Real-life processes are characterized by dynamics involving time. Examples are walking, sleeping, disease progress in medical treatment, and events in a workflow. To understand complex behavior one... Show moreReal-life processes are characterized by dynamics involving time. Examples are walking, sleeping, disease progress in medical treatment, and events in a workflow. To understand complex behavior one needs expressive models, parsimonious enough to gain insight. Uncertainty is often fundamental for process characterization, e.g., because we sometimes can observe phenomena only partially. This makes probabilistic graphical models a suitable framework for process analysis. In this thesis, new probabilistic graphical models that offer the right balance between expressiveness and interpretability are proposed, inspired by the analysis of complex, real-world problems. We first investigate processes by introducing latent variables, which capture abstract notions from observable data (e.g., intelligence, health status). Such models often provide more accurate descriptions of processes. In medicine, such models can also reveal insight on patient treatment, such as predictive symptoms. The second viewpoint looks at processes by identifying time points in the data where the relationships between observable variables change. This provides an alternative characterization of process change. Finally, we try to better understand processes by identifying subgroups of data that deviate from the whole dataset, e.g., process workflows whose event dynamics differ from the general workflow. Show less
The artificial grammar learning experiments reported in this thesis pertain to the question of whether implicit learning is ineluctable and unselective or selective and accidental. The results of... Show moreThe artificial grammar learning experiments reported in this thesis pertain to the question of whether implicit learning is ineluctable and unselective or selective and accidental. The results of Chapter 2 showed that implicit learning was negatively affected by complexity, suggesting that the process does not automatically acquire any structure present in the environment. Chapter 3 provided direct evidence that implicit learning is selective. When two aspects of the structure could facilitate the participants__ task in the induction phase, only the most useful aspect was selected and learned implicitly. Chapter 4 showed that explicit learning could be more successful than implicit learning when participants were specifically instructed to look for rules of letter-order. In Chapter 5, implicit learning was demonstrated when the structure was useful to the participants__ induction task, but not when it was useless. This finding was replicated in 10 to 11-year-old children in Chapter 6. Chapter 7 showed that the negative effect of complexity could not be counterbalanced by adding a semantic reference field. These findings indicate that implicit learning is a selective process that does not occur ineluctably. Rather than being accidental, however, implicit learning seems to occur whenever the structure is useful to one__s current task. Show less
