Bachelor thesis

Cognitive Psychology


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BCP1 - NEW VIEWS ON WORKING MEMORY

SUPERVISOR:  DR. ROB VAN DER LUBBE; DR. SIMONE BORSCI

Recent ideas on working memory propose that it 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. Goal of the current project is to replicate and possibly extend earlier behavioral research (see Ma, Husian & Bays, 2014) that led to this proposal. For example, it was observed that items with increased saliency are remembered better than items with low saliency. These observations also align well with novel views,  according to which attentional selection and memory retrieval share common processes (e.g., see Van der Lubbe et al., 2014).

BCP2 - THE ROLE OF FINGERS USED WHEN EXECUTING PRACTICED MOVEMENT SEQUENCES WHILE LISTENING TO TONES

SUPERVISOR:  PROF. DR. WILLEM VERWEY

An important issue is how people develop motor automaticity. This is the capacity to execute a series of successive movements while you need little attention for executing them. It is as if the limbs know what to do. Such motor skills can be investigated with a sequential key pressing task. In the proposed study, participants will develop motor automaticity by practicing two fixed 6-element key pressing sequences.  After a practice phase, they will produce the same sequences using different fingers and hands. The research question concerns the effect of changing the fingers that are being used, and whether this is affected by a second task. In the proposed bachelor thesis project, this hypothesis will be tested in a laboratory experiment in 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 – CONCEPTUAL LEARNING

SUPERVISOR: PROF. DR. FRANK VAN DER VELDE

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. 

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