Emptying the file drawer: attentional bias in dependent and non-dependent smokers


At my current career stage, my file drawer is not very big. However, it is a waste of everyone’s time if there is data lying around that no one has ever had the chance to see or use. This post outlines my undergraduate third year project (for more information on the background and methodology, the full thesis can be found on ResearchGate) on attentional bias. It is not the most particularly informative experiment due to the small groups sizes, and unless it is included in a meta-analysis it is probably never going to be published. Although the information is available in my thesis, I thought it would be useful to present all the information you would need to perform either a meta-analysis or a power analysis (and it gave me a good excuse to play around with R Markdown). I will start off with a brief explanation of what the study was about, then present the results in three stages: smoking characteristics, attentional bias, and then smoking motives.

The study investigated attentional bias in dependent and non-dependent smokers. Attentional bias is the tendency to fixate attention on drug related cues such as cigarettes, ashtrays, or packaging. Therefore, if you were to present two images side by side, one featuring a drug related image and the other featuring a non-drug related image, attentional bias would be indicated by looking at the drug related image more than the non-drug related image. There are several studies showing that smokers show greater attentional bias towards smoking related cues than non-smokers (I won’t provide references for ease of reading. If you have taken an interest, read the introduction to my thesis for all the background). However, when you start to explore the heterogeneity of smokers, the literature is less consistent. Some research has found light or non-dependent smokers show greater attentional bias towards smoking cues, whilst other research has found heavy or dependent smokers show greater attentional bias towards smoking cues (for more information on the differences between smoking groups, see this relatively short blog post). The aim of this study was to further investigate whether light or heavy smokers exhibit greater attentional bias.

A dot probe task was used which presents two images side by side, one contains a smoking image and the other contains a neutral image. A small dot probe replaces one of the images and the participant has to indicate which side of the screen the dot appeared on. Attentional bias is indicated by faster responses to dots replacing the smoking images in comparison to dots replacing the neutral images. In addition, the images were presented for three different durations (also known as the stimulus onset asynchrony or SOA) of 200ms, 500ms, and 2000ms. Therefore, the design of the study for the main research question was a 3 x 3 mixed design. There was one between-subject factor of smoking group containing light smokers, heavy smokers, and a non-smoking control group. In addition, there was one within-subject factor of SOA for images presented for 200ms, 500ms, and 2000ms.

Packages and data


#Main data file.
data<- read.csv("Dissertation_data.csv", header = TRUE, stringsAsFactors = FALSE)

#Select data including only smokers. 
smoking.data <- data %>% 
  filter(SorNS == "Smoker")

#Data for WISDM analyses, selecting only smokers and the WISDM sub-scales
wisdm.data <- data %>% 
  filter(SorNS == "Smoker") %>%
  select(P_N, FTNDDepend, W_afat:W_weightcon)

#Plotting variables
Pal.2 <- c("#E69F00", "#56B4E9") #colourblind friendly colour palette 
Pal.3 <- c("#E69F00", "#56B4E9", "#009E73")
text.size <- 14

Smoking habits

The first part of the results includes the variables relating to smoking habits. Similar to previous research in the area, non-dependent smokers were defined by scoring 2 or less on the Fagerström Test For Cigarette Dependence (FTCD), and heavy smokers were defined by scoring 3 or more. The following variables are the mean and SD for each smoking related variable broken down by smoking group.

Table 1: Smoking characteristics broken down by smoking group. Abbreviations: FTND; Fagerström Test For Nicotine Dependence, CO; Carbon Monoxide.
Smoking group Smoking charactertic Mean SD N
Dependent Age (years) 28.64 8.65 11
Dependent Cigarette per day 15.27 5.20 11
Dependent CO (ppm) 24.73 19.75 11
Dependent Minutes since last cigarette 108.18 23.59 11
Dependent FTND 4.82 1.33 11
Dependent Years as smoker 11.36 7.39 11
Non-dependent Age (years) 22.14 5.90 7
Non-dependent Cigarette per day 6.43 3.55 7
Non-dependent CO (ppm) 5.14 1.46 7
Non-dependent Minutes since last cigarette 432.86 423.86 7
Non-dependent FTND 1.00 0.82 7
Non-dependent Years as smoker 6.14 4.91 7

An important point to note here is that there are many other ways of defining dependent and non-dependent smokers other than the FTCD, and there are other key smoking measures such as the number of cigarettes smoked per day and the exhaled concentration of CO. These variables are included in the table, but we can take a closer look at their distribution to see how the groups differ.

smoking.data %>%
  ggplot(aes(x = FTNDDepend, y = Cigarettes_day)) + 
  geom_boxplot(fill = Pal.2) + 
  geom_jitter(width = 0.05) + 
  ylab("Cigarettes per day") + 
  xlab("Smoking group") + 
  scale_y_continuous(breaks = c(0,5,10,15,20,25)) + 
  theme_classic(base_size = text.size) + 
  expand_limits(y = c(0, 25))

smoking.data %>%
  ggplot(aes(x = FTNDDepend, y = CO_ppm)) + 
  geom_boxplot(fill = Pal.2) + 
  geom_jitter(width = 0.05) + 
  ylab("Exhaled CO (ppm)") + 
  xlab("Smoking group") + 
  scale_y_continuous(breaks = c(0,10,20,30,40,50,60)) + 
  theme_classic(base_size = text.size) + 
  expand_limits(y = c(0, 60))

smoking.data %>% 
  ggplot(aes(x = FTNDDepend, y = FTND)) + 
  geom_boxplot(fill = Pal.2) + 
  geom_jitter(width = 0.05) + 
  ylab("FTND score") + 
  xlab("Smoking group") + 
  scale_y_continuous(breaks = c(0:7)) + 
  theme_classic(base_size = text.size) + 
  expand_limits(y = c(0, 7))

Attentional Bias

The first piece of information to present for attentional bias is the descriptive statistics for each condition broken down by smoking group. For each smoking group, the mean (and SD) reaction time (RT) to neutral stimuli, smoking stimuli, and the subsequent attentional bias score is presented for each SOA condition.

Table 3: Mean RT condition broken down by smoking group and SOA. Abbreviations: SOA; Stimulus Onset Asynchrony, SD; standard deviation, ms; milliseconds, N; group size.
Smoking group Attentional bias condition SOA condition (ms) Mean RT (ms) SD N
Dependent AB score 200 0.71 31.95 11
Dependent AB score 2000 1.18 46.89 11
Dependent AB score 500 -7.62 38.51 11
Dependent Neutral images 200 568.54 87.69 11
Dependent Neutral images 2000 552.54 77.12 11
Dependent Neutral images 500 558.77 80.29 11
Dependent Smoking images 200 567.83 92.65 11
Dependent Smoking images 2000 551.36 69.92 11
Dependent Smoking images 500 566.39 83.37 11
Non-dependent AB score 200 16.65 30.62 7
Non-dependent AB score 2000 1.98 26.62 7
Non-dependent AB score 500 4.51 26.23 7
Non-dependent Neutral images 200 523.04 39.31 7
Non-dependent Neutral images 2000 526.34 44.68 7
Non-dependent Neutral images 500 524.47 37.37 7
Non-dependent Smoking images 200 506.38 38.40 7
Non-dependent Smoking images 2000 524.36 58.79 7
Non-dependent Smoking images 500 519.96 42.98 7
Non-smoker AB score 200 -3.03 28.61 17
Non-smoker AB score 2000 6.18 37.24 17
Non-smoker AB score 500 -3.72 34.63 17
Non-smoker Neutral images 200 534.27 60.19 17
Non-smoker Neutral images 2000 539.55 56.32 17
Non-smoker Neutral images 500 537.51 59.16 17
Non-smoker Smoking images 200 537.30 52.67 17
Non-smoker Smoking images 2000 533.38 62.80 17
Non-smoker Smoking images 500 541.23 67.88 17

We can also compare each group on their attentional bias score for each SOA condition. Boxplots are presented to show the distribution of attentional bias scores. On each plot, there is a horizontal dashed line on zero to indicate what no attentional bias would look like. Zero would indicate there is no RT difference between smoking and neutral images, positive values indicate attentional bias towards smoking images, and negative values indicate attentional bias towards neutral images.

#input the data set
ggplot(data, aes(FTNDDepend, AB_200)) +
geom_boxplot(fill = Pal.3) +
geom_jitter(width = 0.05) + #perfect amount of jitter on the points             
theme_classic(base_size = text.size) +
xlab("Smoking Group") +
ylab("Attentional Bias Score (200ms)") + 
ylim(-100,100) + 
geom_hline(yintercept = 0, linetype = 2) #add line showing what no AB would look like