## Week of Jan. 10

### Introduction: Belief, reasoning, and probability. Graphical models and probabilistic programs.

Readings:

- ProbMods book chapter 3.
- How to grow a mind: structure, statistics, and abstraction.
J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman
(2011).
*Science.* - ProbMods book chapter 4.
- Optional: ProbMods book chapter 12.

## Week of Jan. 17:

### Generative models and conditioning in Church. Discussion on levels of analysis.

Homework 1 has been posted to the Piazza discussion board.

Readings:

- ProbMods wiki: Generative Models
- ProbMods wiki: Conditioning
- Predicting the future. Griffiths and Tenenbaum (2006).
- Chapter 1 of "Vision." Marr (1982).
- Chapter 1 of "The adaptive character of thought." Anderson (1990).
- Optional: Ten Years of Rational Analysis. Chater, Oaksford (1999).

## Week of Jan. 24:

### Dependency and patterns of inference.

Readings:

- ProbMods wiki: Patterns of Inference. (For Tuesday)
- Bayesian models of object perception. Kersten and Yuille (2003). (For Thursday)
- Causal Reasoning Through Intervention. Hagmayer, Sloman, Lagnado, and Waldmann (2006). (For Thursday)

## Week of Jan. 31:

### Learning as inference.

Readings:

- ProbMods wiki: Learning as Conditional Inference.
- Bayesian modeling of human concept learning. Tenenbaum (1999).
- Word learning as Bayesian inference. Tenenbaum and Xu (2000).
- Optional: Word learning as Bayesian inference: Evidence from preschoolers. Xu and Tenenbaum (2005).
- Optional: Rules and similarity in concept learning. Tenenbaum (2000).

## Week of Feb. 7:

### Occam's razor.

Readings:

- ProbMods wiki: Occam's Razor.
- Structure and strength in causal induction. Griffiths and Tenenbaum (2005).
- (Work on project proposals!)

## Week of Feb. 14:

### Heirarchical models, mixtures, and non-parametrics.

Project proposals due Thursday!

Readings:

- ProbMods wiki: Hierarchical Models
- ProbMods wiki: Mixture and Non-Parametric Models
- Learning overhypotheses. Kemp, Perfors, and Tenenbaum (2006).
- Learning a theory of causality. Goodman, Ullman, and Tenenbaum (2011).
- Learning systems of concepts with an infinite relational model. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T. & Ueda, N. (2006).
- (Optional) Object name learning provides on-the-job training for attention. Smith, Jones, Landau, Gershko-Stowe, and Samuelson (2002).
- (Optional) Learning to learn causal models. Kemp, C., Goodman, N. & Tenenbaum, J. (2010).
- (Optional) Infinite Relational Modeling of Functional Connectivity in Resting State fMRI. Morup, M. and Madsen, K.H. and Dogonowski, A.M. and Siebner, H. and Hansen, L.K. (2010).

## Week of Feb. 21:

### Logic, recursion, and grammar-based induction.

Readings:

- ProbMods wiki: Recursive Models
- A rational analysis of rule-based concept learning. Goodman, Tenenbaum, Feldman, and Griffiths (2008).
- (Review) Learning a theory of causality. Goodman, Ullman, and Tenenbaum (2011).
- (Optional) Learning Structured Generative Concepts. Stuhlmueller, Tenenbaum, and Goodman (2010).
- (Optional) Probabilistic models of language processing and acquisition. Chater and Manning (2006).

## Week of Feb. 28:

### Decision making. Social cognition.

Readings:

- ProbMods wiki: Inference about inference: Nested query
- Goal Inference as Inverse Planning. Baker, Tenenbaum, Saxe (2007).
- Cause and intent: Social reasoning in causal learning. Goodman, Baker, Tenenbaum (2009).
- Quantifying pragmatic inference in language games. Frank and Goodman (under review).
- Knowledge and implicature: Modeling language understanding as social cognition. Goodman and Stuhlmueller (under review).

## Week of March 6:

### Inference algorithms and process models.

Readings:

- Skim the (hidden) section on Inference, we will discuss in class.
- One and done: Globally optimal behavior from locally suboptimal decisions. Vul, Goodman, Griffiths, Tenenbaum (2009).
- Perceptual multistability as Markov chain Monte Carlo inference. Gershman, Vul, & Tenenbaum (2009).
- 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 March 13:

### Project presentations!

Each project team will present a 10min summary. We'll go in alphabetical order.

Project reports due Sunday night (March 18).