Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

BMC: Toolkit for Bayesian analysis of Computational Models using samplers

BackgroundComputational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model.ResultsWe present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved.ConclusionsBCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.Pattern Recognition and Bioinformatic

A Bayesian psychophysical model for angular variables

Contains fulltext : 116551.pdf (publisher's version ) (Closed access

Coordination of gaze and hand movements for tracking and tracing in 3D

Contains fulltext : 76777.pdf (publisher's version ) (Closed access)In this study we have investigated movements in three-dimensional space. Since most studies have investigated planar movements (like ellipses, cloverleaf shapes and “figure eights”) we have compared two generalizations of the two-thirds power law to three dimensions. In particular we have tested whether the two-thirds power law could be best described by tangential velocity and curvature in a plane (compatible with the idea of planar segmentation) or whether tangential velocity and curvature should be calculated in three dimensions. We defined total curvature in three dimensions as the square root of the sum of curvature squared and torsion squared. The results demonstrate that most of the variance is explained by tangential velocity and total curvature. This indicates that all three orthogonal components of movements in 3D are equally important and that movements are truly 3D and do not reflect a concatenation of 2D planar movement segments. In addition, we have studied the coordination of eye and hand movements in 3D by measuring binocular eye movements while subjects move the finger along a curved path. The results show that the directional component and finger position almost superimpose when subjects track a target moving in 3D. However, the vergence component of gaze leads finger position by about 250 msec. For drawing (tracing) the path of a visible 3D shape, the directional component of gaze leads finger position by about 225 msec, and the vergence component leads finger position by about 400 msec. These results are compatible with the idea that gaze leads hand position during drawing movement to assist prediction and planning of hand position in 3D space