Library
Table of contents
Niv Lab primer
These are some useful papers for someone who is new to our area of research. This could be a student doing a rotation with us at Princeton, or anyone who is interested in applying reinforcement learning and Bayesian methods to the understanding of behavior and cognition (particularly in its relevance to questions of psychiatric interest) - but doesn’t know where to start!
Niv (2008): Reinforcement learning in the brain
An introduction to the formal reinforcement learning framework, including a review of the multiple lines of evidence linking reinforcement learning to the function of dopaminergic neurons in the mammalian midbrain and to human imaging experiments.
Gershman, Blei & Niv (2010): Context, learning, and extinction
A paper showing that learning about contexts in conditioning, just like categorization, is well-explained by latent-cause models.
Wilson & Collins (2019): Ten simple rules for the computational modeling of behavioral data
A beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. Covers model design, fitting, evaluation, and comparison.
Niv (2019): Learning task-state representations
Summarizes recent research into the computational and neural foundations of state representations.
Radulescu & Niv (2019): State Representation in Mental Illness
Some ideas about why we think exploring state representations may be valuable in computational psychiatry.
Foundations
Reinforcement learning & decision theory
Niv (2008): Reinforcement learning in the brain
An introduction to the formal reinforcement learning framework, including a review of the multiple lines of evidence linking reinforcement learning to the function of dopaminergic neurons in the mammalian midbrain and to human imaging experiments. (Part of the Niv Lab Primer)
Dayan & Daw (2008): Decision theory, reinforcement learning, and the brain
A review of the Bayesian decision theoretic approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration. Includes paradigmatic psychological and neural examples of each problem.
General learning and memory
Tolman (1948): Cognitive maps in rats and men
Classic and seminal paper introducing the notion of cognitive maps.
McClelland, McNaughton & O’Reilly (1995): Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory
Classic and highly influential computational modeling framework for complementary learning systems (cortex vs. hippocampus). A great use of neural network/distributed representations. The theory has been updated since, but it’s a classic.
Costa & Schoenbaum (2022): Dopamine
An appropriately named primer on dopamine.
Generalization, categorization & latent-cause inference
Shepard (1987): Toward a universal law of generalization for psychological science
The classic paper on generalization, deriving that the probability of generalization from one stimulus to another should decay approximately exponentially with the psychological distance between the stimuli, which is confirmed by a range of empirical evidence.
Anderson (1991): The adaptive nature of human categorization
The paper which introduced the non-parametric model of categorization on which our latent-cause inference models are based (using the Dirichlet process mixture / Chinese restaurant process prior).
Gershman, Blei & Niv (2010): Context, learning, and extinction
A paper showing that learning about contexts in conditioning, just like categorization, is well-explained by latent-cause models. (Part of the Niv Lab Primer)
Computational psychiatry
Huys, Moutoussis & Williams (2011): Are computational models of any use to psychiatry?
A paper discussing the benefits and challenges of using computational models in psychiatry in the form of a debate between two fictional characters.
Huys, Maia & Frank (2016): Computational psychiatry as a bridge from neuroscience to clinical applications
An introduction to computational psychiatry, including a review of theory- and data-driven approaches and examples of each.
Moutoussis et al. (2017): Computation in Psychotherapy, or How Computational Psychiatry Can Aid Learning-Based Psychological Therapies
Introduces how computational modeling can help formalize and test hypotheses regarding how patients make inferences.
Prefrontal cortex and decision making
Rushworth et al. (2011): Frontal Cortex and Reward-Guided Learning and Decision-Making
A lot has been done in the decade since, but it’s nice starting point to value/decision making in the prefrontal cortex, covering both human and monkey studies.
Wilson et al. (2014): Orbitofrontal Cortex as a Cognitive Map of Task Space
A theoretical model of state representation in the orbitofrontal cortex.
Experiments & Data Collection
Barbosa et al. (2022): A practical guide for studying human behavior in the lab
A 10 simple rules paper on designing, piloting, running, and analyzing behavioral experiments.
Zorowitz & Niv (2023): Improving the reliability of cognitive task measures: A narrative review
A review of approaches for improving the psychometric reliability of task measures for use in individual-differences research.
Frank et al. (2023): Experimentology
An open web textbook covering open science approaches to experimental psychology methods.
Online data quality
Zorowitz et al. (2023): Inattentive responding can induce spurious associations between task behavior and symptom measures
Demonstration of how spurious correlations can arise between task and self-report data in the presence of low-effort participants.
Fowler et al. (2022): Frustration and ennui among Amazon MTurk workers
Overview of best practices in the design of online experiments to make your participants are happy :)
Methods & Statistics
Mixed effects models
Shaw & Flake (2022): Introduction to Multilevel Modelling
An online resource to learn the fundamentals of multilevel modelling, from why and when you would use them and how to do so for various research questions and data structures.
Barr et al. (2013): Random effects structure for confirmatory hypothesis testing: Keep it maximal
Recommendations for fitting to linear mixed-effects models to maximize generalizability across experiments.
Meteyard & Davies (2020): Best practice guidance for linear mixed-effects models in psychological science
A set of best practice guidance, focusing on the reporting of linear mixed-effects models.
Yu et al. (2021): Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research
A primer on linear and generalized mixed-effects models that consider data dependence and which provides clear instruction on how to recognize when they are needed and how to apply them.
Hoffman & Walters (2022): Catching Up on Multilevel Modeling
A review focused on the use of multilevel models in psychology and other social sciences. Targeted towards readers aiming to get up to speed on current best practices in the specification of multilevel models.
Multiple comparisons
Rubin (2021): When to adjust alpha during multiple testing: a consideration of disjunction, conjunction, and individual testing
The article outlines the conditions in which multiple comparisons corrections (i.e., alpha adjustment) is appropriate and the conditions in which it is inappropriate.
García-Pérez (2023): Use and misuse of corrections for multiple testing
This paper describes the workings of Bonferroni and false-discovery-rate adjustments, and provides recommendations for the use and reporting of alpha adjustments for a variety of statistical analyses with which they are often implemented.
Gelman et al. (2012): Why We (Usually) Don’t Have to Worry About Multiple Comparisons
This paper describes the multiple comparisons problem from the Bayesian perspective. It argues that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian and partial-pooling perspective.
Cognitive modeling
Palminteri et al. (2017): The Importance of Falsification in Computational Cognitive Modeling
Argues that the simulation of candidate models is necessary to falsify models and thereby support the specific claims of a particular model. Proposes practical guidelines for model comparison and falsification.
Wilson & Collins (2019): Ten simple rules for the computational modeling of behavioral data
A beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. Covers model design, fitting, evaluation, and comparison. (Part of the Niv Lab Primer)
Stan programming language
Johnson et al. (2021): Bayes Rules! An Introduction to Applied Bayesian Modeling
An online resource for learning and understanding Bayesian statistics and software.
Betancourt (2017): A Conceptual Introduction to Hamiltonian Monte Carlo
A comprehensive conceptual account of the foundations of Hamiltonian Monte Carlo, focusing on developing a principled intuition behind the method and its optimal implementations rather of any exhaustive rigor.
Gelman et al. (2020): Bayesian Workflow
Overview of best practices for a workflow using Stan.
Baribault & Collins (2023): Troubleshooting Bayesian cognitive models.
A thorough treatment of the diagnostic checks and procedures that are critical for effective Stan troubleshooting, but are often left underspecified by tutorial papers.
Research skills
Data management
Henry (2021): Eight Principles of Good Data Management
A comprehensive overview of good data management practices.
Haroz (2022): A bare minimum for open empirical data
Bare minimum requirements for what open data should entail in behavioral research.
Lewis (2023): Data Management in Large-Scale Education Research
A detailed guide to good data management practices.
Plotting & visualization
The R Graph Gallery
A collection of charts made with the R programming language. Hundreds of charts are displayed in several sections, always with their reproducible code available.
Muth (2020): How to pick more beautiful colors for your data visualizations
Choosing good colors for your charts is hard. This article tries to make it easier.
Muth (2022): What to consider when using text in data visualizations
Tips for effectively using text in figures.