Software

rfxplot Data visualization toolbox for SPM
NaN Suite descriptive statistics while excluding missing data (MATLAB)
Spkie Analyzer Visualization tool to identify (non-biological) spikes in your fMRI data

Technical Reports and Howtos

Contrast weight tutorial Tutorial on setting up contrast weights in (overparameterized) flexible factorial designs with multiple subjects
SPM struct Brief explanation of the different fields in the SPM struct
Non-spericity correction Brief explanation of the SPM approacht to non-spericity correction

Journal Club on Drift Diffusion Model

The presented papers can be downloaded here.

Julia 01/13/2017 Features of the DDM - Modeling RT distributions and accuracy Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. http://doi.org/10.1162/neco.2008.12-06-420
Christoph 01/20/2017 Mathematical Foundation of the DDM Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review, 113(4), 700–765. http://doi.org/10.1037/0033-295X.113.4.700
Sasa 02/03/2017 Variants of the DDM - collapsing bound models Cisek, P., Puskas G. A., El-Murr, S. (2009). Decisions in Changing Conditions: The Urgency-Gating Model. Journal of Neuroscience, 29(37), 11560-11571. http://dx.doi.org/10.1523/JNEUROSCI.1844-09.2009
Hawkins, G. E., Forstmann, B. U., Wagenmakers, E. J., Ratcliff, R., & Brown, S. D. (2015). Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual Decision-Making. Journal of Neuroscience, 35(6), 2476–2484. http://doi.org/10.1523/JNEUROSCI.2410-14.2015
Sam 02/10/2017 Parameter estimation in the DDM Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9(3), 438–481.doi:10.3758/BF03196302
Wiecki TV, Sofer I and Frank MJ (2013). HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Front. Neuroinform. 7:14. doi: 10.3389/fninf.2013.00014
Tessa 02/17/2017 DDM and value-based decision-making Milosavljevic, M., Malmaud, J., Huth, A., Koch, C., & Rangel, A. (2010). The Drift Diffusion Model Can Account for the Accuracy and Reaction Time of Value-Based Choices Under High and Low Time Pressure. Judgment and Decision-Making, 5(6), 437-449. SSRN Electronic Journal. http://doi.org/10.2139/ssrn.1901533
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 1–9. http://doi.org/10.1038/nn.2635
Jan 03/03/2017 DDM for multi-alternative choices Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008). Decision-making with multiple alternatives. Nature Neuroscience, 11(6), 693–702. http://doi.org/10.1038/nn.2123
Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences of the United States of America, 108(33), 13852–13857. http://doi.org/10.1073/pnas.1101328108
Mareike 03/10/2017 Modeling confidence in the DDM Ratcliff, R., & Starns, J. J. (2009). Modeling confidence and response time in recognition memory. Psychological Review, 116(1), 59–83. http://doi.org/10.1037/a0014086
N.N. 03/03/2017 DDM and Reinforcement Learning Ratcliff, R., & Frank, M. J. (2012). Reinforcement-based decision making in corticostriatal circuits: mutual constraints by neurocomputational and diffusion models. Neural Computation, 24(5), 1186–1229. http://doi.org/10.1162/NECO_a_00270

 

Recommended textbooks

Cognitive Modeling

Lewandowsky & Farrell 2011 Computational Modeling in Cognition A very good introduction to cognitive modeling and the estimation of model parameter using maximum likelihood estimation. It presents important computational models in psychology (e.g. GCM for memory) and also discusses some of the important step in evelopign and validating cognitive models (e.g. model selection)
Lee & Wagenmakers 2014 Bayesian Cognitive Modeling: A Practical Course comment

Bayesian Data Analysis / Hierarchical Modeling

Gelman et al. 2013 Bayesian Data Analysis comment
Kruschke 2015 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd ed.) comment
McElreath 2015 Statistical Rethinking comment

Machine Learning

Bishop 2006 Pattern Recognition and Machine Learning comment

fMRI data analysis

Forstmann & Wagenmakers 2015 An Introduction to Model-based Cognitive Neuroscience comment
Huettel et al. 2015 Functional Magnetic Resonance Imaging (3rd ed.) comment
Friston et al. 2007 Statistical Parametric Mapping comment