The past,present, and future of computational models of cognitive development

Does modeling matter? We address this question by providing a broad survey of the computational models of cognitive development that have been proposed and studied over the last three decades. We begin by noting the advantages and limitations of computational models. We then describe four key dimensions across which models of development can be organized and classified. With this taxonomy in hand, we focus on how the modeling enterprise has evolved over time. In particular, we separate the timeline into three overlapping historical waves and highlight how each wave of models has not only been shaped by developmental theory and behavioral research, but in return also provided valuable insights and innovations to the study of cognitive development.     

Investigating the predictive validity of implicit and explicit measures of motivation on condom use,physical activity and healthy eating

The literature on health-related behaviours and motivation is replete with research involving explicit processes and their relations with intentions and behaviour. Recently, interest has been focused on the impact of implicit processes and measures on health-related behaviours. Dual-systems models have been proposed to provide a framework for understanding the effects of explicit or deliberative and implicit or impulsive processes on health behaviours. Informed by a dual-systems approach and self-determination theory, the aim of this study was to test the effects of implicit and explicit motivation on three health-related behaviours in a sample of undergraduate students (N?=?162). Implicit motives were hypothesised to predict behaviour independent of intentions while explicit motives would be mediated by intentions. Regression analyses indicated that implicit motivation predicted physical activity behaviour only. Across all behaviours, intention mediated the effects of explicit motivational variables from self-determination theory. This study provides limited support for dual-systems models and the role of implicit motivation in the prediction of health-related behaviour. Suggestions for future research into the role of implicit processes in motivation are outlined.     

心理韧性研究诸进路

心理韧性研究自发轫至今出现了若干可供遵循的进路, 主要有：以人为中心模型、以变量为中心模型、时间路径模型和因素-过程模型等。以人为中心进路围绕“人”开展研究, 将甄别出的心理韧性者与非韧性者及未经历严重压力/逆境者进行比较, 尝试理解心理韧性的由来。以变量为中心进路则聚焦于压力/危险逆境指标、心理韧性相关因子及心理社会发展功能指标, 经由分析这些变量的关系以探寻和推测心理韧性发展的机制。时间路径进路专注于时间进程, 追踪研究对象在严重压力/逆境开始前后到相当长一段时期心理社会功能变化过程特点, 以解读人类压力/逆境适应系统的运作特征及其功能差异, 推断心理韧性发展的过程及机制。因素-过程整合进路则以生态系统理论为指导, 兼顾因素与过程, 尝试提供一个更具研究指导性的心理韧性整合模型和一套机制解释策略基准框架。诸心理韧性研究进路各有侧重, 互有长短, 研究者对之了然于胸将有助于选择适切的研究设计与分析策略, 取长避短, 从而提高研究揭示效力。     

“I'd like to be that attractive,but at least I'm smart”: How exposure to ideal advertising models motivates improved decision‐making

The use of idealized advertising models has been heavily criticized in recent years. Existing research typically adopts a social comparison framework and shows that upward comparisons with models can lower self-esteem and affect, as well as produce maladaptive behavior. However, the alternative possibility that consumers can cope with threatening advertising models by excelling in other behavioral domains has not been examined. The present research draws on fluid compensation theory (Tesser, 2000) and shows that idealized models motivate improved performance in consumer domains that fall outside that of the original comparison. These more positive coping effects operate through self-discrepancies induced by idealized models, rather than self-esteem or negative affect. Specifically, self-discrepancies motivate consumers to improve decision-making by: 1) making more optimal choices from well-specified consideration sets, and 2) better self-regulating indulgent choices. More broadly, the current research integrates and extends theories of fluid compensation and self-discrepancy, as well as provides a more complete picture of the ways in which consumers cope with idealized advertising models.     

Social cognition models and health behaviour: A structured review

Abstract

A number of social cognition models have been developed to account for socio-demographic variations in health behaviour. This paper distinguishes between: (a) motivational, (b) behavioural enaction, and (c) multi-stage models of health behaviour. The models are evaluated in terms of advancement of existing knowledge and - where appropriate - predictive utility. Common themes that appear within- and between- these categories are discussed, with consideration of ways in which theory may be advanced by future research. Each approach has associated strengths and weaknesses, suggesting that a “consensus” approach to the study of health behaviour may prove fruitful. Identification of the key constructs across different model types would allow coherent integration and promote further understanding of the psycho-social determinants of health behaviour.     

Le momentum psychologique en sport : vers une perspective complexe et dynamique

Psychological momentum (PM) is a social phenomenon that has triggered the curiosity of sport psychologists since the early 1980s. From early to recent conceptualizations, PM has been considered as a complex construct, inherently related to variations in sport performance. Over the past 30 years, several models have been proposed to untangle how PM is triggered, which psychological changes are involved in PM experiences, and how PM relates to performance. While linear (causal) models have dominated past research on PM, researchers have recently found that PM can change nonlinearly, depending on the performance history of the competition. This systematic review aims to provide an overview of different PM models that have been proposed in the past to increase insights into the complexity of PM, including the transition from linear causal models to nonlinear dynamical models. Moreover, based on the current state of affairs, perspectives for future research are provided.     

Relapse as a nonlinear dynamic system: application to patients with alcohol use disorders

Understanding the relapse process is one of the most important issues in addictive behaviors research. To date, most studies have taken a linear approach toward predicting relapse based on risk factors. Nonlinear dynamical systems theory can be used to describe processes that are not adequately modeled using a linear approach. In particular, the authors propose that catastrophe theory, a subset of nonlinear dynamical systems theory, can be used to describe the relapse process in addictive behaviors. Two small prospective studies using 6-month follow-ups of patients with alcohol use disorders (inpatient, n = 51; outpatient, n = 43) illustrate how cusp catastrophe theory may be used to predict relapse. Results from these preliminary studies indicate that a cusp catastrophe model has more predictive utility than traditional linear models.     

解释性项目反应理论模型：理论与应用

解释性项目反应理论模型(Explanatory Item Response Theory Models, EIRTM)是指基于广义线性混合模型和非线性混合模型构建的项目反应理论(Item Response Theory, IRT)模型。EIRTM能在IRT模型的基础上直接加入预测变量, 从而解决各类测量问题。首先介绍EIRTM的相关概念和参数估计方法, 然后展示如何使用EIRTM处理题目位置效应、测验模式效应、题目功能差异、局部被试依赖和局部题目依赖, 接着提供实例对EIRTM的使用进行说明, 最后对EIRTM的不足之处和应用前景进行讨论。     

Estimating Complex Measurement and Growth Models Using the R Package PLmixed

Measurement models, such as factor analysis and item response theory models, are commonly implemented within educational, psychological, and behavioral science research to mitigate the negative effects of measurement error. These models can be formulated as an extension of generalized linear mixed models within a unifying framework that encompasses various kinds of multilevel models and longitudinal models, such as partially nonlinear latent growth models. We introduce the R package PLmixed, which implements profile maximum likelihood estimation to estimate complex measurement and growth models that can be formulated within the general modeling framework using the existing R package lme4 and function optim. We demonstrate the use of PLmixed through two examples before concluding with a brief overview of other possible models.     

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way we analyse these relationships does not seem to reflect the way we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modelling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such that R ² for worn-out and job satisfaction were significantly higher in the neural networks. Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method, and data.     

Multicomponent latent trait models for ability tests

A major research direction for ability measurement has been to identify the information-processes that are involved in solving test items through mathematical modeling of item difficulty. However, this research has had limited impact on ability measurement, since person parameters are not included in the process models. The current paper presents some multicomponent latent trait models for reproducing test performance from both item and person parameters on processing components. Components are identified from item subtasks, in which performance is a logistic function (i.e., Rasch model) of person and item parameters, and then are combined according to a mathematical model of processing on the composite item.The author would like to thank David Thissen for his invaluable insights concerning this model and an anonymous reviewer for his suggestion about the sample space for the model.This research was partially supported by National Institute of Education grant number NIE-6-7-0156 to Susan E. Whitely, principal investigator. However the opinions expressed herein do not necessarily reflect the position or policy of the National Institute of Education, and no official endorsement by the National Institute of Education should be referred. Part of this paper was presented at the annual meeting of thePsychometric Society, Monterey, California: June, 1979.     

Choice Environment, Market Complexity, and Consumer Behavior: A Theoretical and Empirical Approach for Incorporating Decision Complexity into Models of Consumer Choice

Most empirical models of choice in economics and consumer research assume that the decision maker assesses all alternatives and information in a perfect information-processing sense. The complexity of the choice environment, the ability of the individual to make complex decisions, and the effect of choice context on the decision strategy are generally not considered in statistical model development. One of the reasons for this omission is that theoretical literature on choice complexity and imperfect ability to choose that has developed in psychology and behavioral decision theory (BDT) literatures has not been translated into empirical econometric analysis. Second, the data used in economics and consumer research studies tend to be somewhat different from the data structures used in psychology and BDT literatures. In this paper we outline a theoretical model that simultaneously considers task complexity, effort applied by the consumer, ability to choose, and choice. We then construct a measure of task complexity and incorporate this in an analysis of a number of data series based on the random utility framework. We also examine the performance of our measure of task complexity in a composite data set that allows for increased variability in factors affecting decision context. Our approach provides a mechanism to link research in BDT and econometric modeling of consumer choice. Our findings suggest that task complexity does affect inferences about choice model parameters and that context effects, such as complexity, have a systematic impact on the parameters of econometric models of choice. The modeling approach provides a mechanism for inclusion of results from psychology and BDT in traditional economic models of consumer choice.     

A nonlinear mixed model framework for item response theory

Mixed models take the dependency between observations based on the same cluster into account by introducing 1 or more random effects. Common item response theory (IRT) models introduce latent person variables to model the dependence between responses of the same participant. Assuming a distribution for the latent variables, these IRT models are formally equivalent with nonlinear mixed models. It is shown how a variety of IRT models can be formulated as particular instances of nonlinear mixed models. The unifying framework offers the advantage that relations between different IRT models become explicit and that it is rather straightforward to see how existing IRT models can be adapted and extended. The approach is illustrated with a self-report study on anger.     

Walking through doorways causes forgetting: Event structure or updating disruption?

According to event cognition theory, people segment experience into separate event models. One consequence of this segmentation is that when people transport objects from one location to another, memory is worse than if people move across a large location. In two experiments participants navigated through a virtual environment, and recognition memory was tested in either the presence or the absence of a location shift for objects that were recently interacted with (i.e., just picked up or set down). Of particular concern here is whether this location updating effect is due to (a) differences in retention intervals as a result of the navigation process, (b) a temporary disruption in cognitive processing that may occur as a result of the updating processes, or (c) a need to manage multiple event models, as has been suggested in prior research. Experiment 1 explored whether retention interval is driving this effect by recording travel times from the acquisition of an object and the probe time. The results revealed that travel times were similar, thereby rejecting a retention interval explanation. Experiment 2 explored whether a temporary disruption in processing is producing the effect by introducing a 3-second delay prior to the presentation of a memory probe. The pattern of results was not affected by adding a delay, thereby rejecting a temporary disruption account. These results are interpreted in the context of the event horizon model, which suggests that when there are multiple event models that contain common elements there is interference at retrieval, which compromises performance.     

Understanding Workaholism: Data Synthesis, Theoretical Critique, and Future Design Strategies

Workaholism involves a personal reluctance to disengage from work, which is evidenced by the tendency to work irrespective of external demands. While the term workaholism has been widely used by the public for over 30 years, scientists are only beginning to explore the behavior in depth. To date, most research has occurred on an ad hoc basis, emerging from a wide variety of paradigms without being explicitly linked to theory. The current article presents three methods for defining workaholism and a précis of relevant measures, then provides an integrated overview of research relating workaholism to individual well-being. Three models implicit in existing workaholism research (addiction, learning, and trait theory) are expounded and critiqued, and four new procedures for researching workaholism are suggested. Finally, an integrated, multidisciplinary approach is proposed as an essential element in encouraging rigorous debate and continued development of workaholism theory.     

Latent Growth Models with Floors,Ceilings, and Random Knots

In longitudinal/developmental studies, individual growth trajectories are sometimes bounded by a floor at the beginning of the observation period and/or a ceiling toward the end of the observation period (or vice versa), resulting in inherently nonlinear growth patterns. If the trajectories between the floor and ceiling are approximately linear, such longitudinal growth patterns can be described with a linear piecewise (spline) model in which segments join at knots. In these scenarios, it may be of specific interest for researchers to examine the timing when transition occurs, and in some occasions also to examine the levels of the floors and/or ceilings if they are not known and fixed. In the current study, we propose a reparameterized piecewise latent growth curve model so that a direct estimation of the random knots (and, if needed, a direct estimation of random floors and ceilings) is possible. We derive the model reparameterization using a 4-step structured latent curve modeling approach. We provide two illustrative examples to demonstrate how the proposed reparameterized models can be fitted to longitudinal growth data using the popular SEM software Mplus and we supply the full coding for applied researchers’ reference.     

Linearity of Personality–Performance Relationships: A large‐scale examination

This paper reviews theory and past research concerning nonlinear relationships between personality tests and job performance. We compare linear and nonlinear regression models for each of the 32 Occupational Personality Questionnaire (OPQ32i) scales using job performance as the criterion variable. We evaluate linear, quadratic, and cubic relationships between personality and job performance by calculating changes in R (ΔR) between the linear and nonlinear models. We use 3 decision rules of varying levels for ΔR and divide a large sample randomly into 2 samples to cross‐validate any nonlinear effects found. Few scales exhibited nonlinearity and the magnitude of the departures from linearity were small.     

重参数化的多分属性诊断分类模型及其判准率影响因素

多分属性比传统的二分属性提供更多更详细的诊断反馈信息, 符合对知识技能的多水平要求, 具有较好的应用前景。本文首先介绍了多分属性和多分Q矩阵的概念; 之后重参数化了3个分别满足连接、分离和补偿缩合规则的多分属性诊断分类模型并研究了其判准率影响因素, 结果发现它们的判准率(1)均随多分属性数量的增加而降低, 建议实际使用中不宜高于5个; (2)均随多分属性的最高水平数增加而降低, 建议实际使用中不宜高于4水平; (3)均随多分属性间统计相关性增加而增加, 但影响不大; (4)受多分属性层级结构的影响较大; (4)受被试量影响不大; (5)均随题目数量增加而增加且影响较大。最后, 针对“多分属性与多级评分的关系”和“多分属性与二分属性之间的关系”这两个问题进行了讨论。以期为实证研究者提供相关的理论支持和使用建议。     

On motivational influences,moving beyond valence,and integrating dimensional and discrete views of emotion

ABSTRACT

The field of cognition and emotion has grown considerably over the past 30 years, with an increased emphasis on the relationships between emotional and motivational components and how they contribute to basic perceptual, cognitive, and neural processes. For instance, research has revealed that emotion often influences these processes via emotion’s relationship with motivational dimensions, as when positive emotions low versus high in approach motivational intensity have different influences on attentional and other cognitive processes. Research has also revealed that motivational direction (approach vs. withdrawal) and affective valence (positive vs. negative) are not as closely related as once was theorised; that is, positive affect is not inevitably associated with approach motivation and negative affect is not inevitably associated with withdrawal motivation. These and other lines of research with anger have suggested that the field needs to move beyond a focus on affective valence and it needs to consider an integration of dimensional and discrete models of emotion. The article also includes some suggestions for improving methods of measuring and inducing emotions and some recommendations for future researchers.