From a comment by Eric Loken at: http://andrewgelman.com/2015/04/28/whats-important-thing-statistics-thats-not-textbooks/
Every column of data has a label and numbers. Due to measurement issues, the numbers may have unobserved perturbations, and the label may or may not accurately stand for what the numbers actually represent. And from one model to the next, depending on the other covariates, the numbers actually represent can change. But the label sits on top of the column, and it sits in every summary table, and it all looks so clear cut that it is easy to take for granted that it is what says it is.
At Methlab meeting today, Sanjay introduced the mvtnorm package as a way of generating fake data sets that are correlated with one another.
I think this page has info: http://openmx.psyc.virginia.edu/wiki/generating-simulated-data
http://neuro.debian.net/ has some resources for connecting the Kinect to PsychoPy in Python.
Botvinick, M., & Plaunt, D. C. (2002). Representing task context: proposals based on a connectionist model of action. Psychological Research, 66, 298-311. DOI 10.1007/s00426-002-0103-8
Representations of task context play a crucial role in shaping human behavior. While the nature of these representations remains poorly understood, existing theories share a number of basic assumptions. One of these is that task representations are discrete, independent, and non-overlapping. We present here an alternative view, according to which task representations are instead viewed as graded, distributed patterns occupying a shared, continuous representational space. In recent work, we have implemented this view in a computational model of routine sequential action. In the present article, we focus specifically on this model’s implications for understanding task representation, considering the implications of the account for two influential concepts: (1) cognitive underspecification, the idea that task representations may be imprecise or vague, especially in contexts where errors occur, and (2) information-sharing, the idea that closely related operations rely on common sets of internal representations.