A Comparison of Inverse-Wishart Prior Specifications for Covariance Matrices in Multilevel Autoregressive Models

Multilevel autoregressive models are especially suited for modeling between-person differences in within-person processes. Fitting these models with Bayesian techniques requires the specification of prior distributions for all parameters. Often it is desirable to specify prior distributions that have negligible effects on the resulting parameter estimates. However, the conjugate prior distribution for covariance matrices—the Inverse-Wishart distribution—tends to be informative when variances are close to zero. This is problematic for multilevel autoregressive models, because autoregressive parameters are usually small for each individual, so that the variance of these parameters will be small. We performed a simulation study to compare the performance of three Inverse-Wishart prior specifications suggested in the literature, when one or more variances for the random effects in the multilevel autoregressive model are small. Our results show that the prior specification that uses plug-in ML estimates of the variances performs best. We advise to always include a sensitivity analysis for the prior specification for covariance matrices of random parameters, especially in autoregressive models, and to include a data-based prior specification in this analysis. We illustrate such an analysis by means of an empirical application on repeated measures data on worrying and positive affect

Transitions in smoking behaviour and the design of cessation schemes.

The intake of nicotine by smoking cigarettes is modelled by a dynamical system of differential equations. The variables are the internal level of nicotine and the level of craving. The model is based on the dynamics of neural receptors and the way they enhance craving. Lighting of a cigarette is parametrised by a time-dependent Poisson process. The nicotine intake rate is assumed to be proportional with the parameter of this stochastic process. The effect of craving is damped by a control mechanism in which awareness of the risks of smoking and societal measures play a role. Fluctuations in this damping may cause transitions from smoking to non-smoking and vice versa. With the use of Monte Carlo simulation the effect of abrupt and gradual cessation therapies are evaluated. Combination of the two in a mixed scheme yields a therapy with a duration that can be set at wish

Modeling Affect Dynamics:State of the Art and Future Challenges

The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data (ILD). We do so by introducing the following eight dichotomies that help elucidate what kind of data one has, what process aspects are of interest, and what research questions are being considered: (1) single- versus multiple-person data; (2) univariate versus multivariate models; (3) stationary versus nonstationary models; (4) linear versus nonlinear models; (5) discrete time versus continuous time models; (6) discrete versus continuous variables; (7) time versus frequency domain; and (8) modeling the process versus computing descriptives. In addition, we discuss what we believe to be the most urging future challenges regarding the modeling of affect dynamics

Threat learning impairs subsequent associative inference

Despite it being widely acknowledged that the most important function of memory is to facilitate the prediction of significant events in a complex world, no studies to date have investigated how our ability to infer associations across distinct but overlapping experiences is affected by the inclusion of threat memories. To address this question, participants (n = 35) encoded neutral predictive associations (A → B). The following day these memories were reactivated by pairing B with a new aversive or neutral outcome (B → C(THREAT/NEUTRAL)) while pupil dilation was measured as an index of emotional arousal. Then, again 1 day later, the accuracy of indirect associations (A → C?) was tested. Associative inferences involving a threat learning memory were impaired whereas the initial memories were retroactively strengthened, but these effects were not moderated by pupil dilation at encoding. These results imply that a healthy memory system may compartmentalize episodic information of threat, and so hinders its recall when cued only indirectly. Malfunctioning of this process may cause maladaptive linkage of negative events to distant and benign memories, and thereby contribute to the development of clinical intrusions and anxiety