Here’s a first attempt at a lab manual
Getting into the literature
Gold & Shadlen (2007) : Excellent introduction into the neural basis of decision making, from SDT to DDM.
Johnson and Ratcliff (2014) Overview of various decision making models.
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.
Donkin & Brown : Introduction to different types of evidence accumulation models.
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.
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.
Shinn et al. (2020): pyddm: a flexible framework for fitting the ddm, current state-of-the art?
Niv 2009: the title says it all: reinforcement learning in the brain.
Lockwood & Klein-Flügge (2020): Accessible RL primer with a focus on social cognition.
Miletic et al. (2020): overview about relating reinforcement learning and drift diffusion models.
Pedersen et al. (2016) : introduction to RL-DDM and practical application
Grootswaghers et al. (2017): Overview on multivariate decoding using EEG data.
King & Dehaene (2014): Overview on the temporal generalization method using multivariate EEG decoding.
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:
from matplotlib import pyplot as pl
BayesCog: online course by Lei Zhang on bayesian modeling of cognition (focusing on rstan)