LH - Computational Cognitive Science: Readings

AIMA:

Topic Readings
Introduction
  1. Probabilistic machine learning and artificial intelligence - Ghahramani - Nature (2015)
  2. Deep learning - LeCun, Bengio & Hinton - Nature (2015)
  3. How to grow a mind: Statistics, structure, and abstraction - Tenenbaum et al. - Science (2011)
  4. Building machines that learn and think like people - Lake et al. - Science (2016)
  5. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines - Gershman, Horvitz & Tenenbaum - Science (2015)
Basic Bayesian Cognition
  1. AIMA, Chapter 13: Quantifying uncertainty - Russell & Norvig (2009)
  2. From mere coincidences to meaningful discoveries - Griffiths & Tenenbaum - Cognition (2007)
  3. Online learning of symbolic concepts - Thaker, Tenenbaum & Gershman - J. Mathematical Psychology (2017)
  4. (Optional) Data analysis: A Bayesian tutorial - Sivia & Skilling (2006)
  5. (Optional) Judgement under uncertainty: Heuristics and biases - Tversky & Kahneman - Science (1974)
Bayesian Inference and Bayesian Concept Learning
  1. Machine Learning, Chapter 6: Bayesian learning - Mitchell (1997)
  2. Rules and similarity in concept learning - Tenenbaum - NIPS (2000)
  3. Word learning as Bayesian inference - Xu & Tenenbaum - Psychological Review (2007)
  4. Sensitivity to sampling in Bayesian word learning - Xu & Tenenbaum - Developmental Science (2007)
Human Cognition as Statistical Inference: Computational Theories and Rational Analysis
  1. Vision: A computational investigation into the human representation and processing of visual information, Chapter 1 - Marr (1982)
  2. Ten years of the rational analysis of cognition - Chater & Oaksford - Trends in Cognitive Sciences (1999)
  3. Optimal predictions in everyday cognition - Griffiths & Tenenbaum - Psychological Science (2006)
Graphical Models and Bayes Nets
  1. AIMA, Chapter 14: Probabilistic reasoning - Russell & Norvig (2009)
  2. Bayesian networks without tears - Charniak - AI Magazine (1991)
  3. (Optional) The appeal of parallel distributed processing - McClelland, Rumelhart & Hinton
  4. (Optional) Illusory inferences about probabilities - Johnson-Laird & Savary - Acta Psychologica (1996)
  5. (Optional) The logical primitives of thought - Piantadosi, Tenenbaum & Goodman - Psychological Review (2016)
Probabilistic Programming Languages
  1. Probabilistic models of cognition, 2nd edition - Goodman & Tenenbaum (2016)
  2. The design and implementation of probabilistic programming languages - Goodman & Stuhlmuller (2015
Approximate Probabilistic Inference: MCMC, Gibbs Sampling, Particle Filtering
  1. Introducing Markov Chain Monte Carlo - Gilks, Richardson & Spiegelhalter (1996)
  2. AIMA, Chapter 14: Probabilistic reasoning - Russell & Norvig (2009)
  3. Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model - Vul et al. - NIPS (2009)
  4. Modeling the effects of memory on human online sentence processing with particle filters - Levy, Reali & Griffiths - NIPS (2009)
  5. Detecting and predicting changes - Brown & Steyvers - Cognitive Psychology (2009)
  6. Multistability and perceptual inference - Gershman, Vul & Tenenbaum - Neural Computation (2012)
Probabilistic Programs and Common Sense
  1. Simulation as an engine for physical scene understanding - Battaglia, Hamrick & Tenenbaum - PNAS (2013)
Model Selection and the Bayesian Occam’s Razor
  1. Ockham’s razor and Bayesian analysis - Jefferys & Berger - American Scientist (1992)
  2. Bayesian learning of visual chunks by human observers - Orban et al. - PNAS (2008)
  3. Bayesian theories of conditioning in a changing world - Courville, Daw & Touretzky - Trends in Cognitive Sciences (2006)
  4. (Optional) Structure and strength in causal induction - Griffiths & Tenenbaum - Cognitive Psychology (2005)
  5. (Optional) Occam’s razor - Rasmussen & Ghahramani - NIPS (2001)
Learning with Hierarchical Bayesian Models
  1. Learning overhypotheses with hierarchical Bayesian models - Kemp, Perfors & Tenenbaum - Developmental Science (2007)
  2. One-shot learning with a hierarchical nonparametric Bayesian model - Salakhutdinov, Tenenbaum & Torralba - J. Machine Learning Research (2012)
  3. A tutorial on Bayesian nonparametric models - Gershman & Blei - J. Mathematical Psychology (2012)
  4. (Optional) Topics in semantic representation - Griffiths, Steyvers & Tenenbaum - Psychological Review (2007)
Probabilistic Models for Concept Learning and Categorization
  1. On the genesis of abstract ideas - Posner & Keele - J. Experimental Psychology (1968)
  2. The adaptive nature of categorization - Anderson - Psychological Review (1991)
  3. Rational models: Alternative algorithms for category learning - Sanborn & Griffiths - Psychological Review (2010)
Models of Human Cognitive Development
  1. Constraints on knowledge and cognitive development - Keil - Psychological Review (1981)
  2. Learning domain structures - Kemp, Perfors & Tenenbaum - Proc. Annual Meeting Cognitive Science Society (2004)
  3. Knowledge acquisition: Enrichment or conceptual change - Carey (1999)
  4. The role of theories in conceptual coherence - Murphy & Medin - (1985)
  5. Intuitive statistics by 8-month-old infants - Xu & Garcia - PNAS (2008)
  6. Pure reasoning in 12-month-old infants as probabilistic inference - Teglas et al. - Science (2011)
Planning and Decision Making
  1. Action understanding as inverse planning - Baker, Saxe & Tenenbaum - Cognition (2009)
  2. A rational model of preference learning and choice prediction by children - Lucas et al. - NIPS (2009)
  3. Game Theory of Mind - Yoshida, Dolan & Friston - PLoS Computational Biology (2008)
  4. (Optional) Structure learning in human sequential decision-making - Acuna & Schrater - PLoS Computational Biology (2010)