## Week of Sept. 24

### Introduction. Simulation, computation, and generative models. Probability and belief.

Homework: Excercises on Scheme Basics and Generative Models.

Readings:

- Scheme Basics
- Generative Models
- How to grow a mind: structure, statistics, and abstraction. J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman (2011).
*Science.* - Internal physics models guide probabilistic judgments about object dynamics. Hamrick, Battaglia, Tenenbaum (2011).
- Optional: Structure and Interpretation of Computer Programs. (This is an amazing intro to computer science, through Scheme.)
- Optional: Some Scheme tutorials.
- Optional: Sources of uncertainty in intuitive physics. Smith and Vul (2012).

## Week of Oct. 1

### Conditioning and inference. Causal vs. statistical dependency. Discussion on levels of analysis.

Homework: Excercises on Conditioning.

Readings:

- Conditioning
- Patterns of Inference (first few sections)
- Predicting the future. Griffiths and Tenenbaum (2006).
- Chapter 1 of "The adaptive character of thought." Anderson (1990).
- Optional: Chapter 1 of "Vision." Marr (1982).
- Optional: Ten Years of Rational Analysis. Chater, Oaksford (1999).
- Optional: The Knowledge Level. Newell (1982).
- Optional: Ping Pong in Church: Productive use of concepts in human probabilistic inference. Gerstenberg and Goodman (2012).

## Week of Oct. 8

### Patterns of inference. Sequences of observations.

Homework: Excercises on Patterns of Inference, also work on mini-project.

Mini-project for class on thursday.

Readings:

- (Remainder of) Patterns of Inference
- Models for sequences of observations
- Causal Reasoning Through Intervention. Hagmayer, Sloman, Lagnado, and Waldmann (2006).
- Optional: Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. Sobel, Tenenbaum, Gopnik (2004).
- Optional: Bayesian models of object perception. Kersten and Yuille (2003).

## Week of Oct. 15

### Inference algorithms and process models.

Readings:

- Algorithms for Inference
- One and done: Globally optimal behavior from locally suboptimal decisions. Vul, Goodman, Griffiths, Tenenbaum (2009).
- Burn-in, bias, and the rationality of anchoring. Lieder, Griffiths, and Goodman (2012).
- Optional: Perceptual multistability as Markov chain Monte Carlo inference. Gershman, Vul, Tenenbaum (2009).
- Optional: A more rational model of categorization. Sanborn, Griffiths, Navarro (2006).
- Optional: Theory acquisition as stochastic search. Ullman, Goodman, and Tenenbaum (2010).
- Optional: Exemplar models as a mechanism for performing Bayesian inference. Shi, Griffiths, Feldman, Sanborn (2010).

## Week of Oct. 22

### Social cognition.

Homework: Excercises on Inference about Inference, also work on project proposals.

Readings:

- Inference about Inference
- Goal Inference as Inverse Planning. Baker, Tenenbaum, Saxe (2007).
- Optional: Cause and intent: Social reasoning in causal learning. Goodman, Baker, Tenenbaum (2009).
- Optional: Reasoning about Reasoning by Nested Conditioning: Modeling Theory of Mind with Probabilistic Programs. Stuhlmueller and Goodman (2013).
- Optional: Young children use statistical sampling to infer the preferences of other people. Kushnir, Xu, and Wellman (2010).

## Week of Oct. 29

### Social cognition (continued). Natural language pragmatics.

Project proposals due Friday!

Readings:

- Quantifying pragmatic inference in language games. Frank and Goodman (2012).
- Optional: Teaching games: statistical sampling assumptions for learning in pedagogical situations. Shafto and Goodman (2008).
- Optional: Knowledge and implicature: Modeling language understanding as social cognition. Goodman and Stuhlmueller (2013).
- Optional: Probabilistic Semantics and Pragmatics: Uncertainty in Language and Thought. Goodman and Lassiter (Aug 2013 draft).

## Week of Nov. 5

### Learning as inference.

Readings:

- Learning as Conditional Inference
- A rational analysis of rule-based concept learning. Goodman, Tenenbaum, Feldman, and Griffiths (2008).
- Optional: Rules and similarity in concept learning. Tenenbaum (2000).
- Optional: Learning Structured Generative Concepts. Stuhlmueller, Tenenbaum, and Goodman (2010).

## Week of Nov. 12

### Hierarchical models. Occam's razor.

Readings:

- Hierarchical Models
- Occam's Razor
- Structure and strength in causal induction. Griffiths and Tenenbaum (2005).
- Optional: Bayesian modeling of human concept learning. Tenenbaum (1999).
- Optional: Word learning as Bayesian inference. Tenenbaum and Xu (2000).
- Optional: Word learning as Bayesian inference: Evidence from preschoolers. Xu and Tenenbaum (2005).
- Optional: Learning overhypotheses. Kemp, Perfors, and Tenenbaum (2006).
- Optional: Object name learning provides on-the-job training for attention. Smith, Jones, Landau, Gershko-Stowe, and Samuelson (2002).

## Week of Nov. 19

### Mixture and non-parametric models.

Readings:

- Old pages on Mixture and Non-parametric models: Mixture Models, Non-Parametric Models.
- Learning systems of concepts with an infinite relational model. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., Ueda, N. (2006).
- Optional: Learning to learn causal models. Kemp, C., Goodman, N., Tenenbaum, J. (2010).

## Week of Nov. 26

### Thanksgiving break.

## Week of Dec. 3

### Project presentations!

Presentations will be Dec. 3, 1:00-3:30p. Each project team will present a short summary. We'll go in alphabetical order.

Project reports are due Saturday, Dec. 7, by midnight.