V89.0046 - Lab in Human Cognition

Meeting Time/Place: Meyer 159 from 2:00-3:50 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: ShinWoo Kim (shinwoo.kim@nyu.edu), 482 Meyer, office hours: after class Wednesday or by appt.
Writing Instructor: Luke Fiske (luke.fiske@nyu.edu), by appointment

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 including python (no programming required). Thus, there is nothing to purchase! We may also use Microsoft Excel and perhaps other tools (such as R) for data analysis **.

Readings:

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.

Experiments

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

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.

Writing

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, and all students are required to submit one additional lab report for review at least three working days prior to the due date.

Teams

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.

Readings

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.

Grading

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 Sept 2 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


Class Schedule

Each class session may cover several of a variety of topics and tasks. The schedule below is guaranteed to change! Check back for updates

Date Description Slides from Today's Lecture
Sept. 3, 2008 Introductions, Why Study Human Cognition?

PDF Copy of Syllabus for printing


Pre-Course Survey - Please fill out and email to me (remember [lhc]!!)



Assigned Readings:
Simon, H.A. (1998) "What is an 'explanation' of behavior" in Mind Readings: Introductory Selections on Cognitive Science.


Tulving and Thomson (1973) "Encoding specificity and retrieval processes in episodic memory" Psychological Review 80 (5), 353-373.
Sept. 8, 2008 What make a good (or bad) Experiment? -Designing experiments to test hypothesis (started analysis of Experiment 1 - scoring)
If you are new to class, please read the syllabus and fill out the pre-course survey. Also, check the slides from day 1. See you wednesday - Todd
Sept. 10, 2008 Communicating Results (basic introduction to APA styled papers) Discuss reading for experiment 1
Sept. 15, 2008 A gentle introduction to data analysis and statistics in Excel. Descriptive Statistics refresher


A couple new files provided today:

- Exercise 1 handout

- Interactive Mathematica Notebook of presentation (pdf linked also)

- My excel spreadsheet (for reference on formulas, etc..)
Sept. 17, 2008 Introduction to statistical inference, simple one-sample t-tests, paired t-tests, Experiment 1 data analysis
A couple new files today

- Exp 1 class data *** needed for your lab

- Exercise 2 description and hints

- Interactive Mathematica Notebook of presentation (pdf linked also)

- My excel spreadsheet (for reference on formulas, etc..)
Sept. 22, 2008 Statistics Review. Finish analysis of Experiment 1. What makes a good figure? Creating accurate and informative figures. Error bars and how to (mis)use and (mis)interpret them.
Readings for Monday's class:

- Edward Tufte on Graphical Integrity

- Mind Performance Hack (Chapter 1 - Memory), Particular pay attention to Hint #12 as it talks about encoding specificity principal which is part of Lab 1
Sept. 24, 2008 Presentation by Luke about writing Lab 1, Get started on writing/final analysis. Lab report 1 due Friday Oct. 3rd (draft due Sept 30)


- Description of Lab 1 write up

- Gureckis and Love (in press) - an example of an APA-styled paper

- The test we gave ourselves

- APA template for Microsoft Work with the critical components

Note: if you want a copy of your spreadsheet from class Wednesday (and you saved it to the shared lab folder) email me and I will email it back to you

Sept. 29, 2008 The psychology of Math ability: Intuitive judgments and Symbolic Processing (**** lab 1 draft due tomorrow at noon ****)
From Luke:

I will send the class written comments on the work by Wednesday. For those who want to speak further, I will be in my office on Thursday evening from 6:30 to 8pm, for those who want to discuss the comments further. I am in 411 Lafayette Street, room 409. That is all the time I have this week, unfortunately. But it will productive for those students who do choose to come.

I will focus mainly on the Introduction and the Discussion and the justification and interpretation of the lab work. So students should not see me as an editor to correct typos, but rather as a critically engaged reader interested in the overall framework of their argument.


New York Times article on intuition in mathematics judgments

How Abstract is Symbolic Thought? paper on Math tricks by David Landy

Babys can add- by Karen Wynn

No they can't - By Les Cohen

Dogs can too - West and Young
For next time: David Marr - Vision Ch. 1
Oct. 1, 2008 More on Memory, Charts and Graphs (discuss readings) (**** lab 1 final due Friday at midnight ****)
Note: The final copy of Lab 1 will now be due Tuesday Oct 7th at midnight so that everyone has a chance to discuss their paper with Luke. The note from Luke is that he will be available the following times: Thursday from 6:30-8pm, Friday from 5:30-7pm, and Monday 5-7. Note: We will be picking up the pace significantly after the first paper is due, so be ready!
Oct. 6, 2008 Design and collect experiment 2 data. Learn the basics of R.
- Preliminary Lab 2 directions/description

- Main target article for lab 2 (summarizes current and past research on Stroop including brain imaging studies)


General interest:
What is synesthesia?

- The perceptual reality of synesthetic colors - Palmeri, Blake, Marois, Flanery, and Whetsell (2002), uses stroop-like tasks to assess if synesthesia is real. it is.

- A comprehensive review of Stroop effects (Macleod)

Oct. 8, 2008 Analyzing reaction time (RT), experiment 2 data analysis, more on R
HOW TO USE R (the notes in class that take you through our first set of analyses, along with detailed descriptions. Please read this careful, and practice at home.) You can download R for windows or mac here: http://www.r-project.org/

- UPDATED Lab 2 directions/description

Lab 2 STROOP DATA FILE - if this won't open right on your home computer you might need to open it in excel and then "save as..." (tab delimited txt) to clean it up

No lecture slides
Oct. 13, 2008 No Class.
Oct. 15, 2008 Beyond pairwise contrasts: ANOVA (exp 2 data analysis) more ANALYSIS
- UPDATED (VERSION 3) Lab 2 directions/description, NOTE, I fixed some of the bugs we mentioned in class today

Homework*** - make all the figures you will need for the paper (can captions)

Oct. 20, 2008 Beyond pairwise contrasts: ANOVA (cont.) - Finish lab 1
Oct. 22, 2008 collect experiment 3 data (assign reading 6)
Oct. 27, 2008 Writing exercise, Exp. 3 discussion, begin Experiment 3 data analysis
Readings for Lab 3:

- Posner and Keele (1968)

- Murphy Ch. 3 (big book of concepts)

Oct. 29, 2008 Experiment 3 data analysis, (short, reading 7), Lab report 2 due tonight
CLASS DATA FOR LAB 3
Lab 3 instructions
A nice reference for making/customizing graphs in R


An interesting article on classification/categorization in science
Nov. 3, 2008 Exp 3 data analysis, Discussion of readings
Nov. 5, 2008 Final stuff on Lab 2, Collect experiment 4 data,
Lab 4 instructions
Shepard and Metzler (1971) Takano & Okubo encyclopedia of cognitive science entry on mental rotation
Nov. 10, 2008 Regression and multiple regression, reading 8 discussion, Begin Exp 4. data analysis
lab 4 data
Nov. 12, 2008 Regression and multiple regression (cont. ) Experiment 4 data analysis and discussion
NEW** lab 4 directions
Nov. 17, 2008 Lab report 3 due, Eyetracking discussion with ShinWoo, continue Experiment 4 data analysis
**** A Note from Luke about Writing *** click here
Nov. 19, 2008 Final projects. Ideas, expectations, etc... How to build your own experiment using PyPsyLib, final project proposal due on Friday,
Nov. 24, 2008 Final project time
Nov. 26, 2008 final project time
Dec. 1, 2008 Final project time (work on experiments)
Dec. 3, 2008 Lab report 4 due, Final project time (in class data collection/begin analysis)
Dec. 8, 2008 Final project time/How to give a good talk
Dec. 10, 2008 Final project time/How to give a good talk
Dec. 8, 2008 Mini-conference (paper due Dec. 19th, 12 noon)

Acknowledgments: This course is based in part on the Lab in Human Cognition course taught at NYU in Spring 08 by Harlan Harris, who graciously shared his materials and advice.