Information about labels in data frames:


# dataframe 'quest'
# Display change questionnaire:

# f1: Did you realize something unusual?
# 	yes=1
# 	no=0
# f2: If yes, what exactly?
# 	no answer = 0
# 	fontcolor changed =1
# 	fontsize changed =2
#	words changed =3
# 	none of the above =4
# f3: If yes, in how many sentences?
# 	specification in %

# dataframe 'subjects'
'data.frame':   60 obs. of  4 variables:
 $ id : num  1 2 3 4 5 6 7 8 9 10 ...				unique number per subject
 $ age: num  18 17 21 23 24 20 17 17 17 17 ...		age in years of each subject
 $ exp: num  0 1 0 1 0 1 0 1 0 1 ...				0: Experiment 1 with capitalization
													1: Experiment 2 without capitalization
 $ sex: num  0 0 0 0 0 0 1 0 0 0 ...				0: female -- 1: male
 
# dataframe 'a'
# ... contains reading data for each individual fixation
# FIXATION DURATIONS
 'data.frame':   54888 obs. of  12 variables:
 $ id  : int  1 1 1 1 1 1 1 1 1 1 ...									unique number per subject
 $ sn  : int  29 29 29 29 29 29 29 29 29 86 ...							unique sentence number
 $ nw  : int  10 10 10 10 10 10 10 10 10 11 ...							total number of words per sentence
 $ wn  : int  2 4 4 4 4 6 7 9 10 1 ...									number of word in sentence that is fixated
 $ let : int  0 0 4 9 5 4 4 4 6 3 ...									number of letter in word that is fixated 
																		(0 codes the empty space before the word)
 $ dur : int  942 128 196 212 68 338 510 416 608 290 ...				duration of the current fixation (irrespective of fixation type)
 $ tar : num  0 0 0 0 0 0 1 0 0 0 ...									0: no target word -- 1: target word n+1
 $ pvn1: num  -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 0.5 ...		-0.5: valid preview -- 0.5: invalid preview
 $ n1bb: num  -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...		-0.5: high frequency preview -- 0.5: low frequency preview
 $ sfd : int  NA NA NA NA NA 338 510 416 608 290 ...					single fixation duration
 $ ffd : int  NA 128 NA NA NA 338 510 416 608 290 ...					first fixation duration
 $ gd  : num  942 604 NA NA NA 338 510 416 608 290 ...					gaze duration
 $ tvt : num  942 604 NA NA NA 338 510 416 608 290 ...					total viewing time
 
 
 # dataframe 'em'
 # ... contains reading data on word level 
 # FIXATION PROBABILITIES 
 'data.frame':   54905 obs. of  11 variables:
 $ id  : Factor w/ 60 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...	unique number per subject
 $ sn  : Factor w/ 120 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 2 ...	unique sentence number
 $ wn  : num  1 2 3 4 5 6 7 8 9 1 ...										number of word in sentence that is fixated
 $ n1bb: num  -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...			-0.5: high frequency preview -- 0.5: low frequency preview	
 $ twx : num  0 0 0 0 0 0 1 0 0 0 ...										0: no target word -- 1: target word n+1
 $ nw  : num  9 9 9 9 9 9 9 9 9 9 ...										total number of words per sentence
 $ pvn1: num  -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 0.5 ...			-0.5: valid preview -- 0.5: invalid preview
 $ prx : num  NA NA 0 0 1 0 0 NA NA NA ...									refixation probability
 $ psk : num  1 NA 0 0 0 0 0 NA 1 NA ...									skipping probability
 $ prg : num  0 0 0 0 0 0 0 0 0 0 ...										regression probability
 $ ilp : num  0 NA 0.5 0.286 0.2 ...										initial landing position (letter/(word length +1))

