BCSC 310/311: Syllabus
Time & Location
Mon. & Wed. 2:00 – 3:15 PM
Meliora Hall 269
Instructor: Chigusa Kurumada
Office: Meliora Hall 304
Office hours: by appointment
Course Description & Objectives
You are now approaching the finish line of your undergraduate education. This goal of this course is to provide you all with experience in reading, evaluating, presenting, and discussing primary research in BCS. To further refine your leadership skills, you will also serve as a “student instructor” in one of the presentation/discussion sessions.
- Read: You will choose a topic of particular interest, gain familiarity with the literature on that topic, and select 1 classic research article on that topic, and 1 recent research article that has cited the classic article.
- Present: You will lead discussion on these articles by giving a ~45-60min presentation using a slide deck (e.g., PowerPoint, Google Slides, Keynote). The purpose of this assignment is: (1) to consider where this field began and where it is now; (2) to present the materials in a way that highlights why the work you’ve chosen to review is of interest to cognitive scientists (and yourself); and (3) to learn how to give an academic presentation.
- Discuss: You will then guide a seminar discussion focused on your papers and your topic. The class will read your articles and participate in the discussion. The “student instructor” assigned to your session will help you moderate the discussion.
- Review: Before your presentation, you will submit a written evaluation of ONE of your two articles, as though you were providing a formal peer review for a journal.
- Review-the-Review: The instructor and at least one other student (“student instructor”) will provide written comments on your review
- Rewrite your Review: Upon receiving review feedback, you will rewrite your review to incorporate the comments.
- Participate: When you are not presenting, you are expected to be an active participant by reading the articles and thinking critically about them. Before each seminar discussion day (usually on Wed), everyone in the class must post at least one discussion question each about the articles assigned for that week. Before coming to class on discussion day, make sure to read the posted questions and think about some answers. You are expected to contribute to the discussion. Providing constructive, critical, and helpful comments and asking a good question are a critical skill, almost as important as your abilities to lead a discussion. I therefore take seriously your participation in a class discussion and put more weights on it in terms of your grade evaluation (see below).
- Communicate: Based on your in-class presentation, you will produce a 3-min video describing the core concept discussed in the papers of your choice. You will be presenting your video on the final day of the class.
At the start of the course you will declare a topic of your interest in the domain of cognitive science. You will then pick ONE classic paper you want to read, and find ONE recent paper that is related to the classic paper.
- Read: Within the first few weeks of class, you must obtain approval from the instructor concerning your TWO ARTICLES (one classic and one recent) to ensure that they are (a) in the field, (b) substantial and in a “good” journal, and (c) one “classic” that has stood the test of time and one recent “hot topic” article that has generated recent interest. The guidelines for choosing two articles are posted to Blackboard in the document Articles: How to Find Them. Paper selection is first-come, first-served, so please do not wait until the last moment to select your papers!
- Present: See the section “Presentation” in the Review-Present-Discuss document on Blackboard for more information on your presentation.
- Discuss: See the section “Discussion” in the Review-Present-Discuss document on Blackboard for more information on the seminar discussion.
- Review: Pick ONE of your two articles to review (either classic or recent). Your review is due BEFORE your class presentation (Monday, 9AM). Email the review to the instructor. Guidelines on writing the review can be found in the section “Review: How to Write and Review It” in the Review-Present-Discuss document on Blackboard.
- Review-the-Review: You will be serving as a student instructor in one of the presentation sessions. Your responsibility as a student instructor involves reviewing the presenter’s review and provide feedback. Within one week of receiving a draft review, submit an electronic copy of your review-of-the-review to the instructor (not to the student writer). It will then be forwarded back to the original writer. More details on how to do this are can be found in the section “Review: How to Write and Review It” in the Review-Present-Discuss document on Blackboard.
- Rewrite the Review: Within one week of receiving the review-of-the-review, rewrite your review. Your rewrite will be assessed based on how thoroughly you addressed the comments of the instructor and student reviewer.
- Participate: Participate actively in class discussions, even when you are not the presenter. You are expected to
have read the papers in advance, and you should be prepared to raise questions, comments or concerns regarding
the papers being discussed. Although you may feel that you have little to say about any given paper, if you ask
yourself why you have nothing to say, you might discover questions or opinions that you didn’t know you had.
You are expected to have a copy of the paper being discussed with you in class (or view an electronic version on a
laptop). Presenters should expect everyone in the audience to be able to look at tables or figures in the paper, in
addition to theirs being projected on screen.
Prior to each discussion day (by noon on Tuesday), you are required to post at least ONE discussion question or clarification question to Slack. Please see the section “Discussion” in the Review-Present-Discuss document on Blackboard for more information.
Your participation will be determined by your preparedness (i.e., ability to ask and answer questions and provide comments about the articles), your Slack discussion question posts, your review-of the-review, and your active participation in a class discussion (not your presence or attendance). If you find yourself unable to voice your opinions during class, you should email me your thoughts on a particular topic before the end of the day in order to earn participation points.
- Communicate: You will produce a 3-minute video describing the core concept discussed in the papers of your choice. You can use any software you like. Or I can also recommend relevant applications and tools if you are not familiar with any. The video must be accessible to a general audience unfamiliar with cognitive science research (e.g., first-year students at UR or other institutions). You may include any materials, interviews, or slides as long as they can be licensed under appropriate copyright rules and/or created with explicit consent for public dissemination. Your work product will be hosted and archived on a dedicated website for public viewing
- Presentation/leading the class discussion of your article: 30% (or points). See the sections “Presentation” and “Discussion” in the Review-Present-Discuss document on Blackboard for more info
- Written review of your article: 20%
- Revision of the written review of your article: 10%
- Participation: 20%. Since this is a "seminar" class, it isrun in the style of a journal club, and participation is the most
crucial component of a seminar. The goal of this class is to analyze and think critically about some classic and hot
topics in the field. Even if you don't plan on continuing in the field of cognitive science, this class utilizes skills
that are important for every productive member of society: critical thinking, analyzing arguments, talking
coherently about your ideas on a topic, and presenting your thoughts logically in writing. Your participation grade
is based on 3 major components, each of which demonstrates that you thought critically about the articles and the
reviews written by your peers:
- up to 5 points can be earned for posting 9 discussion board posts on Slack (i.e., all besides your own presentation). Late posts do not count.
- up to 5 points can be earned for your review-of-the-review
- Up to 5 points can be earned for your performances as a student instructor. This includes providing feedback on the assigned student’s presentation as well as on other students’ contributions.
- up to 5 points can be earned for "in-class" participation. Here, I will count any substantive participation during class, email discussions, after-class discussions, or any other interaction regarding the course material.
- Video production: 20% I will evaluate your summary video based on 1) accuracy of content; 2) accessibility of information delivery; 3) creativity of scientific communication; and 4) adherence to rules and best practices regarding copyright/trademark/intellectual property/citation.
Due to the nature of the course, late assignments will be heavily penalized. Failure to do your presentation on the scheduled day without an excuse will lead to you receiving a zero for the assignment. Turning in written assignments late will result in a 25% grade reduction per 24-hour period after the assignment is due.
Attendance is required for this class. If you know that you cannot make it to class or if you become ill, let the instructor know before 8am (1h prior to the starting time of the class). Note that there will be no makeup assignment. Additionally, for each time you are more than 5 minutes late, you will lose points from your participation grade. If you miss class entirely without a medical or university excuse, you will lose 5 points off of your participation grade.
You are expected to uphold the highest standards of academic honesty. Don’t cheat; don’t plagiarize; and declare all collaborative work with your classmates. (Collaboration is highly encouraged!) Cases of suspected misconduct will be immediately referred to the College Board on Academic Honesty. The University of Rochester’s policy on academic honesty is described in detail online. Regarding our policy on ethical AI use, see the section at the end of the syllabus.
For BCSC 311
For BCS 311, you will adhere to all requirements listed for BCS 310. However, the following will be different:
- Your presentation and review must be related to the work of your honors thesis. You will pick a classic paper that is related to your research. You do not need to pick a recent research article (your undergraduate research serves that purpose). I will need to approve your choice of a classic article.
- You will review the classic article (as described above).
- Although your research may not be completed when you give your presentation, you should still follow the format for presentations given for BCS 310. You will be able to give a good background and methodology for your work. If you do not have any results, you should give your presentation with predicted results and discuss the possible pitfalls that may lead to alternative results. You should also discuss the relevance of your work and possible future studies.
Guidelines for ethical and transparent AI use
As part of our discussion of scientific inquiry and the dissemination of knowledge, we will explore and reflect on the role of ChatGPT and other generative AI in cognitive science research.
With respect to our own use of AI technologies in this course, I adopt a general policy of "Unless I Say No," meaning that students are encouraged to use generative AI when it is helpful, unless specifically prohibited for a particular assignment, activity, or other student work. In doing so, we follow the disclosure policy of “Citation and Description” (see an example below). If you have used AI to edit or modify your work product, please submit both an original and an edited version (see Example 2). Note that you will NOT be penalized for using generative AI technologies as long as you clearly and honestly disclose your use. Failure to do so may be considered a violation of academic policy. In case of doubt, I reserve the right to ask for more information or to ask you to reproduce the work product without using AI.
- Citation + Description: Include a citation along with a sentence or two stating how generative AI was used in
the student work.
- Example 1: I generated this text, code, or image in part with ChatGPT-3.5. It was used to identify, calculate and interpret the correct Excel formula. https://chat.openai.com/share/cfc26bf4-96c1-479c-a61cd88866439dea
- Example 2: I generated a draft of this text (attached) and used the browser version of DeepL (https://www.deepl.com/write) to edit it. I incorporated the edits and added further edits to the submitted version.
Because all AI technologies are in their nascent stages, it's impossible for me to provide a finite list of do's and don'ts. I would like to invite you to explore their possibilities in creative ways, while being mindful of ethical concerns. I ask you to be transparent in your use and mindful of copyright and intellectual property regulations. Below are general considerations provided by the Simon School of Education. If you have any questions or concerns, please contact me directly.
- Cautionary Note About Hallucination in Generative AI: Generative AI tools, while immensely powerful, sometimes generate outputs that are inaccurate, made up, or not reflective of reality. Students need to critically analyze AI-generated content and vet for factual errors and inconsistencies. Students remain accountable for the accuracy, integrity, and ethical use of the information in their work.
- Data Privacy: Special care must be taken regarding data privacy. Using generative AI grants the use of the data to the AI tool. Data or documents that are copyrighted should not be uploaded into AI without permission from the copyright holder. Students remain accountable for the accuracy, integrity, and ethical use of the information in their work.
- Data Bias: Special care must be taken regarding data bias. Some of the information provided via generative AI may be biased. Examples of bias include racial, gender, nationality, religion, etc. Students remain accountable for the accuracy, integrity, and ethical use of the information in their work.