The Quality of Response Time Data Inference: A Blinded,Collaborative Assessment of the Validity of Cognitive Models

Psychonomic Bulletin & Review - Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into...     

Bayesian Thurstonian models for ranking data using JAGS

A Thurstonian model for ranking data assumes that observed rankings are consistent with those of a set of underlying continuous variables. This model is appealing since it renders ranking data amenable to familiar models for continuous response variables—namely, linear regression models. To date, however, the use of Thurstonian models for ranking data has been very rare in practice. One reason for this may be that inferences based on these models require specialized technical methods. These methods have been developed to address computational challenges involved in these models but are not easy to implement without considerable technical expertise and are not widely available in software packages. To address this limitation, we show that Bayesian Thurstonian models for ranking data can be very easily implemented with the JAGS software package. We provide JAGS model files for Thurstonian ranking models for general use, discuss their implementation, and illustrate their use in analyses.     

Employing Ethical Codes and Decision-Making Models: A Developmental Process

Counselors often encounter seemingly intractable ethical dilemmas. To assist counselors who face difficult situations, the helping professions have generated both ethical codes and decision-making models. Because the process of decision making seems to be related to cognitive-moral development, this article details ethical codes and decision models and speculates on how counselor level of development may influence use of such codes and models.     

Individual prediction of dyslexia by single versus multiple deficit models

The overall goals of this study were to test single versus multiple cognitive deficit models of dyslexia (reading disability) at the level of individual cases and to determine the clinical utility of these models for prediction and diagnosis of dyslexia. To accomplish these goals, we tested five cognitive models of dyslexia--two single-deficit models, two multiple-deficit models, and one hybrid model--in two large population-based samples, one cross-sectional (Colorado Learning Disability Research Center) and one longitudinal (International longitudinal Twin Study). The cognitive deficits included in these cognitive models were in phonological awareness, language skill, and processing speed and/or naming speed. To determine whether an individual case fit one of these models, we used two methods: 1) the presence or absence of the predicted cognitive deficits, and 2) whether the individual's level of reading skill best fit the regression equation with the relevant cognitive predictors (i.e., whether their reading skill was proportional to those cognitive predictors.) We found that roughly equal proportions of cases met both tests of model fit for the multiple deficit models (30-36%) and single deficit models (24-28%); hence, the hybrid model provided the best overall fit to the data. The remaining roughly 40% of cases in each sample lacked the deficit or deficits that corresponded with their best-fitting regression model. We discuss the clinical implications of these results for both diagnosis of school-age children and preschool prediction of children at risk for dyslexia.     

When parameters collide: a warning about categorization models

Similarity-choice (S—C) models of categorization contain two principal mathematical transformations: an exponential-decay similarity function and a choice rule. However, there is a tension between the psychological processes that models emulate and the mathematics they use to do so. To illustrate this, I show that in these models an unappreciated interaction occurs between the mathematical transformations so that the stages of the model essentially cancel each other out. The result is that the model’s output reflects its input linearly. This cancellation phenomenon has potentially serious implications regarding the interpretation and use of S—C models. The phenomenon also raises questions about the simplification and psychological grounding of categorization models. Modelers broadly might benefit from an internal analysis of their models, such as that described here.     

Analysis of multivariate frequency data by graphical models and generalizations of the multidimensional row-column association model

Models used to analyze cross-classifications of counts from psychological experiments must represent associations between multiple discrete variables and take into account attributes of stimuli, experimental conditions, or characteristics of subjects. The models must also lend themselves to psychological interpretations about underlying structures mediating the relationship between stimuli and responses. To meet these needs, the author extends the graphical latent variable models for nominal and/or ordinal data proposed by C. J. Anderson and J. K. Vermunt (2000) to situations in which dependencies between observed variables are not fully accounted for by the latent variables. The graphical models provide a unified framework for studying multivariate associations that include log-linear models and log-multiplicative association models as special cases.     

多项选择题认知诊断潜能的最大化

多项选择题是认知诊断中常用的测验项目形式, 其正确答案选项和经过特殊编制的干扰项都能提供诊断信息。为了提取干扰项的信息, 需要采用不同于传统认知诊断模型的心理计量模型。分类介绍能使干扰项提供诊断信息的多项选择题编制方法, 分析这类多项选择题的认知诊断模型, 认为未来应加强干扰项编制方法和能提取干扰项信息的认知诊断模型的选择和开发等方面的研究。     

新型农村合作医疗补偿模式现状分析

新型农村合作医疗工作试点推广以来,各地都在探索适合本地实际的运行机制和模式。尝试在归纳我国新型农村合作医疗补偿模式的基础上,分析其发生发展的动因度机制,以供研究和实践者参考。     

D-optimal design for the Rasch counts model with multiple binary predictors

In this paper we derive optimal designs for the Rasch Poisson counts model and its extended version of the (generalized) negative binomial counts model incorporating several binary predictors for the difficulty parameter. To efficiently estimate the regression coefficients of the predictors, locally D-optimal designs are developed. After an introduction to the Rasch Poisson counts model and its extension, we will specify these models as particular generalized linear models. Based on this embedding, optimal designs for both models including several binary explanatory variables will be presented. Therefore, we will derive conditions on the effect sizes for certain designs to be locally D-optimal. Finally, it is pointed out that the results derived for the Rasch Poisson models can be applied for more general Poisson regression models which should receive more attention in future psychological research.     

Vocal syntax development in the white-crowned sparrow (Zonotrichia leucophrys).

Birdsong development exemplifies the interplay between experience and predisposition that occurs during behavioral ontogeny. Songbirds must hear song models to develop normal song, yet they preferentially learn conspecific song when given a choice in the laboratory. To the extent that features guiding this selective learning are pre-encoded in the brain, such features should also develop in the song of young birds not exposed to them in tutor models. To investigate whether song syntax-phrase number and order--is such a feature in the white-crowned sparrow (Zonotrichia leucophrys), the authors tutored males of this species with separate phrase models. Birds learned and assembled these into songs of species-typical sequence, suggesting that syntax is to some degree pre-encoded in white-crowned sparrows. Birds also learned heterospecific phrases, confirming previous evidence that note phonology is not the primary cue for selective song learning in this species.     

Deconstructing and reconstructing ACT-R: Exploring the architectural space

Evaluating variations in the structure of computational models of cognition is as important as evaluating variations in the numerical parameters of such models. However, computational models tend not to be organized in such a way as to directly support such research. To address this need, we have taken the well-known cognitive architecture ACT-R, reduced it to its fundamental components, and reconstructed it. Our new system, Python ACT-R, facilitates exploration of the space of possible models and architectures based on the core ACT-R theory. The result has enabled us to examine the possibility of using basic ACT-R components such as the declarative memory system in new ways; for example, as the basis for a new visual attention system. Python ACT-R allows the same model definition syntax to be used to define both ACT-R models and new ACT-R components, as well as making explicit the processes specified by the ACT-R theory.     

Multi-agent models of social dynamics in children

A series of computational models are presented which address the question of how peer relations change over time. We examine data from a standardized metric (CDC) that places school children in one of five categories: Popular, Rejected, Neglected, Controversial, and Average, and how such classifications change over time. A simple random model is shown to not match the empirical data, while a computational model and an ACT-R model are shown to match equally well, even though they are highly architecturally distinct. To test these models’ ability to give useful predictions in other domains, we introduced variation among the individuals in the models. For both models, we observed equivalent behavior that was consistent with the empirically known effects of Hostile Attribution Bias, variations in social skill, and shyness among others. This indicates that both models are capturing inherent underlying regularities of the social dynamics of peer relations in children. The relationship between these models and its implications are discussed.     

A Bayesian Semiparametric Item Response Model with Dirichlet Process Priors

In Item Response Theory (IRT), item characteristic curves (ICCs) are illustrated through logistic models or normal ogive models, and the probability that examinees give the correct answer is usually a monotonically increasing function of their ability parameters. However, since only limited patterns of shapes can be obtained from logistic models or normal ogive models, there is a possibility that the model applied does not fit the data. As a result, the existing method can be rejected because it cannot deal with various item response patterns. To overcome these problems, we propose a new semiparametric IRT model using a Dirichlet process mixture logistic distribution. Our method does not rely on assumptions but only requires that the ICCs be a monotonically nondecreasing function; that is, our method can deal with more types of item response patterns than the existing methods, such as the one-parameter normal ogive models or the two- or three-parameter logistic models.     

Measuring model flexibility with parameter space partitioning: an introduction and application example

A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data-fitting potential (i.e., flexibility) of the model. In the first part of this article, methods for comparing models and studying their flexibility are reviewed, with a focus on parameter space partitioning (PSP), a general-purpose method for analyzing and comparing all classes of cognitive models. PSP is then demonstrated in the second part of the article in which two connectionist models of speech perception (TRACE and ARTphone) are compared to learn how design differences affect model flexibility.     

Bayesian Estimation of Categorical Dynamic Factor Models

Dynamic factor models have been used to analyze continuous time series behavioral data. We extend 2 main dynamic factor model variations—the direct autoregressive factor score (DAFS) model and the white noise factor score (WNFS) model—to categorical DAFS and WNFS models in the framework of the underlying variable method and illustrate them with a categorical time series data set from an emotion study. To estimate the categorical dynamic factor models, a Bayesian method via Gibbs sampling is used. The results show that today's affect directly influences tomorrow's affect. The results are then validated by means of simulation studies. Differences between continuous and categorical dynamic factor models are examined.     

Global model analysis by parameter space partitioning

To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g., interval-scale performance) often mismatches their testable precision in experiments, where qualitative, ordinal predictions are the norm. Parameter space partitioning is a solution that evaluates model performance at a qualitative level. There exists a partition on the model's parameter space that divides it into regions that correspond to each data pattern. Three application examples demonstrate its potential and versatility for studying the global behavior of psychological models.     

Cognitive and neural modeling of dynamics of trust in competitive trustees

Trust dynamics can be modeled in relation to experiences. In this paper two models to represent human trust dynamics are introduced, namely a model on a cognitive level and a neural model. These models include a number of parameters, providing the possibility to express certain relations between trustees. The behavior of each of the models is further analyzed by means of simulation experiments and formal verification techniques. Thereafter, both models have been compared to see whether they can produce patterns that are comparable. As each of the models has its own specific set of parameters, with values that depend on the type of person modeled, such a comparison is non-trivial. To address this, a special comparison approach is introduced, based on mutual mirroring of the models in each other. More specifically, for a given parameter values set for one model, by an automated parameter estimation procedure the most optimal values for the parameter values of the other model are determined in order to show the same behavior. Roughly spoken the results are that the models can mirror each other up to an accuracy of around 90%.     

The Multiple, Distinct Ways that Personality Contributes to Alcohol Use Disorders

There is little question of whether personality is associated with problematic alcohol involvement (such as alcohol use disorders; AUDs); it clearly is. However, the question remains: how or why is personality related to risky drinking and AUDs? To address this question, theoretical models have been posited regarding the causal effects of personality on alcohol use and related problems. In this article, several of these models are summarized and reviewed. Future research directions are discussed, including possible frameworks that serve to integrate various models of the personality–AUD relation.     

The Butchers, an eclectic approach to family therapy

The thesis of this paper is that differing theoretical constructs offer a variety of rich approaches when responding to the problems families present. To work exclusively within one theoretical framework is to deny oneself the value latent in other models, and thus restrict one's therapeutic competence. It is suggested that various family therapy models can be worked together without contradiction if they are subsumed in an overriding systems approach. It is also pointed out that the various models have particular capabilities and limitations with differing therapeutic goals. Ten interviews with one family are used to illustrate an eclectic approach in therapy.