V89.0046 - Lab in Human Cognition

Meeting Time/Place: Meyers 159 from 2:00AM-3:50 PM on Mondays and Wednesdays
Instructor: Todd M. Gureckis
Office: Meyer 280
Office Hours: Immediately After Class Monday or by appointment
Email: todd.gureckis@nyu.edu
Teaching Assistant: John McDonnell (jmcdon10@gmail.com), 275 Meyer Suite, office hours: Thursdays, 3pm, or by appointment.
Writing Instructor: Luke Fiske (luke.fiske@nyu.edu), office hours: by appt.

Course Description

This course provides hands-on experience with the standard experimental tools used in cognitive psychology research. Students run experiments, collect and analyze data, write research reports, and design and run a new experiment as a final project. Additionally, students read and analyze a number of additional research papers in order to gain a broader perspective on experimental approaches to human cognition. Content areas include memory, categorization, attention, learning, automaticity, and visual perception. Occasional lectures introduce new skills that apply not only in analyzing, communicating, and presenting scientific work, but more broadly (including lectures such as "How to give a good scientific presentation", "How to make an informative graph", "Statistical Thinking").

Laboratory Software

We will be using a combination of open-source tools for running experiments and performing statistical analyses. Thus, there is nothing to purchase! We will make use of Microsoft Excel and perhaps other tools (such as R) for data analysis **.


There is no textbook for this course. However, the following may come in handy in writing your reports:

No readings from this book will be assigned, and much of the content, is available on-line via judicious Google searches. There will be other readings made available as PDF files or handouts in class.


We will be collecting data for four experiments in class, using each other as experimental subjects. The data will be compiled, then analyzed in class and written up outside of class. The final project will involve proposing, implementing, running, and writing up an experiment.


Attendance and participation in lectures and labs is essential. There are in-class tasks and assignments in most class periods that cannot be made up later. Attendance at in-class experimental data collection sessions is mandatory. Students who are absent during data collection will receive a 50% penalty on the lab report for that experiment, no exceptions.


Lab reports will be APA-style research reports. Specific assignments will be explained in handouts and discussed in class. Reports will be graded on the quality of the ideas and thinking, prose style, and on adherance to APA format. Lab Report 1 will be reviewed by the Writing Instructor prior to submission. For all other reports, the writing instructor will read and assign a grade that will count for 10% of the total grade for that lab.


For some (but not all) assignments, students will be assigned to work in teams. Teamwork is an important skill in successful research and in life. In scientific research, papers often include an acknowledgements section which details the contribution of each author. Each assignment completed in a team must include a similar statement of the specific contributions of each person.


To supplement the hands-on-skills developed in this course, we will read a number of real, life (sometimes cutting edge) research papers together. Some of the paper will focus on the different types of data available to experimental psychologist including fMRI, EEG, MEG, eyetracking, etc. There will be short assignments related to these papers, as well as in-class discussion and tours/demonstrations of some of NYU's equipment.


Grades will be weighed as follows:

There may be opportunities for small amounts of extra credit, such as for brief presentations to the class on various topics.

Academic Misconduct

All work that students turn in must be their own work. Group assignments, all work must have been done by the students on the team, and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else's work as your own is unacceptable and will result in a failing grade. On the basis of past experience with intellectually lazy students, I have written an automated algorithm written in python that can detect examples of copying from electronic sources such as Wikipedia in submitted papers (yeah it is so easy to plagiarize even a computer script can do it!). More importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words, or there will be consequences to your academic career.

Research Ethics and Misconduct

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

**Statistics Software

Note that part of the class will be learning to use R and excel software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage:

Consultation will be available starting in Feb on the 6th floor in rooms 620 and 621* via e-mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library's classes page: http://www.library.nyu.edu/forms/research/classes.html