In studies on avoidance learning, a warning signal is followed by an aversive outcome (US) unless the participant makes a particular response. Several theories have been developed to try to explain the mechanisms underlying avoidance learning. Surprisingly, stimulus-response (S-R) theories and cognitive theories of avoidance learning have rarely been compared experimentally. These theories diff...

Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such relations. This paper investigates the learning of CP-logic theories (CP-theories) from training data. Its first contribution is SEM-CP-logic, an algorithm that learns CP-theories by leveraging Bayesian network (BN) lear...

Annotating music poses a cognitive load on listeners and this potentially interferes with the emotions being reported. One solution is to let software agents learn to make the annotator’s task easier and more efficient. Emo is a music annotation prototype that combines inputs from both human and software agents to better study human listening. A compositional theory of musical meaning provides ...

In this paper a generative framework for creativity is presented which extends upon Wallas’s model of creativity (1926), social learning theories and creativity theories. The framework offered here presents a distillation of learning and creativity theory drawing upon salient roots of both creativity and learning theories in the 21st century. The generative framework can be instantiated for use...

Medical education practice is more often the result of tradition, ritual, culture, and history than of any easily expressed theoretical or conceptual framework. The authors explain the importance and nature of the role of theory in the design and conduct of graduate medical education. They outline three groups of theories relevant to graduate medical education: bioscience theories, learning the...

in past, the relationship between traditional learning theories and neurophysiological theories wasn’t a close relationship. recently, new findings in neurological sciences showed relatively simple neuron mechanisms for somewhat complicated learning events, for example observational learning, one of which is mirror neurons. these neurons show that how the brain registers the activity observed a...

Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that the subjective complexity of a theory is determined by the length of its representation in this language. This complexity measure helps to explain how theories are learned from relational data, and h...

Uncertain information is ubiquitous in real world domains and in the Semantic Web. Recently, the problem of representing this uncertainty in description logics has received an increasing attention. In probabilistic Description Logics, knowledge bases contain numeric parameters that are often difficult to specify for a human. Moreover, the information are incomplete and poorly structured. On the...