Logistics

  • Zoom Room
  • Slack
  • TA: ​Patrick Wilson - [email protected]
  • Group List
  • Group Google Drive Folders
  • Meeting Schedule 
    • Sunday
      • 2PM - 2:30PM Flash VA
      • 1PM - 1:30PM Scaling RT
    • Monday
      • 1PM - 1:20PM Gabor RT
      • 1:20PM - 1:40PM Gabor RT & Color RT
      • 1:40PM - 2PM Color RT
​

Data Taking

We have a great advantage during the pandemic because while most in-person research with human subjects has been halted, our experiments can be completed from home by anyone. We can take advantage of our pool of ~35 students to get a large sample size for all of our studies. 

As of now, only the Visual Acuity Flashing experiments (Group 1 & 2) have reached the data taking stage. Please help us out and take data with the experiments, then when your group reaches this stage we'll repeat the process with your experiments! We can all publish our papers quickly with this large sample size.
  • Instructions
    • Instruction Slides
    • Calibration Walk-through (10:21)
    • Validating your Calibration (5:25)
      • The four lines of code are in the video description
    • Before submitting your data, validate your calibration by following along the video above. If your calibration is off, follow the calibration walk-through video closely and re-try
    • Upload your raw data files to the Google Drive (Pending TA Approval folder).
    • I will check the data as it comes in and move it to the approved data folder if it passes.
    • Check the TA Check-Sheet for your status
    • If I reject your data, follow the steps in the calibration walk-through video closely and re-take your data. If your calibration is perfect but I still reject your data, send me an email
    • Complete the Isolated Character and Crowded Periphery Center experiments first and wait for me to check them, so you don't waste your time doing all of the experiments
  • Experiment Code
  • ​To-Do
    • ​Isolated Character 
    • Isolated Character Flashing
    • Crowded Periphery Center
    • Crowded Periphery Center Flashing
    • Crowded Periphery Outer
    • Crowded Periphery Inner
  • Raw Data Upload
​

PsychoPy Materials

  • Simple Reaction Time Experiment
    • In this series of videos I walk through coding a simple reaction time experiment from scratch using PsychoPy. A stimulus is randomly selected from "I", "II", and "III" and displayed at the center of the screen after a randomized delay period. The subject presses the 1, 2, or 3 key depending upon the stimulus and their reaction time is recorded. Download and run the final code to see the experiment
    • Final code
    • Part 1 (7:40)
      • Opening a Window, Displaying a Character, Waiting for Keypress
      • Code
    • Part 2 (4:41)
      • Recording Reaction Time
      • Code
    • Part 3 (4:39)
      • Selecting a Random Stimulus, Checking for Correct Keypress
      • Code
    • Part 4 (3:11)
      • Running Multiple Trials, Randomized Delay
      • Code
    • Part 5 (6:24)
      • Data Output to Csv
      • Code
​

​MATLAB Materials

These videos cover many of the basic coding principles and skills. They are tailored for complete novices, and teach you the tools you need to begin creating more complex programs

There are many videos in the Data Analysis section wherein I go over developing MATLAB scripts to analyze data. They are another good learning resource for MATLAB programming​​

​Data Analysis

This section includes videos explaining key statistics concepts, as well as videos walking through the application of these concepts to the data obtained in our first Visual Acuity paper using MATLAB

The statistics concepts are important for any researcher, and my MATLAB application of them to my data analysis can be adapted to fit your needs. Many of the MATLAB videos provide additional explanation of the concepts

Videos with important statistics concepts are marked with an asterisk (*)
  • Basic Analysis in MATLAB (Scatter Plot & Linear Fit)
  • Chi^2 Concepts
  • Basic Chi^2 Fitting in MATLAB
  • Improved Chi^2 Fitting in MATLAB​​
    • Code
    • Part 1 (3:59) *
      • Removing Outliers
      • Normalized Distribution (y/x) 
    • Part 2 (3:36) 
      • Removing Outliers in MATLAB
      • removeOutliers function
    • Part 3 (1:29)
      • Organizing Your Data
    • Part 4 (6:53)
      • Reading in Data in MATLAB
      • readCsv function
    • Part 5 (5:32) *
      • Estimating Standard Errors of Measurements
      • calculateStandardErrors function 
    • Part 6 (11:44) *
      • Using the readCsv function to get our data
      • Using our improved estimation of the standard errors to improve our fit quality
    • Part 7 (6:59)
      • Creating a pointSlope function to graph our scatter plots
    • Part 8 (7:17) *
      • Fixed intercept Chi^2 minimization fit (y = ax)
    • Part 9 (6:15) *
      • Evaluating reduced Chi^2 (goodness of fit)
    • Part 10 (7:56)
      • Creating functions for our y = ax and y = ax + b Chi^2 fitting
    • Part 11 (17:07) *
      • Graphing Chi^2 vs. slope parameter (y = ax fit)
    • Part 12 (15:08) *
      • Estimating standard error of y = ax fit parameters from Chi^2 contour
      • Improving graph of Chi^2 vs. slope parameter
    • Part 13 (11:47) *
      • Estimating standard error of y = ax + b fit parameters from Chi^2 contour
    • Part 14 (18:35)
      • Graphing Chi^2 vs. slope and intercept parameters (y = ax + b fit)
    • Part 15 (15:39)
      • Graphing Chi^2 vs. slope and intercept parameters (y = ax + b fit) cont.
    • Part 16 (11:34)
      • Adapting our analysis to automatically analyze and graph every protocol for a given subject
    • Part 17 (6:23)
      • Auto-saving plots
    • Part 18 (9:10)
      • Creating templates for structs to store fit parameters for output
    • Part 19 (10:09)
      • Storing fit parameters and Chi^2 values in structs
    • Part 20 (5:33)
      • Outputting fit parameters and Chi^2 values to central spreadsheet
    • Part 21 (3:30)
      • Getting a list of all subjects in the data folder (getSubjects function)
    • Part 22 (9:26)
      • Adapting our analysis to automatically run every subject
  • Assessing Correlation
    • Advantages over population statistics​
    • Plotting correlation in MATLAB
      • Code
  • Hypothesis Testing 
  • Hypothesis Testing in MATLAB
​​​

​Literature Search

As you complete your final data taking and analysis, your group can begin writing your paper and preparing for publication. One of the most important and time consuming steps is the search for existing literature related to your research. There are a few tools which will make this process a lot easier
  • Resources
    • First Visual Acuity Paper
      • Read this paper for an introduction to our research, and for a model for your experiment and publication
    • ​Zotero 
      • ​Zotero is a program which makes saving and organizing research articles, as well as quickly generating citations
    • ​Arisaka's Zotero Library
      • ​Professor Arisaka has compiled a massive library of research articles related to human vision - this is a good place to start your literature search
      • You can send Professor Arisaka new articles to upload to the Zotero library
    • ​Sci-Hub
      • ​I suspect I'm not officially allowed to share Sci-Hub here, but it is an incredibly useful resource. It is a pirating website created to bypass research journal paywalls, motivated by the belief that scientific knowledge should be available to everyone - regardless of income or social status.
      • To use it, I search for the papers I need using google scholar and find the doi number (eg. doi:10.1101/2020.08.02.231803), and simply paste it into the Sci-Hub search bar.