In this folder you can find out how to use the cluster, how to run behavioral experiments, how to run mixed models, how to print at PSI, an introduction to neural nets, the lab manual that you should read on your first day, etc. If you figure out that some of the information is outdated, please update the document. If your questions remain unanswered, have a peak at the hoplab wiki.

Getting into the literature with Review Papers
Decision making
Gold & Shadlen (2007) : Excellent introduction into the neural basis of decision making, from SDT to DDM.
O’Connell et al. (2018): Recent overview linking decision making in modeling and the brain.
Denison et al. (preprint): Good introduction paper relation visual phenomenology to SDT and DDM models.
Decision confidence
Desender et al (2021): relating various expressions of performance monitoring (confidence, error detection, CoM) to post-decisional processing and the Pe component.
Rahnev et al. (2021): Consensus goals for the field of visual metacognition.
Yeung & Summerfield (2012): Overview relating decision confidence and error detection to evidence accumulation models.
Grimaldi et al. (2015): explanations of confidence in signal detection theory, evidence accumulation and Bayesian framework.
Meyniel et al. (2015): Overview about confidence in a Bayesian framework.
Rouault et al. (2018): Overview about the debate whether metacognition is domain-general vs domain-specific.
Computational modeling
Wilson and Collins (2019) Excellent how-to-model intro (10 simple rules) with a slight focus on RL, comes with tutorial code.
Drift Diffusion Modeling and Fitting
Ratcliff & McKoon (2008): review paper on the DDM.
Wiecki et al. (2013): Introducing a hierarchical approach of DDM ditting using MCM
Ravenzwaaij et al. (2018): Graspable explanation of MCMC.
Reinforcement Learning
Daw: excellent book chapter introducing RL models
Niv 2009: the title says it all: reinforcement learning in the brain.
RL-DDM
Miletic et al. (2020): overview about relating reinforcement learning and drift diffusion models.
Pedersen et al. (2016) : introduction to RL-DDM and practical application
EEG decoding
Grootswaghers et al. (2017): Overview on multivariate decoding using EEG data.
King & Dehaene (2014): Overview on the temporal generalization method using multivariate EEG decoding.

Online courses
BayesCog: online course by Lei Zhang on bayesian modeling of cognition (focusing on rstan)

HDDM installation with anaconda
anaconda create -n HDDM python=2.7.18
conda install -c pymc hddm
conda install qtawesome=0.7.3
(or: conda install qtawesome=0.7.3 –channel conda-forge)
conda install spyder seaborn
(or: conda install spyder=3.3.0)

or see here: https://crackedbassoon.com/writing/ddm-figure

If you have issues with spyder, alternatively use iPython:
ipython –matplotlib
from matplotlib import pyplot as pl
pl.figure()