Prepared and reactive inhibition in smokers and non-smokers

Abstract

Introduction: Models of addiction have identified deficits in inhibitory control, or the ability to inhibit inappropriate or unwanted behaviors, as one factor in the development and maintenance of addictive behaviors. Current literature supports disruption of the prefrontal circuits that mediate reactive inhibitory control processes (i.e., inhibition in response to sudden, unplanned changes in environmental demands) in substance use disorders. However, the relationship between disorders of addiction, such as nicotine dependence, and planned inhibitory processes (i.e., inhibition that occurs after advance warning) is unclear. The goal of the present study was to examine the extent to which reactive and planned inhibitory processes are differentially disrupted in nicotine dependent individuals.

Method: We employed an internet-based novel stop signal task wherein participants were instructed to stop a continuous movement at either a predictable or unpredictable time. This task explicitly separated planned and reactive inhibitory processes and assessed group differences in task performance between smokers (N = 281) and non-smokers (N = 164). The smoker group was defined as any participant that identified as a smoker and reported an average daily nicotine consumption of at least 2 mg. The non-smoker group was defined as any participant that identified as a non-smoker and had not been a former smoker that quit within the last year. The smoker group also completed a questionnaire regarding smoking behaviors which included the Fägerstrom Test of Nicotine Dependence (FTND). We used these data to assess the continuous relation between planned stopping, unplanned stopping, and smoking behaviors.

Results: We found significant differences in stop times for both reactive and planned stopping between groups as well as within the smoker group. Additionally, in the smoker group, dependence as measured by the FTND was associated with longer stop times on planned stop trials. Surprisingly, greater daily average consumption of nicotine was related to faster stopping for both trial types.

Conclusion: These results indicate the relevance of measuring both reactive and planned inhibitory processes for elucidating the relationship between nicotine addiction and mechanisms of inhibitory control.

Keywords: Movement termination; Nicotine; Prepared inhibition; Reactive inhibition.

Fig. 1.

Depiction of planned and unplanned stop trials in CMST.

Fig. 2.

Stop times by trial type and group. White diamond indicates group mean. + ++ = p < .001 (a) Stop times on planned stop trials for non-smokers (M=329.9, SD=107.5) and smokers (M=466.2, SD=157.1). (b) Stop times on unplanned stop trials for non-smokers (M=467.7, SD=107.5) and smokers (M=612.4, SD=156). (c) Stop times for planned and unplanned stop trials for the non-smoker group only. (d) Stop times for planned and unplanned stop trials for the smoker group only.

Fig. 3.

Variability in stopping. White diamond indicates group mean. + + = p < .05, + ++ = p < .001 (a) The CV of stop times on planned stop trials is greater for non-smokers (M=.6, SD=.3) compared to smokers (M=.5, SD=.2), p = .002. (b) The CV of stop times on unplanned stop trials is not significantly different between smokers (M =.3, SD =.1) and non-smokers (M=.3, SD=.1) p = .125. (c) The CV of stop times on planned stop trials is greater than the CV of stop times for unplanned stop trials among non-smokers, p < .001. (d) The CV of stop times on planned stop trials is greater than the CV of stop times for unplanned stop trials among smokers, p < .001.

Fig. 4.

Dependence score was significantly and positively related to stop times for planned stop trials (r = 0.217, p = 0.038). Green line indicates line of best fit. Gray shadow is the 95% confidence interval.