Supplemental Materials
This page provides miscellaneous materials regarding research currently conducted in the laboratory. PLEASE REPORT ANY ERRORS TO ME: michaelyoung@ksu.edu
NDSU Workshop (permission required to share first four items)
Video of the presentation (YouTube; Windows users appear to have problems with viewing this - if you can't view it, email me for a pdf version that is too large to post here)
Installing brms (pdf)
Basic Bayesian examples (html)
Bayesian ANOVA example (html)
Supplemental reading by John (pdf)
Supplemental reading by Sarah and Rens (pdf)
Basic multilevel modeling in R using lme4. Note that the brms syntax builds on the lmer/glmer syntax used in the lme4 package: link
How to compute Bayes Factors using various software: link
Bayesian multiple regression resource; provides example code in multiple packages: link
Analyzing Choice Discounting Data (JEAB manuscript support)
Overview of using R for logistic analysis of discounting data (pdf)
Commands used in manuscript (R command file)
Example for analyzing one group (UPDATED: R Markdown output)
Example for analyzing with simple predictors (UPDATED: R Markdown output)
Example of using ggplot for graphical presentation (UPDATED: R Markdown output)
Example one group (csv data file)
Example simple predictors VG (csv data file)
Simulated Choice data revised simplified (csv data file)
Simulated Choice data revised simplified nonzero (csv data file)
Simulated NonZero data (csv data file)
VideoGame DDDG data (csv data file)
Analyzing Indifference Data (JEAB manuscript support)
Overview of using R for nonlinear multilevel modeling (pdf)
Commands used in manuscript (R command file)
Example for analyzing one group (UPDATED: R Markdown output)
Example for analyzing with simple predictors (UPDATED: R Markdown output)
Example of analyzing with interaction (UPDATED: R Markdown output)
Example one group (csv data file)
Young E1 DelayDiscounting (csv data file)
For CO3 Workshop on Multilevel Modeling
PowerPoint Presentation (.pptx)
S-D Mixed Analysis.R (R command file)
S-D Mixed and Num icons entropy.dat (data file)
S-D Mixed disaggregated.dat (data file)
Beckmann analysis.R (R command file)
Beckmann timing data.dat (data file)
Impulsivity (funded by NIDA)
Video game clip of opposition task in CIRP - M+P- condition (youtube)
Example of opposition task as implemented in the continuous impulsivity and risk platform (CIRP). The first two orcs involved a power value of 1.25 whereas the second two involved a power of 0.60.Video game clip of escalating interest task - magnitude (youtube)
Basic escalating interest task clip showing increasing magnitude. This is a short clip showing a power of 1.0 (linear) increase in damage magnitude after a shot is taken.Video game clip of escalating interest task - probability (youtube) - be sure to turn your audio up
This clip shows selected scenes from our preparation examining the study of impulsivity. The blue bar in the lower left corner indicates how much damage will be done (for magnitude manipulations) or the probability of the weapon working (for probability manipulations) when the player pulls the trigger. These scenes are for a probability manipulation in which we occasionally change the shape of the mathematical function that defines the way in which the weapon recharges (the first scene is power = 1.0, the second is power = 1.5, the third is power = 0.5). For more details, see Young, Webb, & Jacobs (2011).Video game clip of delay discounting task - magnitude (youtube)
Delay discounting task clip showing increasing magnitude. This is a short clip of an increase in damage magnitude after a shot is taken. Note that the damage goes to zero after two seconds indicating a commitment to the larger later amount.
Causal Decision Making (funded by AFOSR and NSF)
Video game clip of basic causal task (youtube)
This is a clip showing a participant approaching a trio of potential targets, observing their behavior, and firing at the chosen target. The target's weapon has a delay thus making the decision particulary difficult. The latter part of the video shows a bird's-eye-view of the game region for one of the levels. See Young & Nguyen (2009); Nguyen et al. (2010); Young et al. (2011a; 2011b)Video game clip of continuous causality (youtube) - be sure to turn your audio up
This clip shows a demonstration of our preparation to study continuous causation in which the proximity of the "enemy" to an "enemy detector" (the pole) changes the pitch or sets off (depending on the condition) the detector. Young & Cole (in press).
Variability Discrimination
Example stimuli from Young and Racey (2009, Empirical Studies of the Arts), color versions:
Launching Effect
The following are examples of basic causal interactions that we are studying in the lab. We are exploring a number of variations on this theme.
Two examples from the original study (Young, Rogers, & Beckmann, 2005):
Training movies used with pigeon study (Young, Beckmann, & Wasserman, 2006):
Color change as a method of bridging temporal gaps (Young & Falmier, AJP, 2008):
Continuous 0 ms (top) & Discrete 400 ms (bottom) - from Experiment 2
Discrete 0 ms (top) & Rapid 0 ms (bottom) - from Experiment 1.The effect of animacy on causal judgments (Falmier & Young, 2008):
The effect of gap fillers on judgment (Young & Falmier, QJEP, 2008):
Exp 1 of Spatial Gap study - gap fillers
Exp 2 of Spatial Gap study - line markers
Broca’s area activity in the linguistic coding of visual causal events: An fMRI study (Limongi et al., under review)
- Causal Direct
- Indirect Causal
- Non-Causal
Lexical preference in the linguistic coding of direct causal events: A probabilistic approach (Limongi and Young)
