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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Neurosci. Author manuscript; available in PMC 2013 November 4.
Published in final edited form as:
PMCID: PMC3816361

Spontaneous Action Representation in Smokers when Watching Movie Characters Smoke


Do smokers simulate smoking when they see someone else smoke? For regular smokers, smoking is such a highly practiced motor skill that it often occurs automatically, without conscious awareness. Research on the brain basis of action observation has delineated a frontopareital network that is commonly recruited when people observe, plan or imitate actions. Here, we investigated whether this action observation network would be preferentially recruited in smokers when viewing complex smoking cues, such as those occurring in motion pictures. Seventeen right-handed smokers and seventeen non-smokers watched a popular movie while undergoing functional magnetic resonance imaging. Using a natural stimulus, such as a movie, allowd us to keep both smoking and non-smoking participants naïve to the goals of the experiment. Brain activity evoked by scenes of movie smoking was contrasted with non-smoking control scenes which were matched for frequency and duration. Compared to non-smokers, smokers showed greater activity in left anterior intraparietal sulcus and inferior frontal gyrus, both regions involved in the simulation of contralateral hand-based gestures, when viewing smoking vs. control scenes. These results demonstrate that smokers spontaneously represent the action of smoking when viewing others smoke, the consequence of which may make it more difficult to abstain from smoking.

Keywords: Action Representation, Intraparietal Sulcus, Anterior Cingulate Cortex, Cue Reactivity, Smoking, Cigarette Craving

For smokers, the action of smoking a cigarette is a highly reinforced and overlearned behavior that has been acquired through thousands of exposures to nicotine. Over extensive “practice”, these actions become automatized allowing smokers to perform them effortlessly and even without awareness (Field, Mogg & Bradley, 2006). The automaticity of smoking behavior has important consequences for nicotine dependence and relapse but has largely been overlooked in favor of research on cue-induced craving. This is somewhat surprising in light of evidence that automatic smoking is an important predictor of consumption (Russell, Peto, & Patel, 1974) and relapse (Berlin et al., 2003) in smokers attempting to quit. Although cue-induced craving is an important contributor to smoking relapse, exposure to smoking cues may also trigger automatic smoking behaviors (Tiffany, 1990). Simply observing another person smoking might lead smokers to spontaneously mimic the action of smoking.

Psychologists have long theorized that observing actions increases the likelihood that a person will perform those actions (James, 1890). Extensive research in experimental psychology has demonstrated that simple environmental cues can elicit behavior without participant’s awareness (e.g. Bargh & Ferguson, 2000). For example, seeing someone scratch their faces increases the likelihood that the observer will spontaneously mimic that same action without any awareness that they are doing so (Chartrand & Bargh, 1999).

Neuroimaging research on action observation has shown that observing goal-directed actions recruits regions the superior parietal and lateral prefrontal cortex. This action observation network (AON) is recruited when participants observe meaningful actions (Hamilton & Grafton, 2006), mentally plan actions (Decety et al. 1994; Moll et al., 2000), imitate the actions of others (Buccino et al., 2004) and when experts (e.g. dancers) observe familiar movements (Calvo-Merino, Glaser, Grèzes, Passingham, & Haggard, 2005; Cross, Hamilton & Grafton, 2006). Two regions of the AON are specifically involved in representing goal directed manual actions. The anterior intraparietal sulcus (aIPS) and the lateral inferior frontal gyrus (IFG, BA 45/46) show greater activity when observing (Rizzolatti et al., 1996; Johnson-Frey et al. 2003; Shmuelof & Zohary, 2005) and planning (Grafton et al., 1996; Johnson-Frey, Newman-Norlund, & Grafton, 2005) manual actions such as grasping.

Surprisingly, few neuroimaging studies of smoking cue-reactivity have reported activity in the AON. Smoking is an inherently manual skill and therefore it would be expected that observing smoking cues would recruit regions involved in the representation of hand-based actions (e.g. the aIPS and IFG). A possible explanation may be due to the common usage of static cues (e.g. photographs) that often mix images of smoking actions with images of smoking paraphernalia (e.g. ashtrays). Prior research suggests that movie smoking is an especially potent smoking cue that increases cigarette craving (Sargent, Morgenstern, Isensee & Hanewinkel, 2009) and consumption (Shmueli, Prochaska & Glantz, 2010). Here, we capitalized on the rich dynamic smoking cues present in popular movies to examine smoking cue-related brain activity in the AON. In line with the research outlined above, we predicted that smokers, compared to non-smokers, would spontaneously activate the AON when viewing movie smoking.

Materials and Methods


Twenty right-handed smokers and twenty-one non-smokers were recruited to participate in the present study. Participants had normal or corrected-to-normal visual acuity, no history of neurological problems and were not currently taking medication for blood pressure, depression or other psychiatric. Three smokers and four non-smokers were excluded from the analysis due to excessive head motion (4 subjects) or scanner artifact (3 subjects) leaving a total of 17 smokers (12 women; mean age 23.1 years) and 17 non-smokers (15 women; mean age 21.4). Non-smokers were identified on the basis of having never been a regular or casual smoker and having smoked less than a hundred cigarettes in their lifetime. Smokers were daily smokers who smoked on average 10 cigarettes or more a day and were not currently attempting to quit smoking or taking medication to quit smoking (e.g. Zyban). All participants gave informed consent in accordance with the guidelines set by the Committee for the Protection of Human Subjects at Dartmouth College.


Stimuli consisted of the first thirty minutes of footage from the 2003 film “Matchstick Men”. This film was selected from a database of over 420 top-grossing Hollywood films dating back to 1988 (Dalton et al. 2003). The films in this database have undergone content analysis for scenes of substance use (smoking, alcohol and drugs), violence and sexual content (Sargent, Worth, Beach, Gerrard & Heatherton, 2008). “Matchstick Men” was chosen from this database as a film that contained numerous scenes of smoking but was otherwise low in alcohol use, violence, or sexual content.

Owing to scanner run-length limitations, the film was edited into three ten minute segments; each segment was preceded and followed by 60 seconds of null event trials consisting of a white fixation cross against a black background. In total there were 110 seconds of on-screen smoking distributed throughout the three segments. Participants listened to the audio soundtrack of the film via a pair of MRI compatible headphones (Resonance Technology, Inc.).


Mass advertising was used to recruit smokers and non-smokers. All participants participated in a telephone pre-screening session. Participants were contacted approximately one week after the pre-screening session and invited to participate in a study on the neural correlates of watching movies. This was done to ensure that participants were unaware of selection criteria (i.e. smoking status) and the nature of the study at the time of scanning. All participants were scanned in the morning and were contacted the night before and given a cover story in which they were informed that it was important for accurate measurement of brain activity that they refrain from drinking caffeinated drinks, smoking tobacco or consuming alcohol on the morning prior to scanning, thus ensuring that smokers would arrive craving nicotine. At the time of scanning, participants’ only instructions were to remain awake and pay attention to the film. Afterwards, participants completed a short debriefing consisting of a 100-point likert scale of post-scan cigarette craving (smokers only) and a questionnaire assessing their impressions about the film and its protagonists.

Image Acquisition

Magnetic resonance imaging was conducted with a Philips Achieva 3.0 Tesla scanner using an eight channel phased array coil. Structural images were acquired using a T1-weighted MP-RAGE protocol (160 sagittal slices; TR: 9.9ms; TE: 4.6 ms; flip angle: 8°; 1 × 1 × 1 mm voxels). Functional images were acquired using a T2*-weighted echo-planar sequence (TR: 2500ms; TE: 35ms; flip angle: 90°; field of view: 24cm). For each participant, three runs of 288 whole-brain volumes (30 axial slices per whole-brain volume, 4.5mm thickness, 0.5mm gap; 3 × 3 mm in-plane resolution) were collected.

Image Preprocessing

FMRI data were analyzed using the general linear model for event-related designs in SPM8 (Wellcome Department of Cognitive Neurology, London, UK). For each functional run, data were preprocessed to remove sources of noise and artifact. Images were corrected for differences in acquisition time between slices and realigned within and across runs via a rigid body transformation in order to correct for head movement. Images were then unwarped to reduce residual movement-related image distortions not corrected by realignment. Functional data were normalized into a standard stereotaxic space (3mm isotropic voxels) based on the SPM8 EPI template which conforms to the ICBM 152 brain template space (Montreal Neurological Institute) and approximates the Talairach and Tournoux atlas space. Finally, normalized images were spatially smoothed (6mm full-width-at-half-maximum) using a Gaussian kernel to increase the signal to noise ratio and to reduce the impact of anatomical variability not corrected for by stereotaxic normalization. Volumes were inspected for scanner and motion-related artifact based on examination of the realignment parameters and voxel-wise standard deviations for each run and subject.

Modeling of conditions

Modeling continuous stimuli, such as films, presents a special challenge for the analysis of functional neuroimaging data. Moran and colleagues (2004) successfully employed a method for the analysis of brain activity time-locked to specific events (e.g. humor detection and appreciation) in participants viewing full television episodes. In the present study we followed a similar approach by examining brain activity time-locked to smoking and non-smoking scenes. Seventeen discrete smoking scenes were identified with durations ranging from 2.5 seconds to 30 seconds (total time 117s). An equal duration of non-smoking scenes were randomly selected from the remaining portion of the film and matched for duration and within-run frequency to the smoking scenes (Figure 1). The remaining portion of the film was explicitly modeled, but was not used for further comparison. As our model was constrained by the specific temporal sequence of smoking scenes in the film, we were unable to explicitly optimize our design for maximum estimability of the conditions of interest. However, the natural temporal order of smoking and control scenes was such that the conditions were inherently “jittered” (correlation between predictors was 0.09).

Figure 1
Schematic of the study design. Smoking and non-smoking control scenes were matched for duration and frequency per run. TR = Repetition Time.

Data Analysis

For each participant a general linear model incorporating task effects and covariates of no interest (a session mean, a linear trend to account for low-frequency drift and 6 movement parameters derived from realignment correction) was convolved with a canonical hemodynamic response function (HRF) and used to compute contrast images (containing weighted parameter estimates) for the comparison of smoking scenes vs. control scenes at each voxel.

Contrast images were entered into a 2nd level random effects analysis with participant (smokers and non-smokers) treated as the random effect. The resulting whole-brain statistical parametric map representing regions in which activity was greater for smoking vs. control scenes for all participants was thresholded at p < 0.005 uncorrected with an extent threshold of 10 contiguous voxels. This combined map of smokers and non-smokers was not itself our comparison of interest but rather served to identify regions exhibiting smoking cue-related activity(the difference between the neural response to smoking vs. control scenes) in order to further interrogate these regions for an effect of group. In order to investigate between-group differences, a region of interest (ROI) analysis was conducted by extracting parameter estimates (β) from the contrast of smoking vs. control scenes using 6mm spherical ROIs centered on the peak voxel of clusters demonstrating an effect of smoking vs. control scenes across both groups. In this way, ROIs are defined in an unbiased manner since both smokers and non-smokers contribute equally to the statistical parametric map used for ROI selection. Comparisons in a priori regions of the AON and in cue-reactivity regions are presented at alpha = 0.05. Non-hypothesized areas were Bonferroni adjusted for the number of ROIs interrogated (alpha = 0.003).


Whole-brain random effects analysis averaging across both smokers and non-smokers showed greater smoking cue-related activity in the aIPS, left IFG (BA 45/46) and premotor cortex (Figure 2a). In addition, regions commonly implicated in the reward and craving component of drug cue-related activity were also recruited during smoking compared to non-smoking scenes, namely the dorsal anterior cingulate cortex, orbitofrontal cortex and dorsolateral prefrontal cortex (Table 1).

Figure 2
(a) Brain regions showing a greater BOLD signal response during smoking scenes compared to control scenes in all subjects (p < 0.005, uncorrected, cluster extent threshold of 10 contiguous voxels). This parametric map was used to generate unbiased ...
Table 1
Brain regions showing greater BOLD signal response to smoking scenes compared to non-smoking scenes for all participants (smokers and non-smokers)

Regions differentiating between smokers and non-smokers

Regions of interest analysis on ROIs derived from the orthogonal contrast of smoking vs. control scenes across all participants revealed between-group differences in both regions of the AON and regions involved in reward and craving. Specifically, smokers demonstrated greater smoking cue-related activity in the left, but not right, aIPS (t(32) = 2.8, p = 0.009) and in the left IFG pars triangularis region (t(32) = 2.16, p = 0.38) (Figure 2b). In addition, smokers also demonstrated greater smoking cue-related activity in the dorsal anterior cingulate (t(32) = 2.55, p = 0.016), orbiofrontal cortex (t(32) = 2.95, p = 0.006) and bilaterally in the dorsal lateral prefrontal cortex (left: t(32) = 2.61, p = 0.014; right: t(32) = 2.41, p = 0.022). Conversely, non-smokers did not show greater smoking cue-related activity when compared to smokers in any of the regions identified in the across-subject contrast of smoking vs. control scenes.

Finally, group differences were specific to the AON and cue-reactivity regions. There were no between group differences in non-hypothesized ROIs from the contrast of smoking vs. non-smoking scenes (both at the corrected alpha of 0.003 but also uncorrected).

Correlations with post-scan cigarette craving

To examine the relationship between cigarette craving and smoking cue-related activity in smokers we correlated post-scan ratings of cigarette craving (smokers only) with parameter estimates extracted from the ROIs defined above. Only the dorsal anterior cingulate (dACC: MNI coordinates: 9,30,27) cortex was significantly correlated with post-scan ratings (r = 0.57, p < 0.018); (Figure 3).

Figure 3
Relationship between post-scan cigarette craving in smokers and BOLD response difference between smoking and control scenes in the dorsal Anterior Cingulate Cortex (r2 = 0.33, p < 0.018; MNI coordinates: 9,30,27). Dashed lines indicate the 95% ...


In this study we investigated both action representation and reward-related responses to dynamic smoking cues under natural viewing conditions. Compared with nonsmokers, smokers showed robust neural activity in response to smoking cues in action representation regions (left aIPS and left IFG) as well as cue-reactivity regions (dACC, OFC and dLPFC). The dACC, OFC and DLPFC are frequently observed in drug cue-reactivity studies (e.g. Grant et al. 1996; Brody et al. 2002; Due, Huettel, Hall, & Rubin, 2002; David et al., 2005; McBride, Barrett, Kelly, Aw &Dagher, 2006; see also Wilson, Sayette & Fiez, 2004) and are thought to be involved in the motivational and cognitive control aspects of drug-seeking behavior (Goldstein & Volkow, 2002). The dACC in particular has often been linked to the subjective experience of craving, and a number of previous studies have demonstrated a correlation between cue-reactivity in the dACC and tobacco craving (e.g. Brody et al. 2004; McClernon, Hiott, Huettel, & Rose, 2006; Zubieta et al., 2005). Here, we replicated the relation between post-scan tobacco craving and cue-induced changes in dACC activity, but in this instance it occurred spontaneously, in subjects who were unaware that they were being exposed to smoking cues

Of greater interest was the finding that the left aIPS and left IFG showed greater cue-related activity for smokers than nonsmokers. Both are regions involved in representing goal-directed manual actions, with the left IFG being especially important for planning manual actions (Johnson-Frey, Newman-Norlund & Grafton, 2005). Recently it’s been argued that the aIPS is preferentially tuned to the actions of the contralateral hand. For instance, the left aIPS shows greater activity when viewing right handed gestures, both when viewing them from the perspective of the person performing the action and also when viewing them as though facing the person performing the action (i.e. an allocentric perspective) (Shmuelof & Zohan, 2008). Further evidence comes from a study showing that disrupting activity in the aIPS with TMS impairs grasping of the contralateral -but not ipsilateral- hand (Rice, Tunik, Cross, & Grafton, 2007). In our study, all participants were right-handed, as was the hand used for smoking by the movie’s protagonists. This suggests that our finding of differential left—but not right aIPS activity between smokers and non-smokers is due in part to this region’s bias towards representing actions performed by the contralateral hand, even though, as in Schmuelof & Zohary (2008), all scenes of smoking were observed from an allocentric perspective.

Despite smoking being an inherently manual action, there has been little prior evidence to indicate that when smokers see smoking cues they activate manual action representations in the AON. We propose that this may be largely due to the use of static photographs depicting smoking and smoking paraphernalia by most, but by no means all (e.g. Brody et al. 2002), studies of cue reactivity. This is in contradistinction to research on action observation of manual gestures which relies on dynamic stimuli in the form of video clips (e.g. Hamilton & Grafton 2006; Buccino et al., 2004; Cross, Hamilton & Grafton, 2006; Schmuelof & Zohary, 2008). This is not to say that dynamic stimuli are more ideally suited to drug cue-reactivity research -indeed static cues have proved instrumental to the study of cue induced craving- but rather that dynamic action stimuli may be better suited for studying the motor component of drug taking behavior.

An unanswered question in cue-reactivity research is whether smokers would spontaneously demonstrate neural cue-reactivity when smoking cues are processed incidentally. Here, we used a cover story to ensure that smokers remained unaware of the nature of the experiment. In so doing, we avoid potential demand characteristics that can arise when smokers participate in a study in which they either knowingly, or can easily infer, that they are expected to react to smoking cues. Our findings largely replicate prior neuroimaging research on smoking cue-reactivity, demonstrating that the explicit awareness of smoking cues is not essential for the demonstration of drug cue reactivity. In addition, we demonstrated that the previously reported relationship between subjective craving and cue-related activity in the dACC obtains even when participants are unaware of the smoking cues.

For smokers trying to quit, movie smoking can be a particularly difficult cue to avoid, one that smokers may be unlikely to consider. Research has shown that movie smoking predicts smoking initiation among adolescents (Dalton et al. 2003; Hanewinkel et al. 2008; Dalton et al. 2009) and increases cigarette craving and consumption among adults leaving the movie theatre (Sargent, Morgenstern, Isensee & Hanewinkel, 2009; Shmueli, Prochaska & Glantz, 2010). In this study we demonstrated that movie smoking, even when processed incidentally, is processed much like an explicit smoking cue and recruits the same brain regions commonly observed in cue-reactivity studies. In addition, we demonstrate that movie smoking activates the AON. This network is involved in observation, planning and motor simulation and is often characterized as a motor resonance system and forms part of what some researchers have termed the human mirror system (Rizzolatti & Craighero, 2004).

Research on behavioral mimicry has demonstrated that participants spontaneously mimic the incidental actions of others (Chartrand & Bargh, 1999) Recently this research has been extended to eating behavior, showing that people spontaneously mimic the eating behavior of a confederate, and do so without any awareness of this external influence over their behavior (Johnston, 2002; Tanner, Ferraro, Chartrand, Bettman, Van Baaren, 2008). In the present study, smokers demonstrated increased cue-related activity in regions of the AON suggesting that they are spontaneously representing the action of smoking. Due the physical constraints of the scanner environment we were not able to assess behavioral mimicry, but the prior research outlined above suggests seeing smoking in a movie would increase the likelihood that a smoker would subsequently light up a cigarette.

Studies of drug cue-reactivity have largely focused on the link between cue-induced craving and drug-use, however it has long been suggested that another route through which drug cues may induce increased drug use is through activation of conditioned sensorimotor associations (Tiffany, 1990). Further study of this automatic component of cue-reactivity, especially in middle-aged smokers who have decades of smoking experience, may help elucidate why smokers trying to quit find it more difficult to overcome the habitual and automatic aspects of smoking compared to physical withdrawal (Russell, Peto, & Patel, 1974; Berlin et al. 2003). Moreover, for smokers attempting abstinence, movie smoking is a particularly difficult cue to escape, given the ubiquity of movies and television. Although an abstinent smoker can remove smoking paraphernalia from the home and avoid high-risk situations which may lead to smoking relapse, they are unlikely to refrain from watching a movie due to its smoking content. Finally, our results further the understanding of how complex human behaviors can be shaped by subtle contextual cues, such as how smokers might be tempted to share a cigarette with onscreen smokers.


This research was supported by a grant from the National Institutes of Drug Addiction (R01DA22582) to TFH., a Cancer Control Research Program Prouty Pilot Grant from the Norris Cotton Cancer Center to SD & JDS, and a Strategic Training Fellowship in Tobacco Research from the Canadian Institutes of Health Research awarded to SD. We would like to thank William Kelley and Keilah Worth for suggestions during the design and analysis of this research.


Author Contributions D.D.W. collected, analyzed and interpreted the data and wrote the first draft of the manuscript. D.D.W. S.D.C. W.M.K. & T.F.H. designed the experiment. S.D.C. J.S. & T.F.H. contributed to the interpretation of the data and to the final version of the manuscript.


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