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In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models—the Ising model from physics—and one of the most important latent variable models—the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection. [ABSTRACT FROM PUBLISHER]

Estimated generalized least squares (EGLS) electromagnetic source analysis is used to downweight noisy and correlated data. Standard EGLS requires many trials to accurately estimate the noise covariances and, thus, the source parameters. Alternatively, the noise covariances can be modeled parametrically. Only the parameters of the model describing the noise covariances need to be estimated and, therefore, less trials are required. This method is referred to as parametric egls (PEGLS). In this paper, PEGLS is developed and its performance is tested in a simulation study and in a pseudoempirical study.

A simple connectionist model for the Stroop task was implemented to investigate a defective inhibition hypothesis first postulated by Fox in 1994 for individuals of high anxiety. Simulations with this model indicated that Stroop interference was positively, instead of negatively, related to inhibition. An alternative excitation hypothesis assuming that high anxiety corresponds to stronger verbal connections to the distracting material could also be investigated in the model. Stronger verbal connections indeed corresponded to larger interference. It is argued that this type of simulation could be useful, not because it proves in any way the supposed mechanisms, but because it clarifies the relation between theory and empirical data and so stimulates further specification of theoretical assumptions and invites empirical research into these assumptions.