We analyze data from a very large (n=854064) sample of players of an online game involving rapid perception, decision-making and motor responding. Use of game data allows us to connect, for the first time, rich details of training history with measures of performance, for participants who are engaged for a sustained amount of time in effortful practice. We show that lawful relations exist between practice amount and subsequent performance, and between practice spacing and subsequent performance. This allows an in-situ confirmation of results long established in the experimental literature on skill acquisition. Additionally, we show that higher initial variation in performance is linked to subsequent higher performance, a result we link to the exploration-exploitation trade-off from the computational framework of reinforcement learning. We discuss the benefits and opportunities of behavioral datasets with very large sample sizes and suggest that this approach could be particularly fecund for studies of skill acquisition.

Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. Using ideas from statistics, signal processing, and ecology, we provide a predictive framework able to assimilate data and give confidence estimates on the predictions. We demonstrate our methods on the WikiLeaks Afghan War Diary. Our results show that the approach allows deeper insights into conflict dynamics and allows a strikingly statistically accurate forward prediction of armed opposition group activity in 2010, based solely on data from previous years.conflict prediction | point processes | variational Bayes T he last decade has witnessed a tremendous increase in the availability of data relating to conflicts. For example, the collection of media reports in the ' Armed Conflict Location and Event Dataset' (1) provides a small scale but highly curated record of conflict events. More prominently, the release of confidential documents by the WikiLeaks whistleblower website in July 2010 has provided for the first time a large scale (but uncurated) description of the current Afghan conflict. However, most analyses of these and similar data sources do not go beyond visualization and descriptive statistical methods (2-5), for good reasons: first, conflict data is highly heterogeneous and often poorly annotated. For example, the WikiLeaks Afghan War Diary (AWD) data used in this study (Dataset S1) consists of event entries as diverse as elaborate preplanned military activity and spontaneous stop-and-search events. Any plausible attempt to model this data will need to be statistical in nature in order to handle the high levels of noise. Second, it is very difficult to define simple mechanisms that would allow the bottom-up construction of a plausible model.Here, we develop statistical dynamical modelling methodologies to provide a predictive framework that may be used in policy making. We show that the temporal and spatial dependencies (6, 7) as well as diffusion and advection effects (8, 9) inherent in conflict data make it suitable for the use of a broad class of models, widely employed in ecology and epidemiology, in order to describe the dynamics of disaggregated data. We then develop tools based on ideas from point process statistics (10) to constrain the models. The approach enables us to leverage powerful techniques from point process filtering theory and spatiotemporal statistics (11-14) to carry out inference of the underlying system's dynamics and to predict the future behavior of the system.We test the performance of our methods on the AWD, a WikiLeaks release which contains over 75,000 military logs by th...

This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch-Tung-Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.