EFFECTS OF A FIXED‐TIME SCHEDULE ON ABERRANT AND ADAPTIVE BEHAVIOR

Fixed‐time (FT) schedules of reinforcement have been used to decrease destructive behavior. However, the effects of FT schedules on acquisition and maintenance of appropriate behavior remain unclear. In this study, we present a case in which an FT schedule produced an increase in adaptive behavior and resulted in a significant decrease in destructive behavior.     

多面体的一面:第二语言学习的社会化过程

第二语言学习过程是社会互动情景中的言语活动,"热"的社会化过程是第二语言学习过程的有机组成部分;形成第二语言情景下的文化心理参照系认知能力和文化适应能力是第二语言的言语能力重要内容;第二语言学习过程中的言语变异现象部分源于学习者的社会心理结构特征。     

具有认知诊断功能的计算机化自适应测验的研究与实现

构造具有认知诊断功能的计算机化自适应测验（Computerized Adaptive Testing,CAT）,关键在于设计不同于传统CAT的选题策略。本文采用先认知诊断后估计能力的方法,在诊断阶段用状态转换图描述特定认知领域中所有知识状态及这些状态之间的联系,以图的深度优先算法为基础设计选题策略;而在能力估计精细化阶段,每个被试所测项目,不仅与其能力估计值相匹配,且只与其所掌握的属性相关。本文采用蒙特卡罗模拟针对三种不同的属性结构进行试验,结果良好     

The perfectionism of Nussbaum's adaptive preferences

Although the problem of adaptiveness plays an important motivating role in her work on human capabilities, Martha Nussbaum never gives a clear account of the controversial concept of adaptive preferences on which she relies. In this paper, I aim both to reconstruct the most plausible account of the concept that may be attributed to Nussbaum and to provide a critical appraisal of that account. Although her broader work on the capabilities approach moves progressively towards political liberalism as time passes, I aim to show that her account of adaptive preferences continues to maintain her earlier commitment to perfectionism about the good. I then distinguish between two obligatory kinds of respect for persons, which I call, respectively, primary and secondary recognition respect. This distinction allows us to see that her perfectionist account of adaptive preferences allows her to show persons primary but not secondary recognition respect. Ultimately, I claim that an acceptable account of adaptive preferences must succeed in showing persons both types of respect. I conclude with some preliminary remarks on what such an account might look like.     

A Systematic Review and Psychometric Evaluation of Adaptive Behavior Scales and Recommendations for Practice

Adaptive behavior scales are vital in assessing children and adolescents who experience a range of disabling conditions in school settings. This article presents the results of an evaluation of the design characteristics, norming, scale characteristics, reliability and validity evidence, and bias identification studies supporting 14 norm-referenced, informant-based interviews and rating scales designed to measure adaptive behaviors. To derive these results, the manuals for each of these scales were reviewed using a standardized coding procedure, and information about each scale was double-coded by reviewers. Findings reveal that several evidence-based adaptive behavior scales are available to school psychologists. Concluding recommendations address selection and use of adaptive behavior scales as part of a comprehensive assessment, using the optimal methods of administration of adaptive behavior scales, and interpreting resultant scores that have demonstrated the highest levels of reliability and the largest body of validity evidence.     

Behavioral variability in an evolutionary theory of behavior dynamics

McDowell’s evolutionary theory of behavior dynamics (McDowell, 2004) instantiates populations of behaviors (abstractly represented by integers) that evolve under the selection pressure of the environment in the form of positive reinforcement. Each generation gives rise to the next via low‐level Darwinian processes of selection, recombination, and mutation. The emergent patterns can be analyzed and compared to those produced by biological organisms. The purpose of this project was to explore the effects of high mutation rates on behavioral variability in environments that arranged different reinforcer rates and magnitudes. Behavioral variability increased with the rate of mutation. High reinforcer rates and magnitudes reduced these effects; low reinforcer rates and magnitudes augmented them. These results are in agreement with live‐organism research on behavioral variability. Various combinations of mutation rates, reinforcer rates, and reinforcer magnitudes produced similar high‐level outcomes (equifinality). These findings suggest that the independent variables that describe an experimental condition interact; that is, they do not influence behavior independently. These conclusions have implications for the interpretation of high levels of variability, mathematical undermatching, and the matching theory. The last part of the discussion centers on a potential biological counterpart for the rate of mutation, namely spontaneous fluctuations in the brain's default mode network.     

On initial item selection in cognitive diagnostic computerized adaptive testing

There has recently been much interest in computerized adaptive testing (CAT) for cognitive diagnosis. While there exist various item selection criteria and different asymptotically optimal designs, these are mostly constructed based on the asymptotic theory assuming the test length goes to infinity. In practice, with limited test lengths, the desired asymptotic optimality may not always apply, and there are few studies in the literature concerning the optimal design of finite items. Related questions, such as how many items we need in order to be able to identify the attribute pattern of an examinee and what types of initial items provide the optimal classification results, are still open. This paper aims to answer these questions by providing non‐asymptotic theory of the optimal selection of initial items in cognitive diagnostic CAT. In particular, for the optimal design, we provide necessary and sufficient conditions for the Q ‐matrix structure of the initial items. The theoretical development is suitable for a general family of cognitive diagnostic models. The results not only provide a guideline for the design of optimal item selection procedures, but also may be applied to guide item bank construction.     

Do Smarter People Employ Better Decision Strategies? The Influence of Intelligence on Adaptive Use of the Recognition Heuristic

Within the adaptive toolbox approach, it has repeatedly been shown that, on average, people tend to adapt their decision strategies to the decision context. Building upon these results, we investigated whether individuals systematically differ in their ability to successfully adapt to the situation when applying the fast‐and‐frugal recognition heuristic (RH). In decisions between recognized and unrecognized choice objects, individuals can base their choices solely on recognition, as predicted by the RH, or they can use further knowledge retrieved from memory. Since intelligence has been conceived as the ability to successfully adapt to different situations, we expected intelligence to influence the degree of adaptive use of the RH. To test this hypothesis, we first re‐analyzed data that referred to a decision domain for which RH‐use is known to perform well. As expected, individual RH‐use increased with general intelligence. Next, we designed an experiment addressing individual RH‐use in two new decision domains, one domain for which RH‐use was less effective than knowledge‐use and another domain for which both strategies were about equally effective. In addition, we tested whether fluid or crystallized intelligence best predicts adaptive use of the RH. RH‐use was found to decrease with fluid but not crystallized intelligence when RH‐use was less effective than use of further knowledge. In contrast, there was no significant association between either type of intelligence and RH‐use when none of the two strategies was optimal. We conclude that adaptive use versus non‐use of the RH is moderated by fluid intelligence. Copyright © 2017 John Wiley & Sons, Ltd.     

Computational modeling of organisational learning by self-modeling networks

Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.