Establishing normative data for repeated cognitive assessment: A comparison of different statistical methods

Serial cognitive assessment is conducted to monitor changes in the cognitive abilities of patients over time. At present, mainly the regression-based change and the ANCOVA approaches are used to establish normative data for serial cognitive assessment. These methods are straightforward, but they have some severe drawbacks. For example, they can only consider the data of two measurement occasions. In this article, we propose three alternative normative methods that are not hampered by these problems—that is, multivariate regression, the standard linear mixed model (LMM), and the linear mixed model combined with multiple imputation (LMM with MI) approaches. The multivariate regression method is primarily useful when a small number of repeated measurements are taken at fixed time points. When the data are more unbalanced, the standard LMM and the LMM with MI methods are more appropriate because they allow for a more adequate modeling of the covariance structure. The standard LMM has the advantage that it is easier to conduct and that it does not require a Monte Carlo component. The LMM with MI, on the other hand, has the advantage that it can flexibly deal with missing responses and missing covariate values at the same time. The different normative methods are illustrated on the basis of the data of a large longitudinal study in which a cognitive test (the Stroop Color Word Test) was administered at four measurement occasions (i.e., at baseline and 3, 6, and 12 years later). The results are discussed and suggestions for future research are provided.     

The DINA model as a constrained general diagnostic model: Two variants of a model equivalency

The ‘deterministic‐input noisy‐AND’ (DINA) model is one of the more frequently applied diagnostic classification models for binary observed responses and binary latent variables. The purpose of this paper is to show that the model is equivalent to a special case of a more general compensatory family of diagnostic models. Two equivalencies are presented. Both project the original DINA skill space and design Q ‐matrix using mappings into a transformed skill space as well as a transformed Q ‐matrix space. Both variants of the equivalency produce a compensatory model that is mathematically equivalent to the (conjunctive) DINA model. This equivalency holds for all DINA models with any type of Q ‐matrix, not only for trivial (simple‐structure) cases. The two versions of the equivalency presented in this paper are not implied by the recently suggested log‐linear cognitive diagnosis model or the generalized DINA approach. The equivalencies presented here exist independent of these recently derived models since they solely require a linear – compensatory – general diagnostic model without any skill interaction terms. Whenever it can be shown that one model can be viewed as a special case of another more general one, conclusions derived from any particular model‐based estimates are drawn into question. It is widely known that multidimensional models can often be specified in multiple ways while the model‐based probabilities of observed variables stay the same. This paper goes beyond this type of equivalency by showing that a conjunctive diagnostic classification model can be expressed as a constrained special case of a general compensatory diagnostic modelling framework.     

Kuhn and the Question of Pursuit Worthiness

The aim of this paper is, on the one hand, to critically investigate Kuhn’s stance on the assessment of the pursuit worthiness of scientific theories, and, on the other hand, to show the actuality of some of Kuhn’s points on this issue, in view of their critical analysis. To this end we show that Kuhn presents certain tools, which may help scientists to overcome communication breakdowns when engaging in the process of rational deliberation regarding the question whether a theory is worthy of further pursuit. These tools are persuasion, translation and interpretation. However, we argue that the perspective of epistemic semantic monism present in Kuhn’s work obstructs the full applicability of these tools. We show that dropping this perspective makes the notions of persuasion and interpretation more fruitful, and moreover, allows for a pluralism of scientific theories and practices that complements the pluralism based on disagreement among scientists, emphasized by Kuhn.     

Meaning making,self‐determination theory,and the question of wisdom in personality

Self‐determination theory (SDT) has advanced the most comprehensive model of motives for human flourishing in the field of personality psychology and beyond. In this article, we evaluate SDT relative to the process of meaning making, particularly from a narrative perspective, showing what SDT can and cannot explain about the construction of self‐identity and its relation to human flourishing. On the one hand, SDT explains how subjective assessments of need fulfillment drive the process of self‐determined living. The internal motives that follow such fulfillment serve as important themes in people's life stories that predict several markers of hedonic and eudaimonic well‐being. On the other hand, SDT's focus on subjective fulfillment limits what SDT can explain about how wisdom, which is a canonical good of both eudaimonia and meaning making, helps people make sense of life's more difficult or unfulfilling events. SDT may facilitate a facet of wisdom that is more subjective and experiential but not the critical facet of wisdom defined by objectively more complex structures of interpretation.     

The Ornstein-Uhlenbeck model for decision time in cognitive tasks: An example of control of nonlinear network dynamics

The Ornstein-Uhlenbeck (OU) model for human decision-making has been successfully applied to account for response accuracy and response time (RT) data in recent two-choice decision models. A variant of the OU model is shown to arise from the response dynamics of a nonlinear network consisting of randomly connected neural processing units. When feedback control of the network is effected by the stimulus onset, the average network response is an autocorrelated random signal satisfying the stochastic differential equation for the OU process. An alternative, more general, stimulus detection procedure is proposed which involves the use of an adaptive Kalman filter process to track any temporal change in autoregressive parameters. The predicted decision time distributions suggest that both the OU and the Kalman filter processes can serve as alternative models for RT data in experimental tasks. Received: 15 October 1998 / Accepted: 27 May 1999     

Learning representations in a gated prefrontal cortex model of dynamic task switching

The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based on a temporal‐differences reward‐prediction learning mechanism. Unlike prior models of this type, the present model develops all of its internal representations via learning mechanisms as shaped by the demands of continuous periodic task switching. This advance opens up a new domain of research into the interactions between working memory task demands and the representations that develop to meet them. Results on a version of the Wisconsin card sorting task are presented for the full model and a number of comparison networks that test the importance of various model features. Furthermore, we show that a lesioned model produces perseverative errors like those seen in frontal patients.     

Modelling non‐normal data: The relationship between the skew‐normal factor model and the quadratic factor model

Maximum likelihood estimation of the linear factor model for continuous items assumes normally distributed item scores. We consider deviations from normality by means of a skew‐normally distributed factor model or a quadratic factor model. We show that the item distributions under a skew‐normal factor are equivalent to those under a quadratic model up to third‐order moments. The reverse only holds if the quadratic loadings are equal to each other and within certain bounds. We illustrate that observed data which follow any skew‐normal factor model can be so well approximated with the quadratic factor model that the models are empirically indistinguishable, and that the reverse does not hold in general. The choice between the two models to account for deviations of normality is illustrated by an empirical example from clinical psychology.     

Coherent psychometric modelling with Bayesian nonparametrics

In this paper we argue that model selection, as commonly practised in psychometrics, violates certain principles of coherence. On the other hand, we show that Bayesian nonparametrics provides a coherent basis for model selection, through the use of a ‘nonparametric’ prior distribution that has a large support on the space of sampling distributions. We illustrate model selection under the Bayesian nonparametric approach, through the analysis of real questionnaire data. Also, we present ways to use the Bayesian nonparametric framework to define very flexible psychometric models, through the specification of a nonparametric prior distribution that supports all distribution functions for the inverse link, including the standard logistic distribution functions. The Bayesian nonparametric approach provides a coherent method for model selection that can be applied to any statistical model, including psychometric models. Moreover, under a ‘non‐informative’ choice of nonparametric prior, the Bayesian nonparametric approach is easy to apply, and selects the model that maximizes the log likelihood. Thus, under this choice of prior, the approach can be extended to non‐Bayesian settings where the parameters of the competing models are estimated by likelihood maximization, and it can be used with any psychometric software package that routinely reports the model log likelihood.     

A Latent Gaussian process model for analysing intensive longitudinal data

Intensive longitudinal studies are becoming progressively more prevalent across many social science areas, and especially in psychology. New technologies such as smart-phones, fitness trackers, and the Internet of Things make it much easier than in the past to collect data for intensive longitudinal studies, providing an opportunity to look deep into the underlying characteristics of individuals under a high temporal resolution. In this paper we introduce a new modelling framework for latent curve analysis that is more suitable for the analysis of intensive longitudinal data than existing latent curve models. Specifically, through the modelling of an individual-specific continuous-time latent process, some unique features of intensive longitudinal data are better captured, including intensive measurements in time and unequally spaced time points of observations. Technically, the continuous-time latent process is modelled by a Gaussian process model. This model can be regarded as a semi-parametric extension of the classical latent curve models and falls under the framework of structural equation modelling. Procedures for parameter estimation and statistical inference are provided under an empirical Bayes framework and evaluated by simulation studies. We illustrate the use of the proposed model though the analysis of an ecological momentary assessment data set.     

A neurocognitive model of meditation based on activation likelihood estimation (ALE) meta-analysis

Meditation comprises a series of practices mainly developed in eastern cultures aiming at controlling emotions and enhancing attentional processes. Several authors proposed to divide meditation techniques in focused attention (FA) and open monitoring (OM) techniques. Previous studies have reported differences in brain networks underlying FA and OM. On the other hand common activations across different meditative practices have been reported. Despite differences between forms of meditation and their underlying cognitive processes, we propose that all meditative techniques could share a central process that would be supported by a core network for meditation since their general common goal is to induce relaxation, regulating attention and developing an attitude of detachment from one's own thoughts. To test this hypothesis, we conducted a quantitative meta-analysis based on activation likelihood estimation (ALE) of 10 neuroimaging studies (91 subjects) on different meditative techniques to evidence the core cortical network subserving meditation. We showed activation of basal ganglia (caudate body), limbic system (enthorinal cortex) and medial prefrontal cortex (MPFC). We discuss the functional role of these structures in meditation and we tentatively propose a neurocognitive model of meditation that could guide future research.     

Abstract argumentation and explanation applied to scientific debates

Abstract argumentation has been shown to be a powerful tool within many fields such as artificial intelligence, logic and legal reasoning. In this paper we enhance Dung’s well-known abstract argumentation framework with explanatory capabilities. We show that an explanatory argumentation framework (EAF) obtained in this way is a useful tool for the modeling of scientific debates. On the one hand, EAFs allow for the representation of explanatory and justificatory arguments constituting rivaling scientific views. On the other hand, different procedures for selecting arguments, corresponding to different methodological and epistemic requirements of theory evaluation, can be formulated in view of our framework.     

Book reviews

Results from past studies show that the central executive, as conceptualised in the working memory model of Baddeley and Hitch (), plays an important role in simple mental arithmetic. According to this model the central executive is viewed as a unitary system. Recent research, however, suggests that the “central executive” can be fractionated in more autonomous executive functions, such as inhibition, response selection, planning, and input monitoring. In four experiments we studied the role of two possible executive functions (input monitoring and response selection) in solving simple sums. Subjects solved one‐digit sums (e.g., 5 + 7) in a single‐task condition as well as in dual‐task conditions. The results show that secondary tasks, which require a choice, impair the calculation of simple sums. On the other hand, an increased degree of input monitoring in the secondary tasks does not seem to impair the calculation of the sums. These findings show that response selection is strongly involved in simple arithmetic and may be one of the core executive processes in mental arithmetic.     

The structure of self‐reported emotional experiences: A mixed‐effects Poisson factor model

Multivariate count data are commonly analysed by using Poisson distributions with varying intensity parameters, resulting in a random‐effects model. In the analysis of a data set on the frequency of different emotion experiences we find that a Poisson model with a single random effect does not yield an adequate fit. An alternative model that requires as many random effects as emotion categories requires high‐dimensional integration and the estimation of a large number of parameters. As a solution to these computational problems, we propose a factor‐analytic Poisson model and show that a two‐dimensional factor model fits the reported data very well. Moreover, it yields a substantively satisfactory solution: one factor describing the degree of pleasantness and unpleasantness of emotions and the other factor describing the activation levels of the emotions. We discuss the incorporation of covariates to facilitate rigorous tests of the random‐effects structure. Marginal maximum likelihood methods lead to straight‐forward estimation of the model, for which goodness‐of‐fit tests are also presented.     

The contrasting effects of negative word of mouth in the post-consumption stage

We explore the effects of negative word of mouth (NWOM) from worse-off or similar others in the post-consumption stage. In four experiments, we show that the ramifications of NWOM are more complex than portrayed in the literature. Specifically, we demonstrate that attribute-based NWOM has a negative (i.e., aggravating) effect on dissatisfied consumers, whereas experience-based NWOM has a positive (i.e., alleviating) effect. Thought-listing data reveal distinct processes underlying the contrasting effects. On one hand, these results are consistent with the predictions of attitude polarization and downward comparison research. On the other hand, they are explainable in terms of the disconfirmation model.     

A circular and dynamic model of the process of job design

We present a model of the process of job design that attempts to develop theory in five ways. First, critical variables in the model include performance, perceived competence, trust, knowledge and self‐efficacy. Second, job design is proposed to be a dynamic and circular process, with the variables acting as both predictors and outcomes. Third, the process is seen, at least in part, as a social one, involving job‐holders, supervisors and peers. Fourth, we identify some of the contingencies affecting the model. Finally, we specify the causal ordering of events and speculate on the nature of the time lags involved in the process. We make a number of model‐based predictions and consider the implications of the model for theory, research and practice.     

Temporal control and coordination: the multiple timer model.

We consider the psychological and neurological mechanisms involved in timed behaviors, motor or perceptual tasks that emphasize the temporal relationship between successive events. Two general models for representing temporal information are described. In one model, temporal information is based on the oscillatory activity of an endogenous pacemaker; in the other model, temporal information is interval-based with distinct elements devoted to representing different intervals. We incorporate the interval hypothesis into a process model, the multiple timer model, to account for the timing and coordination of repetitive movements. The model accounts for the patterns of temporal stability observed within each effector and offers a novel account of between-effector coordination. Finally, we consider how timing and temporal coordination may be instantiated in the nervous system.     

A latent trait model for response times on tests employing the proportional hazards model

For computer-administered tests, response times can be recorded conjointly with the corresponding responses. This broadens the scope of potential modelling approaches because response times can be analysed in addition to analysing the responses themselves. For this purpose, we present a new latent trait model for response times on tests. This model is based on the Cox proportional hazards model. According to this model, latent variables alter a baseline hazard function. Two different approaches to item parameter estimation are described: the first approach uses a variant of the Cox model for discrete time, whereas the second approach is based on a profile likelihood function. Properties of each estimator will be compared in a simulation study. Compared to the estimator for discrete time, the profile likelihood estimator is more efficient, that is, has smaller variance. Additionally, we show how the fit of the model can be evaluated and how the latent traits can be estimated. Finally, the applicability of the model to an empirical data set is demonstrated.     

A mixture hierarchical model for response times and response accuracy

In real testing, examinees may manifest different types of test‐taking behaviours. In this paper we focus on two types that appear to be among the more frequently occurring behaviours – solution behaviour and rapid guessing behaviour. Rapid guessing usually happens in high‐stakes tests when there is insufficient time, and in low‐stakes tests when there is lack of effort. These two qualitatively different test‐taking behaviours, if ignored, will lead to violation of the local independence assumption and, as a result, yield biased item/person parameter estimation. We propose a mixture hierarchical model to account for differences among item responses and response time patterns arising from these two behaviours. The model is also able to identify the specific behaviour an examinee engages in when answering an item. A Monte Carlo expectation maximization algorithm is proposed for model calibration. A simulation study shows that the new model yields more accurate item and person parameter estimates than a non‐mixture model when the data indeed come from two types of behaviour. The model also fits real, high‐stakes test data better than a non‐mixture model, and therefore the new model can better identify the underlying test‐taking behaviour an examinee engages in on a certain item.