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PSYCH 291: Causal Cognition (https://tobiasgerstenberg.github.io/causal_cognition/)

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Causality is central to our understanding of the world and of each other. We think causally when we predict what will happen in the future, infer what happened in the past, and interpret other people's actions and emotions. Causality is intimately linked to explanation -- to answering questions about why something happened. In this discussion-based seminar class, we will first read foundational work in philosophy that introduces the main frameworks for thinking about causation. We will then read some work on formal and computational theories of causation that was inspired by these philosophical frameworks. Equipped with this background, we will study the psychology of causal learning, reasoning, and judgment. We will tackle questions such as: How can we learn about the causal structure of the world through observation and active intervention? What is the relationship between causal reasoning and mental simulation? Why do we select to talk about some causes over others when several causes led to an outcome? Toward the end of the course, we will discuss how what we have learned about causation in psychology may inform other fields of inquiry, such as legal science as well as machine learning and artificial intelligence.

<hr>

### Instructor info

[Tobi Gerstenberg](http://cicl.stanford.edu/member/tobias_gerstenberg), Assistant Professor of Cognitive Psychology

__Office hours__: Friday 10-11am (or by appointment),

__Office__: [420-302](https://campus-map.stanford.edu/?id=01-420&lat=37.42826985&lng=-122.16901666&zoom=17&srch=420-302) (in Jordan Hall)

__Email__: gerstenberg@stanford.edu

### Schedule overview

The class meets on Mondays	between __3:30__ and __6:20PM__ in [160-323](https://campus-map.stanford.edu/?id=01-160&lat=37.42826985&lng=-122.16901666&zoom=17&srch=160-323) (Wallenberg Hall).

__Note__: This syllabus is not final. I will adapt the readings based on your interests!

- 4/1 Introduction & logistics 
- 4/8 Causality in thought 
- 4/15 Philosophy of causation
- 4/22 Theories of causation
- 4/29 Causal perception 
- 5/6 Causal learning 
- 5/13 Causal reasoning
- 5/20 Causal judgment
- 5/27 Memorial day (no class)
- 6/3 Causality in AI and in the Law

A sign-up sheet for leading discussion in class is available [here](http://bit.do/psych291signup).

### Detailed schedule

The readings will be made available through [Canvas](https://canvas.stanford.edu/).

##### 4/1 Introduction & logistics

##### 4/8 Causality in thought

__Note__: We'll have class until 5pm and it would be great if you joined in afterwards for my talk at the Center for Mind, Brain, Computation and Technology.

__Causality in cognition__

- Sloman, S. A. & Lagnado, D. (2015). Causality in thought. Annual Review of Psychology, 66(1), 223--247. 
- Alicke, M. D., Mandel, D. R., Hilton, D., Gerstenberg, T., & Lagnado, D. A. (2015). Causal conceptions in social explanation and moral evaluation: A historical tour. Perspectives on Psychological Science, 10(6), 790--812.

__Understanding "why": The role of causality in cognition__

- Talk by Prof. Tobias Gerstenberg
- Location: Sloan Hall, Math Building 380, Room 380-C
- Details: [https://neuroscience.stanford.edu/mbct/events/understanding-why-role-causality-cognition](https://neuroscience.stanford.edu/mbct/events/understanding-why-role-causality-cognition)

##### 4/15 Philosophy of causation

__Philosophy & Psychology of causation__

- __White, P. A. (1990). Ideas about causation in philosophy and psychology. Psychological Bulletin, 108(1), 3--18.__
- Woodward, J. (2012). Causation: Interactions between philosophical theories and psychological research. Philosophy of Science, 79(5), 961--972.

__Concepts of causation__

- __Hall, N. (2004). Two concepts of causation. In Causation and Counterfactuals. MIT Press.__
- Schaffer, J. (2005). Contrastive causation. The Philosophical Review, 114(3), 327--358.

__Optional__:

- Lombrozo, T. (2010). Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions. Cognitive Psychology, 61(4), 303--332. 
- [Menzies, Peter, "Counterfactual Theories of Causation", The Stanford Encyclopedia of Philosophy (Winter 2017 Edition), Edward N. Zalta (ed.).](https://plato.stanford.edu/archives/win2017/entries/causation-counterfactual/)
- [Starr, William, "Counterfactuals", The Stanford Encyclopedia of Philosophy (Spring 2019 Edition), Edward N. Zalta (ed.).](https://plato.stanford.edu/archives/spr2019/entries/counterfactuals/)

##### 4/22 Theories of causation

__Process theories__

- Wolff, P. (2007). Representing causation. Journal of Experimental Psychology: General, 136(1), 82-111.

__Dependence theories__

- Halpern, J. Y. & Pearl, J. (2005). Causes and explanations: A structural-model approach. Part I: Causes. The British Journal for the Philosophy of Science, 56(4), 843--887.

__Optional__

- [Hitchcock, Christopher, "Causal Models", The Stanford Encyclopedia of Philosophy (Fall 2018 Edition), Edward N. Zalta (ed.)](https://plato.stanford.edu/archives/fall2018/entries/causal-models/) 
- Wolff, P., Barbey, A. K., & Hausknecht, M. (2010). For want of a nail: How absences cause events. Journal of Experimental Psychology: General, 139(2), 191--221. 
- Halpern, J. Y. & Hitchcock, C. (2015). Graded causation and defaults. British Journal for the Philosophy of Science, 66, 413--457. 
- Cartwright, N. (2001). What Is Wrong With Bayes Nets?. Monist, 84(2), 242--264.

##### 4/29 Causal perception

__Perceiving causation__

- __Rips, L. J. (2011). Causation from perception. Perspectives on Psychological Science, 6(1), 77--97.__ 
- Scholl, B. J. & Tremoulet, P. D. (2000). Perceptual causality and animacy. Trends in Cognitive Sciences, 4(8), 299--309. 
- Rolfs, M., Dambacher, M., & Cavanagh, P. (2013). Visual adaptation of the perception of causality. Current Biology, 23(3), 250--254.

__Perception or cognition?__

- __Bechlivanidis, C., Schlottmann, A., & Lagnado, D. A. (2019). Causation without Realism. Journal of Experimental Psychology: General.__ 
- Woodward, J. (2011). Causal perception and causal cognition. Perception, Causation, and Objectivity, 229--263. 
- Bechlivanidis, C. & Lagnado, D. A. (2016). Time reordered: Causal perception guides the interpretation of temporal order. Cognition, 146, 58--66.

__Optional__

- Saxe, R. & Carey, S. (2006). The perception of causality in infancy. Acta Psychologica, 123(1-2), 144--165.

##### 5/6 Causal learning

__The development of causal reasoning__

- __Gopnik, A., Glymour, C., Sobel, D., Schulz, L., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 1--31.__
- Frosch, C. A., McCormack, T., Lagnado, D. A., & Burns, P. (2012). Are causal structure and intervention judgments inextricably linked? A developmental study. Cognitive Science, 36, 261--285.

__Causal learning in adults__

- __Griffiths, T. L. & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116(4), 661--716.__
- Lagnado, D. A., Waldmann, M. R., Hagmayer, Y., & Sloman, S. A. (2007). Beyond covariation. In Causal learning: Psychology, philosophy, and computation (pp. 154--172). Oxford University Press.
- Griffiths, T. L. & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51(4), 334--384. 
- Bramley, N. R., Gerstenberg, T., Tenenbaum, J. B., & Gureckis, T. M. (2018). Intuitive experimentation in the physical world. Cognitive Psychology, 105, 9--38.

##### 5/13 Causal reasoning

__Causal reasoning and counterfactuals__

- __Chater, N. & Oaksford, M. (2013). Programs as causal models: Speculations on mental programs and mental representation. Cognitive Science, 37(6), 1171--1191.__

__The problem of causal selection__

- __Hesslow, G. (1988). The problem of causal selection. In Contemporary science and natural explanation: Commonsense conceptions of causality, Brighton, UK, 1988 (pp. 11--32). Harvester Press.__
- Hitchcock, C. & Knobe, J. (2009). Cause and norm. Journal of Philosophy, 11, 587--612. 
- Icard, T. F., Kominsky, J. F., & Knobe, J. (2017). Normality and actual causal strength. Cognition, 161, 80--93.

__Optional__

- Sloman, S. A. & Lagnado, D. A. (2005). Do we "do"?. Cognitive Science, 29(1), 5--39. 
- Waldmann, M. R. & Hagmayer, Y. (2005). Seeing versus doing: two modes of accessing causal knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(2), 216--227. 
- Woodward, J. (2015). The problem of variable choice. Synthese, 193(4), 1047--1072. 
- Woodward, J. (2006). Sensitive and insensitive causation. The Philosophical Review, 115(1), 1--50.
- Krynski, T. R. & Tenenbaum, J. B. (2007). The role of causality in judgment under uncertainty. Journal of Experimental Psychology: General, 136(3), 430--450. 
- Kahneman, D. & Miller, D. T. (1986). Norm theory: Comparing reality to its alternatives. Psychological Review, 93(2), 136--153.

##### 5/20 Causal judgment

__Causation and physics__

- __White, P. A. (2006). The causal asymmetry. Psychological Review, 113(1), 132--147.__ 
- Mayrhofer, R. & Waldmann, M. R. (2014). Indicators of causal agency in physical interactions: The role of the prior context. Cognition, 132(3), 485--490. 
- Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013). Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review, 120(2), 411--437.

__Intuitive theories__

- __Gerstenberg, T., Goodman, N. D., Lagnado, D. A., & Tenenbaum, J. B. (in preparation). A counterfactual simulation model of causal judgment.__ 
- Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A., & Tenenbaum, J. B. (2017). Eye-Tracking causality. Psychological Science, 28(12), 1731--1744.

__Optional__

- Goodman, N. D., Tenenbaum, J. B., & Gerstenberg, T. (2015). Concepts in a probabilistic language of thought. In The Conceptual Mind: New Directions in the Study of Concepts (pp. 623--653). MIT Press.

##### 5/27 Memorial day (no class)

##### 6/3 Causality in AI and in the Law

__Causality in AI__

- __Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2016). Building machines that learn and think like people. Behavioral and Brain Sciences, 40.__ 
- [Miller, T. (2017). Explanation in Artificial Intelligence: Insights from the Social Sciences. arXiv:1706.07269.](https://arxiv.org/abs/1706.07269) 
- Pearl, J. (2019). The seven tools of causal inference, with reflections on machine learning. Communications of the ACM, 62(3), 54--60.

__Causality in the law__

- __Summers, A. (2018). Common-Sense Causation in the Law. Oxford Journal of Legal Studies, 38(4), 793--821.__ 
- Schaffer, J. (2010). Contrastive causation in the law. Legal Theory, 16(04), 259--297. 
- Lagnado, D. A. & Gerstenberg, T. (2017). Causation in legal and moral reasoning. In Oxford Handbook of Causal Reasoning (pp. 565--602). Oxford University Press.

__Optional__

- Moore, M. S. (1999). Causation and responsibility. Social Philosophy and Policy, 16(2), 1--51.

### General information

#### What to expect?

In ["A Vision for Stanford"](https://ourvision.stanford.edu/), university president Marc Tessier-Lavigne states that Stanford wants to be

>"an inspired, inclusive and collaborative community of diverse scholars, students and staff, where all are supported and empowered to thrive."

Let's try our best together in this seminar to make this happen!

##### What you can expect from me

_I will ..._

- be in class 5 minutes before it starts to set up and stay 10 minutes afterwards for questions. 
- start and end each class on time. 
- meet with you to discuss the handout during [office hours](#course-info) on Friday before class.
- provide an introduction to the field of causal cognition in the first session. 
- help facilitate the discussion in subsequent sessions. 
- send announcements via Canvas to guide the reading for each upcoming week.

##### What I expect from you

_You will ..._

- attend all of [the classes](#schedule) and participate in class discussion.
- lead the discussion for one session in class (you may use slides to help structure the discussion). 
- prepare a handout for the session that you're leading. 
- discuss the handout with me during office hours __on Friday before your presentation__. 
- write short [reaction papers](#reaction-posts) based on the readings and upload each paper on Canvas by 10pm (at the latest) the night before class. 
- write a final paper (and submit a paper proposal). 
- students who audit the class are expected to write reaction posts and participate in discussion in class.

#### Grading

- 40% final paper
- 20% handout and leading discussion 
- 20% reaction posts to readings
- 20% participation and discussion in class

##### Final paper

The final project may be one of the following three:

1. An empirical project proposal. 
2. A literature review based on one of the class topics. 
3. An essay. 
	- Here is a list of example essay questions: 
		+ What is the relationship between causal cognition and causal perception? 
		+ In what way are counterfactual thinking and causal thinking related? 
		+ How many concepts of causation are there? What are the arguments in favor of causal pluralism versus a unified concept of causation? 
		+ What role does causality play in artificial intelligence? 
		+ Should legal theorists care about the ordinary person's concept of causation? 
		+ How do children and adults learn about the causal structure of the world?

A short final paper proposal (1/2 page) will be due on __May, 26th at 10pm__.

The final paper (2000--3000 words) will be due on __June, 9th at 10pm__.

##### Reaction posts

Here are some guiding thoughts on how to write a good reaction post:

- Structure your posts (headings, subheadings, etc.). 
- Express your opinion rather than summarize the paper(s).
- Try to connect the ideas expressed in the paper to concrete every-day experiences. 
- Identify strengths and weaknesses of the paper. 
- Relate different papers to each other. 
- Ask questions that go beyond what the paper discusses (what's missing, where should we go next)?

The reaction posts should be concise (one or two paragraphs per paper), and should be submitted by __Sunday at 10pm__ the latest via Canvas.

#### Policies

Please familiarize yourself with [Stanford's honor code](https://communitystandards.stanford.edu/policies-and-guidance/honor-code). We will adhere to it and follow through on its penalty guidelines.

#### Support

Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education
([OAE](https://oae.stanford.edu/)). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is being made. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (phone: 723-1066, URL: http://oae.stanford.edu).

Stanford is committed to ensuring that all courses are financially accessible to its students. If you require assistance with the cost of course textbooks, supplies, materials and/or fees, you should contact the Diversity & First-Gen Office ([D-Gen](https://diversityandfirstgen.stanford.edu/)) at opportunityfund@stanford.edu to learn about the FLIbrary and other resources they have available for support.

Stanford offers several tutoring and coaching services:

- [Academic Skills Coaching](https://learningconnection.stanford.edu/academic-skills-coaching)
- [Tutor for learning differences](https://slc.stanford.edu/slc-services/tutor-connection-service-0)
- [English language learners](https://teachingcommons.stanford.edu/teachingwriting/pwr-guide/teaching-multilingual-students/hume-center-support-ells)
- [Hume Center for Writing and Speaking](https://undergrad.stanford.edu/tutoring-support/hume-center)

#### Feedback

I welcome feedback regarding the course at any point. Please feel free to talk with me after class, come to office hours, email me, or leave anonymous feedback using this [online form](http://bit.do/psych291feedback).

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