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Why? Not always lots of RA opportunities in our laboratory from semester to semester. Provide opportunity for awesome students who applied or from recent classes to gain some experiences. To translate some cognitive science into

  1. Why? • Not always lots of RA opportunities in our laboratory from semester to semester. • Provide opportunity for awesome students who applied or from recent classes to gain some experiences. • To translate some cognitive science into day-to- day practice , hone training materials, disseminate resources, etc. Caveats Goals • This workshop will be a rough draft . • Learn some solid RStudio . • Material may not always be super clear, however I will be • Learn how to plot and describe data that is here to collaborate on RStudio. organized in time. • Apply this knowledge to real-world case studies. • Today : we start slow and simple just to get everyone on the same page.

  2. COMPLEX DYNAMICAL SYSTEMS IN SOCIAL AND PERSONALITY PSYCHOLOGY 269 Time Series Types 15 50 Limb Posi � on 40 12 Anxiety 30 9 20 6 10 measurement sampling 0 3 0 5 10 15 20 25 30 35 40 45 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Time (seconds) Session 9 1500 8 Self-Esteem 7 RT (msec) 1000 6 regular irregular 5 500 4 3 2 0 1 65 129 193 257 321 385 449 513 577 641 705 769 833 897 961 64 128 192 256 320 384 448 512 Trial Trial measurement type 6 3 Daily Hedonic Level 2 5 Numeric Code 1 4 second-by-second word sequence in a 3 categorical 0 emotion type conversation -1 2 -2 1 -3 0 1 8 15 22 29 36 43 50 57 64 71 78 0 500 1000 1500 2000 2500 3000 3500 Time (seconds) Time (msec) brain waves or motion reaction time , or arm while walking (Harrison & Richardson, 2009). In Figure 11.8. Hypothetical examples of several types of behav- continuous ioral time series. (top left) Change in anxiety level for an indi- other cases the patterns of change over time are highly tracking keystrokes ( trial series ) vidual over 50 therapy sessions. (middle left) An individual’s complex and appear to be nondeterministic or stochas- self-esteem recorded on a 9-point Likert-scale twice a day for tic (i.e., random): an individual’s self-esteem over the 512 days. (bottom left) An individual’s daily hedonic (mood) course of 1.5 years (see Deligni` eres et al., 2004) and level recorded over 12 weeks. (top right) Motion sensor record- the trial-by-trial RT and an individual completing a ing of a individuals right arm movements while walking. (mid- 512 trial lexical decision task (see Holden, 2005). Oth- dle right) Reaction times of a participant completing a 512 ers seem to fall somewhere in between, containing trial lexical decision task. (bottom right) A time series repre- senting categorical data obtained from eye movement behav- semi-periodic patterns or other complex regularities. Recap Day 1 • Setting up RStudio • Navigating your computer to get to your working directory (setwd) • Loading in a table (read.table) for inspection and plotting (plot) • Time series concepts.

  3. Time Series Types reaction time! measurement sampling 1500 RT (msec) 1000 continuous trial series 500 regular irregular 0 64 128 192 256 320 384 448 512 Trial 6 measurement type motion tracking! second-by-second word sequence in a categorical emotion type conversation 15 Limb Posi � on 12 continuous regular 9 6 3 brain waves or motion reaction time , or 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Time (seconds) continuous tracking keystrokes ( trial series ) quantities for continuous time series Goals Day 2 15 Limb Posi � on 12 mean • Taking the mean and standard deviation (sd) of deviation 9 your time series. 6 (sd) 3 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 • The concept of entropy as a measure of “disorder” Time (seconds) • Taking the difference (diff) of your time series to explore how “stable” a process it. • E.g., mental processing during typing • E.g., stock prices

  4. examples quantities for continuous time series lower entropy 15 Limb Posi � on 12 RT mean deviation 9 6 (sd) t 3 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Time (seconds) + a new measure of disorder higher entropy entropy RT how (in)consistent the higher the is the time series in entropy, the more general its values? “disorder” in the time series t examples lower entropy RT t Exercise 4 higher entropy RT t

  5. example of taking the difference entropy of differences RT t RT t example of taking the difference example of taking the difference 0 0 0 0 0 0 lower entropy 100 100 100 100 100 100 100 100 0 0 RT 0 0 RT 0 0 t t higher entropy 32 34 … 32 34 … 24 21 24 21 RT RT 18 18 t t

  6. how do we get the difference, like this? entropy of differences 0 0 0 lower entropy 100 100 100 100 0 RT 0 0 t (0, 100, 0, 100, 0, 100, 0, 0, …) entropy of differences Exercise 5

  7. Recap Days 1, 2 • Setting up RStudio • Taking the mean and standard deviation ( sd ) of your time series. • Navigating your • The concept of entropy as a computer to get to measure of “disorder” your working directory ( setwd ) • Taking the difference ( diff ) of your time series to explore how “stable” • Loading in a table a process it. ( read.table ) for inspection and plotting • E.g., mental processing during ( plot ) typing • Time series concepts . • E.g., stock prices Goals Day 3 more fun • How to subset data. with dynamic • E.g.: Deleting outliers from your data (like a 47-second data keystroke!?) • “ Devilish details .” • Analyzing typing speed for individuals characters (e.g., ‘e’ vs. ‘p’). • Which do you think would be faster? • Experience collecting dynamic data with eye tracking.

  8. Plan for Eye Tracking • Used a “relay” method for training • I will get things prepped at the back of the room. Exercise 6 • Kevin will join me, and act as my subject as I show him the tracker. • Kevin will then act as me, and train Mario on the eye tracker. • Mario will then act as Kevin, and train Mitzy on the eye tracker, etc. • … Recap Days 1, 2, 3 How to subset data. • • Taking the mean and standard deviation ( sd ) of Setting up RStudio • E.g.: Deleting outliers from your time series. • your data (like a 47-second keystroke!?) • The concept of entropy Navigating your • as a measure of computer to get to your “disorder” working directory “ Devilish details .” • ( setwd ) • Taking the difference Analyzing typing speed for • ( diff ) of your time series Loading in a table individuals characters (e.g., ‘e’ • to explore how “stable” a vs. ‘p’). ( read.table ) for process it. inspection and plotting ( plot ) • E.g., mental Which do you think would be • processing during faster? typing Time series concepts . • Experience collecting dynamic • • E.g., stock prices data with eye tracking.

  9. Goals Last Day! Promise of Data • It is our era… for example, today… • More hands-on training on dynamic data collection ( eye tracking glasses ). society • Mario VR demo!? • Case study in a cultural domain: word frequencies over historical time . • Case study challenge : I give you some data, some basic code, and you hack at it . self Strategies for Next Steps 3 Strategies most structured • 1. Find a structured course online. • E.g.: Coursera. What kind of 
 • 2. Find videos and other structured resources. learner are you? • https://www.youtube.com/channel/ UC5ktyacv_aPSBmKB7uX5Piw • 3. Hack, hack away using Google and manuals most disorder

  10. Skill Concepts • Program planning (“logic in pseudo-code”) • Not even actually programming • Debugging process • When starting out, any time you are writing a script, run each line as you write it . • Learn how to maximize use of online resources • Become familiar with help(function) or an RStudio reference site that can help (e.g.: r-dir.com).

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