See Master thesis

Cognitive Psychology

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MASTER THESIS

  • MCP1 - ON THE POSSIBILITY OF OPTIMIZING THE BEHAVIORAL EFFECTS OF TDCS BY CONSIDERING FUNCTIONAL PROPERTIES OF THE BRAIN (IFADO DORTMUND)

    SUPERVISORS: PROF. DR. WILLEM VERWEY (UT), DR. MOHSEN MOSAYEBI SAMANI (IFADO)

    35EC

    Traditionally, transcranial Direct Current Stimulation involves two relatively large electrodes placed on the scalp surface. While this approach has several advantages, including simplicity, lower time/cost of the experiment, this might have also some limitations. Indeed, the main motivation of this approach is to manipulate the excitability of cortical area under the stimulation electrodes. However, recent studies showed that this conventional tDCS montage influences a large area under, but also between, two electrodes, results in a low focality of stimulation over the targeted cortical region, and is also affecting distant brain areas in an unspecific/uncontrolled manner [1]. This might probably be a source of limited efficacy of the technique. In addition, it is well known that brain regions do not operate in isolation, but communicate with other regions, in distributed brain networks [2]. As an example, functional magnetic resonance imaging studies have shown the involvement of several brain regions during performance of a simple sequence learning reaction time task (SRTT), including the dorsolateral prefrontal cortex, ventrolateral prefrontal, premotor, anterior cingulate, and dorsal and inferior parietal cortices [3]. Therefore, stimulation of one cortical area may influence and be impacted on by other regions of this network. This suggests that it might be advantageous to manipulate multiple cortical target areas to further control the current distribution of tDCS, but also improve the efficacy of the intervention.

    Recent experimental studies have shown that network-targeted tDCS enhances the neurophysiological effects of stimulation beyond conventional stimulation targeting a single brain region [4]. However, it is not yet clear whether these promising results extend to behavioral effects of the intervention. Therefore, in this project we aim to test if multi-target tDCS can enhance the behavioral efficacy of conventional tDCS. To address this, twenty-four healthy young participants will take part in three randomized tDCS sessions (with one week interval between each session to avoid carry over effects) including 1) 1mA conventional M1 anodal tDCS, 2) multi-target tDCS, and 3) sham stimulation. The results (for the SRTT task outcome including reaction time, error rates and variability) will then be compared between conventional and multi-target tDCS. Expected Duration (including data acquisition, analysis, and writing up): about 5 months.  

    The experiment will be performed at the IfADo in Dortmund (www.ifado.de). Students are welcome to contact the following email for more details: mosayebi@ifado.de.

    References

    [1]         Makarov SN, Noetscher GM, Nummenmaa A. Brain and Human Body Modeling 2020. Springer; 2020.

    [2]         Sporns O, Chialvo DR, Kaiser M, Hilgetag CC. Organization, development and function of complex brain networks. Trends in cognitive sciences 2004;8(9):418-25.

    [3]         Schendan HE, Searl MM, Melrose RJ, Stern CE. An FMRI study of the role of the medial temporal lobe in implicit and explicit sequence learning. Neuron 2003;37(6):1013-25.

    [4]         Fischer DB, Fried PJ, Ruffini G, Ripolles O, Salvador R, Banus J, et al. Multifocal tDCS targeting the resting state motor network increases cortical excitability beyond traditional tDCS targeting unilateral motor cortex. NeuroImage 2017;157:34-44.

  • MCP2 - INVESTIGATING THE INFLUENCE OF TASK RELEVANCE ON COGNITIVE ENGAGEMENT BY MEANS OF EEG (IFADO DORTMUND)

    SUPERVISORS: PROF. DR. WILLEM VERWEY (UT), NATHALIE LIEGEL , MSc (IFADO) & DR. STEFAN ARNAU (IFADO)

    35EC

    Human performance depends to a large extent on how the difficulty of a given task compares to the skill-level of the performing individual. A sufficiently high skill level, however, is not enough to ensure an adequate performance. It is also crucial that the performer allocates sufficient cognitive resources to the task so that the skill actually translates to performance (Shenhav et al., 2017). The phenomenon of allocating cognitive resources to a task at hand is also referred to as cognitive engagement.

    Cognitive engagement is scalable (Inzlicht et al., 2018). The degree of cognitive engagement, that is the amount of cognitive resources that are allocated to a task, depends on the motivation for task performance. Recent theoretical frameworks conceptualize the motivation for task performance as an adaptive signal indicating whether task performance is most likely beneficial to an individual or not. As the mechanisms that drive this signal, the amount of energy already invested in a given task (Boksem & Tops, 2008), as well as the opportunity costs of current behavior (Kurzban et al., 2013) have been proposed.

    In order to investigate the influence of the expected benefit of behavior and the opportunity costs, we set up an EEG experiment in which the participants perform in a dual task scenario in which scores are awarded based on the performance in both tasks. The participants are instructed to aim for an overall score as high as possible. The relative importance of the tasks for the achievable score is manipulated on a trial by trial basis and the participants are explicitly informed about the current importance of each task. Behavioral data as well as electrophysiological parameters of task engagement will be analyzed in order to investigate the distribution of cognitive resources across the tasks.

    The experiment will be carried out at the IfADo in Dortmund, Germany (www.ifado.de), and will be supervised by Nathalie Liegel and Stefan Arnau. Please contact Nathalie Liegel (liegel@ifado.de) or Stefan Arnau (arnau@ifado.de) for further information.

    References:

    Boksem, M. A. S., & Tops, M. (2008). Mental fatigue: Costs and benefits. Brain Research Reviews, 59(1), 125–139. https://doi.org/10.1016/j.brainresrev.2008.07.001

    Inzlicht, M., Shenhav, A., & Olivola, C. Y. (2018). The Effort Paradox: Effort Is Both Costly and Valued. Trends in Cognitive Sciences, 22(4), 337–349. https://doi.org/10.1016/j.tics.2018.01.007

    Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Sciences, 36(06), 661– 679. https://doi.org/10.1017/s0140525x12003196

    Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017). Toward a Rational and Mechanistic Account of Mental Effort. Annual Review of Neuroscience, 40(1), 99–124. https://doi.org/10.1146/annurev-neuro-072116-031526

  • MCP3 - ENHANCING MOTOR LEARNING WITH MEDITATION AND INDIVIDUALIZED TRAINING

    SUPERVISORS: DR. RUSSELL CHAN, PROF. DR. WILLEM VERWEY

    35EC

    Motor learning is an important activity that underlies most physical tasks such as driving to playing music.  Learning motor skills quickly and accurately is therefore an asset to enhance these activities.  Despite this, little is known about how it can be enhanced from the cognitive domain.  Recently, there has been increased importance for cognitive enhancement activities such as meditation to further increase cognitive control (Chan et al., 2020; Hommel & Colzato, 2017).  The goal of the project is therefore to investigate if a particular kind of meditation known as open-monitoring meditation can enable faster learning in the Discrete Sequence Production task (DSP) (Verwey, Shea, & Wright, 2015).  This is coupled together with modifications to sequence scheduling could induce faster learning. As a masters student, you will measure motor learning performance such as reaction time and memory recall in a keyboard-based version of the DSP task and have the opportunity to learn more about meditation as a cognitive practice.  Analysis will be performed using mixed-effects models in R/Python to understand changes pre-post program changes in a randomized controlled fashion.

    Requirement: 1 Masters student with some understanding of R and/or Python for analysis.

    Predevelopment and pilot from April/May 2021. Aim for data collection start in June/July 2021 onwards.

    Primary supervisor: Dr. Russell Chan

    Secondary supervisor: Prof. Willem Verwey

    Readings:

    Chan, R. W., Alday, P. M., Zou-Williams, L., Lushington, K., Schlesewsky, M., Bornkessel-Schlesewsky, I., & Immink, M. A. (2020). Focused-attention meditation increases cognitive control during motor sequence performance: Evidence from the N2 cortical evoked potential. Behav Brain Res, 384, 112536. doi:https://doi.org/10.1016/j.bbr.2020.112536

    Hommel, B., & Colzato, L. S. (2017). Meditation and Metacontrol. Journal of Cognitive Enhancement, 1(2), 115-121. doi:10.1007/s41465-017-0017-4

    Verwey, W. B., Shea, C. H., & Wright, D. L. (2015). A cognitive framework for explaining serial processing and sequence execution strategies. Psychon Bull Rev, 22(1), 54-77. doi:10.3758/s13423-014-0773-4

  • MCP4 - INTERNAL AND EXTERNAL SPATIAL ATTENTIONAL EXAMINED WITH LATERALIZED EEG POWER SPECTRA

    SUPERVISORS: DR. ROB VAN DER LUBBE, DR. SIMONE BORSCI

    35EC

    Data have to be gathered.

    Several authors argued that retrieval of an item from visual short term memory (internal spatial attention) and focusing attention on an externally presented item (external spatial attention) are similar. In a recent EEG study (Van der Lubbe et al., 2014) we presented four-stimulus arrays and observed increased power in the alpha and theta bands at ipsilateral sites above occipital cortex with precues and with postcues appearing 3,000 ms after array offset. These findings indeed support the idea of a common underlying mechanism. Nevertheless, this support may crucially depend on the time interval between the stimulus array and the postcue, and also on the specific strategy employed. In the planned research project we want to examine whether participants shift to a more abstract non-spatial type of representation in the case of longer time intervals. Thus, goal of the project is to determine the boundary conditions for overlapping mechanisms by systematically varying the array-postcue time interval.

  • MCP5 - ON THE ORIGIN OF FRONTAL THETA WHILE MENTALLY SIMULATING A SEQUENCE OF FINGER MOVEMENTS

    SUPERVISOR: DR. ROB VAN DER LUBBE

    35EC

    Data are already present.

    In a recent study, it was demonstrated that during the mental simulation (or motor imagery) of a sequence of finger movements in a GoNoGo version of the discrete sequence production task an increase of frontal theta power was observed. In several other tasks, frontal theta has been related to mental effort, and has been ascribed to the dorsolateral prefrontal cortex (DLPFC). An alternative possibility is that the increased activity originates from the pre-supplementary motor area, which may then be better interpreted as reflecting reactive inhibition. By performing beam-former source analyses with specialized EEG software, the likely source of the increased activity may be determined. The outcome will facilitate  our understanding of the processes involved during motor imagery, and may provide more support for the motor-cognitive model of motor imagery.

  • MCP6 – CONCEPTUAL LEARNING

    SUPERVISOR: PROF. DR. FRANK VAN DER VELDE

    25/35EC

    Abstract

    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.

    Literature:

    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