Computational Models in Cognitive Science

Spring 2015


COMP150-03 "Computational Models in Cognitive Science" (3 cr)

This course will provide an overview of different computational models and modeling techniques in cognitive science.  Students will work with different kinds of computer simulations to develop models and study their properties.  A term project will require students to develop and implement their own model for a task of their choice.

Required Readings:

All readings will be made available on the course page.

(Optional) Additional Readings:

Ron Sun, ed. (2008). The Cambridge Handbook of Computational Psychology. Series: Cambridge Handbooks in Psychology. Cambridge University Press. ISBN: 9780521674102, 768 pages.

Jerome R. Busemeyer and Adele Diederich (2010). Cognitive Modeling. SAGE Publications, Inc. ISBN: 9780761924500, 224 pages.

Instructor: Matthias Scheutz

Office Hours: location and time by appointment only
Phone: (617) 627-0453 or internal 7-0453

Teaching Assistant: Sepideh Sadeghi

Office Hours: 2-4 pm Friday, Halligan TA Room (121)

Course Goals:

The main goals of this course are to get a good understanding of the role of computational modeling in cognitive science and to become familiar with the basic modeling techniques (connectionist, Bayesian, dynamic-systems-based, logic/symbol-based, production-based, etc.).  As such, students should be able to understand computational models developed in different paradigms as they are presented in research papers and should also be able to develop simple computational models of their own.

 Topics:  Appr. number of weeks
 Connectionist models 
 Bayesian models
 Dynamic system models
 Logic/symbol-based models
 Classical cognitive architectures/production models 
 Models of specific cognitive functions 

Note that the number of lectures on each topic (as well as some of the various subtopics covered by the lectures) is subject to change (e.g., based on the respective research projects picked by students).

Computer Usage:

All assignments in this course will use the comprehensive R software.


In line with the course goals, the semester grade will reflect both the students' mastery of basic modeling techniques (through individual assignments and the mid-term exam) as well as the students' ability to develop simple computational models (through the term project and final paper).

Undergraduate students: Graduate students: The following grade breakdown will be used:
 92 - 100  A 
 89 - 91  A-
 86 - 88   B+
 82 - 85  B
 79 - 81  B-
 76 - 78  C+
 72 - 75  C
 69 - 71  C-
 62 - 68  D
   0 - 61  F

Class presentations and assignments

Each graduate student will give a short presentation of a computational model to the whole class during the second half of the semester.  This presentation will be one component of the additional graduate student effort.  All assignments (including write-ups, data files, etc.) that have to be turned in will have to be submitted electronically via email to the TA before the deadline.

Late Policy

Late assignments are in general not accepted, hence will not earn any credit (except in extraordinary formally documented circumstances).

Class Attendance, Participation, and Strict Policy on Electronic Devices

Although no rigorous attendance policy will be implemented for this course, students are expected to attend all classes (students with excessive absences will be very unlikely to pass the course).  Everybody is encouraged to participate actively and contribute to the course (e.g., by asking questions and sharing information in the online web-based forum). 

This course follows the Faculty of Arts, Sciences and Engineering Guidelines Pertaining to Religious Observances. You are not required to prove attendance at religious services or events to obtain an accommodation for religious observance, but you are requested to provide indication of such any accommodation requests early in the semester.

Electronic devices can be massively disturbing during class time (from noises due to typing, to the distractions that result from being connected to the Internet) and will lead to reduced attention/participation of the user (there are lots of studies confirming that!), and likely the people around the user.  Instead of coping with the temptations of texting, emailing, web browsing, and other non-class-related activities enabled by electronic devices during class time, this course implements a strict "no electronic devices during lectures" policy: no cell phones, smart phones, PDAs, tablets, laptops, or other similar electronic devices are allowed during class time.  Handouts will be distributed for each lecture (with extra space for additional notes students might want to take) and will also be made available online.


Per Tufts policy, incompletes will be granted under only the most exceptional of circumstances (out of your control) and only in cases where most of the course work has already been completed. Examples of exceptional circumstances include a death in the family or major illness that keeps you out of the classroom for a significant period of time. Note that getting behind in the class due to other obligations outside the classroom (other classes, job) doesn't warrant granting an incomplete.


There will be a final term paper (instead of a final exam) due at the end of the course.  For undergraduate students, the paper should take the form of a final project report.  For graduate students, the paper should be written in the style of a conference publication and will thus (individual)be graded based on typical criteria used for conference publications.  Guidelines and templates for the final papers will be provided.

Academic Honesty:

This course is conducted in accordance with the Academic Integrity standards as described in the School of Arts and Sciences / School of Engineering Academic Integrity Handbook.  Specifically, it is considered cheating if you obtain any kind of information about answers and solutions to any of the assignments in this course from any non-intended source (including your peers) or conversely transfer such information to others. When in doubt, ask the instructor. Nobody begins the semester with the intention of cheating. Students who cheat do so because they fall behind gradually, and then panic at the last minute. Some students get into this situation because they are afraid of an unpleasant conversation with an instructor if they admit to not understanding something. I would much rather deal with your misunderstanding early than deal with its consequences later. Please, feel free to ask for help as soon as you need it.  And remember: plagiarism violates academic honesty and Tufts faculty are required by Tufts policies to report any form of plagiarism.

Statement for Students with Disabilities:
The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact Disability Services.

This page is maintained by Matthias Scheutz.
Last revised on February 25, 2015.