In list-method directed forgetting, people are cued to forget a previously studied item list and to learn a new list instead. Such cuing typically leads to forgetting of the first list and to memory enhancement of the second, referred to as list 1 forgetting and list 2 enhancement. In the present study, two experiments are reported that examined influences of items' serial learning position in ...

We model the forgetting of propositional variables in a modal logical context where agents become ignorant and are aware of each others’ or their own resulting ignorance. The resulting logic is sound and complete. It can be compared to variable-forgetting as abstraction from information, wherein agents become unaware of certain variables: by employing elementary results for bisimulation, it fol...

Often autonomous mobile robots operate in environment for which prior maps are incomplete or inaccurate. They require the safe execution for a collision free motion to a goal position. This paper addresses a complete navigation method for a mobile robot that moves in unknown environment. Thus, a novel method called DVFF combining the Virtual Force Field (VFF) obstacle avoidance approach and glo...

Often autonomous mobile robots operate in environment for which prior maps are incomplete or inaccurate. They require the safe execution for a collision free motion to a goal position. This paper addresses a complete navigation method for a mobile robot that moves in unknown environment. Thus, a novel method called DVFF combining the Virtual Force Field (VFF) obstacle avoidance approach and glo...

We investigated influences of item generation and emotional valence on retrieval-induced forgetting. Drawing on postulates of the three-factor theory of generation effects, generation tasks differentially affecting the processing of inter-item relations were applied. Whereas retrieval-induced forgetting of freely generated items was moderated by the emotional valence as well as retrieval-induce...

Radial basis function (RBF) models are often adapted on-line using exponential forgetting with a single forgetting factor. However, this technique applies forgetting uniformly to the past data in the entire operating space and is not appropriate for non-linear systems where dynamics are different in different operating regions. This paper describes a new development in local forgetting for onli...

Even when provided with feedback after every movement, adaptation levels off before biases are completely removed. Incomplete adaptation has recently been attributed to forgetting: the adaptation is already partially forgotten by the time the next movement is made. Here we test whether this idea is correct. If so, the final level of adaptation is determined by a balance between learning and for...