| Week | Date | Weekly Module |
|---|---|---|
| 1 | Tuesday, September 3, 2024 | W1: Getting started |
| 1 | Thursday, September 5, 2024 | W1 continued… |
| 1 | Sunday, September, 8, 2024 | Week 1 Reflection Due |
| 2 | Tuesday, September 10, 2024 | W2: Experiment Anatomy |
| 2 | Thursday, September 12, 2024 | W2 continued… |
| 2 | Sunday, September, 15, 2024 | Project Milestone #1 (Team Plan + Lit Review) Due |
| 3 | Tuesday, September 17, 2024 | W3: jsPsych 101 |
| 3 | Thursday, September 19, 2024 | W3 continued… |
| 3 | Sunday, September 22, 2024 | Project Milestone #2 (Project Proposal) Due |
| 4 | Tuesday, September 24, 2024 | W4: Experiment Timeline |
| 4 | Thursday, September 26, 2024 | W4 continued… |
| 4 | Sunday, September 29, 2024 | Project Milestone #3 (Design Draft) Due |
| 5 | Tuesday, October 1, 2024 | W5: Recording Data |
| 5 | Thursday, October 3, 2024 | W5 continued… |
| 6 | Tuesday, October 8, 2024 | Fall Break!! NO CLASS |
| 6 | Thursday, October 10, 2024 | W6: Experiment Workflow |
| 6 | Monday, October 14, 2024 | Formative Assignment (jsPsych) Due |
| 7 | Tuesday, October 15, 2024 | W7: Visualize Data |
| 7 | Thursday, October 17, 2024 | W7 continued… |
| 7 | Monday, Oct 21, 2024 | Project Milestone #4 (Full Experiment) Due |
| 8 | Tuesday, October 22, 2024 | W8: Manipulate Data |
| 8 | Thursday, October 24, 2024 | W8 continued… |
| 8 | Sunday, October 27, 2024 | Formative Assignment (jsPsych) Resubmission Due |
| 9 | Tuesday, October 29, 2024 | W9: Project Work |
| 9 | Thursday, October 31, 2024 | W9 continued… |
| 9 | Monday, November 4, 2024 | Formative Assignment (R Descriptive) Due |
| 10 | Tuesday, November 5, 2024 | Weeks 10-12: Data Collection |
| 10 | Thursday, November 7, 2024 | Weeks 10-12: Data Collection |
| 10 | Monday, November 11, 2024 | Project Milestone #5 (Pre-Registration + Checklist) Due |
| 11 | Tuesday, November 12, 2024 | Weeks 10-12: Data Collection |
| 11 | Thursday, November 14, 2024 | Weeks 10-12: Data Collection |
| 11 | Sunday, November 17, 2024 | Formative Assignment (R Descriptive) Resubmission Due |
| 11 | Monday, November 18, 2024 | Project Milestone #6a (Analyses: Deadline 1) Due |
| 12 | Tuesday, November 19, 2024 | Weeks 10-12: Data Collection |
| 12 | Thursday, November 21, 2024 | Weeks 10-12: Data Collection |
| 12 | Friday, November 22, 2024 | Project Milestone #6b (Analyses: Deadline 2) Due |
| 13 | Tuesday, November 26, 2024 | THANKSGIVING BREAK!!! NO CLASS |
| 13 | Thursday, November 28, 2024 | THANKSGIVING BREAK!!! NO CLASS |
| 14 | Monday, December 2, 2024 | Formative Assignment (R Inferential) Due |
| 14 | Tuesday, December 3, 2024 | W14: Odds and Ends |
| 14 | Wednesday, December 4, 2024 | Project Milestone #7a (Poster Draft) Due |
| 14 | Thursday, December 5, 2024 | W14 continued… |
| 14 | Tuesday, December 10, 2024 | Project Milestone #7b (Poster Final) Due |
| 15 | Tuesday, December 10, 2024 | W15: Wrapping Up |
| 15 | Thursday, December 12, 2024 | Project Milestone #8a (Poster Symposium) |
| 15 | Thursday, December 12, 2024 | Project Milestone 8b (Collaboration) Due |
| 16 | Sunday, December 15, 2024 | Formative Assignment (R Inferential) Resubmission Due |
| 16 | Friday, December 20, 2024 | Project Milestone #9 (Team Skills) |
| 16 | Friday, December 20, 2024 | Project Milestone #10 (Final Report) Due |
Syllabus
Lab in Cognitive Science (PSYC 2740)
Welcome! This page hosts the syllabus for PSYC 2740
Basic Course Information
Course number: PSYC 2740
Semester: Fall 2024
When: Tuesdays & Thursdays, 10.05-11.30 AM
Where: Druckenmiller Hall 024
Website: You are here!
Pre-requisites: Three of:
- either PSYC 2040 or PSYC 2060 (same as NEUR 2060) or PSYC 2055 (same as NEUR 2055)
- PSYC 2510
- PSYC 2520 (Concurrent Registration Allowed)
Who is your instructor?
Abhilasha Kumar: Hear my name!
Pronouns: she/her
About me: I am a cognitive scientist who is fascinated by how humans think, learn, and communicate. My work involves conducting psychological experiments to understand different aspects of human behavior such as how we learn the meaning of words, how we search for information, and how we cooperate with one another. When I am not working, I enjoy playing board games, learning new recipes, and playing tennis (badly)!
Email: a.kumar@bowdoin.edu
Office: Kanbar Hall, Room 217
Who is your learning assistant?
Uma Mohamed
Pronouns: she/her
About me: I am a senior majoring in Psychology and Digital Computational Studies with a minor in Africana Studies. I spent the spring semester studying in Jordan and Switzerland where I completed a research project on the perceptions and behaviors of young Jordanian woman on their second-class citizenship. Also, I spent the summer in Boston interning at Wayfair as a Business Analyst. Cognitive psych classes have been my favorite courses at Bowdoin so far, so I am excited to be a learning assistant for this course! In my free time, I love cooking, trying to create pottery and watching a lot of random YouTube videos.
Email: umohamed@bowdoin.edu
Office: Kanbar Hall, Room 200
Student/Office Hours
Prof Kumar’s office hours (Kanbar 217):
- Tuesdays, 1-2 pm
- Tuesdays, 5-6 pm
- Thursdays, 9-10 am
- Thursdays, 4.20-5.30 pm
Uma’s office hours (Kanbar 101)
- Sundays, 12-2 pm
You are strongly encouraged to drop-in during student/office hours - this is time specifically dedicated to you and any thoughts, questions, or concerns you may wish to share with us. If the designated hours don’t work for you, please email us to find a different time.
What is this course about?
The mind is one of the biggest human puzzles, and scientific exploration of different aspects of the mind is the central goal of cognitive science. This lab course will expose you to modern techniques used in cognitive science that can be applied to the study of cognition. Together, we will learn how to develop a research question, conceptualize scientific experiments from start to finish, analyze experimental data, and communicate important insights about human cognition.
Why take this course? a.k.a. learning goals
In addition to delving into some key aspects of cognition, my hope as an instructor is to empower you with an analytical toolkit that can be applied to other areas of your life. At the end of this course, you will be able to:
- Design and conduct a web-based experiment about cognition [Department Goal #6]
- Describe and analyze data [Department Goal #5]
- Communicate scientific findings [Department Goal #7]
Course materials
All of the course materials will be available in a timely fashion on this course website and/or posted on Canvas.
Students do not need to purchase or download any textbook - all materials will be made available (or are already available) on this course website or on Canvas
Course structure
There are 15 total weeks in this course. In this course, we will learn how to design, analyze, and communicate the findings from a research study, through the lens of replication. We will learn how to design a web-based experiment that mirrors a recently published study on learning new words. We will then use data collected as part of a direct replication of this study to understand the basics of descriptive and inferential statistics. Armed with these tools, we will propose and implement conceptual replications and extensions of this study, collect and analyze real behavioral data, and communicate findings in a poster session as well as through a short report.
This is an in-person class, and students are expected to attend all class sessions.
Weekly module structure
Weekly learning modules include three components to encourage engagement with the domain of intelligence in different ways.
PREP: Most modules either have assigned readings or surveys/experiments. All material is freely available and posted on Canvas or the course website. Student are expected to complete the assigned material before classes where the material is discussed.
TRY: We will actively work on designing, analyzing, or communicating about research in class. This component will involve you actively participating in class.
APPLY: At the end of each week or every other week, you will also have some formative assignments or project milestones due. These assignments will help you build on the skills you learn in class, and apply them to actual tasks and problems you may encounter in psychological research.
Course Schedule
Grading
The grading scale for this class is as follows:
| Letter grade | Points |
|---|---|
| A | 95 - 100+ |
| A- | 90 - 94.99 |
| B+ | 87 - 89.99 |
| B | 83 - 86.99 |
| B- | 80 - 82.99 |
| C+ | 77 - 79.99 |
| C | 73 - 77.99 |
| C- | 70 - 72.99 |
| D | 60 - 69.99 |
| F | fewer than 60% |
Grades will be determined based on the following rubric, which emphasizes our three learning goals (Design, Analyze and Communicate)
There are multiple ways for students to engage with class and course materials and achieve their desired grade. Students are encouraged to choose the assignments that work best for them. Course assessments that occur throughout the semester are designed to help students set and achieve their own goals for engaging in course content.
Points
| Component | Points |
|---|---|
| Formative assignments | 40 |
| Final project | 60 |
| Extra credit | 5 |
| Total | 105 |
Formative Assignments (40 points)
At different points during the semester, there will be 3 formative assignments that will help you assimilate the information you learn in class, and apply them in new contexts. These assignments will require you to adapt or write simple code to build parts of an experiment, test different aspects of design, or describe, manipulate, or infer statistics from data.
Overall, these formative assignments contribute 40 points towards your final grade.
Making mistakes is part of the learning process. To incentivize correcting your mistakes on your formative assignments, you will be allowed to redo ONLY formative assignments after receiving feedback on your initial attempt. Your initial attempt will be worth 3 points and your second attempt will be worth 10 points of your total grade on that assignment. You will be awarded the final 1 point if you successfully submit a first attempt that is at least 80% correct for each of the three assignments.
Final Project (60 points)
In this course, you will design a follow-up experiment based on a cognitive research study in a group, collect and analyze data from this follow-up study, and present your findings in a poster session and via a short report. Given that this will be a group project, you will be working with other classmates on several parts of the scientific process.
Groups will be assigned in the first or second week of class based on student interests and schedule compatibility.
Milestones
There will be several formative low-stakes milestones during the semester to ensure you are making steady progress towards the final project. Details about the project milestones are available on Canvas and linked in weekly assignments.
Due dates for these milestones are available on Canvas. The breakdown of how the milestones contribute to your final project grade is below:
| Milestone | Points |
|---|---|
| 1: Team plan + Literature review | 5 |
| 2: Project proposal | 5 |
| 3: Design draft | 2 |
| 4: Full Experiment | 7 |
| 5: Pre-registration | 5 |
| 6: Analyses | 10 |
| 7: Poster draft | 5 |
| 8: Poster presentation | 5 |
| 9: Final report | 10 |
| 10: Team skills | 5 |
| Total | 60 |
Milestone 1: Team Plan + Litertaure Review
This assessment will happen relatively early in the semester. You will be expected to submit a document where you have discussed a plan for your project as well as write summaries for 6 research articles that your team has collectively read and reviewed.
Milestone 2: Project Proposal
This assessment will help you consolidate your ideas and research into an informal research proposal. You will submit a jointly written 5-page literature review and proposal for your study. This milestone will also involve understanding the concrete elements of your research design. You will be required to submit a detailed mock-up of your experiment, including the stimuli and phases that you envision your experiment to contain. Note that your study must be a conceptual extension of the main study we will be replicating in class.
Milestone 3: Design Draft
This assessment is intended to help you put your ideas into practice. You will be required to submit code where you have implemented your experiment in jsPsych/HTML. This is a low-stakes assignment, where you will be provided feedback if you are running into issues.
Milestone 4: Full Experiment
For this assessment, you will consolidate all the feedback from the previous milestone and submit the code for a nearly-final online experiment. At this point, you should have ironed out all the kinks in your design and your experiment should be ready for data collection.
Milestone 5: Pre-registration
In this assessment, you will submit a pre-registration draft where you will clearly outline your hypotheses and planned analyses for your study. This draft will be reviewed by me, and you will actually pre-register your study after my approval.
Milestone 6: Analyses
In this assessment, you will be required to compute some basic descriptive statistics from the data you have collected from your experiment and submit R code. You will also be expected to conduct statistical analyses and examine whether the data you have collected support your predictions and hypotheses. The goal is to get your comfortable with gleaning patterns and making inferences from real-world data.
Milestone 7: Poster Draft
Using all the work you have done so far, you will now create a visually appealing poster draft that you will present in a poster symposium at the end of the semester. Your poster should deliver all the information a third observer will need to understand your study. You will be provided feedback on this draft.
Milestone 8: Poster Presentation
At the end of the semester, all groups will present their poster to the Psychology department through a poster symposium. Your final poster and presentation skills will be observed and you will be expected to communicate your findings effectively to the audience.
Milestone 9: Final Report
Although all previous milestones will be graded on a group level, you will submit an individually written final report that combines your literature review, methods, and results into a coherent story and presents all the information in a short APA-style report.
Milestone 10: Team Skills
No submission is required for this milestone. Your team skills will be assessed by your peers.
Extra credit (5 points)
There will be some opportunities to earn extra credit during the semester. These opportunities are described below:
Complete class surveys (2 points) : There will be 3 surveys (beginning, mid-semester, end of semester) to gather your reflections and suggestions to improve the course. With the exception of the pre-class survey (which is mandatory), all other surveys will be anonymous, and you will be able to earn 1 point for each survey you complete.
Win Star Coder (2 points): You will submit 3 formative coding assignments during the semester. The student who scores the combined highest score on the FIRST attempt for these assignments will earn 1 extra credit.
Win Team Player (1 point): Throughout the course, I will also evaluate who stood out as a team player, by observing how you participate in groups and contribute to group work. The student who stands out in this respect will earn 1 extra credit point.
Course Policies
Academic honesty and plagiarism
We, as a class, will abide by the Bowdoin College Academic Honor Code. While you are encouraged to discuss ideas and thoughts with your classmates, plagiarism in any form will be subject to grade reductions and disciplinary action. Specifically, you are permitted to make use of online resources and/or search platforms. However, directly copying or adapting written material and/or your classmates’ answers or ideas will be considered plagiarism. This policy applies to both individual and group assignments.
Please refer to this page for a list of resources related to plagiarism and other academic integrity issues. Here is another helpful infographic about plagiarism that you are encouraged to go over.
Use of generative artificial intelligence tools
Acknowledgement: This policy about generative AI was generated using the Generative AI Syllabus Statement Tool provided by Seaver College
Generative artificial intelligence tools — software that creates new text, images, computer code, audio, video, and other content—have become widely available. Well-known examples include ChatGPT/Bard for text and DALL-E for images. This policy governs all such tools, including those released during our semester together. You may use generative AI tools for work in this course to help with idea generation, understanding code, literature review, drafting, and other such academic work. If you do use generative AI tools on assignments in this class, you must properly document and credit the tools themselves. Cite the tool you used, following the pattern for computer software provided by the American Psychological Association (APA) guide. If you choose to use generative AI tools, please remember that they are typically trained on limited datasets that may be out of date. Additionally, generative AI datasets are trained on pre-existing material, including copyrighted material; therefore, relying on a generative AI tool may result in plagiarism or copyright violations. Finally, keep in mind that the goal of generative AI tools is to produce content that seems to have been produced by a human, not to produce accurate or reliable content; therefore, relying on a generative AI tool may result in your submission of inaccurate content. It is your responsibility as a scholar — NOT the tool’s — to assure the quality, integrity, and accuracy of work you submit in any college course. Although you have wide latitude to determine how you use generative AI tools in this course, you must be wary of unintentional plagiarism or fabrication of data. Please act with integrity, for the sake of both your personal character and your academic record.
Electronic devices
Most of our class time will be spent in active learning and in-person/online activities that require the use of technology. Therefore, please bring a Macbook and iPad to class. Some students find it helpful to code on their Mac and use their iPad for looking at slides, so please consider bringing both devices to class.
Please make sure that your devices are fully charged when you arrive to class. In order to minimize distractions, please close or exit out of all other programs except those needed during class, and put your Mac/iPad on “work” mode. All class time should be devoted to working on in-class activities and group projects.
Attendance: How many classes can you miss?
It not only benefits your learning, but benefits all of our learning to be present together in the same space. Class time is designed to take advantage of our presence together. To that end, your attendance will count towards your class participation.
Of course, emergencies (illness or family emergencies) can and do occur. Note: If you are sick, please stay home. However, I would greatly appreciate that you email me if you will be missing class. If you miss more than 2 classes and I haven’t heard from you, I will be in touch to check in on you. Hopefully you will have connections to other students in the class and can find out what you missed from a classmate. I will also try my best to upload slides and other course materials on the website and/or on Canvas.
Late Work Policy
Sometimes, life doesn’t go as planned and you have way too much going on to turn things in on time. That is OKAY! This course has the following policies for late work:
- Each student has 3 “flex” days that they can use at their discretion throughout the semester. You can use all 3 days at once for a single assignment and turn in one assignment 3 days late (with no questions asked), OR you can spread the love across different assignments.
- Flex days can be used for ANY assignments (formative assignments or project milestones) unless otherwise stated in class. However, if an assignment is a GROUP assignment (as will be the case for projects), EACH member of the group will need to count off their individual flex day for the flex day to count.
- If you need to turn in work late and do not have any flex days left, I will consider extensions based on legitimate reasons, which ONLY include verified illnesses and/or family emergencies. In these cases, you are encouraged to reach out to me at least 24 hours in advance of the due date.
- Work that is handed in late beyond the flex days or without an approved extension will automatically be graded on 50% of the original points.
- Using ONE flex day means you get a 24-hour extension. Please note that this is a strict extension and any work that is handed in beyond 24 hours after a flex day was requested will either need to use an additional flex day or will be graded on 50% of the original points.
- To request a flex day, you can send me an email within the 24-hour extension window, or leave a comment on your submitted assignment on Canvas.
Inclusion
I will do my very best to ensure that students from all backgrounds and perspectives receive equitable access and opportunity in this course, that students’ learning needs be addressed both in and out of class, and that the diversity students bring to this class is viewed as a resource, strength, and benefit. I hope to employ materials and engage in activities and dialogue that are respectful of different aspects of your identity.
Religious Holidays
No student is required to take an examination or fulfill other scheduled course requirements on recognized religious holidays. Students are expected to declare their intention to observe these holidays at the beginning of the semester.
Accommodations
Students with documented Bowdoin-granted accommodations have a right to have these met. I encourage you to see me in the first 2 weeks of class to discuss how your accommodations may support your learning process in this course. I highly encourage all students to meet with me in the first few weeks of class to discuss your learning preferences, challenges you may face learning this semester, and how we can create an effective learning experience for you. If you are interested in learning more about accommodations please see Lesley Levy in the Office of Student Accessibility or visit Bowdoin’s website on accessibility-related policies and resources.
Counseling Resources
As a student, you may experience a range of issues that can cause barriers to learning, such as strained relationships, increased anxiety, alcohol/drug problems, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may lead to diminished academic performance or reduced ability to participate in daily activities. Bowdoin College is committed to advancing the mental health and well-being of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. You can learn more about the broad range of confidential mental health services available on campus at this link. The Dean of Students Office is also a resource for students facing personal and academic challenges. I encourage you to reach out to the amazing people in the dean’s office for a meeting anytime.
Learning Resources
The Baldwin Center for Learning and Teaching offers peer-to-peer resources including mentors, Q-Tutors, and Writing Assistants. If you or your family are multilingual, you may also take advantage of Lisa Flanagan to work on writing and speaking assignments and projects. Tina Chong is available as an Academic Coach to work with you on goal setting, managing time, study habits and other strategies to support academic success. You are encouraged to make an appointment and learn more about how the Center can support your learning.
Other resources
If you are on a budget or would benefit from financial assistance of any kind at any point in the semester, I encourage you to contact your Dean and explore the Supplemental and Emergency Funding website. The Office of the Dean of Students has grant and loan funds available to remove financial barriers so that students can more fully access the Bowdoin experience or to assist students with unexpected financial needs.
Mandated Reporter
As a faculty member, I am considered a Responsible Employee, per the Student Sexual Misconduct and Gender Based Violence Policy. As a Responsible Employee I am required to report disclosures of sexual misconduct, dating violence, stalking, and/or sexual and gender-based harassment to Bowdoin’s Title IX Coordinator, Kate O’Grady. My reporting does NOT mean that any actions will be taken beyond Kate reaching out to you and trying to schedule a time to talk to see what assistance you might need to be successful as a student here at Bowdoin. For more information please check out Bowdoin’s Title IX page.