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Table of contents

  1. Foundations
    1. Reinforcement learning & decision theory
    2. General learning and memory
    3. Generalization, categorization & latent-cause inference
    4. Computational psychiatry
    5. Prefrontal cortex and decision making
  2. Experiments & Data Collection
    1. Online data quality
  3. Methods & Statistics
    1. Mixed effects models
    2. Cognitive modeling
    3. Stan programming language
  4. Reserach skills
    1. Data management


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.

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 modelling 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.

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/CRP 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.

Computational psychiatry

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.

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.

Moutoussis et al. (2017)

Computation in Psychotherapy, or HowComputational 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 theoratical model of state representation in the orbitofrtonal 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 behaivoral experiments.

Online data quality

Agley et al. (2021)

Quality control questions on Amazon’s Mechanical Turk (MTurk): A randomized trial of impact on the USAUDIT, PHQ-9, and GAD-7

An investigation of different attention checks in the online environment, and their resulting impacts in preventing skew in common psychiatric screening measures. Different quality control approaches significantly affect outcome scores on each of the screening tools.

Zorowitz et al. (2021)

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.

Methods & Statistics

Mixed effects models

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 onMultilevel 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.

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.

Stan programming language

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.

Baribault & Collins (2021)

Troubleshooting Bayesian cognitive models: A tutorial with matstanlib

A deep treatment of the diagnostic checks and procedures that are critical for effective Stan troubleshooting, but are often left underspecified by tutorial papers.

Reserach 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.