对归纳推理贝叶斯模型的检验

作者用实验比较检验特征归纳的贝叶斯模型、关联相似性模型、基于特征的归纳推理模型和相似性覆盖模型这四种模型。以大学生为被试的实验结果表明：1）在强关联强度一致时,被试的归纳推理基本符合贝叶斯模型和关联相似性模型的预测,在弱关联强度一致时,不符合这四种模型预测;2）在关联强度不一致时,关联强度效应不符合贝叶斯模型的预测;3）在关联强度一致时,贝叶斯模型和关联相似性模型的预测结果是一致的,不能区分两个模型。实验结果较多地支持贝叶斯模型和关联相似性模型。     

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.     

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.     

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.     

The case for the comparator model as an explanation of the sense of agency and its breakdowns

I compare Frith and colleagues’ influential comparator account of how the sense of agency is elicited to the multifactorial weighting model advocated by Synofzik and colleagues. I defend the comparator model from the common objection that the actual sensory consequences of action are not needed to elicit the sense of agency. I examine the comparator model’s ability to explain the performance of healthy subjects and those suffering from delusions of alien control on various self-attribution tasks. It transpires that the comparator model needs case-by-case adjustment to deal with problematic data. In response to this, the multifactorial weighting model of Synofzik and colleagues is introduced. Although this model is incomplete, it is more naturally constrained by the cases that are problematic for the comparator model. However, this model may be untestable. I conclude that currently the comparator model approach has stronger support than the multifactorial weighting model approach.     

Hierarchical versus nonhierarchical models of movement sequence control: a reply to Klein

Klein (1983) has proposed a nonhierarchical logogen model to account for response timing and error data from a rapid finger-tapping task reported by Rosenbaum, Kenny, and Derr (1983). On grounds of parsimony, Klein's model cannot be easily distinguished from the hierarchical model proposed by Rosenbaum et al. However, the two models can be distinguished on other grounds. First, the logogen model cannot account for results from a second experiment reported in the Rosenbaum et al. article that the hierarchical model can account for. Second, the logogen model gives rise to a preponderence of incorrect response orders, whereas the hierarchical model does not. Third, the logogen model, unlike the hierarchical model, cannot account for the fact that people can produce complex, extended sequences of responses from memory.     

数学认知模型评介--加法事实存储与提取

本文介绍了国外心理学界近年来提出的有关加法事实存储与提取的筱模型,即小值模型、网络恢复模型、联结强度分布模型、网络干扰模型以及比较模型,并对其进行了简评。     

虚拟团队共享心理模型与团队效能的关系

通过对62个虚拟团队的调研,检验了虚拟团队认同式共享心理模型与分布式共享心理模型构思,并运用多层线性建模技术,对团队特征变量、共享心理模型与虚拟团队效能间的关系进行了分析。结果表明：两类共享心理模型与团队效能间存在显著正相关;团队时间会影响共享心理模型与虚拟团队任务效能间的关系,团队时间会削弱认同式共享心理模型对任务效能间的正向关系,但能加强分布式共享心理模型对任务效能的正面影响;团队规模则主要影响共享心理模型对合作效能的效应,团队规模增加会加强认同式共享心理模型与合作效能间的相关,却会削弱分布式共享心理模型与合作效能间的关系     

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.     

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。     

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.     

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.     

An approach to combine the logistic threshold model of psychophysics with the Bradley-Terry-Luce models of choice theory

The paper presents a straightforward extension of the Bradley-Terry-Luce model (BTL model) that can be derived from the logistic threshold model of psychophysics which assumes that psychometric functions are logistic probability functions. It is shown that (under weak side conditions) the logistic threshold model is a submodel of the extended BTL model. Moreover, representation and uniqueness theorems are proven that provide some evidence that the extended BTL model is a useful and widely applicable generalization of the ordinary BTL model. Finally, the logistic shape of the psychometric function is derived from axioms about binary choice probabilities. This characterization of the logistic threshold model can replace goodness of fit tests for the logistic probability distribution.     

现代测量理论下四大认知诊断模型述评

该文介绍并比较了现代测量理论下四大认知诊断模型的思想方法、模型结构及各自的特点性能等。LLTM是一个较早的认知诊断模型,它实现了认知与测量的结合;规则空间模型实现了对认知结构的诊断,并创造性地提出了Q矩阵理论;统一模型与融合模型是同一类模型：两者均沿用了规则空间模型的Q矩阵方法,但克服了规则空间模型中的一些不足;融合模型被认为是二十一世纪初创立的一个很成功的认知诊断模型。     

A critique of the correlated trait-correlated method and correlated uniqueness models for multitrait-multimethod data

The correlated trait-correlated method (CT-CM) and correlated uniqueness (CU) confirmatory factor analysis models for multitrait-multimethod data are critiqued. Although the CU model often returns convergent and admissible factor solutions when the CT-CM model does not, the CU model is shown to have theoretical and substantive shortcomings. On the basis of this critique, the authors recommend that the CT-CM model be regarded as the generally preferred model and that the CU model be invoked only when the CT-CM model fails.     

基于非逻辑机制的条件推理模式：P—Q映射模型及其实证检验

研究结合数学分析方法,提出了基于非逻辑机制的条件推理模型：P-Q映射模型。并根据这个模型,对人们在不同命题类型奈件下的推理行为进行了预测。预测结果显示,当推理前提为LH和HL型命题时,基于P-Q映射模型的预测结果与基于条件概率模型的预测结果完全一致。但当推理前提为LL和HH型命题时,两种模型给出的预测结果存在差异。实验结果表明,当前提命题为LL和HH型命题时,被试的条件推理行为与P-Q映射模型的预言完全一致。     

MASS COMMUNICATION AND BELIEF CHANGE: A TEST OF THREE MATHEMATICAL MODELS

Three mathematical models of communication and belief change were proposed and tested: a proportional change model, a belief certainty model, and an accumulated information model. A quick correlational check of the three models suggested that the accumulated information model was the superior with the belief certainty model being the most inferior of the three. Stronger support for the accumulated information model obtained using a more stringent test: a nonlinear bivariate regression which produced visual “plots” of empirical data that nearly duplicated the visual “plots” produced by the theoretical model. The accumulated information model states that belief change is proportional to the discrepancy between the original belief and the belief communicated in the message, and inversely proportional to the amount of information which the receiver has about the topic at the time the message is received. The belief certainty model was the most inferior of the three indicating that the degree to which a receiver is certain in conviction is unrelated to the communication-belief change relationship.     

Finding downbeats with a relaxation oscillator

A relaxation oscillator model of neural spiking dynamics is applied to the task of finding downbeats in rhythmical patterns. The importance of downbeat discovery or 'beat induction' is discussed, and the relaxation oscillator model is compared to other oscillator models. In a set of computer simulations the model is tested on 35 rhythmical patterns. The model performs well, making good predictions in 34 of 35 cases. In an analysis we identify some shortcomings of the model and relate model behavior to dynamical properties of relaxation oscillators.