Psychological Measurement in the Information Age: Machine-Learned Computational Models

Exploring Variable Ranges in Machine Learned Models

Experiments, simulations and machine learned models can all be used as ways of classifying a set of data into a certain class. When the dimensionality of the input data starts getting larger, classification results like this become difficult for humans to comprehend; it’s no longer possible to easily visualize how the various parameters contribute to the given answer. A novel approach for explo...

Answer Set Programming for Computational Psychological Models

Our work explores the use of Answer Set Programming (ASP) (Gelfond & Lifschitz, 1991; Marek & Truszczynski, 1999) to formalize and reason about psychological knowledge. Whereas some psychological models have a clear quantitative nature, which allows modeling e.g. with neural networks or Bayesian networks, other models have a more logical or qualitative nature and are not suitable for formalizat...

Computational Models for Information Reuse

Techniques that optimize computations by reusing partial results have a long tradition in computer science. Seen from the point of view of sequential computations, all these techniques share the common execution strategy of storing partial results in a centralized data structure, while they differ as to how the results are computed, e.g. in a data-driven or constraint-driven fashion. Concurrent...

Psychological Function in Computational Models of Neural Networks

Computational models in the age of large datasets.

Technological advances in experimental neuroscience are generating vast quantities of data, from the dynamics of single molecules to the structure and activity patterns of large networks of neurons. How do we make sense of these voluminous, complex, disparate and often incomplete data? How do we find general principles in the morass of detail? Computational models are invaluable and necessary i...