In the present study the effects of contextual interference on the retention and transfer performance of reflectives and impulsives on a maze task were studied. Forty-seven subjects were randomly assigned to either a high contextual interference group or to a low contextual interference group. Within the two groups subjects were further classified according to their preferred modes of respondin...

Robotics systems need to be robust and adaptable to multiple operational conditions, in order to be deployable in different application domains. Contextual knowledge can be used for achieving greater flexibility and robustness in tackling the main tasks of a robot, namely mission execution, adaptability to environmental conditions and self-assessment of performance. In this chapter, we review t...

Fictional information presents a unique challenge to the developing child. Children must learn when it is appropriate to transfer information from the fictional space to the real world and what contextual cues should be considered in this decision. The current research explores children’s causal inferences between fictional representations and reality by examining their developing sensitivity t...

For the scale of data in process keep increasing, data provenance also becomes large and constantly growing, which brings challenges to the efficiency of provenance tracking in process analysis. This paper proposes a kind of dependency view to extract a global data provenance description of the data process instance, and then defines a contextual query language based on dependency view to imple...

Expression of learned stimulus-reward associations based on context is essential for regulation of behavior to meet situational demands. Contextual regulation improves during development, although the developmental progression of relevant neural and cognitive processes is not fully specified. We therefore measured neural correlates of flexible, contextual expression of stimulus-reward associati...

This work proposes a methodology for the construction of state abstraction from a set of empirically derived models of system behav-ion from a set of empirically derived models of system behavior. The idea is to treat the agent in its environment as a dynamical system and augment its observation space with contextual cues extracted empirically as the agent exercises each element out of the set ...

Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identify...

The continuing shortage of IT professionals is a global problem and is exacerbated by the lack of women, representing nearly half the potential workforce, who are attracted to the industry. This paper contributes to the growing body of literature by examining the perspectives and experiences of both male and female IT professionals in New Zealand. Using a narrative inquiry methodology the study...

A computational model of learning in visual attention is described, and it is shown how it can integrate information over time to form a representation of the visual context. This visual context is subsequently used to guide the focus of attention in a visual search task. Computer simulations shows that the model can learn to use contextual cues when they are available. The performance using co...

Recently, the field of automatic recognition of users’ affective states has gained a great deal of attention. Automatic, implicit recognition of affective states has many applications, ranging from personalized content recommendation to automatic tutoring systems. In this work, we present some promising results of our research in classification of emotions induced by watching music videos. We s...