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Expectations about an impeding pain stimulus strongly shape its perception, yet the degree that uncertainty might affect perception is far less understood. To explore the influence of uncertainty on pain ratings, we performed a close replication of the study of Yoshida, Seymour, Koltzenburg, and Dolan (2013), who manipulated vicarious information about upcoming heat pain and found evidence for uncertainty-induced hyperalgesia. In our study, we presented eight fictitious ratings of previous participants prior the delivery of electrocutaneous pain. The vicarious information was either biased to over- or underreport pain levels based on the participant's psychometric function. We induced uncertainty by manipulating the variation of the vicarious information. As in Yoshida et al. (2013), four computational models were formulated, such that each model represented a different way of how the pain ratings might have been generated by the physical stimulus and the vicarious information. The four competing models were tested against the data of each participant separately. Using a formal model selection criterion, the best model was selected and interpreted. Contrary to the original study, the preferred model for the majority of participants suggested that pain ratings were biased towards the average vicarious information, ignoring the degree of uncertainty. Possible reasons for these diverging results are discussed.

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BACKGROUND: Affective dysregulation is widely regarded as being the core problem in patients with borderline personality disorder (BPD). Moreover, BPD is the disorder mainly associated with affective dysregulation. However, the empirical confirmation of the specificity of affective dysregulation for BPD is still pending. We used a validated approach from basic affective science that allows for simultaneously analyzing three interdependent components of affective dysregulation that are disturbed in patients with BPD: homebase, variability, and attractor strength (return to baseline). METHODS: We applied two types of multilevel models on two e-diary datasets to investigate group differences regarding three subcomponents between BPD patients (n = 43; n = 51) and patients with posttraumatic stress disorder (PTSD; n = 28) and those with bulimia nervosa (BN; n = 20) as clinical control groups in dataset 1, and patients with panic disorder (PD; n = 26) and those with major depression (MD; n = 25) as clinical control groups in dataset 2. In addition, healthy controls (n = 28; n = 40) were included in the analyses. In both studies, e-diaries were used to repeatedly collect data about affective experiences during participants' daily lives. In study 1 a high-frequency sampling strategy with assessments in 15 min-intervals over 24 h was applied, whereas the assessments occurred every waking hour over 48 h in study 2. The local ethics committees approved both studies, and all participants provided written informed consent. RESULTS: In contradiction to our hypotheses, BPD patients did not consistently show altered affective dysregulation compared to the clinical patient groups. The only differences in affective dynamics in BPD patients emerged with regard to one of three subcomponents, affective homebase. However, these results were not even consistent. Conversely, comparing the patients to healthy controls revealed a pattern of more negative affective homebases, higher levels of affective variability, and (partially) reduced returns to baseline in the patient groups. CONCLUSIONS: Our results indicate that affective dysregulation constitutes a transdiagnostic mechanism that manifests in similar ways in several different mental disorders. We point out promising prospects that might help to elucidate the common and distinctive mechanisms that underlie several different disorders and that should be addressed in future studies.

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BACKGROUND: Structured interviews and questionnaires are important tools to screen for major depressive disorder. Recent research suggests that, in addition to studying the mean level of total scores, researchers should focus on the dynamic relations among depressive symptoms as they unfold over time. Using network analysis, this paper is the first to investigate these patterns of short-term (i.e. session to session) dynamics for a widely used psychological questionnaire for depression - the Beck Depression Inventory (BDI-II). METHOD: With the newly developed vector autoregressive (VAR) multilevel method we estimated the network of symptom dynamics that characterizes the BDI-II, based on repeated administrations of the questionnaire to a group of depressed individuals who participated in a treatment study of an average of 14 weekly assessments. Also the centrality of symptoms and the community structure of the network were examined. RESULTS: The analysis showed that all BDI-II symptoms are directly or indirectly connected through patterns of temporal influence. In addition, these influences are mutually reinforcing, 'loss of pleasure' being the most central item in the network. Community analyses indicated that the dynamic structure of the BDI-II involves two clusters, which is consistent with earlier psychometric analyses. CONCLUSION: The network approach expands the range of depression research, making it possible to investigate the dynamic architecture of depression and opening up a whole new range of questions and analyses. Regarding clinical practice, network analyses may be used to indicate which symptoms should be targeted, and in this sense may help in setting up treatment strategies.

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BACKGROUND: From the perspective of the cognitive mediational paradigm, we focus in this study on students' conceptions of the relationship between instructional interventions and learning: 'instructional knowledge'. AIMS: Task perception has been investigated as a procedural manifestation of instructional knowledge. Four research questions directed the study: (1) how do students perceive a task; (2) by which structure can the relations between categories of task perception be represented; (3) do students differ in their task perception; and (4) is there a significant relationship between students' task perception and the learning activities they plan and/or execute. SAMPLE: The sample consisted of 149 university freshmen in educational sciences. METHODS: Students were confronted with a concrete task in a natural setting. Correlations were searched for by phi coefficient. Hierarchical classes analysis was used to search for hierarchical relations and inter-individual differences. Goodman-Kruskal lambda was calculated to estimate the association between students' task perception and the learning activities they planned and executed. The questionnaire and the design of the coding systems were first tried out in a pilot study. RESULTS: Students' task perception can be described in 11 categories. Correlations between those categories were low, but a simple hierarchical structure was discovered. Students can be distinguished according to their task perception into eight groups. Finally, the results indicate a statistically significant association between students' task perception and the learning activities they plan and execute. CONCLUSIONS: The study provides additional evidence to involve 'instructional knowledge' and students' task perception as part of it, as mediating variables in future research.

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Most item response theory models assume conditional independence, and it is known that interactions between items affect the estimated item discrimination. In this article, this effect is further investigated from a theoretical perspective and by means of simulation studies. To this end, a parametric model for item interactions is introduced. Next, it is shown that ignoring a positive interaction results in an overestimation of the discrimination parameter in the two-parameter logistic model (2PLM), whereas ignoring a negative interaction leads to an underestimation of the parameter. Furthermore, it is demonstrated that in some cases the item characteristic curves of the 2PLM and of an item involved in an interaction are quite similar, indicating that the 2PLM can provide a good fit to data with interactions.

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Four methods for the simulation of the Wiener process with constant drift and variance are described. These four methods are (1) approximating the diffusion process by a random walk with very small time steps; (2) drawing directly from the joint density of responses and reaction time by means of a (possibly) repeated application of a rejection algorithm; (3) using a discrete approximation to the stochastic differential equation describing the diffusion process; and (4) a probability integral transform method approximating the inverse of the cumulative distribution function of the diffusion process. The four methods for simulating response probabilities and response times are compared on two criteria: simulation speed and accuracy of the simulation. It is concluded that the rejection-based and probability integral transform method perform best on both criteria, and that the stochastic differential approximation is worst. An important drawback of the rejection method is that it is applicable only to the Wiener process, whereas the probability integral transform method is more general.

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