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Open projects and theses

On this webpage we present topics that can lead to BSc/MSc projects or theses in Computer Science or Cognitive Science. As our research focus is highly interdisciplinary we can offer a bandwith of projects that may have an empirical/experimental or more theoretic approach. In any case please contact Marco Ragni directly, if you are interested.

Topic 1: Probabilities and Ranks in Human Non-Monotonic Reasoning

Target group: MSc (Computer Science)

Bayes-in-the-head theories of cognition assume that humans calculate or approximate Bayesian statistics for everyday reasoning. Other approaches, however, assume mental representations with a qualitative ordering on models. From a formal perspective, it is an open question which of these approaches predicts human reasoning behavior the best. The goal is to compare, implement and evaluate the theories against empirical data.

Requirements:

  • Programming in Python
  • Interest in formal logics

Topic 2: Predictive models for individual human reasoning

Target group: BSc/MSc (Computer Science / Cognitive Science) (several theses)

For given information what inferences do humans draw in general? Going one step further: Is it possible to predict for some background knowledge like working memory size and inferences drawn for some problem instances what the subject draws for an inference for a similar problem? What about a not so similar problem?

Requirements:

  • Knowledge of Python
  • Interest in predictive modeling

 

Topic 3: Intentional forgetting of previously learned information

Target group: BSc/MSc (Computer Science / Cognitive Science)

Although forgetting is often associated with frustration in our everyday lives, forgetting is a highly adaptive mechanisms that helps us to deal with the great amount of information we are confronted with on a daily basis. In fact, people are even able to intentionally forget previously learned information. Yet, the cognitive underpinnings of intentional forgetting are not yet well understood. We are currently running several experiments to gain additional insights on how people are able to intentionally forget different kinds of information (e.g., semantic memory or motor representations). Furthermore, we are developing a cognitive model that can account for such mechanisms. At the same time, we aim to use data-driven approaches to obtain upper bounds of predictive performance for these models.

Requirements:

  • Varies: Knowledge of R/ Python /...
  • Basic experience in data analysis (e.g., clustering)

Topic 4: Genetic programming for automatic generation of heuristics

Target group: BSc/MSc (Computer Science / Cognitive Science)

Decisions human make can depend on heuristics. In this thesis you will start with analyzing existing heuristics and then try to automatically generate heuristics that can fit better human decisions than existing ones.

Requirements:

  • Knowledge of Python
  • Some knowledge about AI foundations

 

Topic 5: Formalization and Evaluation of Cognitive Theories

Target group: MSc/BSc (Computer Science / Cognitive Science)

How can we compare different cognitive theories? Mathematical psychology offers interesting approaches to evaluate theories using AIC, BIC, DIC etc. Multinomial process trees offer an excellent possibility to formalize cognitive theories (especially for research on recognition memory. Their expressiveness, however, and their application on human reasoning still requires more research. 

Requirements:

  • Knowledge of R
  • Mathematical knowledge (statistics)

Topic 6: Modelling Reasoning in the Neural Engineering Framework

Target group: BSc/MSc (Computer Science/Cognitive Science)

This project is about the modelling of reasoning processes in the Neural Engineering Framework (NEF). The NEF is a cognitive architecture framework which is based on physiologically plausible neural clusters. The goal is to implement a model which is able to solve reasoning and tasks and afterwards to evaluate the results and the model. In the end, we would like to find out how algorithms have to be implemented in neurons in order to exhibit human-like behavior.

Requirements:

  • Programming in Python
  • Some knowledge about cognitive neuroscience and/or interest in learning about it

Topic 7: Modeling common sense reasoning

Target group: MSc (Computer Science / Cognitive Science)

Problems such as the Winnograd challenge or PDP-Problems are considered very hard AI problems. Such problems are typically very easy to solve for human reasoners but are very difficult for current Machine Learning approaches as they require some form of semantic understanding. 

Requirements:

  • Knowledge of Python
  • Some knowledge about logic/machine learning

Topic 8: Predictor Analysis in Syllogistic Reasoning

Target group: BSc/MSc (Computer Science / Cognitive Science)

The syllogistic reasoning domain stretches over 64 distinct problems. As a result, experiments testing participants on all instances take a significant amount of time resulting in a potential loss of data quality due to fatigue, boredom, or a general loss of concentration. This project investigates the impact/influence of specific syllogisms on predictive cognitive modeling with the goal to propose a reduced set of problems balancing data quality and richness.

Requirements:

  • Knowledge of Python
  • Basic experience in data analysis (e.g., clustering)