Relational reasoning is central in human cognition. Numerous computational models address the component processes of relational reasoning, however these models require the modeler to hand-code the vocabulary of relations on which the model operates. The acquisition of relational concepts remains poorly understood. We present a theory of relation discovery instantiated in a symbolic-connectionis...

Episodic memory retrieval most often recruits multiple separate processes that are thought to involve different temporal regions. Previous studies suggest dissociable regions in the left lateral parietal cortex that are associated with the retrieval processes. Moreover, studies using resting-state functional connectivity (RSFC) have provided evidence for the temporo-parietal memory networks tha...

Conceptual modelling involves many higher order cognitive processes, such as relational reasoning and abstraction, which are based on integration and maintenance of information. Evidence from cognitive psychology suggests that these processes are subject to individual differences which cannot be explained by training and experience alone. In this review, we study how the cognitive processes tha...

We present in this paper the extraction of relational patterns on a hospital discharge database to monitor for quality of care. We delineate the requirements set for the extraction of meaningful medical practices from the data and describe how relational patterns address these requirements. Patterns representing practices regarding hospitalization instances are comprehensive and easy to interpr...

Several promising variants of hidden Markov models (HMMs) have recently been developed to efficiently deal with large state and observation spaces and relational structure. Many application domains, however, have an apriori componential structure such as parts in musical scores. In this case, exact inference within relational HMMs still grows exponentially in the number of components. In this p...

This thesis addresses two challenges in extracting patterns from social data generated by modern sensor systems and electronic mechanisms. First, that such data often combine spatial, temporal, and relational evidence, requiring models that properly utilize the regularities of each domain. Second, that data from open-ended systems often contain a mixture between entities and relationships that ...

Agents that learn and act in real-world environments have to cope with both complex state descriptions and non-deterministic transition behavior of the world. Standard statistical relational learning techniques can capture this complexity, but are often inefficient. We present a simple probabilistic model for such environments based on CP-Logic. Efficiency is maintained by restriction to a full...

Traditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known in advance, and this is impractical ...

The purpose of the present investigation was to examine the association between physiological reactivity to peer stressors and physical and relational aggression. Potential moderation by actual experiences of peer maltreatment (i.e., physical and relational victimization) and gender were also explored. One hundred ninety-six children (M = 10.11 years, SD = 0.64) participated in a laboratory str...

Several promising variants of hidden Markov models (HMMs) have recently been developed to efficiently deal with large state and observation spaces and relational structure. Many application domains, however, have an apriori componential structure such as parts in musical scores. In this case, exact inference within relational HMMs still grows exponentially in the number of components. In this p...