OpenMx is free, full–featured, open source, structural equation modeling (SEM) software. OpenMx runs within the R statistical programming environment on Windows, Mac OS–X, and Linux computers. The rationale for developing OpenMx is discussed along with the philosophy behind the user interface. The OpenMx data structures are introduced — these novel structures define the user interface framework and provide new opportunities for model specification. Two short example scripts for the specification and fitting of a confirmatory factor model are next presented. We end with an abbreviated list of modeling applications available in OpenMx 1.0 and a discussion of directions for future development.

The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly-written CSOLNP. Entire new methodologies such as Item Factor analysis (IRT) and State-space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

Lucid dreaming is a state of awareness that one is dreaming, without leaving the sleep state. Dream reports show that self-reflection and volitional control are more pronounced in lucid compared with nonlucid dreams. Mostly on these grounds, lucid dreaming has been associated with metacognition. However, the link to lucid dreaming at the neural level has not yet been explored. We sought for relationships between the neural correlates of lucid dreaming and thought monitoring.Human participants completed a questionnaire assessing lucid dreaming ability, and underwent structural and functional MRI. We split participants based on their reported dream lucidity. Participants in the high-lucidity group showed greater gray matter volume in the frontopolar cortex (BA9/10) compared with those in the low-lucidity group. Further, differences in brain structure were mirrored by differences in brain function. The BA9/10 regions identified through structural analyses showed increases in blood oxygen leveldependent signal during thought monitoring in both groups, and more strongly in the high-lucidity group.Our results reveal shared neural systems between lucid dreaming and metacognitive function, in particular in the domain of thought monitoring. This finding contributes to our understanding of the mechanisms enabling higher-order consciousness in dreams.

Robots that interact with humans face-to-face using natural language need to be responsive to the way humans use language in those situations. We propose a psychologicallyinspired natural language processing system for robots which performs incremental semantic interpretation of spoken utterances, integrating tightly with the robot's perceptual and motor systems.