A unitary Neo-Piagetian/Neo-Eriksonian model of development: Fundamental assumptions and meta-issues

The article briefly reviews a stage model of change in human development that integrates a Neo-Piagetian model and a Neo-Eriksonian model into one unitary stage model across the lifespan (as presented in Young, 2011). The model suggests a generic model of stages in change. The article explores the fundamental assumptions underlying the model as well as considering meta-issues. The article adds to the book by describing the Neo-Eriksonian stages as reflections of a social “participatory” process. It adds to the change process in development by describing it as possibly reflecting “punctuated disequilibrium.” Finally, it adds a “multiple psychodynamics” model.     

Expanding the rasch model to a general model having more than one dimension

The well-known Rasch model is generalized to a multicomponent model, so that observations of component events are not needed to apply the model. It is shown that the generalized model has retained the property of the specific objectivity of the Rasch model. For a restricted variant of the model, maximum likelihood estimates of its parameters and a statistical test of the model are given. The results of an application to a mathematics test involving six components are described.     

Model Predictive Impedance Control: A Model for Joint Movement

Impedance control has been suggested as the strategy employed by the central nervous system to control human postures and movements. A realization of this strategy is presented that uses a model predictive control algorithm as a higher motor controller. External disturbances are explicitly included in the model. The combination of 3 key factors-joint impedance control, model predictive controller, and external disturbance input-forms the basis for the generality of this model. The model was applied to 3 different types of joint movements: a tracking movement with an unpredicted disturbance, a rhythmic movement, and an unstable biped model of human walking. Computer simulation results showed excellent performance of the model in all 3 cases for optimal values of active joint impedances and an exact match between the musculoskeletal system and the model internal to the model predictive controller. The controller was also able to maintain acceptable performance in the presence of a 25% mismatch between the musculoskeletal system and its internal model.     

G-DINA认知诊断模型在语言测验中的验证

G-DINA模型是DINA 模型的一般化模型,具有补偿性和饱和性两个主要特征。G-DINA模型的补偿性特征契合了语言测验的综合性和多元性,G-DINA模型的饱和性特征则可以比较理想地应对语言技能的抽象性和难区分性。此项研究以代表性的语言测验类型阅读测验为案例,应用G-DINA模型对1029名被试的PISA英语阅读测验结果进行实证分析,证明了两个假设：补偿饱和型认知诊断模型对多元抽象的语言测验的适应程度较高;G-DINA这一新生认知诊断模型可以被用来诊断较为复杂抽象的语言测验,且经得起统计学和语言学理论的双重考验。     

Developing TODAM: Three models for serial-order information

TODAM2, a theory of distributed associative memory, shows how item and associative information can be considered special cases of serial-order information. Consequently, it is important to get the right model for serial-order information. Here, we analyze and compare three distributed-memory models for serial-order information that use TODAM’s convolution-correlation formalism. These models are the chaining model, the chunking model, and a new model, the power-set model. The chaining model associates each item with its predecessor; the chunking model uses multiple convolutions andn-grams to form chunks; and the power-set model interassociates all items in a set in a particular way to form a chunk. The models are compared in terms of their performance on seven basic tests of serial-order information—namely, serial recall, backward recall, recall of missing items, sequential probe tests, positional probe tests, serial-to-paired-associate transfer, and item recognition. The strengths and weaknesses of each model are discussed.     

Obtaining separate measures for implicit and explicit memory

In this paper a new model, the Implicit/Explicit Separation (IES) model, is developed and applied. The model is designed to obtain separate probability measures for explicit memory storage, implicit memory storage, fractional storage, and non-storage. The model is needed because memory research has shown the importance of the distinction between a conscious memory of a target event and a memory residue that is unable to support conscious recall or confident recognition, but it is still able to support guessing at rates above chance. Maximum likelihood and population-parameter mapping estimates for the parameters of the IES model are provided. The accuracy of parameter estimates is studied as a function of sample size. Three experiments are reported to demonstrate how the IES model is used to achieve a more fine grained assessment of the quality of information storage. These experiments also provide strong validation support for the IES model itself. Across the three experiments, each of the four components for representing target information (explicit, implicit, fractional, and non-storage) demonstrates a different pattern. The IES model is discussed in terms of alternative models such as the dual-process model and the process-dissociation model.     

A quantum probability account of order effects in inference

Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a state vector with different sequences of operators for different orderings of information. We demonstrate this process by fitting the quantum model to data collected in a medical diagnostic task and a jury decision-making task. To further test the quantum inference model, a new jury decision-making experiment is developed. Using the results of this experiment, we compare the quantum inference model with two versions of the belief-adjustment model, the adding model and the averaging model. We show that both the quantum model and the adding model provide good fits to the data. To distinguish the quantum model from the adding model, we develop a new experiment involving extreme evidence. The results from this new experiment suggest that the adding model faces limitations when accounting for tasks involving extreme evidence, whereas the quantum inference model does not. Ultimately, we argue that the quantum model provides a more coherent account for order effects that was not possible before.     

An accumulator model for responses and response times in tests based on the proportional hazards model

Latent trait models for responses and response times in tests often lack a substantial interpretation in terms of a cognitive process model. This is a drawback because process models are helpful in clarifying the meaning of the latent traits. In the present paper, a new model for responses and response times in tests is presented. The model is based on the proportional hazards model for competing risks. Two processes are assumed, one reflecting the increase in knowledge and the second the tendency to discontinue. The processes can be characterized by two proportional hazards models whose baseline hazard functions correspond to the temporary increase in knowledge and discouragement. The model can be calibrated with marginal maximum likelihood estimation and an application of the ECM algorithm. Two tests of model fit are proposed. The amenability of the proposed approaches to model calibration and model evaluation is demonstrated in a simulation study. Finally, the model is used for the analysis of two empirical data sets.     

Marginal and Random Intercepts Models for Longitudinal Binary Data With Examples From Criminology

Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides individual-level information including information about heterogeneity of growth. It is shown how a type of numerical averaging can be used with the random intercepts model to obtain group-level information, thus approximating individual and marginal aspects of the LMM. The types of inferences associated with each model are illustrated with longitudinal criminal offending data based on N = 506 males followed over a 22-year period. Violent offending indexed by official records and self-report were analyzed, with the marginal model estimated using generalized estimating equations and the random intercepts model estimated using maximum likelihood. The results show that the numerical averaging based on the random intercepts can produce prediction curves almost identical to those obtained directly from the marginal model parameter estimates. The results provide a basis for contrasting the models and the estimation procedures and key features are discussed to aid in selecting a method for empirical analysis.     

Perceived Coaching Behaviors and College Athletes' Intrinsic Motivation: A Test of Self-Determination Theory

We investigated whether a shared mental model is present in elite ice hockey and handball teams. In total, 231 male players participated in the study. Shared mental models were found to exist. Relationships between shared mental models and coaching efforts to develop a general training shared mental model and an opponent-specific model were explored. The relationship between role clarity and shared mental model, general training shared mental model, and opponent-specific model was positive. The shared mental model is a useful construct for analyzing elite team practice and coaching behavior. Coaches and sport psychologists should be aware that establishing a shared mental model in elite teams is essential in facilitating performance.     

Exemplars, prototypes, and the flexibility of classification models

J. P. Minda and J. D. Smith (2001) showed that a prototype model outperforms an exemplar model, especially in larger categories or categories that contained more complex stimuli. R. M. Nosofsky and S. R. Zaki (2002) showed that an exemplar model with a response-scaling mechanism outperforms a prototype model. The authors of the current study investigated whether excessive model flexibility could explain these results. Using cross-validation, the authors demonstrated that both the prototype model and the exemplar model with a response-scaling mechanism suffered from overfilling in the linearly separable category structure. The results illustrate the need to make sure that the best-fitting model is not chasing error variance instead of variance attributed to the cognitive process it is supposed to model.     

Consistently modeling the same movement strategy is more important than model skill level in observational learning contexts

The experiment undertaken was designed to elucidate the impact of model skill level on observational learning processes. The task was bimanual circle tracing with a 90° relative phase lead of one hand over the other hand. Observer groups watched videos of either an instruction model, a discovery model, or a skilled model. The instruction and skilled model always performed the task with the same movement strategy, the right-arm traced clockwise and the left-arm counterclockwise around circle templates with the right-arm leading. The discovery model used several movement strategies (tracing-direction/hand-lead) during practice. Observation of the instruction and skilled model provided a significant benefit compared to the discovery model when performing the 90° relative phase pattern in a post-observation test. The observers of the discovery model had significant room for improvement and benefited from post-observation practice of the 90° pattern. The benefit of a model is found in the consistency with which that model uses the same movement strategy, and not within the skill level of the model. It is the consistency in strategy modeled that allows observers to develop an abstract perceptual representation of the task that can be implemented into a coordinated action. Theoretically, the results show that movement strategy information (relative motion direction, hand lead) and relative phase information can be detected through visual perception processes and be successfully mapped to outgoing motor commands within an observational learning context.     

The transition from algorithm to retrieval in memory-based theories of automaticity

Two memory-based theories of automaticity were compared. The mixture model and the race model both describe automatization as a transition from algorithmic processing to memory retrieval. The mixture model predicts that, with training, the variability of reaction time will initially increase, and later decrease in a concave downward manner, whereas the race model predicts the variability will decrease only in a concave upward manner. The mixture model predicts that using both algorithm and retrieval on a single trial will be slower than using the algorithm alone, whereas the race model predicts the reverse. The experiments used an alphabet arithmetic task, in which subjects verified equations of the form H + 3 = K and made subjective reports of their strategies on individual trials. Both the variability of reaction times and the pattern of reaction times associated with the strategy reports supported the race model.     

Prospective analysis of comorbidity: tobacco and alcohol use disorders

Alcohol use disorders (AUD) and tobacco use disorders (TD) frequently co-occur. The authors examined AUD-TD comorbidity over time using a state-trait (ST) model. The ST model represents variance in AUD/TD as a traitlike factor that spans measurement occasion and identifies distinct sources of variance in AUD-TD comorbidity. The ST model was evaluated on 450 young adults (baseline age = 18.5 years; 51% with family history of alcoholism) assessed 5 times over 7 years. The ST model demonstrated superior fit over a first-order autoregressive model. The tendency to diagnose with AUD and TD was partially explained by family history of alcoholism; this relationship was mediated by childhood stressors, alcohol expectancies, and behavioral undercontrol. Results supported a common third-variable influence (vs. directional) model of comorbidity. The ST model is an important conceptual and methodological approach to the prospective study of comorbidity in general.     

A generalized longitudinal mixture IRT model for measuring differential growth in learning environments

This article describes a generalized longitudinal mixture item response theory (IRT) model that allows for detecting latent group differences in item response data obtained from electronic learning (e-learning) environments or other learning environments that result in large numbers of items. The described model can be viewed as a combination of a longitudinal Rasch model, a mixture Rasch model, and a random-item IRT model, and it includes some features of the explanatory IRT modeling framework. The model assumes the possible presence of latent classes in item response patterns, due to initial person-level differences before learning takes place, to latent class-specific learning trajectories, or to a combination of both. Moreover, it allows for differential item functioning over the classes. A Bayesian model estimation procedure is described, and the results of a simulation study are presented that indicate that the parameters are recovered well, particularly for conditions with large item sample sizes. The model is also illustrated with an empirical sample data set from a Web-based e-learning environment.     

Regularities of source recognition: ROC analysis

Source memory has become the focus of a growing number of investigations in a variety of fields. An appropriate model for source memory is, therefore, of increasing importance. A simple 2-dimensional signal-detection model of source recognition is presented. The receiver operating characteristics (ROCs) obtained from 3 experiments are then used to test the model. The data demonstrate 3 regularities: convex ROCs, z-ROCs with linear slopes of 1.00, and slightly concave z-ROCs. Two of these regularities support the model. The 3rd requires a revision of the model. This revised model is fitted to the data. The implications of these regularities for other theories are also discussed.     

Analysis of asymmetry by a slide-vector

The slide-vector scaling model attempts to account for the asymmetry of a proximity matrix by a uniform shift in a fixed direction imposed on a symmetric Euclidean representation of the scaled objects. Although no method for fitting the slide-vector model seems available in the literature, the model can be viewed as a constrained version of the unfolding model, which does suggest one possible algorithm. The slide-vector model is generalized to handle three-way data, and two examples from market structure analysis are presented.     

Paired comparisons with individual differences

A model of Bradley-Terry type for paired comparisons is considered. In addition to the usual parameters, the model allows for individual parameters and corresponding parameters for the choice-alternatives. The model is applied to a set of data from a Danish investigation of the attitude of blue collar workers towards alternative social gains. The proposed model is finally compared to a model recently suggested by Schönemann and Wang.     

Bayesian estimation of a multilevel IRT model using gibbs sampling

In this article, a two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to observed scores, latent scores are test-independent, which offers the possibility of using results from different tests in one analysis where the parameters of the IRT model and the multilevel model can be concurrently estimated. The two-parameter normal ogive model is used for the IRT measurement model. It will be shown that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. Examples using simulated and real data are given.