A Process Model for Prevention Program Research

Interventions attempted before the onset of a counseling problem are primary prevention efforts. A process model for prevention program research is the focus of this paper. The process model features four essential steps: assessment, goal setting, strategy implementation, and evaluation. The likely problems encountered by a prevention researcher at each step of the model are discussed.     

An anthropomorphic method for number sequence problems

Number sequence problems appear frequently in IQ tests, where the task is to extrapolate finite sequences of integers. This paper presents a computational method for solving number sequence problems appearing in IQ tests. The assumption that these problems are solvable by humans is actively exploited to keep the computational complexity manageable. The method combines elements of artificial intelligence and cognitive psychology and is referred to as anthropomorphic because it makes use of a model of human reasoning. This model features a set of cognitive resources, a repertoire of patterns that encode integer sequences, and a notion of bounded computation for decoding patterns. The model facilitates the search for patterns matching a given integer sequence by quickly discarding many patterns on the grounds that they are too demanding to decode. The computational method was implemented as a computer program called Asolver and then tested against the programs Mathematica and Maple. On the number sequence problems of the IQ test PJP, Asolver scored above IQ 140, whereas the other programs scored below IQ 100.     

The Origin and Evolution of the Behavior Analysis Program at the University of Nevada,Reno

The origins of the Behavior Analysis program at the University of Nevada, Reno by way of a self-capitalized model through its transition to a more typical graduate program is described. Details of the original proposal to establish the program and the funding model are described. Some of the unusual features of the program executed in this way are discussed, along with problems engendered by the model. Also included is the diversification of faculty interests over time. The status of the program, now, after 25 years of operation, is presented.     

Teaching students to recognize structural similarities between statistics word problems

An important skill in mathematical problem solving is recognizing that the problem one is working on (target problem) can be solved using the same method as a problem one already knows (source problem). In a preliminary study, college students displayed a higher level of structural awareness (i.e. sorting word problems on the basis of structural features) at the end rather than the beginning of their first statistics course. In the main study, we taught college students to sort statistics word problems on the basis of structural features (i.e. whether the problem could be solved by t‐test, correlation, or chi‐square statistics) rather than surface features (i.e. the problem's cover story). These results support a structural awareness theory in which students learn to form problem schemas by abstracting the underlying structural features of a problem statement and organizing them into a generalized problem model. Copyright © 2002 John Wiley & Sons, Ltd.     

Cognitive specificity in internalizing and externalizing problems in community and clinic-referred children

Examined whether cognitive features in Beck's (1967) model of depression and his cognitive content-specificity hypothesis (Beck, Brown, Steer, Eidelson, & Riskind, 1987) are broadband specific features that distinguish internalizing problems from externalizing problems. From a community (n = 389; 196 boys, 193 girls; age M = 9.98, SD = 1.52) and clinic (n = 82; 55 boys, 27 girls; age M = 10.42, SD = 2.91) sample, 4 groups were defined on behavioral measures and then examined on cognitive measures: externalizing only (n = 33), internalizing only (n = 41), comorbid externalizing and internalizing (n = 53), and control (neither externalizing nor internalizing; n = 35). Both the internalizing only and comorbid groups reported significantly more cognitive disturbances (negative cognitive triad, cognitive processing distortions, and "depressive" and "anxious" thought content) than both of the externalizing only and control groups in both the community and clinic samples. The results were not related to either overall level of psychopathology (as reported by mothers) or social desirability in children's reporting. These findings suggest that the cognitive features in Beck's model distinguish internalizing from externalizing problems and add to a growing literature that calls for further conceptual refinement of cognitive models for understanding both narrow-band and broadband syndromes.     

代数应用题项目生成的结构分析方法

自动化项目生成是近年来兴起的测量领域, 是一种以项目认知加工理论为基础的原则性项目设计(principled item design)模式。其中, 如何在项目认知模型基础上, 通过任务结构分析的方式系统全面的鉴别和提取任务特征是一个关键环节。基于已有文献中代数应用题的命题分析法、网络语言分析法、关系-函数分析法、任务分析地图等四种结构分析方法, 研究探索了能够服务于自动化项目生成的代数应用题任务结构分析方法。该分析表明, 前三种方法分别对应于个体解题过程需要形成的三种中介表征, 即问题陈述背后的命题表征、事件时空关系的情境模型、以及变量间数量关系的问题模型, 第四种方法从过程角度分析了问题解决的认知需求。然而, 要实现项目生成的特征提取需求, 尚需对现有四种方法所揭示问题特征的心理现实性、特征提取的系统性和完备性、任务领域的适用范围、以及不同方法的整合等问题开展进一步研究。     

Borderline personality features predict alcohol use problems

This study assessed the relation between BPD features and problems associated with alcohol use 2 years later in young adults. Approximately 5,000 nonclinical young adults were screened for BPD features, and two cohorts of participants completed the laboratory phase of the study at Time 1 (total N = 421) and then again 2 years later at Time 2 (total N = 356). Measures included self-report and interview-based assessments of personality disorders, psychopathology in biological parents, Axis I psychopathology, and alcohol use problems. BPD features were found to significantly predict alcohol use problems 2 years later after controlling for parents' substance use disorders, Axis I psycho-pathology (including alcohol abuse/dependence), and non-BPD personality disorders. In addition, the BPD subscale that assesses impulsivity was found to be significantly associated with alcohol use problems once all predictors were entered into the model.     

Persönlichkeitsdiagnostik im DSM-5

The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) was published in May 2013 and features (in Section III) a new model for the diagnosis of personality disorders. The aim of this paper is to introduce this model to a broader German-speaking readership. The article begins by explaining why a new model seems necessary. Secondly, the different components of the new DSM-5 model are presented, the theoretical and empirical background is explained and it is shown how the model works using a clinical case example. Thirdly, several advantages of the new model as compared to the DSM-IV diagnostic categories (which will be retained in DSM-5 Section II) are discussed. Finally, the article highlights some open questions and problems that require further conceptual and empirical research.     

代数应用题项目生成中的认知过程与任务特征分析

认知项目设计起源于理论驱动的测量模式,要求在详尽阐述测量建构理论的基础上,鉴别测验项目类型的任务特征,建立和标定与测量建构认知变量之间的对应关系,从而通过系统变换任务特征生成项目.本文以Greeno (1994)提出的代理者-情境交互作用(agent-situation interaction)的问题解决观为基础,对代数应用题解决的认知过程和任务蕴含性(affordance)进行了系统分析.具体而言,代数应用题被视为以文本形式呈现的,嵌套在现实情境中的某种量化结构.在代数应用题解决的转译、整合、计划、执行的认知过程中需要形成三种不同的表征,即文本基、情境模型和问题模型.其形成及解决过程受到个体问题图式知识的深刻影响.对应于不同表征方式,对代数应用题语法/语义结构、量化元素及结构、问题情境特征进行了系统分析和综合.该分析表明,为了保证代数应用题项目生成的科学性,当前研究的关键在于建立任务特征与问题解决认知变量之间的经验关系,并在问题领域整体水平上检验这种关系的可推广性.     

Category and feature identification

This paper considers a family of inductive problems where reasoners must identify familiar categories or features on the basis of limited information. Problems of this kind are encountered, for example, when word learners acquire novel labels for pre-existing concepts. We develop a probabilistic model of identification and evaluate it in three experiments. Our first two experiments explore problems where a single category or feature must be identified, and our third experiment explores cases where participants must combine several pieces of information in order to simultaneously identify a category and a feature. Humans readily solve all of these problems, and we show that our model accounts for human inferences better than several alternative approaches.     

A tuned-trace theory of interval-timing dynamics

Animals on interval schedules of reinforcement can rapidly adjust a temporal dependent variable, such as wait time, to changes in the prevailing interreinforcement interval. We describe data on the effects of impulse, step, sine-cyclic, and variable-interval schedules and show that they can be explained by a tuned-trace timing model with a one-back threshold-setting rule. The model can also explain steady-state timing properties such as proportional and Weber law timing and the effects of reinforcement magnitude. The model assumes that food reinforcers and other time markers have a decaying effect (trace) with properties that can be derived from the rate-sensitive property of habituation (the multiple-time-scale model). In timing experiments, response threshold is determined by the trace value at the time of the most recent reinforcement. The model provides a partial account for the learning of multiple intervals, but does not account for scalloping and other postpause features of responding on interval schedules and has some problems with square-wave schedules.     

Developmental trajectories of childhood anxiety: Identifying continuity and change in anxious emotion

This paper outlines a way for thinking about continuity and change in childhood anxiety symptoms. Considerations for a model of continuity and change in anxious emotion are discussed first. Then, a perspective which may resolve inconsistencies across studies on the stability of childhood anxiety problems overtime is presented. The perspective views dysregulation of the anxiety response system and distress/negative affect as the core primary and necessary features of maladaptive anxious emotion and views the disorder-specific symptoms (generalized anxiety disorder, social phobia, separation anxiety, panic disorder, and simple phobia) as secondary characteristics of maladaptive anxious emotion. The perspective emphasizes ordered complexity in the developmental expression of anxious emotion, and delineates expectations for continuity and change in the features of anxious emotion by describing major normative trajectories across childhood and positing multiple sub-trajectories.     

Dimensions and categories: the "big five" factors and the DSM personality disorders

The five-factor model of personality, which has been widely studied in personality psychology, has been hypothesized to have specific relevance for DSM-defined personality disorders. To evaluate hypothesized relationships of the five-factor model of personality to personality disorders, 144 patients with personality disorders (diagnosed via a structured interview) completed an inventory to assess the five-factor model. Results indicated that the majority of the personality disorders can be differentiated in theoretically predictable ways using the five-factor model of personality. However, while the personality disorders as a whole appear to be differentiable from normal personality functioning on the five factors, the patterns are quite similar across the disorders, a finding that may provide some insight into the general nature of personality pathology but may also suggest problems with discriminant validity. Third, it does not appear that considering disorders as special combinations of features (as might be expected in some categorical models) is more informative than considering them as the sum of certain features (as might be expected in a dimensional model).     

The development of linear syllogistic reasoning

Between 24 and 26 children in each of grades 3, 5, 7, 9, and 11 (with mean ages of 8.5, 10.2, 13.0, 15.0, and 16.6 years, respectively) were tested in their ability to solve linear syllogisms, problems such as “John is taller than Mary. Mary is taller than Pete. Who is tallest? John, Mary, Pete.” Response latencies and error rates decreased across grade levels and sessions. Component latencies also generally decreased with increasing age. Four alternative information-processing models were fit to the group latency data at each grade level. These models were (a) a spatial model, according to which people represent the terms of a linear syllogism in the form of a linear spatial array; (b) a linguistic model, according to which linear syllogisms are solved via inferences on functional relations represented by linguistic deep structures; (c) an algorithmic model, according to which people solve linear syllogisms by applying a series of simple, essentially mechanical steps, and (d) a mixed model, which combines selected features of the spatial and linguistic models, and adds new features of its own. The latency data supported the mixed model at each grade level, although in grade 9 the model was not the preferred one until the second session of testing.     

An integrated account of generalization across objects and features

Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single feature that is unobserved for one or more objects. We explore problems where people must make inferences about multiple objects and features, and propose that people solve these problems by integrating knowledge about features with knowledge about objects. We evaluate three computational methods for integrating multiple systems of knowledge: the output combination approach combines the outputs produced by these systems, the distribution combination approach combines the probability distributions captured by these systems, and the structure combination approach combines a graph structure over features with a graph structure over objects. Three experiments explore problems where participants make inferences that draw on causal relationships between features and taxonomic relationships between animals, and we find that the structure combination approach provides the best account of our data.     

New parenthood as a risk factor for the development of obsessional problems

Research on emotional disturbance during pregnancy and the postpartum period has focused primarily on mood disorders and psychosis, yet preliminary evidence suggests that early parenthood is also associated with an increased risk for the development and exacerbation of obsessional problems. In this article we describe the nature of "postpartum obsessive-compulsive disorder" (ppOCD) and present a cognitive-behavioural model to account for these signs and symptoms. The model outlines features of early parenthood that might increase vulnerability to ppOCD and proposes a conceptual framework similar to that described in cognitive-behavioural models of OCD in general. The empirical status of the model described herein is discussed, along with suggestions for future research and implications for treatment.     

Multi-layer architecture for adaptive fuzzy inference system with a large number of input features

The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method.     

Co-occurring mental health and substance use problems in offenders: implications for risk assessment

We undertook a secondary data analysis to study issues relevant to co-occurring mental health and substance disorder in a combined sample of offenders (N = 3,197). Using the Personality Assessment Inventory, we compared the frequency of depressive, traumatic stress, and personality disorder symptom elevations across offenders with and without substance problems, identified the extent to which co-occurring problems were accompanied by risk factors for suicide and aggression, and tested for gender differences. Offenders with substance problems were more likely than others to have increased mental health problems and risk factors for suicide or aggression. Women with substance problems, compared with men, had higher depression, traumatic stress, and borderline features, in addition to lower antisocial features. The frequency with which suicide and aggression risk factors were associated with mental health problems was generally similar across men and women. Measurement issues relevant to co-occurring disorder and risk assessment are discussed.