Impairments in inhibition or cognitive control in psychological disorders

Contributions to this special issue of Applied & Preventive Psychology richly elucidate connections between a variety of psychological disorders and performance in a number of tasks that are used to reason about inhibitory deficits. This commentary calls attention to the different uses of the concept of inhibition – vernacular, neural, operational, and theoretical – and suggests that the term cognitive control avoids claims about dampened memory representations that are difficult to support. Central findings from the reviews are summarized; evidence concerning suppression-induced forgetting is featured and directions to foster application are discussed.     

Higher-order latent trait models for cognitive diagnosis

Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling theϑ of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example. This research was funded by National Institute of Health grant R01 CA81068. We would like to thank William Stout and Sarah Hartz for many useful discussions, three anonymous reviewers for helpful comments and suggestions, and Kikumi Tatsuoka and Curtis Tatsuoka for generously sharing data.     

使用验证性补偿多维IRT模型进行认知诊断评估

随着人们对测验反馈结果精细化的需求逐渐提高, 具有认知诊断功能的测量方法逐渐受到人们的关注。在认知诊断模型(CDMs)闪耀着光芒的同时, 另一类能够在连续量尺上提供精细反馈的多维IRT模型(MIRTMs)似乎受到些许冷落。为探究MIRTMs潜在的认知诊断功能, 本文以补偿模型为视角, 聚焦于分别属于MIRTMs的多维两参数logistic模型(M2PLM)和属于CDMs的线性logistic模型(LLM); 之后为使两者具有可比性, 可对补偿M2PLM引入验证性矩阵(Q矩阵)来界定题目与维度之间的关系, 进而得到验证性的补偿M2PLM (CC-M2PLM), 并通过把潜在特质按切点划分为跨界属性, 以期使CC-M2PLM展现出其本应具有的认知诊断功能; 预研究表明logistic量尺上的0点可作为相对合理的切点; 然后, 通过模拟研究对比探究CC-M2PLM和LLM的认知诊断功能, 结果表明CC-M2PLM可用于分析诊断测验数据, 且认知诊断功能与直接使用LLM的效果相当; 最后, 以两则实证数据为例来说明CC-M2PLM在实际诊断测验分析中的可行性。     

Malingering of psychological disorders

Differences between clinical and forensic approaches to evaluating individuals for malingering are discussed. Several important studies are discussed relative to malingering in clinical evaluations, objective and projective psychological testing, and neuropsychological assessment procedures.     

Dimensions in data: testing psychological models using state-trace analysis

Cognitive science is replete with fertile and forceful debates about the need for one or more underlying mental processes or systems to explain empirical observations. Such debates can be found in many areas, including learning, memory, categorization, reasoning and decision-making. Multiple-process models are often advanced on the basis of dissociations in data. We argue and illustrate that using dissociation logic to draw conclusions about the dimensionality of data is flawed. We propose that a more widespread adoption of 'state-trace analysis'--an approach that overcomes these flaws--could lead to a re-evaluation of the need for multiple-process models and to a re-appraisal of how these models should be formulated and tested.     

A general proof of consistency of heuristic classification for cognitive diagnosis models

The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633–665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non‐parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non‐parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.     

Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits

Findings from a complex decision-making task (the Iowa gambling task) show that individuals with neuropsychological disorders are characterized by decision-making deficits that lead to maladaptive risk-taking behavior. This article describes a cognitive model that distills performance in this task into three different underlying psychological components: the relative impact of rewards and punishments on evaluations of options, the rate that the contingent payoffs are learned, and the consistency between learning and responding. Findings from 10 studies are organized by distilling the observed decision deficits into the three basic components and locating the neuropsychological disorders in this component space. The results reveal a cluster of populations characterized by making risky choices despite high attention to losses, perhaps because of difficulties in creating emotive representations. These findings demonstrate the potential contribution of cognitive models in building bridges between neuroscience and behavior.     

Intrinsic cognitive models

Theories concerning the structure, or format, of mental representation should (1) be formulated in mechanistic, rather than metaphorical terms; (2) do justice to several philosophical intuitions about mental representation; and (3) explain the human capacity to predict the consequences of worldly alterations (i.e., to think before we act). The hypothesis that thinking involves the application of syntax‐sensitive inference rules to syntactically structured mental representations has been said to satisfy all three conditions. An alternative hypothesis is that thinking requires the construction and manipulation of the cognitive equivalent of scale models. A reading of this hypothesis is provided that satisfies condition (1) and which, even though it may not fully satisfy condition (2), turns out (in light of the frame problem) to be the only known way to satisfy condition (3).     

基于个体的退休心理模型

退休是个体在特定年龄领取养老金的行为及动态过程。退休模型包括退休的影响因素模型、退休过程模型和退休结果模型三类。其中,退休的影响因素模型包括前因变量模型和退休多水平模型,分别从个人-环境和宏观角度提出了影响退休的各类因素;退休过程模型包括退休决策三阶段模型、退休时间过程模型和退休适应动态模型,分别从阶段性、个体差异性和动态性的角度揭示了退休过程的心理特点;退休结果模型分析了退休对个人和组织的影响。退休四阶段模型是在西方模型的基础上结合中国国情提出的一个整合模型。未来研究应丰富研究对象的类型、揭示影响退休的心理学变量的作用机制、加强应用性并进行本土化研究。     

Computer models of cognitive processes

Conclusion From the small sample of achievements that I have had time to mention, we can only conclude that automation is here to stay. Nor is there any doubt that more powerful automata will be built. A great many of the higher human abilities will be given to machines. The great rush to automation is sure to stimulate psychologists to learn more about the human symbolic processes being mimicked by the machines. And the computers, which are the ultimate cause of the feverish scramble toward automation, are providing both the framework for describing complex models of behavior and also the means for testing these models. With both the means and the motivation at hand, psychologists are sure to make rapid progress in understanding complex human behavior.Operated with support from the United States Army, Navy, and Air Force.     

Object-oriented models of cognitive processing

Information-processing models of vision and cognition are inspired by procedural programming languages. Models that emphasize object-based representations are closely related to object-oriented programming languages. The concepts underlying object-oriented languages provide a theoretical framework for cognitive processing that differs markedly from that offered by procedural languages. This framework is well-suited to a system designed to deal flexibly with discrete objects and unpredictable events in the world.     

Neuronal models of cognitive functions

Understanding the neural bases of cognition has become a scientifically tractable problem, and neurally plausible models are proposed to establish a causal link between biological structure and cognitive function. To this end, levels of organization have to be defined within the functional architecture of neuronal systems. Transitions from any one of these interacting levels to the next are viewed in an evolutionary perspective. They are assumed to involve: (1) the production of multiple transient variations and (2) the selection of some of them by higher levels via the interaction with the outside world. The time-scale of these "evolutions" is expected to differ from one level to the other. In the course of development and in the adult this internal evolution is epigenetic and does not require alteration of the structure of the genome. A selective stabilization (and elimination) of synaptic connections by spontaneous and/or evoked activity in developing neuronal networks is postulated to contribute to the shaping of the adult connectivity within an envelope of genetically encoded forms. At a higher level, models of mental representations, as states of activity of defined populations of neurons, are discussed in terms of statistical physics, and their storage is viewed as a process of selection among variable and transient pre-representations. Theoretical models illustrate that cognitive functions such as short-term memory and handling of temporal sequences may be constrained by "microscopic" physical parameters. Finally, speculations are offered about plausible neuronal models and selectionist implementations of intentions.     

Discrimination nets as psychological models

Simulations of human cognitive processes often employ discrimination nets to model the access of permanent memory. We consider two types of discrimination nets—EPAM and positive-proper-only nets—and argue that they have insufficient psychological validity. Their deficiencies arise from negative properties, insufficient sensitivity to the discriminativeness of properties, extreme sensitivity to missing or incorrect properties, inefficiency in representing multiple knowledge domains, and seriality. We argue that these deficiencies stem from a high degree of test contingency in utilizing property information during acquisition and memory search. Discrimination nets are compared to other models that have less or no test contingency (e.g., PANDEMONIUM) and that thereby avoid the problems of discrimination nets. We propose that understanding test contingency and discovering psychologically valid ways to implement it will be central to understanding and simulating memory indexing in human cognition.     

Toward integrating cognitive components with computational models of emotion using software design patterns

Computational models of emotion (CMEs) are software systems designed to imitate particular aspects of human emotions. The main purpose of this type of computational model is to capture the complexity of the human emotion process in a software system that is incorporated into a cognitive agent architecture. However, creating a CME that closely imitates the actual functioning of emotions demands to address some challenges such as (i) sharing information among independently developed cognitive and affective components, and (ii) interconnecting complex cognitive and affective components that must interact with one another in order to generate realistic emotions, which may even affect agents’ decision making. This paper proposes an architectural pattern aimed at cataloging and describing fundamental components of CMEs and their interrelationships with cognitive components. In this architectural pattern, external cognitive components and internal affective components of CMEs do not interact directly but are extended by including message exchange methods in order to use a publish-subscribe channel, which enables their intercommunication, thus attenuating issues such as software heterogeneity. This structural approach centralizes communication management and separates the inherent complexity of the cognitive and affective processes from the complexity of their interaction mechanisms. In so doing, it enables the design of CMEs’ architectures composed of independently developed affective and cognitive components. The proposed architectural pattern attempts to make progress in capturing the complex process of human emotions in a software system that adheres to software engineering best practices and that incorporates quality attributes such as flexibility and interoperability.     

Connectionist models of neuropsychological disorders

Artificial neural networks ('connectionist models') embody aspects of real neuronal systems. But does studying the breakdown of performance in such models help us to understand cognitive impairments in humans following brain damage? Here we review recent attempts to capture different neuropsychological disorders using connectionist models with simulated lesions. We show how such lesion studies can be used to evaluate some of the standard assumptions made in neuropsychological research, concerning both double dissociations and associations between patterns of impairment. We also illustrate how lesioned models, like humans, can sometimes be more impaired on the easier of two tasks and demonstrate that connectionist models can incorporate forms of internal structure. Finally we discuss the utility of the models for understanding and predicting the effectiveness of different rehabilitation strategies. Future questions concern the role and possible development of internal structure within these models, whether the models can be generalized to larger-scale simulations, and whether they can accommodate higher-order linguistic disorders.