Treatment rejecting and treatment seeking personality disorders: Type R and Type S

An important distinguishing feature of one group of personality disorders is the wish of the sufferer to seek treatment. For another group this wish is rarely entertained. Although there is some variation between different types of personality disorder the wish to change is not confined to any one diagnostic category. A useful subclassification of personality disorders is therefore into Type R (treatment rejecting) and Type S (treatment seeking) personality disorders, and these are defined operationally. The classification of 68 personality disordered patients on the caseload of an assertive community team using a simple scale showed a 3 to 1 ratio between Type R and Type S personality disorders with Cluster C personality disorders being significantly more likely to be Type S, and paranoid and schizoid (Cluster A) personality disorders significantly more likely to be Type R than others. It is suggested that this typology is useful for those contemplating treatment with those who have personality disorders.     

The material–weight illusion induced by expectations alone

In the material-weight illusion (MWI), equally weighted objects that appear to be made from different materials are incorrectly perceived as having different weights when they are lifted one after the other. Here, we show that continuous visual experience of the lift is not a prerequisite for this compelling misperception of weight; merely priming the lifters' expectations of heaviness is sufficient for them to experience a robust MWI. Furthermore, these expectations continued to influence the load force used to lift MWI-inducing stimuli trial after trial, supporting the notion that vision plays an important role in the skillful lifting of objects.     

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.