SYMSYS 1 / LINGUIST 35 / PHIL 99 / PSYCH 35 class
Minds and Machines
Winter 2019, 4 units
Stanford University

Paul Skokowski
Symbolic Systems & Philosophy
office: Philosophy Bldg 100, Room 102M
office hours: Tue 4-5:30pm & by appointment -- Professor Skokowski is happy to see any student for any reason during office hours.

Note: First contact for any questions should be with your TA, or for general inquiries use Piazza. Please do not email the instructor except in case of emergency. Also be mindful when using Piazza to not post explicit answers to questions in the problem sets, or, whenever necessary, do so as a "private" post that is only visible to the TAs and course instructor. Piazza link: ord/winter2019/symsys1. (Students can also join by going to and searching for SymSys 1 in the search box.
Teaching Assistants

Julian Alvarez, julian42
office hours: Fri 2:40-4:00pm
location: 460-040E
Nicholas Barbier, nbarb
office hours: Tue 3:00-4:20pm
location: 460-040E
Pedro Garzon, pgarzon
office hours: Thur 1:30-2:50pm
location: 460-040E
Luigi Sambuy, lsambuy
office hours: Thur 1:30-2:50pm
location: 460-040E
Leela Srinivasan, leelas
office hours: Wed 5:30-7:00pm
location: 460-040E
George Supaniratisai, sspkpl
office hours: Fri 10am-4pm,
location: 460-040E
Imran Thobani, ithobani
office hours: Fri 12-1:30pm
location: 460-040E
Eva Wallack, ewallack
office hours: Fri 12-1:30pm
location: 460-040E
Thomas Yim, thomascy
office hours: Fri 2:40-4pm
location: 460-040E

Note: office hours begin the second week of classes (from 1/14/19)
Meeting times Tuesdays and Thursdays 10:30-11:50 am, starting January 8, 2019
plus sections (beginning week 2)
Location Cubberley Auditorium
Description An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Undergraduates considering a major in Symbolic Systems should take this course as early as possible in their program of study.
Contact information For most course-related inquiries, your first point of contact will be your teaching assistant. For general inquiries use Piazza. Also be mindful when using Piazza to not post explicit answers to questions in the problem sets, or, whenever necessary, do so as a "private" post that is only visible to the TAs and course instructor. Except in case of emergency or sensitive personal issues, do not email the instructor directly.

Beginning with the Class of 2018, students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites.

NOTE: SYMSYS1P ("A Practical Introduction to Symbolic Systems") is offered as an optional 2-unit supplement to this course, aimed especially at prospective Sym Sys undergraduates and covering "nuts and bolts" topics related to majoring in Symbolic Systems: concentrations and course selection, advising, research opportunities, and career planning. It meets on Wednesdays, 4:30-5:50pm in 380-380C. For questions about SYMSYS 1P, please contact the instructor, Todd Davies (
Assignments There will be weekly assignments, starting the second Friday (Jan 18) and due every Friday thereafter. All assignments will be submitted via Canvas. See the assignment schedule on Canvas for details.
Sections meet every week beginning the second week of class, on Monday January 14. They will cover the material presented in the previous week's lectures. Section attendance constitutes 9% of your final grade (1% per section.)
Important! Only sign up for Sections on CANVAS! (Do NOT sign up for sections on Axess!)
Signup window for sections on CANVAS will be announced sometime during 1st week.
Readings Students should do all assigned readings in advance of the class for which they are assigned. This is crucial for success in the class, and lectures will assume prior familiarity with the contents of the readings. The course will require the following book and reader:
  • Danny Hillis, The Pattern on the Stone, 2nd edition, available in the Stanford Bookstore (and the usual alternative sources)

  • Course reader ($30), available 1/7 - 1/18 during business hours (M-F, 9-5) in the Thornton Center lobby, 379 Santa Teresa Street. After 1/18 you can buy a copy from Copy America, contact: 650-454-0937 --
Course contract: electronics in class
By enrolling in "Minds & Machines", you are signing up for the following contract: No laptop computers, smartphones, iPads, or other internet-enabled devices during class meetings. Students should bring a notebook/notepad and pen/pencil to class for note-taking purposes, as well as the course reader or Hillis book as applicable. This contract has been created in response to a large body of educational research demonstrating that laptop and phone use in class is detrimental to learning. We will discuss this research further in the first course meeting.
Grading is still being determined, but will be approximately as given below. There will be four components:
  • Take-home final: 24%
  • Weekly assignments: 60%
  • Class participation component 7%
  • Section attendance: 9%
Note that there is no midterm.

The final exam will be released the last day of class, March 15, at 5PM. It will be due exactly one week later, on March 22 at 5PM. No late finals will be accepted.

All assignment submissions and gradings will be done through Gradescope.

Late policy. Every student gets two penalty-free late days total for the quarter (Weekly assignments only, not the Final), where a day is charged for lateness between 0 and 24 hours after the time the assignment is due. After the two late days are used, late assignments will not be accepted, and a grade of 0 will be given for a late assignment. The homework grading policy is posted he re.

Students with Documented Disabilities
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).  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:
Collaboration & plagiarism policy
The SymSys1 Collaboration and Plagiarism Policy is posted here. Download this document and read it carefully as these policies will be applied in Winter 2019.

You should also consult
Stanford's plagiarism policy carefully. If you use ideas from someone else, you should cite a source. If you use someone else's words, you should indicate this by using a quotation and citing a source.

Failure to follow the plagiarism policy is a serious offense and can lead to major sanctions, including failing the class and official sanctions through the Office of Community Standards.
Preliminary Schedule (There Will Be Changes!)
Extras Lecture

Thinking about Intelligence Human-Level Artificial Intelligence? Be Serious! (all) (fun) The Robots are Winning
(serious) Laptops in class: [one] [two] [three] [four ] etc. etc.
Bodies, Minds, and Machines I  
  • Sel. from "Minds and Bodies" (all)
  • "Meat machines" (7-15; 25-27)
Leibniz: The Nature and Communication of Substances
CANVAS: On the hypothesis that animals are automata

Bodies, Minds and Machines II CANVAS: On the hypothesis that animals are automata
Wittgenstei n
CANVAS: Sels. from Place and Smart
CANVAS: Sel. from Ryle

From embodied to abstract machines ch.1-2 and ch.4 of The Pattern on the Stone
(1-38; 61-76)

Machines for natural language grammars "How language works" (83-103) [slides]

Brains as machines?

  • Sel. from Computing the Mind (26-44)
  • "Chinese room argument" (all)
Mapping communication networks in the brain [slides]
Noah Goodman

The probabilistic language of thought

  • "Language of thought" (all)
  • "4 Problems Solved by the Probabilistic Language of Thought" (all)
Helpful probability notes [slides]

Rules & representations, levels of analysis

  • "Rules & representations" (all)
  • Marr, sel. from Vision (all)
Can Neuroscience Understand Donkey Kong, Let Alone a Brain?
From circuits to behavior: A bridge too far?

Representation and computation in animal & human cognition

"Learning & representation" (all) Donahoe review of Gallistel & King book
Gallistel response to Donahoe
Brian Knutson

Decision and the brain

"When brain beats behavior: Neuroforecasting crowdfunding outcomes" (all) CANVAS: Neuroforecasting Aggregate Choice [slides]

Bayesian inference, Realism and Anti-Realism

"Bayesian models of cognition: What's built in after all?" (all) How machines learned to think statistically
CANVAS: Arguments concerning scientific realism (van Fraassen)
Justin Gardner

Vision and the brain

  • CANVAS: Gardner, "Optimality and heuristics in perceptual neuroscience" (all)
  • "Bayes' rule in perception action and cognition" (all)
Ch.1 of Basic Vision (all) -Snowden et al. [slides]
David Eagleman

Brain and Perception

  • CANVAS: Eagleman and Sejnowski, "Motion Integration and Postdiction in Visual Awareness" (all)
  • CANVAS: Eagleman, "Human time perception and its illusions" (all)
Ch.1 of Basic Vision (all) -Snowden et al. [slides]

Neural networks intro

  • Pattern on the Stone, ch.8 (all)
  • Marcus, "Multilayer perceptrons" (7-20; 25-34)
Richard Socher

Deep Learning

"Deep machine learning" (all) Intro background: LeCun, "Machines who learn"
Advanced: LeCun et al., "Deep learning"
Bill Newsome

Neuroscience: the Path Forward
Can a human brain understand itself?

"The BRAIN Initiative: developing technology to catalyse neuroscience discovery" [slides]

Philosophy and Neuroscience

  • CANVAS: Dennett, "Intentional Stance"
  • CANVAS: Gold & Stoljar, "A Neuron Doctrine in Philosophy of Neuroscience"
CANVAS: Paul Churchland, Eliminative Materialism and the Propositional Attitudes
Arianna Yuan

Parallel distributed processing

"Parallel distributed processing at 25"

Bats, zombies, and qualia

CANVAS: Nagel, Tye, Jackson [slides]

Review & discussion
(take-home final released 5PM)