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鼠标追踪技术学习记录

Agent Based model在社会心理学中的应用 基于个体建模（Agent based

Introduction to JATOS 一些在线的互动任务可以借鉴这个网站上的示例 https://volunteerscience.com/experiments/templates/ https://www.jatos.org/Whats-JATOS.html JATOS (Just Another Tool

Introdcution to nodeGame nodeGame: Overview Common online labor markets include Amazon Mechanical Turk, oDesk, Freelancer, Elance, Guru, CrowdFlower, Innocentive, or SurveySampling, etc. If monetary incentives are not required, it is

Introdcution to jsPsych jsPsych is a JavaScript library for running behavioral experiments in a web browser. The library provides a flexible framework for building a wide range of laboratory-like experiments

R中试错运行语句 采用tryCatch({ error = function(e) { } } for (i in 1:n_subj)

Introduction to mouse tracking Using mouse cursor tracking to investigate online cognition: Mouse cursor tracking has become a prominent method for characterizing cognitive processes, used in a wide variety of

library(SimDesign) library(cmdstanr) [Update: Use parallel computing with two cores.] Adapted from https://cran.r-project.org/web/packages/SimDesign/vignettes/SimDesign-intro.html See https://mc-stan.org/cmdstanr/articles/cmdstanr.html for using cmdstanr Design <- createDesign(sample_size = c(30, 60, 120, 240), distribution = c('norm', 'chi')) Design ## # A tibble: 8 × 2 ## sample_size distribution ## <dbl> <chr> ## 1 30 norm ## 2 60 norm ## 3 120 norm ## 4 240 norm ## 5 30 chi ## 6 60 chi ## 7 120 chi ## 8 240 chi Generate <- function(condition, fixed_objects = NULL) { N <- condition$sample_size dist <- condition$distribution if(dist == 'norm'){ dat <- rnorm(N, mean = 3) } else if(dist == 'chi'){ dat <- rchisq(N, df = 3) } dat } Define Bayes estimator of the mean with STAN

Load Required Packages Data Description Data Import Mixed ANOVA ANOVA Table Plot Sample Result Reporting Load Required Packages library(afex) ## Loading required package: lme4 ## Loading required package: Matrix ## ************ ## Welcome to afex.

One thing that I always felt uncomfortable in multilevel modeling (MLM) is the concept of a unit-specific (US)/subject-specific model vs. a population-average (PA) model. I’ve come across it several times, but for some reason I haven’t really made an effort to fully understand it.