Correction to Ferguson and Rule.

Reports an error in the article original article "Effects of Inferential Set, Outcome Severity, and Basis for Responsibility on Children's Evaluations of Aggressive Acts" by Tamara J. Ferguson and Brendan Gail Rule (Developmental Psychology, Vol. 16. [2],141-146). On page 144 there was an error in Table 1. The mean value for the older children/foreseeable cell should be 2.39, not 3.39. (The following abstract of this article originally appeared in record 1980-07374-001). 49 boys and 58 girls in Grades 2 and 8 made moral judgments about stories describing a same-sexed child expressing aggression. Each child, under either a situation-matching or a value-maintenance set, judged 8 behaviors that represented 4 of F. Heider's (1958) responsibility levels and had mild or serious consequences. Moral judgments of older Ss were sensitive to Heider's differing levels of responsibility, whereas younger Ss' judgments were more sensitive to outcome severity. The aggression was viewed as less reprehensible by older than by younger Ss; this difference was especially pronounced for girls. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Towards a human–robot symbiotic system

Partnership between a person and a robot could be simplified if the robot were intelligent enough to understand human intentions and perform accordingly. During the last decade, we have been developing such an intelligent robot called ISAC. Originally, ISAC was designed to assist the physically disabled, but gradually became a test bed for more robust human–robot teaming (see http://eecs.vanderbilt.edu/CIS/). In this paper, we will describe a framework for human–robot interaction, a multi-agent based robot control architecture, and short- and long-term memory structures for the robot brain. Two applications will illustrate how ISAC interacts with the human.     

Explaining the segmentation effect in learning from animations: The role of pausing and temporal cueing

Segmentation of animations, that is presenting them in pieces rather than as a continuous stream of information, has been shown to have a beneficial effect on cognitive load and learning for novices. Two different explanations of this segmentation effect have been proposed. Firstly, pauses are usually inserted between the segments, which may give learners extra time to perform necessary cognitive processes. Secondly, because segmentation divides animations into meaningful pieces, it provides a form of temporal cueing which may support learners in perceiving the underlying structure of the process or procedure depicted in the animation. This study investigates which of these explanations is the most plausible. Secondary education students (N = 161) studied animations on probability calculation, after having been randomly assigned to one of four conditions: non-segmented animations, animations segmented by pauses only, animations segmented by temporarily darkening the screen only, and animations segmented by both pauses and temporarily darkening the screen. The results suggest that both pauses and cues play a role in the segmentation effect, but in a different way.     

Flow through packed bed reactors: 1. Single-phase flow

Single-phase pressure drop was studied in a region of flow rates that is of particular interest to trickle bed reactors . Bed packings were made of uniformly sized spherical and non-spherical particles (cylinders, rings, trilobes, and quadralobes). Particles were packed by means of two methods: random close or dense packing (RCP) and random loose packing (RLP) obtaining bed porosities in the range of 0.37–0.52. It is shown that wall effects on pressure drop are negligible as long as the column-to-particle diameter ratio is above 10. Furthermore, the capillary model approach such as the Ergun equation is proven to be a sufficient approximation for typical values of bed porosities encountered in packed bed reactors. However, it is demonstrated that the original Ergun equation is only able to accurately predict the pressure drop of single-phase flow over spherical particles, whereas it systematically under predicts the pressure drop of single-phase flow over non-spherical particles. Special features of differently shaped non-spherical particles have been taken into account through phenomenological and empirical analyses in order to correct/upgrade the original Ergun equation. With the proposed upgraded Ergun equation one is able to predict single-phase pressure drop in a packed bed of arbitrary shaped particles to within ±10% on average. This approach has been shown to be far superior to any other available at this time.     

Effects of identical example–problem and problem–example pairs on learning

Examples are often an integral part of online learning environments directed at the acquisition of problem-solving skills. An unresolved issue, however, is when examples should be provided to learners. Prior research has suggested that example–problem pairs are more effective than problem–example pairs for novice learners. However, in those studies, the problem–example pairs condition may have been hindered by the fact that the examples and problems were not identical within and across pairs. The present experiment therefore employed a between-subjects design with two conditions to compare the effects of learning to solve one particular problem from studying/practicing either in an example–problem–example–problem sequence (EP condition; n = 16) or in a problem–example–problem–example sequence (PE condition; n = 16). Results show that participants in the EP condition outperformed their counterparts in the PE condition during the learning phase, but that this difference had disappeared on the test tasks after participants in the PE group had also studied the example a second time.     

Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives

When describing robot motion with dynamic movement primitives (DMPs), goal (trajectory endpoint), shape and temporal scaling parameters are used. In reinforcement learning with DMPs, usually goals and temporal scaling parameters are predefined and only the weights for shaping a DMP are learned. Many tasks, however, exist where the best goal position is not a priori known, requiring to learn it. Thus, here we specifically address the question of how to simultaneously combine goal and shape parameter learning. This is a difficult problem because learning of both parameters could easily interfere in a destructive way. We apply value function approximation techniques for goal learning and direct policy search methods for shape learning. Specifically, we use “policy improvement with path integrals” and “natural actor critic” for the policy search. We solve a learning-to-pour-liquid task in simulations as well as using a Pa10 robot arm. Results for learning from scratch, learning initialized by human demonstration, as well as for modifying the tool for the learned DMPs are presented. We observe that the combination of goal and shape learning is stable and robust within large parameter regimes. Learning converges quickly even in the presence of disturbances, which makes this combined method suitable for robotic applications.     

Distributions of the potential and electric field of an electrode elliptic array used in tumor electrotherapy: Analytical and numerical solutions

To estimate the potential and electric field generated by any electrode array is very useful in effective tumor destruction. At present, an electrode array that takes into account the ellipsoidal geometry of the solid tumors has not been proposed. We present both analytical and numerical solutions for the potential and electric field in a solid tumor established by an electrode array with elliptic shape which may be used in vitro, in vivo and in clinical studies for cancer treatment with electrotherapy. These analytical and numerical solutions are obtained using multipole expansion and the finite difference method. Distributions of potential and electric field magnitudes are computed in function of the eccentricity of an elliptical array and compared with those obtained with a circular array of electrode. Maximum difference and Root Means Square Error are used to compare the distributions of the potential and electric field in leading-order and first-order correction and between the analytical and numerical solutions. The results show a good agreement between these distributions in both orders and the analytical and numerical solutions. It was concluded that the mathematical approach presented in this study is a tool for a rapid design of electrode elliptical arrays in order to induce the maximum destruction of the tumor. Moreover, it is shown that, for all values of eccentricity, there is a good correspondence between the distributions of the potential and the electric field for leading-order and first-order correction and for both the analytical and numerical solutions.