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 Laboratory in Cognitive Science  posted by  duggu   on 12/12/2007  Add Courseware to favorites Add To Favorites  
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Abstract/Syllabus:

Statistically-analyzed PET scan data superimposed on structural MRI scan.

Statistically-analyzed PET scan data superimposed on structural MRI scan (front of brain is at right) shows areas in the anterior and posterior cingulate where panic disorder patients had nearly one third fewer serotonin 5-HT1A receptors compared to healthy control subjects. The lighter the color, the greater the difference between patients and controls. (Image courtesy of the National Institute of Mental Health.)

Course Highlights

This course features a complete set of lecture notes.

Course Description

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Technical Requirements

Special software is required to use some of the files in this course: .xls, .m

Syllabus

 

Description

The goal of this course is to prepare you for conducting and critiquing research in cognitive science. To achieve this goal, the course will cover the following five topics.

1. Basic research methodologies. A series of lectures and case studies (many of them deliberately flawed) will introduce you to fundamental issues of experimental design, sample collection and ethics.

2. Data analysis. How can one summarize experimental data and make intelligent inferences from them? You will learn about important statistical analysis techniques in a 'hands-on' fashion - by examining how these techniques have been used in several real research papers.

3. Writing research papers and presenting your work. No matter how good a scientist you are, if you cannot communicate your ideas effectively in your papers and talks, the impact of your research will likely be greatly diminished. The course will provide some pointers about good writing and presenting style.

4. Studying and critiquing past experiments. A necessary part of being a scientist is reading past and ongoing work from other laboratories and understanding the strengths and limitations of their findings. Through example case studies, we shall try to indicate how to develop a discerning attitude when reading papers.

5. Conducting independent experiments. To reinforce your learning of research methodologies and writing style, you will design, run and writeup several experiments in various areas of cognitive science including (but not limited to) human attention, vision, memory, imagery and reasoning. Not only will this exercise help bring together all the topics touched upon in the course, it may also allow you to produce a publishable paper.

Requirements

Your grade in class will be determined by:

In-class participation (5%)
Mid-term examination (20%)
3 write-ups and presentations of team experiments (45%)
1 write-up of an independently conducted experiment (30%)
Satisfaction of learning (priceless)

 

Calendar

 

         
  LEC #       TOPICS       ASSIGNMENTS
         
         
  1       Goals of the class and overview of content and Administrivia        
         
         
  2       The genesis and nature of research ideas
Basics of data collection and analysis
       
         
         
  3       Experimental design - I        
         
         
  4       Data analysis - I        
         
         
  5       Experimental design - II        
         
         
  6       Data analysis - II        
         
         
  7       Home experiment presentations       Home experiments (set 2) handed out
         
         
  8       Data analysis in Excel; Presenting        
         
         
  9       Tutorial on Matlab (data analysis and Psych Toolbox)        
         
         
  10       Correlational research        
         
         
  11       Small N designs        
         
         
  12       Quasi-experimental designs/ Applied research/ Descriptive research        
         
         
  13       Home experiments (set 2) presentations       Home experiments (set 3) handed out
         
         
  14       Mid-term Exam        
         
         
  15       Ethics in conducting and reporting research        
         
         
  16       Flawed papers - I       Papers for home experiments (set 2) due in class
         
         
  17       Flawed papers - II        
         
         
  18       Home experiment presentations (set 3)        
         
         
  19       Flawed papers - III        
         
         
  20       Areas of research at MIT and the neighborhood       Papers for home experiments (set 3) due in class
         
         
  21       Final project presentations        
         
         
  22       Final project presentations        
         
         
  23       Final project presentations        
         
         
  24       Final project presentations        
         
         
  25       Show-case presentations        
         
 

 




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