The experimental sample was composed by 39 volunteers (12 M, 27 F; average age = 18.308 ± 2.726 years old, min = 15, max = 24 years old), 13 HS, 11 Light Smokers (LS), 15 NS. Participants smoking more than five cigarettes per day were classified as HS; participants who did not smoke any cigarette were classified as NS. Starting from these classification, the HS group smoked an average of 9.703 ± 3.728 and the LS group an average of 1.127 ± 1.456 cigarettes/day. All subjects were given of detailed information about the study and signed an informed consent. The experiment was performed in accord to the principles outlined in the Declaration of Helsinki of 1975, as revised in 2000, and it was approved by the Sapienza University of Rome ethical committee in charge for the Department of Molecular Medicine. During the execution of the test, participants were sitting on a comfortable chair in front of a computer screen and they were not instructed with any particular task, just to be relaxed and to restrict head and body movements as much as possible.

Participants were asked to watch a video composed by six neutral images taken from IAPS database (Lang et al., 2008) used as baseline, followed by a train of 10 antismoking PSAs images displayed in a randomized order (so to prevent in the participants reaction the eventual bias attributable to a positional effect), followed by the images baseline. Images were displayed for 9 s each and between each pair of them a black cross on a white field was shown, so to reestablish a central fixation point. The stimuli were presented on a 19″ flat screen with a distance from the subject varying from 50–60 cm. Before the exposure to the experimental stimuli, participants were asked to look at a black screen for 60 s, and the EEG activity recorded in correspondence of this open eyes condition was then used for the IAF calculation (Goljahani et al., 2012).

For the EEG, autonomic responses and ET investigation six images have been selected, because presenting both text and visual elements. In particular, each image have been identified two kinds of areas of interest (AOIs): “visual” (such as the image of a lady who underwent a tracheotomy; or the picture of a young man with a sarcastic expression, depicted while smoking a cigarette) and “text” (text of the claim or the antismoking message). PSAs have been divided in three groups: “Effective,” “Ineffective” and “Awarded.” The “Effective” and “Ineffective” PSAs were the PSAs that produced the best and the worst documented answers by the general public, respectively, according to the published documentation available (Coffman, 2002; Varcoe, 2004). Such answers are measured through a number of metrics including the number of phone calls to the quit-smoking centers after the campaign, the decrement/increment of ambulatory visit for the attempt to quit smoking and so forth. The third kind of PSAs investigated has been previously classified as “Awarded” on the basis of the evaluation obtained by specialized committees, expressed as number of prizes received. In particular, the selected PSAs were:

-Effective:

-Ineffective:

- Awarded:

At the end of the observation of the PSA images, it has been asked to the participants to spontaneously recall the PSAs writing a list of the images just seen.

The EEG activity was recorded using a 10-electrodes-based EEG frontal band (Fpz, Fp1, Fp2, AFz, AF3, AF4, AF5, AF6, AF7, AF8) by means of a portable 24-channels system (BEmicro, EBneuro, Italy). The signals have been acquired at a sampling rate of 256 Hz and the impedances were kept below 10 kΩ. First, a notch filter (50 Hz) was applied in order to reject the main current interference. Second, the gathered signal has been digitally band-pass filtered by a 5th order Butterworth filter ([2÷30] Hz), in order to reject the continuous component as well as high-frequencies interferences, such as muscular artifacts. Then, the Independent Component Analysis (ICA) has been applied to EEG data in order to identify and remove other artifacts-related components, such as blinks and eye movements, since their contribution is overlapped to the EEG bands of interest in the present study (Di Flumeri et al., 2016a). In order to take into account any subjective difference in terms of brain rhythms, for each subject the Individual Alpha Frequency (IAF) was computed on the 60-s-long Open Eyes segment (Goljahani et al., 2012), recorded at the beginning of the experimental task, in order to define the EEG bands of interest according to the method suggested in the current scientific literature, i.e., each band is defined as “IAF ± x,” where IAF is the Individual Alpha Frequency, in Hertz, and × an integer in the frequency domain (Klimesch, 1999). Thus, the EEG activity was divided, by filtering the EEG signals in the time-domain, in two main frequency bands: theta [IAF-6 ÷ IAF-2 Hz] and alpha [IAF-2 ÷ IAF+2 Hz].

To summarize the activity of the cortical areas of interest in a specific frequency band, the Global Field Power (GFP) was then computed. This is a measurement introduced by Lehmann and Michel (1990) some decades ago to summarize the synchronization level of the brain activity over the scalp surface. In general, the measure of the GFP corresponds to the spatial standard deviation, and it estimates the quantity of activity at each time point in the potential field, simultaneously considering data from all recording electrodes, producing a reference-independent descriptor of the field (Skrandies, 1990). The underlying idea of this procedure is that potential fields with few field lines presumably contain little information while scalp fields displaying much activity reflect the synchronous activation of a large number of intracranial neuronal elements. The GFP has been used in studies of perceptual, attentional, cognitive processing (e.g., Dierks and Maurer, 1989; Skrandies, 1991; Rodin, 1991; Michel et al., 1993; Rau et al., 2002; Boonstra et al., 2013; Ahonen et al., 2016) as well as in clinical studies (e.g., Rodin, 1990; Maurer et al., 1990; Strik et al., 1994; Favrod et al., 2017; Giroud et al., 2017; Iannilli et al., 2017). The GFP constitutes a good index for the temporal determination of information from cognitive studies, furthermore it has become a commonly used parameter for the time-domain analysis of EEG (as in the present study), since it allows to identify the maps of maximal electric field strength (hilliness).

In particular, in this study the GFP has been computed from a specific set of electrodes (the set depends on the investigated brain area, in the following it will be specified for each index) by performing the sum of squared values of EEG potential at each electrode, averaged for the number of involved electrodes, resulting in a time-varying waveform related to the increase or decrease of the global power in the analyzed EEG. The GFP formula is presented in the following:

where ϑ is the considered EEG band, Frontal is the considered cortical area, N is the number of electrodes included in the area of interest (AOI; in this example the Frontal area), and i is the electrodes’ index. Also, in the present study the GFP function, initially with the same time-resolution of the EEG gathered signal (i.e., equal to the sampling rate of 256 Hz), has been averaged on 1-s-long signal windows, in order to comply with the EEG signal stationarity hypothesis (Elul, 1969). According to the cognitive phenomenon to investigate, in the following the considered electrodes and brain rhythms are described.

It has been repeatedly evidenced in literature, thanks to the application to several kinds of stimuli (e.g., Davidson, 2004; Borghini et al., 2015; Maglione et al., 2015; Di Flumeri et al., 2016b, 2017), that the frontal cortex constitutes AOI for the analysis of the approach or withdrawal attitude (Davidson, 2004), while the frontal theta as index of cerebral effort and mental processing (Klimesch, 1999; Gevins and Smith, 2003; Wascher et al., 2014).

The formula defining the frontal alpha asymmetry index is the following:

where the GFPα_right and GFPalpha_left stand for the GFP calculated among right (Fp2, AF4, AF6, AF8) and left (Fp1, AF3, AF5, AF7) electrodes respectively, in the alpha (α) band. The frontal alpha asymmetry index was estimated for each second, and then standardized on the basis of the baseline (the IAPS images) EEG activity acquired at the beginning and at the end of the experiment. Higher frontal alpha asymmetry values, reported by the subjects, stood for an approach motivation toward the stimulus, while lower frontal alpha asymmetry values for a withdrawal motivation (Davidson, 2004).

To evaluate the mental effort/processing, EEG activity in the theta band over all the frontal electrodes (Fp2, AF4, AF6, AF8, AF7, AF3, Fp1, AF5) has been considered for the GFP computation. Thus, the obtained GFP values have been standardized according to the baseline EEG activity acquired at the beginning and at the end of the experiment, similarly to the procedure applied for the frontal alpha asymmetry index. An increase in the frontal theta (i.e., mental effort) would imply an increase in the task difficulty (Wisniewski et al., 2015).

GSR and Electrocardiographic (ECG) signals were recorded with a sampling rate of 128 Hz through a NeXus-10 (Mindmedia, Netherlands) system. For the recording of the cardiac activity, disposable electrodes (Mindmedia company) were placed on the subject’s wrists. In order to estimate the HR from the ECG signal, it has been used the Pan-Tompkins algorithm (Pan and Tompkins, 1985). The constant voltage method (0.5 V) was employed for the acquisition of the GSR, also named skin conductance as the signal related to the skin sweating. The electrodes were placed to the palmar side of the middle phalanges of the second and third fingers, on the non-dominant hand of the participant, according to published procedures (Boucsein et al., 2012). Employing the LEDAlab software (Benedek and Kaernbach, 2010), the tonic component of the skin conductance (Skin Conductance Level, SCL) was estimated. The circumplex model of affect plane was adopted to collapse information about a stimulus deriving from SCL and HR (Russell and Barrett, 1999; Posner et al., 2005). In this model, the x axis reported the HR values, reflecting the valence dimension of the stimulus-related emotional state, while the y axis reported the SCL values, mirroring the related arousal dimension (for a review see Mauss and Robinson, 2009). Adopting this theoretical framework, it was possible to obtain a synthetic monodimensional variable, named the EI, providing information concerning the emotional status of a participant, as defined in previous studies (Vecchiato et al., 2014a). The EI has been calculated as defined in the just mentioned previous studies so to vary between −1 and +1. EI results interpretation predicts that higher values would mirror a more positive and engaging emotion experienced by the subject, while lower values would reflect more negative and less engaging emotion experienced by participants.

ET data have been acquired by a remote eye-tracker (Eye Tribe) with a sampling frequency of 30 Hz, in order to identify eye fixations on the proposed stimuli. As first step, all the artifactual or not physiological points of gaze were automatically removed. Then, all the points belonging eye movements (such as saccades or smooth pursuit) were removed by an Dispersion-Threshold Identification (I-DT) algorithm (Salvucci and Goldberg, 2000). Such algorithm uses two thresholds, a temporal one, set at 100 ms, and a spatial one, set at 60 pixels (for the identification of a fixation point on a picture (Rayner, 2009). Then, ET data were analyzed through a software for the extraction of information about fixations in each AOI, such as number of fixations and total time of fixation spent on each AOI. Each AOI (graphical and text) has been measured in relation to the size of the entire image in order to weight the time of fixation spent on the AOI. This since evidences show that the size of a visual target influences the amount of time spent on it (Lohse, 1997). Therefore, an index of VA has been defined as follows:

where %Total Fixation Duration (TFD) is the total time spent fixating an AOI weighted on the total time the image is shown on the screen, and %AREA is the area of the AOI weighted on the total dimension of the image. This formula returns a non-dimensional index suitable for the comparison among different AOIs. It can be predicted that elements gaining higher VA values would have more visual saliency for the participants. In addition, since the essence of a scene is acquired very quickly (about 100 ms) in the first fixations (Pieters and Wedel, 2012), it was hypothesized that elements characterized by higher VA values would elicit more interest in the subjects, with a top-down mechanism.

ANOVA test has been performed on the frontal alpha asymmetry, frontal theta, EI and ET data. The between variable was the “Smoking Habit,” with three levels: HS, LS and NS; the within variable was the “PSA kind,” with three levels: Effective, Ineffective and Awarded. Concerning ET data also the within variable Element (with two levels: Text and Visual) has been included in the ANOVA analysis. Duncan post hoc has been employed on the statistically significant results from ANOVA. Linear regression analysis has been performed among the number of cigarettes smoked by participants (independent variable) and frontal alpha asymmetry, frontal theta and EI values (dependent variable), in order to investigate the possible correlation between these two kinds of data. Since the number of factors considered in the study is lower than 4, the ANOVA test has a sufficient statistical power to deal with the analysis also of moderately small number of participants (Zar, 2000). Finally, the sample size employed in the study has been set on the basis of the best practice adopted in numerous previous studies in literature, concerning the study of the electroencephalographic and autonomic indices employed in the research. In particular, in scientific literature for frontal alpha asymmetry, frontal theta as well as EI has been demonstrated as a sample size of 12–15 persons could be enough to capture less than 10% variation of these indexes (e.g., Gevins et al., 1997; Dussault et al., 2005; Domino et al., 2009; Maglione et al., 2014; Wascher et al., 2014; Cartocci et al., 2015, 2017; Wisniewski et al., 2015, 2017; Soria Morillo et al., 2016; Di Flumeri et al., 2016b; Cherubino et al., 2016a; Liu et al., 2017; Aghajani et al., 2017; Mennella et al., 2017).

Frontal alpha asymmetry results highlighted a statistically significant effect of the Smoking Habit (F_(2,35) = 6.229; p = 0.005) (Figure 1A), in particular the post hoc analysis revealed that lower frontal alpha asymmetry values were reported by NS in comparison to both HS (Cohen’s d = 0.746; p = 0.003) and LS (Cohen’s d = 0.477; p = 0.036) groups, while no difference has been observed between HS and LS (Cohen’s d = 0.233; p = 0.251). Moreover, it has been found a statistically significant interaction between the variables Smoking Habit and PSA kind (F_(4,70) = 3.226; p = 0.017) (Figure 1B). The post hoc analysis showed an increase of the frontal alpha asymmetry values relative to the Effective PSAs for the HS group in comparison to both the LS (Cohen’s d = 1.898; p = 0.002) and NS groups (Cohen’s d = 1.536; p = 0.004). While there was no statistical difference in the frontal alpha asymmetry values relative to the Ineffective and Awarded PSAs among the experimental groups.

In addition, by the linear regression analysis it has been evidenced a correlation between the number of cigarettes regularly smoked by participants and the relative frontal alpha asymmetry values obtained in response to the vision of the Effective PSAs (η² = 0.423 R = 0.650 p < 0.001): the higher the number of smoked cigarettes the higher the frontal alpha asymmetry values (Figure 2).

Frontal theta activity results showed a statistically significant effect of the Smoking Habit (F_(2,35) = 6.535; p = 0.004) (Figure 3A), and the post hoc analysis highlited an increase reported by LS in comparison to both HS (Cohen’s d = 0.844; p = 0.001) and NS (Cohen’s d = 0.471; p = 0.040), with the HS group reporting the lowest values. Additionally, there was a significant effect of the PSA kind (F_(2,70) = 24.771; p < 0.001) (Figure 3B), characterized by the increase of the values relative to the Awarded PSAs in comparison to the Effective and Ineffective ones (Cohen’s d = −0.899 and Cohen’s d = −1.098 respectively p < 0.001 both).

The linear regression analysis revealed a correlation between the number of cigarettes regularly smoked and the decrease of the frontal theta values for both the Effective (η² = 0.142 R = 0.378 p = 0.019) (Figure 4A) and Ineffective (η² = 0.155 R = 0.394 p = 0.014) (Figure 4B) PSAs. In other words, the higher the number of cigarettes smoked by participants and the lower the frontal theta values reported.

The EI analysis showed a statistically significant effect of the Smoking Habit (F_(2,36) = 8.139; p = 0.001) (Figure 5A), with the HS group expressing a decrease in the EI values in comparison to LS and NS groups (Cohen’s d = 0.8 and Cohen’s d = 0.852 respectively; p = 0.002 both). Furthermore it has been found a statistically significant effect of the PSA kind (F_(2,72) = 6.470; p = 0.003) (Figure 5B), with an increase of the EI values reported for the Effective PSAs in comparison to both the Ineffective (Cohen’s d = 0.585; p = 0.004) and Awarded (Cohen’s d = 0.620; p = 0.002) ones.

There was also a correlation, as evidenced by the linear regression analysis, between the number of cigarettes smoked by participants and the EI values reported in response to the observation of the Effective (η² = 0.106; R = 0.325; p = 0.043) (Figure 6A) and Awarded (η² = 0.126; R = 0.355; p = 0.026) (Figure 6B) PSAs: the lower the number of cigarettes smoked and the more positive the corresponding EI values.

ET data evidenced a statistically signficant effect of the PSA kind (F_(2,50) = 8.899; p < 0.001) (Figure 7A), with the Ineffective PSA obtaining the higher VA values in comparison to the Effective (Cohen’s d = 0.431; p = 0.003) and the Awarded (Cohen’s d = 0.662; p < 0.001) PSAs. Moreover there was a significant interaction between the variable PSA kind and the variable Element (F_(2,50) = 16.264; p < 0.001; Figure 7B). The post hoc analysis showed that within each PSA kind there was a significant difference between Text and Visual Element, with the Visual element gaining more VA than the Text element for the Effective (Cohen’s d = −0.787; p = 0.002) and Awarded (Cohen’s d = −0.907; p = 0.019), vice versa for the Ineffective PSAs the Text element presented higher VA values than the Visual one (Cohen’s d = 0.772; p = 0.001). In addition, the Text of the Ineffective PSAs obtained higher VA values than the Text elements present in the Effective and Awarded PSAs (Cohen’s d = 1.324 and Cohen’s d = 1.406 respectively; p < 0.001 both), but also than all the Visual elements present in the different kinds of PSAs (Cohen’s d = 0.418; Effective p = 0.025, Cohen’s d = 0.772; Ineffective p = 0.001, Cohen’s d = 0.770; Awarded p = 0.002).

Concerning the spontaneous recall of the PSAs there was a difference in the distribution among the groups (Figure 8). In particular, the NS remembered the Effective PSAs with a higher occurrence in comparison to the HS and LS groups, that instead reported the observation of the Awarded PSAs with a higher percentage. The chi-square test reported a statistical significant difference among the groups (χ² > 15.357; p = 0.004).

The present study presents several methodologic limitations that we would like to discuss in this section.

The first issue is related to the sample size investigated for each one of the statistical cells employed. In fact, it may be argued that 12–15 persons per each cell could be not adequate to drawn statistically robust conclusion in a multivariate analysis, especially for the occurrence of the type-I error (Zar, 2000). While the above mentioned limitation is clear, it must be underlined as the ANOVA is a statistical technique that it is robust and it has sufficient statistical power in conjunction with the execution of appropriate post hoc tests such as Duncan’s test employed here (Zar, 2000). A second issue is related to the fact that it may be argued as the ANOVAs have been performed without previously checking the normality of the data. While this is true in general, due to the statistical assumptions underlying the ANOVA methodology, it is also true that it has been evidenced how ANOVA is not very sensitive to deviations from normality. In particular, simulation studies using several non-normal distributions have shown that the false positive rate is not affected very much by this violation of the assumption (Glass et al., 1972; Harwell et al., 1992; Lix et al., 1996).

A third issue is related to the material presented for the analysis. It may be argued that since the stimulus materials in this experiment are presented once to each participant, an insufficient number of trials or length for the cerebral and autonomic indexes to be able to detect reliable and valid responses from these neurometric signals could be gathered. However, it has to be noted that we were interested in this study to the reaction of the subjects to the unfamiliar PSAs. In fact, as reported in literature, repeated exposure to a stimulus causes increased liking of that stimulus (Zajonc, 1968). Thus, it makes sense to expose subjects just once to the stimuli to be tested. In addition, recent studies by other research groups follows the adoption of a single exposure to advertising to be tested (Rossiter et al., 2001; Silberstein and Nield, 2012; Yang et al., 2015).

Hypothesis 2, stated that a higher approach would have been shown by HS in comparison to NS and LS in response to the anti-smoking PSAs. The results here obtained suggested that such hypothesis is true, although, among smokers the statistically significant difference between HS and NS was limited to the Effective PSAs.

The NS group reported the lowest and negative frontal alpha asymmetry values in comparison to HS and LS ones, so suggesting a tendency of avoidance toward the advertised material (Davidson, 2004). Such results are in agreement with the strength of the negative preexisting beliefs about smoking and smokers, in particular after the exposure to antismoking material in youth (Pechmann and Ratneshwar, 1994). On the other hand, HS showed higher frontal alpha asymmetry values in comparison to LS (even if not reaching the statistical significant difference) and the NS. Furthermore, the Effective PSAs included in the present study were characterized by fear arousing appeal and paternalistic communication styles. Those PSAs obtained the more positive frontal alpha asymmetry from HS group in comparison to LS and NS groups. This evidence suggests a tendency of approach that could be symmetrical to the emotional reaction linked to the reactance theory (Brehm and Brehm, 2013). Therefore, the choice of the fear arousing appeal and paternalistic communication styles would be of interest for young smokers, even a possible dangerous trigger of reactance. Furthermore, the negative frontal alpha asymmetry value reported by NS in response to the Effective PSAs suggests that those communication styles (fear arousing appeal and paternalistic) could be effective in young NS in preventing the smoking begin. A similar result has been reported for the LS group in reaction to the vision of Effective PSAs. In this case, those particular PSAs would be effective in preventing the rise of the number of cigarettes daily smoked by participants. These results are basically in accord to the Hypothesis 2, predicting a higher approach tendency by HS in comparison to NS and LS in response to the anti-smoking PSAs. It is indeed interesting that the statistically significant difference between HS and LS was limited to the Effective PSAs. Moreover, the correlation between the number of smoked cigarettes and the frontal alpha asymmetry values in response to the Effective PSAs exposure, could be linked to the quantitative effect of daily tobacco use. In fact, such effect was found to affect the neural response, as measured by the amplitude of the P300 event related potential (Haarer and Polich, 2000). Moreover, the extent of nicotine dependence seems to moderate the cue reactivity toward antismoking stimuli (Vollstädt-Klein et al., 2011). In smokers and non-smokers groups, a different response lateralization between the two hemispheres, in the present study indexed by positive (left) and negative (right) frontal alpha asymmetry values, has been already showed. Specifically, it has been shown an increased P300 amplitude produced by smokers in the right hemisphere in response to both tobacco promoting and antismoking images, vice versa in the left hemisphere by non-smokers (Jang et al., 2007). These results appear in contrast with the present data showing a higher response in the left hemisphere in HS and in the right hemisphere in the NS groups, nevertheless this could be attributable to the different definition of smokers between the two researches (i.e., more than two cigarettes per day, while in the present study participants have been divided between HS and LS on the basis of the threshold of five cigarettes per day) and to the different age of the experimental sample (mean age 30.6 years ± 7.5 in Jang et al.’s (2007) study, while 18.308 ± 2.726 years in the present research).

Hypothesis 3 stated that Effective PSAs would have shown increased frontal theta activity in comparison to Ineffective PSAs, mirroring a higher anti-smoking message processing. The results here obtained suggested that such hypothesis is partially true, since in the present sample there was only a trend of such increase. However, it must be noted that repeating the statistical analysis excluding the Awarded PSAs category, while leaving all the other factors unvaried, such increase reached the statistical significance (F_(1,35) = 5.660, p = 0.023).

In fact, the Awarded images, obtained the highest frontal theta levels. This could be explained by the peculiar content and style of the images potentially requiring a high decoding effort, linked to the increase of EEG power in theta band for correctly gaining the antismoking information. In fact, in the case of the picture depicting a cigarette symbolizing an artery filled of fatty deposits, this was probably due to the inferior common knowledge about the vascular consequences of smoking habits among the population¹ (WHO report 2011), in comparison to the more famous pulmonary effects. Moreover, in the case of the young man smoking a cigarette, it could be explained by the sarcastic nature of the sentence above and below him. Furthermore, present data agrees with evidences of a discrimination of awarded advertising through, among the others, EEG theta band, in the right fronto-temporal regions (Wang et al., 2018). In addition, it has been evidenced that in moderately dependent smokers brain activity elicited by tobacco advertisements was higher in comparison to highly dependent smokers (Vollstädt-Klein et al., 2011). This phenomenon could also take shape in the higher frontal theta levels identified in the LS group in comparison to the HS and NS groups. This result suggests that LS group would be particularly receptive to aesthetic features in anti-smoking PSAs, thus producing a spontaneous targeting of the PSA that could be employed for the delivery of the messages to that specific population.

Finally, the correlation between the number of cigarettes regularly smoked by participants and the EEG frontal power in theta band values suggests that HS would percept Effective antismoking PSAs as less effortful or eliciting a lower processing than the NS group. This could be connected to the reason why those participants actually did not smoke, that is, NS participants performed a higher cognitive processing of the antismoking content of the PSAs in comparison to HS group. The same consideration could be extended also to the correlation between the number of smoked cigarettes and the EEG frontal power in theta band in correspondence of the exposure to Ineffective PSAs, even if in this case the cerebral frontal theta resulted to be unuseful for the obtainment of compelling antismoking messages. About the levels of frontal theta elicited by the Effective PSAs, the pre-test of the advertising for the frontal theta evaluation in target populations appears useful. Concerning the Awarded PSAs, the absence of a statistical correlation with the number of cigarettes smoked by participants support the hypothesis formulated above of their stylistic complexity in general, not promoting attitudinal/beliefs changes in the population, so without differences among the groups.

Hypothesis 1, according to the theory of the psychological reactance, stated that HS would present a more negative emotional reaction to antismoking PSAs in comparison to LS and NS. The results here obtained suggested that such hypothesis is true.

Concerning EI results, the evidence that HS group presented the most negative emotional reaction toward stimuli can be linked to the theory of psychological reactance (Brehm and Brehm, 2013), that states why efforts to convince adolescents not to smoke may lead to an opposite effect. In addition, it has been shown that youth smoking prevention advertisements have been rated more negatively by adolescents with high psychological reactance than participants with low psychological reactance (Henriksen et al., 2006). Therefore, it is possible to hypothesize that HS participants presented a higher potential for reactance, and an indicator of the oppositional attitudes of adolescents toward authority are associated with tobacco use (Wakefield et al., 2005). It could be hypothesized that the cognitive reaction, as estimated by the frontal alpha asymmetry index, toward the Effective PSAs is symmetrical to the emotional reaction emboding the reactance response. The sum of these evidences suggests that in young HS group the Effective PSAs would elicit positive frontal alpha asymmetry values and negative EI values. Therefore, this pattern of indicators levels could be the best mix to realize antismoking PSAs able to promote quitting. In fact, the Effective PSAs are campaigns that already received a favorable acceptance by the public. It is indeed reasonable that the PSA material from the governs could be usefully pre-tested in such population in order to estimate the neurometric reaction before their launch on air. The verification of the presence of such pattern in frontal alpha asymmetry and negative EI values before the launch could reinforce the idea that the advertising could be a success, and viceversa.

The correlation between the number of cigarettes regularly smoked by participants and the EI values reported in response to the Effective and Awarded PSAs suggests that congruent, threatening and well-delivered antismoking messages would elicit a greater emotional reaction in smokers than in non-smokers, probably because as felt as more involving themselves.

Looking at the two extremes of the smoking habit behavior (HS and NS), summing evidences from frontal alpha asymmetry, frontal theta and EI, Effective PSAs should present in a priori test different patterns. In particular, PSAs to be effective in HS would present positive (approach tendency) frontal alpha asymmetry, low frontal theta and negative EI levels; while in NS Effective PSAs would present negative (withdrawal tendency) frontal alpha asymmetry, higher frontal theta and more positive EI values. These two patterns would be promoting two distinct reactions: “quitting” in HS and “prevention from starting” in NS, respectively.

Hypothesis 3 stated that a priori assessment of effective anti-smoking material is reliable. The results here obtained suggested that such hypothesis is true.

The analysis of the VA index showed that participants tended to look more at the informative elements (text or graphical elements) of the Ineffective PSAs. This result could be explained by the poor delivery of the message promoted by this kind of PSAs in accordance with a study by Rayner (2009), where is highlighted a direct correlation between the time spent on an AOI and the inability of comprehension of the message: smoking cessation habits in this particular case. Looking at the interaction of PSAs kind and the Elements (Text and Visual) of such PSAs, it is evident the presence of different trends in the VA for the different PSAs categories. In particular, in the Effective and in the Awarded PSAs the visual elements were the most attractive in contrast to the Ineffective PSAs, where the texts seemed to be the main focus of attention. This could reflect the lack of information about anti-smoking contents in the Ineffective PSAs, where the perceived message appears unclear and poorly related to the issue. Specifically in fact, the Ineffective PSAs were constituted by: one visual element consisting in a set of young models without any reference to the tobacco consumption (Feel free to say no PSA), and the second was a drawing of a coughing boy, but in a cluttered and hard to decode picture, where the embedded antismoking message was probably difficult to perceive (Pieters et al., 2007).

Evidences suggest that the text is the main focus on a printed advertising (Rayner et al., 2001), but in the present study concerning antismoking campaigns, this holds true only for the Ineffective PSAs.

This could be explained by the reason that for the Effective and Awarded PSAs, where the message proposed by the visual element was clearer and more immediate (e.g., the lady who underwent the tracheotomy), there was less needing to obtain information from the textual element. The graphical elements in this case would be self-explanatory and strictly related to the issue, so there was no need to further investigate the purpose or the message of the campaign by spending more time on the text elements.

The results from ET strongly suggest that Effective PSAs should present a clear and immediate message, strictly connected to the textual element. Therefore a priori pre-test of the PSA would be useful for the evaluation through VA metrics of the ET pattern elicited by the advertisement.