TOKEN REINFORCEMENT DURING THE INSTATEMENT AND SHAPING OF FLUENCY IN THE TREATMENT OF STUTTERING

The value of token reinforcement in the instatement and shaping of fluency was examined in an intensive treatment program for adult stutterers. Experiment 1 examined the effect of removing the tangible back-up reinforcers for the token system and found that clients' performance in the program was equally good with or without these back-up reinforcers, suggesting that a strict token economy may not be crucial to rapid progress through treatment. Experiment 2 compared contingent and noncontingent token reinforcement, while controlling for some variables that may have confounded the results of earlier research, and found no difference in clients' performance. Experiment 3 examined the effect of the entire removal of token reinforcement. Performance was found to be no worse under a “no tokens” system than under a system of tokens with back-up reinforcers. It is argued that in a highly structured treatment program where many other reinforcers are operating, token reinforcement may be largely redundant. Clinical and theoretical implications of the findings are discussed.     

离散交叠作业中反应选择与反应组织加工关系的研究

实验结果支持反应选择瓶颈理论,并证实反应组织是在反应选择阶段完成的多任务之一。因此,反应组织不具备瓶颈性质。     

汉语口语词汇产生中的多重音韵激活：单词翻译任务的ERP研究

口语词汇产生过程中, 非目标词是否会产生音韵激活是独立两阶段模型和交互激活模型的争论焦点之一。研究运用事件相关电位技术, 考察了被试在翻译命名任务中是否受到背景图片音韵或语义干扰词的影响。行为反应时中未发现显著的音韵效应, 而语义效应显著, 表明非目标词不会产生音韵激活。事件相关电位的结果显示在目标单词呈现后的400~600 ms时间窗口中出现了显著的语义效应, 在600~700 ms时间窗口内出现了边缘显著的语义效应和音韵效应, 均表现为相关条件比无关条件波幅更正。上述结果表明在将英语翻译成汉语的过程中, 尽管在脑电上呈现出可能存在微弱的多重音韵激活, 但行为结果并不会显示出非目标项的音韵激活。研究结果支持了汉语口语词汇产生遵循独立两阶段模式的观点。     

Adaptive sampling for active learning with genetic programming

Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling for GP gives to the algorithm a new sample periodically, often each generation, without considering the state of the evolution. In so doing, individuals do not have enough time to extract the hidden knowledge. An alternative approach is to use some information about the learning state to adapt the periodicity of the training data change. In this work, we propose an adaptive sampling strategy for classification tasks based on the state of solved fitness cases throughout learning. It is a flexible approach that could be applied with any dynamic sampling. We implemented some sampling algorithms extended with dynamic and adaptive controlling re-sampling frequency. We experimented them to solve the KDD intrusion detection and the Adult incomes prediction problems with GP. The experimental study demonstrates how the sampling frequency control preserves the power of dynamic sampling with possible improvements in learning time and quality. We also demonstrate that adaptive sampling can be an alternative to multi-level sampling. This work opens many new relevant extension paths.     

Regularized ELM bagging model for Tropical Cyclone Tracks prediction in South China Sea

This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) with 24 h lead time. The model proposed in this paper is a regularized extreme learning machine (ELM) ensemble using bagging. The method which turns the original problem into quadratic programming (QP) problem is proposed in this paper to solve lasso and elastic net problem in ELM. The forecast error of TCTs data set is the distance between real position and forecast position. Compared with the stepwise regression method widely used in TCTs, 8.26 km accuracy improvement is obtained by our model based on the dataset with 70/1680 testing/training records. By contrast, the improvement using this model is 16.49 km based on a smaller dataset with 30/720 testing/training records. Results show that the regularized ELM bagging has a general better generalization capacity on TCTs data set.     

Teaching help‐seeking when lost to individuals with autism spectrum disorder

Deficits in safety skills and communication deficits place individuals with autism spectrum disorder (ASD) at an increased risk of danger. We used a multiple‐probe across‐participants design to evaluate the effects of video modeling and programming common stimuli to teach low‐ and high‐tech help‐seeking responses to children with ASD when lost. Participants acquired answering or making a FaceTime^® call and exchanging an identification card in contrived and natural settings. Responses generalized to novel community settings and maintained during a one‐ and two‐week follow‐up. Social validity measures showed that the procedures and outcomes of the study were acceptable to indirect and direct consumers, and immediate and extended community members. Implications are that children with ASD can effectively be taught both low‐ and high‐tech help‐seeking responses when lost.     

A Recourse Stochastic Goal Programming Approach for the Multi‐objective Stochastic Vehicle Routing Problem

This paper addresses a multi‐objective stochastic vehicle routing problem where several conflicting objectives such as the travel time, the number of vehicles in use and the probability of an accident are simultaneously minimized. We suppose that demands and travel durations are of a stochastic nature. In order to build a certainty equivalent program to the multi‐objective stochastic vehicle routing problem, we propose a solution strategy based on a recourse approach, a chance‐constrained approach and a goal‐programming approach. The resulting certainty equivalent program is solved to optimality using CPLEX. Copyright © 2016 John Wiley & Sons, Ltd.     

An interactive multiobjective programming approach to combinatorial data analysis

Combinatorial optimization problems in the social and behavioral sciences are frequently associated with a variety of alternative objective criteria. Multiobjective programming is an operations research methodology that enables the quantitative analyst to investigate tradeoffs among relevant objective criteria. In this paper, we describe an interactive procedure for multiobjective asymmetric unidimensional seriation problems. This procedure uses a dynamic-programming algorithm to partially generate the efficient set of sequences for small to medium-sized problems, and a multioperation heuristic to estimate the efficient set for larger problems. The interactive multiobjective procedure is applied to an empirical data set from the psychometric literature. We conclude with a discussion of other potential areas of application in combinatorial data analysis.Stephanie Stahl is a freelance writer and editor. She can be reached via e-mail at s-stahl@worldnet.att.net.     

Executable specifications for hypothesis-based reasoning with Prolog and Constraint Handling Rules

Constraint Handling Rules (CHR) is an extension to Prolog which opens up a spectrum of hypothesis-based reasoning in logic programs without additional interpretation overhead. Abduction with integrity constraints is one example of hypothesis-based reasoning which can be implemented directly in Prolog and CHR with a straightforward use of available and efficiently implemented facilities.The present paper clarifies the semantic foundations for this way of doing abduction in CHR and Prolog as well as other examples of hypothesis-based reasoning that is possible, including assumptive logic programming, hypotheses with priority and scope, and nonmonotonic reasoning.Examples are presented as executable code so the paper may also serve the additional purpose of a practical guide for developing such programs, and it is demonstrated that the approach provides a seamless integration with existing constraint solvers.     

Using aspiration levels in an interior primal-dual multiobjective linear programming algorithm

We introduce in this paper a new multiple-objective linear programming (MOLP) algorithm. The algorithm is based on the single-objective path-following primal—dual linear programming algorithm and combines it with aspiration levels and the use of achievement scalarizing functions. The resulting algorithm falls in the class of interactive MOLP algorithms, as it requires interaction with the decision maker (DM) during the iterative process to obtain statements of aspirations for levels of objectives of the MOLP problem. The interior point algorithm is then used to trace a path of interates from a current (interior) solution and approach as closely as desired a non-dominated solution corresponding to the optimum of the achievement scalarizing function. The timing of the interaction with the DM is dependent on the progress of the interior algorithm. It can take place every few, pre-specified, iterations or after the duality gap achieved for the stated aspirations has fallen below a certain threshold. It is expected that an interior algorithm will speed up the overall process of searching and finding the most preferred MOLP solution—especially in large-scale problems—by avoiding the need for numerous pivot operations and their corresponding interactive sessions inherent in simplex-based algorithms.