Selective directed forgetting: Eliminating output order and demand characteristics explanations

Previous research has shown that giving an instruction to forget part of a studied list of items impairs the subsequent retrieval of these items compared with those not cued to be forgotten. This selective directed forgetting (SDF) effect has been found with slightly different procedures and in adolescents and young adults. While recent research has suggested that executive control might underlie SDF, alternative explanations that rely on procedural issues still have not been investigated. Specifically, SDF might essentially reflect output interference from the items cued to be remembered, so that the earlier recalled items interfere with the later recalled items. The effect could also result from demand characteristics: Participants might withhold the to-be-forgotten items to comply with the experimenter’s implicit goals or might not be willing to engage in the effort of retrieving all studied information. The results from two experiments showed that (1) the to-be-forgotten items were less accessible and were not influenced by output interference from to-be-remembered items (Experiment 1), and (2) SDF was still present when participants were offered monetary reward for retrieving as many items as possible (Experiment 2). Hence, the findings do not provide support to explanations of SDF based on output interference and demand characteristics.

A new set-membership estimation method based on zonotopes and ellipsoids

This paper presents a new approach for guaranteed state estimation based on zonotopes and ellipsoids for linear discrete-time systems with unknown but bounded perturbations and noises. At each sample time, a predicted state set is calculated by zonotopes and an outer bounding ellipsoid of the predicted state set is calculated. State estimation is calculated directly by intersects with an outer bounding ellipsoid and strip. The precision of the state estimation will increase as output order improves by this method. Two examples have been provided to clarify the algorithm.