Predictive models in HCI, such as models of user performance, are often expressed as multivariate nonlinear regressions. This approach has been preferred, because it is compact and allows scrutiny. However, existing modeling tools in HCI, along with the common statistical packages, are limited to predefined nonlinear models or support linear models only. To assist researchers in the task of identifying novel nonlinear models, we propose a stochastic local search method that constructs equations iteratively. Instead of predefining a model equation, the researcher defines constraints that guide the search process. Comparison of outputs to published baselines in HCI shows improvements in model fit in seven out of 11 cases. We present a few ways in which the method can help HCI researchers explore modeling problems. We conclude that the approach is particularly suitable for complex datasets that have many predictor variables.
PDF copy of the paper: Automated Nonlinear Regression Modeling for HCI Oulasvirta, A. Proceedings of the 2014 Annual Conference on Human factors in Computing Systems (CHI'14), ACM Press (2014), to appear 

Presentation slides in Slideshare 
The original sources are described in the paper. See Table 1. Values are commaseparated. The first column is the dependent variable (often movement time MT) and the rest are predictors. Variable names for the predictors are given in Table 1 and described in more length in the original papers.
1  dataset1.csv  Stylus tapping  
2  dataset2.csv  Stylus tapping with W_e  
3  dataset3.csv  Mouse pointing  
4  dataset4.csv  Mouse pointing with W_e  
5  dataset5.csv  Trackball dragging  
6  dataset6.csv  Trackball dragging with W_e  
7  dataset7.csv  Magic Lens pointing  
8  dataset8.csv  Tactile guidance  
9  dataset9.csv  Pointing, angular Exp. 2  
10  dataset10.csv  Two thumb tapping  
11  dataset11.csv  Menu selection 
This is a proofofconcept study. It shows that known techniques in symbolic programming can be successfully applied to modeling tasks in HCI. Performance can be vastly improved by considering more advanced techniques in the literature. Therefore, the code is (very) experimental. The version shared here is able to replicate the results reported in Table 1 in the paper. I recommend using the random search mode.
Installation:My present system is Mac OS X 10.7.5 with Python 2.7.6. To run the optimizer, you need the following modules: ols, array, csv, string, random, numpy, sys, re, math, scipy, utilities. The modules ols.py and utilities.py are in the zip file. They must be placed in the directory as the main file. You need to have a folder named "logs" for outputs.
Usage: From command line python NES.py FILENAME numberoffreeparams numberofiterations
. Note that because the intercept is considered as a parameter, the actual number of free parameters is one less. The input file must be in the same directory. The outputs are stored to logsfolder.
@article{oulasvirta2014automated,
title={Automated Nonlinear Regression Modeling for HCI},
author={Oulasvirta, Antti},
booktitle={Proceedings of the 2014 Annual Conference on Human factors in Computing Systems
(CHI'14)},
year={2014},
organization={ACM Press}
}