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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!
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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.
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A paper showing that learning about contexts in conditioning, just like categorization, is well-explained by latent-cause models.
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A beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. Covers model design, fitting, evaluation, and comparison.
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Summarizes recent research into the computational and neural foundations of state representations.
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Some ideas about why we think exploring state representations may be valuable in computational psychiatry.
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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)
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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.
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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
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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.
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An appropriately named primer on dopamine.
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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.
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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).
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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)
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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
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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
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Introduces how computational modeling can help formalize and test hypotheses regarding how patients make inferences.
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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.
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A theoretical model of state representation in the orbitofrontal cortex.
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A 10 simple rules paper on designing, piloting, running, and analyzing behavioral experiments.
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A review of approaches for improving the psychometric reliability of task measures for use in individual-differences research.
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An open web textbook covering open science approaches to experimental psychology methods.
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Zorowitz et al. (2023): Inattentive responding can induce spurious associations between task behavior and symptom measures
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Demonstration of how spurious correlations can arise between task and self-report data in the presence of low-effort participants.
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Overview of best practices in the design of online experiments to make your participants are happy :)
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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.
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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
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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
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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.
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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.
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Rubin (2021): When to adjust alpha during multiple testing: a consideration of disjunction, conjunction, and individual testing
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The article outlines the conditions in which multiple comparisons corrections (i.e., alpha adjustment) is appropriate and the conditions in which it is inappropriate.
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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.
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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.
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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.
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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)
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An online resource for learning and understanding Bayesian statistics and software.
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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.
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Overview of best practices for a workflow using Stan.
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A thorough treatment of the diagnostic checks and procedures that are critical for effective Stan troubleshooting, but are often left underspecified by tutorial papers.
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A comprehensive overview of good data management practices.
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Bare minimum requirements for what open data should entail in behavioral research.
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A detailed guide to good data management practices.
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A collection of charts made with the R programming language. Hundreds of charts are displayed in several sections, always with their reproducible code available.
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Choosing good colors for your charts is hard. This article tries to make it easier.
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Tips for effectively using text in figures.