Syllabus

BCSC 204: Syllabus

Spring 2025

Time and Location

Mondays and Wednesdays, 11:50am – 1:05pm
Room: Meliora 210

Personnel

Instructor: Dr. Martin Yang
Office: Meliora 322
Office Hours (both in person and Zoom): 10:30 – 11:30am Mon/Wed (starting 1/27)

Graduate TA: Aaron Huynh
Tuesday 10-11am @ Meliora 324

Undergraduate TA: Kathleen Zhou
Thursday 2-3pm @Meliora 324

Course Description

This lab course introduces methods used in cognitive neuroscience, a field that examines cognitive phenomena in terms of their underpinnings in the brain. It covers brain imaging approaches to studying cognition and provides hands-on knowledge for functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) experiment design and data analysis. This course will give you a sense of what lab research in cog-neuro is like and better prepare you for other research opportunities. Another major requirement of the course is academic writing, so you will also learn how to write a research proposal.

This course follows the College credit hour policy for four-credit courses. This course meets twice weekly for 3 academic hours per week. The course also includes independent out-of-class assignments (group projects, lab notes, and readings) for at least 1 academic hour per week.

Textbooks (recommended but not required)

An introduction to the event-related potential technique by Steven Luck (2e) (available as an E-Book in the Library)
Handbook of functional MRI data analysis by Poldrack, Mumford and Nichols (2011) (on Library reserve). Selected chapters are already posted on Blackboard under “Reserves”.

Software

You will learn to use EEGLAB and SPM12, both of which requires MATLAB. We will also use Excel for behavioral data processing.

Course Requirements

  1. Participation (20.5%): attendance is a basic and important requirement in a lab (and also in any lab where you want to gain research experiences). That said, active participation in the discussions, exercises and lab activities/demos are expected and required. The following items will also be counted toward your participation:
    1. In-class exercises and lab activities (10%): This doesn’t require submissions on Blackboard. You can just show us your completed work while we walk around during the data analysis sessions, and we will keep track of it. If you couldn’t finish the in-class work for any reason (e.g., absence from the class due to illness/emergencies or technical issues), email your make-up work to Kathleen (with explanations of why you couldn’t finish it in class) by noon on the next day after the class. (Each class is worth 1 point of participation grade). However, if you keep running into the same technical issue that prevents you from completing the work and cannot submit it by noon of the next day, let us know and we will help you out. (Note that the datasets for the in-class work are different from those used in the lab assignments and lab report.) The lab demos are required and also counted as in-class exercises.
    2. 3 short quizzes (3% each) (see schedule below for dates): Each quiz contains 5-6 questions (multiple-choice and T/F questions). As long as you pay attention to the lectures/lab instructions/lab demo and actively participate in the lab exercises, these quizzes should be easy.
    3. Participation in other groups’ presentations (1.5%): everyone should ask at least 3 questions or give at least 3 comments/suggestions in total throughout the semester (not 3 questions per presentation). We will keep track of this!
  2. Research ethics (3%)
    1. Plagiarism quiz (1%) (graded as Complete/Incomplete)
    2. CITI training (2%) (graded as Complete/Incomplete)
  3. Lab assignments (10%): mostly just output of graphs from data processing (submitted individually)
    1. EEG: two assignments (2.5% each)
    2. fMRI: two assignments (2.5% each)
  4. Lab report (min. 6 pages plus one page of references) (6%): Write up a brief report including the following: introduction (including a thorough literature review), method, results, discussion and conclusion. Detailed instructions will be posted on Blackboard and also given in class. (Individual submission)
  5. Literature review: A review of journal articles discussed in class OR articles of your choice that you found interesting. Critique the study and discuss how the study might be done differently if you were to replicate it. Detailed instructions will be posted on Blackboard. The review should be at least 3 pages long plus 1 page of references. (4.5%) (Individual submission)
  6. Proposal writing (23.5%) Each of the following items will be due on different dates. Instructions for each item will be posted on Blackboard under “Assignments”. Each group will choose one topic and work on it together. Equal contribution from each group member to the project is expected. When submitting your group assignments, please either put the link to your Google Docs in the submission comment box OR use two different font colors to differentiate the parts written by each group member.
    1. Submission of 3 possible topics on Blackboard as a word document (3.5%) (group submission)
    2. Write-up of motivation/hypotheses/research questions (3.5%) (group submission)
    3. Write-up of experimental design (3.5%) (group submission)
    4. Final proposal (12 pages of text and figures, plus a one-page abstract and references) (13%) (group submission)
    All written assignments should follow the same format: 1” margins, double-spaced, 12-pt Times New Roman, course number and your name on the header instead of the main body of text. For every single written assignment, be sure to properly cite your resources. Citation should follow the APA format. A maximum of 5 pts will be deducted for all graded written assignments if the format fails to meet the stated requirements here.
  7. Project presentations (31%): Grading is based on several criteria, including clarity of slides, organization, delivery, and handling of questions from the audience.

    For each presentation, please indicate the percentage of the contribution by each group member on the bottom of the title slide
    (e.g., Hypothesis: Martin 40%; Aaron 60%;
    Literature review; Martin 50%; Aaron 50%;
    Research questions: Martin 50%; Aaron 50%)
    1. Group presentation of topic, motivation and hypothesis (9.5%)
    2. Group presentation of detailed experimental design and analysis (9.5%)
    3. Group presentation of final project (12%)
  8. Reflection (1.5%)

Class policies

  1. Academic misconduct
    All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. PPlagiarism and any form of academic misconduct will be reported following the guidelines set by the University.
    Be sure to carefully read “AvoidingPlagiarism.pdf” by Remmel uploaded to Blackboard.
  2. Considering the prevalence of AI nowadays, the use of AI tools is only approved for the following two purposes in this class:
    1. Searching for relevant studies when searching on Google doesn’t give you the results you want. But beware of possible fake references generated by AI!
    2. Proofreading your written assignments for grammatical errors and typos. The use of AI for this purpose should be limited to changing only words/phrases and can’t be used for generating whole sentences. If you do use AI-suggested word choices or short phrases (e.g., by using Grammarly), it should be no more than 2% of the text (e.g., in a 2000-word paper, the AI-suggested changes of words/phrases should be no more than 40.) And you “must” highlight the parts (by using a different font color) that are suggested by AI.
    If you do use AI for any part of your work (not just the actual writing but also brainstorming or planning), please clearly state what you used it for on the first page of the assignment (or as a footnote on the first page).

    All other purposes of AI use not listed above will be considered as “unauthorized aid” and be reported to the Academic Honesty Board. Multiple AI content detectors will be used when grading your assignments. Whenever you’re not sure whether your use of AI tools complies with the course policy, please do feel free to consult me or Aaron BEFORE you start using it.
  3. Special accommodation: If you need special accommodation (e.g., medical or family emergencies, observance of religious holidays/cultural events, etc.), please let us know as early as possible. We will do our best to accommodate! If you can’t take a quiz or complete an assignment on time for health reasons, documentations are required for make-ups or late submissions (see below for COVID/flu-related symptoms). Otherwise, late submissions won’t be accepted.

    If you have flu/COVID symptoms or have been in close contact with someone who got COVID, please let me know first by sending me an email at your earliest convenience. We can discuss how you can make up the missed work. If unfortunately you test positive for COVID, please carefully follow the instructions and procedures.
  4. Mobile devices
    Please silence your mobile devices. No cellphone/smartphone or any other entertainment devices are allowed while class is in session. However, laptop use is strongly encouraged for data analysis and presentations.

Grading

The final grades will be based upon the following scale:

  • A 93 -100
  • A- 90 - 92
  • B+ 87 - 89
  • B 84 – 86
  • B- 80 - 83
  • C+ 77 – 79
  • C 73 – 76
  • C- 70 – 72
  • D+ 67 – 69
  • D 63 – 66
  • D- 60 – 62
  • E below 60