Supervisors: prof.dr. Willem Verwey
In collaboration with prof. Michael Nitsche at Ifado (http://www.ifado.de/neurowissenschaft/neuromodulation/), a Transcranial Magnetic Stimulation (TMS, https://en.wikipedia.org/wiki/Transcranial_magnetic_stimulation) study will be carried out while participants are executing a motor sequencing task. Earlier studies suggest that the supplementary motor area (SMA) is heavily involved in learning and producing motor sequences. We previously tested this in two studies with TMS at the preSMA and the SMAproper (Ruitenberg et al., 2014, Verwey et al., 2002), and found different effects, suggesting different functional roles for preSMA and SMAproper. However, these studies were somewhat different and carried out in different laboratories. In this master assignment, we intend to re-examine the different roles of the preSMA and the SMAproper in a single study, possibly including 1 or experiments. The experiment will be carried out at the Ifado in Dortmund, Germany, and will be supervised by researchers at the Ifado.
Ruitenberg, M. F. L., Verwey, W. B., Schutter, D. J. L. G., & Abrahamse, E. L. (2014). Cognitive and neural foundations of discrete sequence skill: A TMS study. Neuropsychologia, 56, 229-238.
Verwey, W. B., Lammens, R., & van Honk, J. (2002). On the role of the SMA in the discrete sequence production task: a TMS study. Neuropsychologia, 40(8), 1268-1276.
Supervisors: prof.dr. Willem Verwey
While a lot of research has been devoted to the learning of motor skills, a particular task that has attracted little attention is chording. Chording skill is used when people are pressing various keys simultaneously, like when playing the piano or a saxophone. The interest in this task is increasing because there are indications that motor learning at the level of primary motor cortex involves learning bodily postures, and these may well also include the hand postures used to perform a chording task. As only a few studies have addressed chording task, in this bachelor thesis project the development with practice of chording responses will be assessed, and most importantly, the hypothesis will be tested that this skill develops differently for those practicing the task with one and with two hands. According to the notion that left and right motor cortex learn postures of one hand, one would expect that one-handed and two-handed chording develop differently. For that reason, chording will be assessed in two groups of participants, one using one hand, the other using to hands. This research will be carried out with participants in the BMS laboratory and will be based on a recent experiment. The results of the experiments will be submitted for publication in the case of positive findings.
Seibel, R. (1962). Performance on a five-finger chord keyboard. Journal of applied psychology, 46(3), 165.
Wifall, T., McMurray, B., & Hazeltine, E. (2012). Perceptual similarity affects the learning curve (but not necessarily learning). Journal of Experimental Psychology: General, 143(1), 312-331.
Supervisors: dr. Rob Van der Lubbe, prof.dr. Willem Verwey
Learning to execute a sequence of movements, for example, when learning to play a piece of music on the piano, involves multiple cortical brain areas. Part of this sequence learning, especially for novel players, will be independent of the specific piece, while another part will be piece-specific. Goal of this project is to determine the differences between sequence-specific and sequence-nonspecific learning by using measures derived from the EEG. A go/nogo variant of the discrete sequence production paradigm will be used (see De Kleine & Van der Lubbe, 2011) wherein one sequence stays constant during a series of several blocks while another sequence changes from block to block. Event-related (de)synchronization (ER(D)/S; e.g., see Pfurtscheller & Neuper. 1997) will be employed to examine changes in the topography of alpha and beta power over time. Results may provide new insights into sequence learning and especially the differences between sequence-specific and non-specific learning.
Supervisors: dr. Rob Van der Lubbe
Patients that are in a so-called vegetative state (VS) do not produce overt motor behavior to command and are therefore considered to be unaware of themselves and of their environments. Recent findings, however, suggest that some of these patients may be more aware than was previously thought (e.g., see Owen et al., 2006). Several studies reported that motor imagery and motor execution induce comparable lateralized brain activity in the alpha and/or beta bands. Earlier research suggested that lateralized activity in the case of motor execution can be demonstrated at an individual level (Van der Lubbe et al., 2018). If it also can be demonstrated that motor imagery of the left or right hand can be demonstrated at an individual level this provides us with a diagnostic tool that may help to determine whether a VS patient is aware enough to follow the instruction to imagine a left or right hand movement (see also Cruse et al., 2012).
Supervisors: dr. Rob Van der Lubbe, dr. Simone Borsci
Studies on working memory load have revealed that an increase in load by using the N-back task leads to diminished performance and an increase in frontal theta activity (measured with the EEG). In the current project, a variant of the memory search task originally developed by Sternberg (e.g., see Gerven et al., 2004) will be used with varying loads of the memory set (1,2, or 4 items), The idea is to examine the 3 loads across in total 30 blocks, to examine how decreased vigilance affects behavioral performance, and is reflected in changes in frontal theta activity and also posterior alpha.
Supervisors: prof.dr. Frank Van der Velde
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 modeled with computer modeling, such as Deep Learning or other techniques.
This is a general topic that can be specified into a 25EC or a 35EC thesis project.
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