such as actions) are typically learned in (certain) relations to each other, a 'conceptual space' (or knowledge base) can arise, which forms the basis for our semantic cognition.  

How we learn concepts and conceptual spaces, and how they are represented in the brain, is a topic of very active research. Learning of concepts and relations is also an important theme in machine learning. The key issue in this project concerns the way in which concepts and their relations in a given domain are learned and how they are combined to form a conceptual space. The domain can be chosen one, such as the "sport domain" (with concepts like "player" or "game") or the "health domain" (with concepts like "virus" or "medicine"). Or it could be designed for the project to study how humans learn such a new domain. The chosen topic can be studied with experimental techniques such as card sorting or priming studies. Or the conceptual space in a chosen domain could be designed (e.g., for use in machines) and evaluated by humans, for example by using questionnaires. Aspects of concept learning and conceptual spaces can also be modelled with computer modelling, such as Deep Learning or other techniques. 


Rouder, Jeffrey; Ratcliff, Roger (2006). "Comparing Exemplar and Rule-Based Theories of Categorization". Current Directions in Psychological Science. 15: 9–13. doi:10.1111/j.0963-7214.2006.00397.x. 

Lambon-Ralph, M. A., Jefferies, E., Patterson, K. and Timothy T. Rogers, T. T. (2017). The neural and computational bases of semantic cognition. Nature Reviews Neuroscience 18, 42–55. doi:10.1038/nrn.2016.150

Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016). Natural speech reveals the semantic maps that tile human cerebral cortex. Nature, 532(7600), 453-458. doi:10.1038/nature17637