A Joint Modeling Approach for Reaction Time and Accuracy in Psycholinguistic Experiments

In the psycholinguistic literature, reaction times and accuracy can be analyzed separately using mixed (logistic) effects models with crossed random effects for item and subject. Given the potential correlation between these two outcomes, a joint model for the reaction time and accuracy may provide further insight. In this paper, a Bayesian hierarchical framework is proposed that allows estimation of the correlation between time intensity and difficulty at the item level, and between speed and ability at the subject level. The framework is shown to be flexible in that reaction times can follow a (log-) normal or (shifted) Weibull distribution. A simulation study reveals the reduction in bias gains possible when using joint models, and an analysis of an example from a Dutch–English word recognition study illustrates the proposed method.     

Determining a long term ambulatory load profile of the shoulder joint: neural networks predicting input for a musculoskeletal model

To gain more insight in the development of joint damage, a long term load profile of the shoulder joint under daily living conditions is desirable. Standard musculoskeletal models estimate joint load using kinematics and exerted force. However, the latter cannot be measured continuously in ambulatory settings, hampering the use of these models. This paper describes a method for obtaining such a load profile, by training a Neural Network (NN), using kinematics and EMG. A small data set of specified movements with known exerted forces is used in two ways. First, the model calculates several variables of joint load, and a set of Generalized Forces and Net Moments (GFNM) around the model's degrees of freedom. Second, using kinematics and EMG, an NN is trained to predict these GFNM, which can concurrently be used as input for the model, resulting in full model output independent of exerted force. The method is validated with an independent trial. The NN could predict GFNM within 10% relative RMS, compared to output of the model. The NN-model combination estimated joint reaction forces with relative RMS values of 7 to 17%, enabling the estimation of a detailed load profile of the shoulder under daily conditions.     

Construct representation and definitions in psychopathology: the case of delusion

Background  

Delusion is one of the most intriguing psychopathological phenomena and its conceptualization remains the subject of genuine debate. Claims that it is ill-defined, however, are typically grounded on essentialist expectations that a given definition should capture the core of every instance acknowledged as delusion in the clinical setting.