# exp1: Data from Experiment 1 in Gauvrit, Singmann, Soler-Toscano &... In acss: Algorithmic Complexity for Short Strings

## Description

34 participants were asked to produce at their own pace a series of 10 symbols among "A", "B", "C", and "D" that would "look as random as possible, so that if someone else sees the sequence, she will believe it is a truly random one".

## Usage

 `1` ```exp1 ```

## Format

A data.frame with 34 rows and 2 variables.

## Source

Gauvrit, Singmann, Soler-Toscano & Zenil (submitted). Complexity for psychology. A user-friendly implementation of the coding theorem method.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```# load data data(exp1) # summary statistics nrow(exp1) summary(exp1\$age) mean(exp1\$age) sd(exp1\$age) ## Not run: # this uses code from likelihood_d() to calculate the mean complexity K # for all strings of length 10 with alphabet = 4: tmp <- acss_data[nchar(rownames(acss_data)) == 10, "K.4", drop = FALSE] tmp <- tmp[!is.na(tmp[,"K.4"]),,drop = FALSE] tmp\$count <- count_class(rownames(tmp), alphabet = 4) (mean_K <- with(tmp, sum(K.4*count)/sum(count))) t.test(acss(exp1\$string, 4)[,"K.4"], mu = mean_K) ## End(Not run) ```

acss documentation built on May 1, 2019, 7:56 p.m.