Olivier Lareyre
email: olivier.lareyre@univ-montp3.fr

Mathieu Gourlan
email: mathieu.gourlan@icm.unicancer.fr

Anne Stoebner-Delbarre

Florence Cousson-Gélie
email: florence.cousson-gelie@univ-montp3.fr

Characteristics and impact of theory of planned behavior interventions on smoking behavior: A systematic review of the literature

Keywords: tobacco, health promotion, psychosocial theories, systematic review

Introduction

Despite years of progress supported by the World Health Organization (WHO) Framework Convention on Tobacco Control to guide national policies, 19% of the world population is currently smoking, causing 8 million deaths every year (World Health Organization, 2019). In order to prevent tobacco smoking, many countries are supporting actions of tobacco control for a long time, with nonetheless a high variability in the effectiveness of the implemented interventions (e.g., [START_REF] Rice | Nursing interventions for smoking cessation[END_REF]. Applying theory to design and evaluate behavior change interventions is currently viewed as good practice [START_REF] Conner | Predicting and changing health behaviour: A social cognition approach[END_REF]. As highlighted by some authors [START_REF] Michie | Are interventions theory-based? Development of a theory coding scheme[END_REF], implementing theory-based interventions provides several benefits, including the identification of key constructs to target and the selection of appropriate intervention techniques to use. Moreover, collecting empirical data from theory-based interventions can also aid in the understanding of the underlying mechanisms (proposed by the theory) implied in the effectiveness of interventions [START_REF] Michie | From theory-inspired to theory-based interventions: A protocol for developing and testing a methodology for linking behaviour change techniques to theoretical mechanisms of action[END_REF]. In this context, using theory to design interventions is advocated to contribute to the development of potentially efficient and replicable programs, to aid in the understanding of mechanisms of behavior change, and to provide a basis for theorists to refine their models [START_REF] Rothman | Is there nothing more practical than a good theory? »: Why innovations and advances in health behavior change will arise if interventions are used to test and refine theory[END_REF].

To design interventions, many theories of behavior change are available [START_REF] Michie | ABC of behaviour change theories[END_REF].

One popular model that has received wide attention in health behavior research is the theory of planned behavior (TPB) [START_REF] Ajzen | The theory of planned behavior[END_REF]. The TPB is a model of rational decision-making that proposes that behavior is determined by a number of potentially changeable cognitions.

More precisely, the model proposes that behavioral intention (i.e., an individual's motivation to act) is the immediate antecedent of behavior. Intention, in turn, is determined by attitude (i.e., favorable or unfavorable evaluation of the targeted behavior), subjective norm (i.e., perceived social pressure from important others about performing the behavior and level of adoption of the behavior by important others) and perceived behavioral control (PBC) (i.e., perceived ability to perform the behavior). This latter construct is also hypothesized to directly predict behavior. Numerous reviews and meta-analyses have reported the validity of the TPB to explain various health behaviors such as physical activity, dietary behavior, or alcohol consumption [START_REF] Cooke | How well does the theory of planned behaviour predict alcohol consumption? A systematic review and metaanalysis[END_REF]Hagger et al., 2016;[START_REF] Mcdermott | The theory of planned behaviour and dietary patterns: A systematic review and meta-analysis[END_REF][START_REF] Mceachan | Prospective prediction of health-related behaviours with the theory of planned behaviour: A metaanalysis[END_REF]. Concerning smoking behavior, if studies could vary with regard to the behavioral outcome (e.g., smoking reduction, smoking cessation) or the kind of products explored (e.g., cigarettes only, drugs including tobacco), previous meta-analyses and reviews have also reported the validity of the TPB in those different contexts [START_REF] Mceachan | Prospective prediction of health-related behaviours with the theory of planned behaviour: A metaanalysis[END_REF][START_REF] Topa | Theory of planned behavior and smoking: Meta-analysis and SEM model[END_REF].

Otherwise, the TPB has also been used as a theoretical framework for designing health behavior interventions. Previous reviews and meta-analyses have reported that TPB-based interventions appear efficient in various behavioral domains, including the reduction of sexual risk behaviors [START_REF] Tyson | Theory of planned behavior interventions for reducing heterosexual risk behaviors: A meta-analysis[END_REF], or the promotion of physical activity [START_REF] Gourlan | Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials[END_REF]. However, information concerning the efficacy of TPB-based interventions specifically in the domain of smoking behavior remains sparse. [START_REF] Hardeman | Application of the theory of planned behaviour in behaviour change interventions: A systematic review[END_REF] reported impact on smoking behavior and TPB variables (i.e., attitude, subjective norm, PBC, intention). Regarding the existing reviews and meta-analyses (e.g., [START_REF] Hardeman | Application of the theory of planned behaviour in behaviour change interventions: A systematic review[END_REF][START_REF] Steinmetz | How effective are behavior change interventions based on the theory of planned behavior? A three-level meta-analysis[END_REF], this work appears as the first to inform on the efficacy of TPB-based interventions specifically on smoking behavior. Moreover, the present review will also provide a detailed insight into the ways and contexts in which the TPB was used to change smoking behavior. Second, to evaluate the level of methodological quality of the included studies (e.g., design, data collection methods). This point appears of particular importance as it gives some crucial information about the validity of the results presented and the robustness of the conclusions that can be drawn from this review. Third, to assess the quality of the theoretical implementation in the included studies. As pointed out by some authors [START_REF] Michie | Are interventions theory-based? Development of a theory coding scheme[END_REF], implementing a theory-based intervention protocol is a rigorous scientific process that can be evaluated to determine to what extent the theory was used to develop the intervention, to explain intervention effects, and to provide considerations for future theorybased research. Nevertheless, [START_REF] Prestwich | Does theory influence the effectiveness of health behavior interventions? Meta-analysis[END_REF] reported in a meta-analysis that theories were rarely used extensively to develop or evaluate physical activity and healthy eating interventions. This review will explore how the TPB was implemented in smoking interventions.

Method

Inclusion and exclusion criteria

Studies were included in the present systematic review if they met the following criteria: (i) the primary component or one of the components of the intervention targeted smoking behavior, (ii) the intervention implemented was based on the TPB (i.e., TPB had to be explicitly named in the text), (iii) smoking behavior or intention related to smoking behavior was measured at least at post-intervention time point. The exclusion criteria were (i) studies only evaluating the intention of subjects to participate in a program, (ii) interventions which could target one or several variables included in the TPB (e.g., attitude, PBC) but not explicitly citing the model, and (iii) results published in another language than English or French. Of note, the studies could be either experimental (using pre-and post-tests) or randomized controlled trials, the intervention could combine the TPB with other theoretical frameworks and could be oriented toward smoking prevention and/or treatment. No restriction was either placed upon the mode of delivery of the intervention (e.g., face to face, internet) or upon the age of participants (e.g., adolescents, adults).

Search strategies

A comprehensive investigation was completed using four search strategies. First, studies were identified by searching through the MEDLINE, PsycINFO, and PsycARTICLES, Academic Search Premier, Eric, Francis, Pascal, Psychology and Behavioral Sciences Collection, and SocIndex databases (last search November 19, 2019). Mesh and text-words were used in MEDLINE. Additional searches were carried out on the search engines of Google Scholar (scholar.google.com), Researchgate (researchgate.net), and the National Institute of Health (nih.gov) (more details in Supplementary File 1). Second, additional manual searches were also carried out. More precisely, references were screened in the bibliography section of Ajzen's website, in the registry of clinical trials website, in Cochrane Reviews related to tobacco retrieved from the Cochrane Library, and in relevant published TPB reviews, metaanalyses, books, and book chapters (more details in Supplementary File 2). Third, messages were sent on thematic forums of the American Psychological Association, on the listserv of the Society for Personality and Social Psychology, and on Researchgate forum discussion.

Fourth, e-mails were sent to prominent authors of the TPB (i.e., Armitage, Ajzen, Conner, Godin, Hardeman, McEachan, Rhodes, Taylor, West). References of articles retrieved from thematic forums and e-mails sent by prominent authors were also screened manually.

The titles and abstracts found in the electronic databases were initially and independently screened by two trained reviewers (OL, Ph.D.; MG, Ph.D.) to determine their adequacy with this systematic review. Similarly, those two reviewers also independently screened references from the additional manual searches and from articles retrieved from thematic forums and emails sent by prominent authors. The final selection was based on full-text reading and performed by four trained reviewers (OL; MG; AS, Ph.D., MD; FCG, Ph.D.). Any disagreements were resolved by discussion.

Coding procedure

Descriptive data were extracted by (i) study population and design: Year, country of the intervention, number of participants analyzed, gender, sample characteristics (e.g., smokers, university students), (ii) characteristics of the intervention: Objective (i.e., prevention to maintain non-smoking, treatment to reduce, abstain or quit smoking, combined intervention to reduce smoking prevalence in a group), components implemented (e.g., education/information, skill training), mode of delivery (e.g., internet, face to face), length, number of sessions, (iii) outcomes: Presence of a measure of smoking behavior and/or TPB variables, significant impact of the intervention (i.e., ps < .05 for the statistical tests performed) on smoking behavior and/or TPB variables.

Additionally, the methodological quality of each study was examined using the 14 items from the Effective Public Health Practice Project (EPHPP) [START_REF] Thomas | A process for systematically reviewing the literature: Providing the research evidence for public health nursing interventions[END_REF]. This tool has demonstrated content and construct validity [START_REF] Thomas | A process for systematically reviewing the literature: Providing the research evidence for public health nursing interventions[END_REF]. In a report assessing over 190 bias assessment tools for intervention studies, the EPHPP was one of six shown to be appropriate for use in systematic reviews [START_REF] Deeks | Evaluating non-randomised intervention studies[END_REF]. The quality categories that were rated included selection bias (items 1-2; i.e., representativeness of the sample, proportion of individuals who agreed to participate), study design (items 3-6; i.e., design of the study, method of randomization), confounders (items 7-8; i.e., presence of significant differences between groups prior the intervention, control of confounders in main analyses), blinding (items 9-10; i.e., outcome assessor aware of the intervention status of participants, participants aware of the research question), data collection methods (items 11-12; i.e., reliability -validity of outcome measures), and withdrawals and drop-outs (items 13-14; i.e., information about drop-outs, proportion of completers). For each study, each quality category was rated as "strong," "moderate" or "weak." Then, a global quality score was given to each study. More precisely, studies with a "strong" global rating did not have any "weak" for any quality category, studies with a "moderate" global rating had one "weak" rating in one of the quality categories, and studies with a "weak" global rating had two or more "weak" ratings in the quality categories (see [START_REF] Thomas | A process for systematically reviewing the literature: Providing the research evidence for public health nursing interventions[END_REF].

Finally, the quality of the theoretical implementation of the TPB in the included studies was coded using 23 items from the Theory Coding Scheme (TCS) [START_REF] Michie | Are interventions theory-based? Development of a theory coding scheme[END_REF].

The few adaptations of the scale used in the present review as compared to the original work of Michie and Prestwich (2010) (e.g., two items not included as they already represented inclusion criteria) are presented in Supplementary File 3. All items were coded as "yes" or "no." For each study, four composite scores were created to report the score of: Theoretical basis of the intervention (items 1-9; i.e., mention of the TPB variables targeted, mention of another theoretical model, TPB variables use to select participants -to tailor intervention, interrelationships between intervention techniques and TPB variables), quality of the measure of the theoretical constructs (items 10-15; i.e., assessment of TPB variables, validity and reliability of TPB measures), nature of the results on theoretical constructs (items 16-20; i.e., impact of the intervention on TPB measures, mediational analyses), and feedback and considerations on the theory (items 21-23; i.e., results use to discuss, validate, refine the TPB). An overall theoretical implementation score summing the 23 items was also created for each study.

Two reviewers (OL-MG) independently coded the data for each included study. Results of the coding were then compared. The interrater reliability for the variables coded was at least substantial (κ > .63), with a satisfactory global mean κ = .88 (SD = .13) (more details in Supplementary File 4). Any disagreements were resolved by discussion with AS and FCG.

Results

Search results

Figure 1 displays the process of study selection. The four search strategies used in this systematic review initially returned a total of 8279 articles. Based on the screening of titles and abstracts, a total of 7039 articles were excluded using the following criteria: (i) not an intervention and/or not about tobacco, (ii) other languages than English or French. After having removed the duplicates between the four search strategies, 536 full-text articles were screened. A total of 519 articles were excluded using the following criteria: (i) not an intervention about tobacco, (ii) intervention not based on the TPB, (iii) neither smoking behavior nor intention is measured at post-test. As a result, 17 studies -involving 17 independent samples -met all inclusion criteria and were included in this review.

Characteristics of the studies

The general characteristics of the 17 included studies are presented in Table 1. The majority (n = 15, 88%) were published after the year 2010, while 2 (12%) were in 2008. The samples were mixed-sex in 11 studies (65%) and included men exclusively in the remaining 6 studies (35%). The population targeted was described as students (i.e., elementary school, high school, university) in 11 studies (65%). A total of 3 other studies (18%) targeted individuals in contexts of health risks (i.e., in preoperative evaluation, fathers with a sick child, at risk of type 2 diabetes and/or cardiovascular diseases). Last, 3 studies (18%) targeted only smokers in various contexts (i.e., Korean American, public employees).

Regarding the objective of the 17 interventions, 8 (47%) focused on tobacco treatment for smokers, 7 (41%) were combined programs addressed to groups of both smokers and nonsmokers and 2 (12%) aimed at preventing initiation among non-smokers. All the programs included an education/information component. This consisted of the provision of health messages related to topics such as deleterious effects of smoking, or benefits of quitting smoking. In 10 studies (59%), the intervention also included skill training components such as learning to manage stress, encouraging self-affirmation or learning to deal with relapse-prone situations through various means (e.g., planning, problem-solving strategies). Interventions were delivered in a variety of ways, most often combining several modes of delivery (n = 14, 82%), predominantly by face to face (n = 10, 59%) and printed materials (n = 9, 53%).

Measure and impact of interventions on smoking behavior and TPB variables

As reported in Table 2, of the 17 included studies, 13 (76%) reported results on smoking behavior, and 12 (71%) on intention. A total of 7 studies (41%) reported results on both smoking behavior and all TPB variables, 3 (18%) only on smoking behavior, and 3 (18%) only on all TPB variables. Of note, no study addressing smoking prevention reported results on smoking behavior, and 6/8 studies on smoking treatment did not report results on at least one variable of the TPB. A significant impact of the intervention was reported in 6/13 studies (46%) on smoking behavior, in 6/12 (50%) on intention, in 6/13 (46%) on attitude, in 5/12 (42%) on subjective norm, and in 6/12 (50%) on PBC (see Table 2). Of note, of the 17 included studies, 1 (6%) reported a significant impact on both smoking behavior and all TPB variables, 3 (18%) reported a significant impact on smoking behavior but not on any TPB variables, and 1 (6%) reported a significant impact on all TPB variables but not on behavior.

Methodological quality of interventions

Results of the EPHPP coding are summarized in Table 3 (see Supplementary File 5 for the detailed result of each item of the EPHPP for each study). Of the 17 included studies, 10 (59%) were rated as "weak" for their global methodological quality score, 5 (29%) as "moderate," and 2 (12%) as "strong." When more specifically exploring the quality categories, 14 studies (82%) were rated as "strong" for the study design, since 13 were randomized controlled trials and 1 was a controlled clinical trial. No study was rated as "weak" for this category. Regarding confounders, when applicable (confounders were not assessed for one-group studies), a majority of studies (n = 8/14, 57%) were rated as "strong," but 5/14 (36%) were rated as "weak" (i.e., potential confounders not controlled in analyses, or insufficient information available). A total of 7 studies (41%) were rated as "weak" for selection bias (i.e., sample not or insufficiently representative of the targeted population), and 7 as "moderate" (41%) (i.e., insufficient information available). Similarly, 7 studies (41%) were rated as "weak" for data collection methods (i.e., insufficient information about validity and reliability of the tools measuring smoking behavior), and 6 (35%) as "moderate" (i.e., insufficient information whether for validity or reliability). A majority of studies (n = 12, 71%) were rated as "moderate" for blinding (i.e., insufficient information available). Finally, 5 studies (29%) were rated as "strong" for withdrawal and drop-outs, 5 (29%) as "weak" (i.e., no information available, or follow-up rate < 60%), and 7 (41%) as "moderate" (i.e., followup rate between 60% and 79%).

Theoretical implementation of interventions

Results of the TCS for the 17 included studies are summarized in Table 4 (see Supplementary File 6 for the detailed result of each item of the TCS for each study). The mean theoretical implementation score (23 items, maximum score = 19) was 7.41 (SD = 2.85, median = 8), ranging from 1 to 13. Regarding the theoretical basis of interventions (9 items, maximum score = 7), the mean score was 3.24 (SD = 0.90, median = 3). Sixteen studies (94%) mentioned the TPB variables as predictors of smoking behavior. No study has used TPB variables to select participants or to tailor the intervention. Eight studies (47%) explicitly linked all their intervention techniques to at least one TPB variable. There was also a total of 8 studies (47%) that explicitly linked all TPB variables to at least one technique. Concerning the measure of theoretical constructs (6 items, maximum score = 4), the mean score was 2.71 (SD = 1.45, median = 3). It can be noted that 9 studies (53%) reported some evidence of the reliability of every TPB measure (e.g., Cronbach's alphas), and 8 (47%) reported having followed the existing recommendations for the construction of a TPB questionnaire (e.g., [START_REF] Ajzen | Constructing a TPB questionnaire[END_REF][START_REF] Francis | Constructing questionnaires based on the theory of planned behaviour: A manual for health services researchers[END_REF]. Regarding the results of interventions on theoretical constructs (5 items, maximum score = 5), the mean score was 0.76 (SD = 1.20, median = 1).

Only 1 study (6%) analyzed and reported that the impact of the intervention on smoking behavior was significantly mediated by a TPB variable. Last, concerning feedback and considerations on the theory (3 items, maximum score = 3), the mean score was 0.71 (SD = 0.47, median = 1). Results were discussed in relation to TPB in 11 studies (65%), but only 1 (6%) provided an appropriate support to the theory, and none used results to make some propositions to refine the theory.

Discussion

The present systematic review is the first to specifically focus on TPB-based interventions on smoking behavior. A total of 17 studies were identified, which is lower than the number of observational studies identify (n ≥ 27) when TPB is used to explore determinants of smoking behavior [START_REF] Mceachan | Prospective prediction of health-related behaviours with the theory of planned behaviour: A metaanalysis[END_REF][START_REF] Topa | Theory of planned behavior and smoking: Meta-analysis and SEM model[END_REF]. The publication of the results of the included studies mainly begins after 2010. The use of the TPB to design and evaluate programs targeting smoking behavior appears thus a little bit more recent than for some other health behaviors such as risky sexual behaviors [START_REF] Tyson | Theory of planned behavior interventions for reducing heterosexual risk behaviors: A meta-analysis[END_REF] or physical activity [START_REF] Gourlan | Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials[END_REF]. In the current review, all the programs minimally included the provision of health messages. As previously reported in other health contexts (Hagger & Chatzisarantis, 2016;[START_REF] Hardeman | Application of the theory of planned behaviour in behaviour change interventions: A systematic review[END_REF] more consistently focus on preventing its initiation. In addition, the studies addressing smoking prevention did not measure post-intervention behavior, and a majority of the studies on smoking treatment did not measure all TPB variables. In line with the existing recommendations [START_REF] Michie | Are interventions theory-based? Development of a theory coding scheme[END_REF], future TPB-based interventions should assess both smoking behavior and TPB variables, whatever the objective pursued. Last, when measured, no more than half of the studies reported a significant impact on smoking behavior, intention, attitude, subjective norm, or PBC. The efficacy of TPB-based interventions on various health behaviors such as risky sexual behaviors, physical activity or nutrition has been reported elsewhere [START_REF] Gourlan | Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials[END_REF][START_REF] Hardeman | Application of the theory of planned behaviour in behaviour change interventions: A systematic review[END_REF][START_REF] Steinmetz | How effective are behavior change interventions based on the theory of planned behavior? A three-level meta-analysis[END_REF][START_REF] Tyson | Theory of planned behavior interventions for reducing heterosexual risk behaviors: A meta-analysis[END_REF]. Nonetheless, [START_REF] Steinmetz | How effective are behavior change interventions based on the theory of planned behavior? A three-level meta-analysis[END_REF] also reported in their meta-analysis that TPB-based interventions in the "alcohol and drugs" domain, which included smoking behavior, had a less consistent impact compared to other behavioral domains. From all of those results, it appears that despite some evidence of the efficacy of TPB-based interventions in various health domains, the impact of those interventions on TPB variables and smoking behavior remains to be confirmed, and should be further explored.

The global methodological quality score was rated as "low" for a majority of studies (59%), and "strong" for only a small minority (12%). For most quality categories, a majority of studies were rated as "low" or "moderate." The proportion of studies rated as "low" for selection bias and data collection methods was quite high (41%). The presence of these types of biases is known to both threaten external validity [START_REF] Van Heuvelen | Differences between participants and non-participants in an RCT on physical activity and psychological interventions for older persons[END_REF] and overestimate the beneficial impact of interventions [START_REF] Bernard | Moderators of theory-based interventions to promote physical activity in 77 randomized controlled trials[END_REF]. Additionally, the proportion of studies that were rated as "moderate" was quite high for selection bias, data collection methods, and withdrawal and drop-outs (41%), and especially high for blinding (71%). When using the EPHPP [START_REF] Thomas | A process for systematically reviewing the literature: Providing the research evidence for public health nursing interventions[END_REF], a "moderate" rating mostly indicates unclear risks of biases due to insufficient information available. Numerous previous reviews and meta-analyses have reported that the globally low methodological quality found in TPBbased [START_REF] Tyson | Theory of planned behavior interventions for reducing heterosexual risk behaviors: A meta-analysis[END_REF], theory-based [START_REF] Lock | Efficacy of theory-informed workplace physical activity interventions: A systematic literature review with meta-analyses[END_REF] , or health promotion interventions [START_REF] Armanasco | Preventive health behavior change text message interventions: A meta-analysis[END_REF], was notably due to a lack of details provided in many studies. From those results, future research should thus more systematically rely on existing recommendations on methodological quality when implementing and reporting interventions [START_REF] Boutron | Methods and processes of the CONSORT Group: Example of an extension for trials assessing nonpharmacologic treatments[END_REF][START_REF] Des Jarlais | Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: The TREND statement[END_REF][START_REF] Thomas | A process for systematically reviewing the literature: Providing the research evidence for public health nursing interventions[END_REF]. When more specifically considering recommendation toward the measurement of smoking behavior (i.e., data collection methods), given the current knowledge, smoking measurement should be notably based on WHO recommendations (Global Adult Tobacco Survey Collaborative Group, 2011).

Concerning the theoretical implementation, it can be noted that no studies used TPB variables to select participants or tailor the program (items 3 and 4 of the TCS; [START_REF] Michie | Are interventions theory-based? Development of a theory coding scheme[END_REF]. Interventions based on a continuum theory as the TPB are traditionally designed to simultaneously target the determinants of intention (i.e., attitude, subjective norm, PBC), and contrast with interventions tailored to individual profiles [START_REF] Weinstein | Stage theories of health behavior: Conceptual and methodological issues[END_REF]. That said, Ajzen (2006a) also specifies that it may be relevant to focus a TPB-based intervention only on the variable(s) that have a meaningful impact on intention and behavior in a given population. So, to the extent a TPB variable is identified as a key determinant of intention and smoking behavior, such as PBC in adolescents with asthma (e.g., [START_REF] Van De Ven | A longitudinal test of the theory of planned behavior predicting smoking onset among asthmatic and non-asthmatic adolescents[END_REF], future TPB-based interventions could focus on this variable, and select and/or tailor the program in function of the level of the participants on the variable. Otherwise, it can be noted that less than half (47%) of the studies did report a precise link between all intervention techniques and at least one TPB variable (item 5 of the TCS), and also less than half (47%) of the studies did report a precise link between all TPB variables and at least one intervention technique (item 8). Those proportions appear in line with previous meta-analyses exploring the efficacy of theory-based interventions on diet or physical activity [START_REF] Gourlan | Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials[END_REF][START_REF] Prestwich | Does theory influence the effectiveness of health behavior interventions? Meta-analysis[END_REF]. Establishing a precise link between all intervention techniques and all theoretical concepts is an important step to identify the factors involved in the efficacy of theory-based interventions [START_REF] Michie | From theory to intervention: Mapping theoretically derived behavioural determinants to behaviour change techniques[END_REF]. Future TPB-based interventions are thus invited to systematically develop a precise rational that consider all the hypothesized relationships between the intervention techniques used to impact smoking behavior and the TPB variables targeted. Regarding the measurement of TPB variables, about half of the studies did not report any information on the reliability (53%) and/or validity of the measurement of theoretical constructs (47%). From this, it appears of particular importance for future research to thoroughly follow the existing guidelines regarding the conceptualization, development and analysis of TPB questionnaires (e.g., [START_REF] Ajzen | Constructing a TPB questionnaire[END_REF][START_REF] Francis | Constructing questionnaires based on the theory of planned behaviour: A manual for health services researchers[END_REF], to ensure the validity of the results presented regarding TPB variables. Finally, despite the recognized importance of empirically exploring the mediators of behavior change interventions [START_REF] Lubans | A review of mediators of behavior in interventions to promote physical activity among children and adolescents[END_REF], only one study (6%) performed such mediation analyses (items 17-20 of the TCS). As a logical consequence, most studies could not propose an adapted feedback and an in-depth discussion of the TPB (items 22-23 of the TCS). From all of those reports, the contribution of TPB-based interventions to aid in the understanding of mechanisms of smoking behavior change, and to confirm or refine the TPB in this behavioral domain can be currently considered as particularly modest [START_REF] Rothman | Is there nothing more practical than a good theory? »: Why innovations and advances in health behavior change will arise if interventions are used to test and refine theory[END_REF]. Some limitations should be noted for this systematic review. First, as only 17 studies implemented in only 6 countries by a limited number of research teams could be included, more research appears necessary before any major conclusion can be made regarding TPBbased interventions targeting smoking behavior. Second, the inclusion of only English and French language studies can be considered as another limitation. Third, despite the quite comprehensive investigation of the existing literature (i.e., see Figure 1), a publication bias cannot be totally excluded.

Conclusion

This systematic review appears as the first to specifically focus on TPB-based interventions targeting smoking behavior. The proportion of studies that reported a significant impact on smoking behavior, intention, attitude, subjective norm, or PBC appeared relatively modest (42%-50%). Moreover, numerous flaws have been identified, both regarding methodological aspects and the theoretical implementation of the interventions. Future research should be aware that theory-based interventions have the potential to be truly efficient and to enrich the underlying theoretical frameworks only to the extent rigorous methods are applied when designing, implementing, and reporting the research protocol and the theory-based intervention [START_REF] Hoffmann | Better reporting of interventions: Template for intervention description and replication (TIDieR) checklist and guide[END_REF][START_REF] Michie | From theory-inspired to theory-based interventions: A protocol for developing and testing a methodology for linking behaviour change techniques to theoretical mechanisms of action[END_REF][START_REF] Peters | Full disclosure: Doing behavioural science necessitates sharing[END_REF]. As no studies using a factorial design were identified in this systematic review, future studies are also encouraged to develop research protocols using this kind of design [START_REF] Weinstein | Misleading tests of health behavior theories[END_REF]. For example, testing independent effects of manipulating attitude, subjective norm, and PBC as well as their synergetic effect on smoking behavior (e.g., 2*2*2 factorial design; [START_REF] Sniehotta | An experimental test of the theory of planned behavior[END_REF], represents a fruitful perspective to better identify the component techniques and underlying mechanisms involved in the impact of TPB-based interventions on smoking behavior.

Funding sources: Olivier Lareyre and Mathieu Gourlan were supported by the SIRIC Montpellier Cancer (Grant INCa-DGOS-Inserm 6045). The funding source had no role in the collection, analysis and interpretation of data, or the right to approve the finished manuscript prior to publication.

Conflicts of interest statement:

The authors declare that there are no conflicts of interest. Note: F = Female, M = Male, NA = not applicable, NI = not informed.

  systematically reviewed the application of the TPB in behavior change interventions. They noted that at this time only two papers out of 30 addressed smoking behavior and that those papers did not examine the impact of TPB-based interventions on smoking behavior per se but solely evaluated the interest of subjects to participate in a program of smoking cessation. More recently, Steinmetz et al. (2016) reported in their meta-analysis a significant global effect of TPB-based interventions to promote change in various health behaviors and change in TPB variables. However, interventions targeting smoking behavior were regrouped within "alcohol and drugs" studies, which preclude the possibility of establishing a detailed synthesis of TPBbased interventions specifically in the smoking domain. The present systematic review aims to identify and synthesize knowledge concerning the efficacy of TPB-based interventions on smoking behavior of minors or adults in studies using quasi-experimental (pre-and post-tests) or randomized controlled trial designs, through three objectives: First, to determine the characteristics of TPB-based interventions and their

  , persuasive communication is the most widely component used in TPB-based interventions. Of note, this provision of information was regularly completed with at least another component such as skill training or counseling sessions. Regarding program objectives, it appears that only 2 programs were aimed at preventing initiation among non-smokers. As smoking cessation remains a challenge (U.S. Department of Health and Human Services, 2020), future TPB-based interventions should

Table 1 .

 1 Characteristics of included studies.

	Study	Country	Population	Gender Number of	Objective of the intervention	Components of the	Mode of delivery of	Length	Number
					participants		intervention	the intervention		of
					analyzed					sessions
	Barati (2015) Iran	High school students M	114	Combined intervention to reduce smoking	Education/information,	Internet, videos	6 weeks	NA
						prevalence in a group of both smokers and	skill training
						non-smokers
	Cameron	United	New undergraduate	M/F	1495	Combined intervention to reduce smoking	Education/information,	Internet, videos	24 weeks NA
	(2015)	Kingdom	university students			prevalence in a group of both smokers and	skill training,
						non-smokers	implementation
							intentions
	Chan (2008) China	Smoking fathers with	M	1483	Treatment to reduce, abstain or quit	Education/information,	Wife involvement,	12 months NA
			sick child			smoking	implementation of social	printed materials,
							support, counselling	telephone
	Dehdari	Iran	College students with	M	90	Treatment to reduce, abstain or quit	Education/information	Face to face (group),	3 weeks	4
	(2013)		positive history of			smoking		printed materials
			water pipe use
	Epton (2013,	United	New undergraduate	M/F	1107	Combined intervention to reduce smoking	Education/information,	Internet, videos	24 weeks NA
	2014, 2015)	Kingdom	university students			prevalence in a group of both smokers and	skill training,
						non-smokers	implementation
							intentions
	Fallin (2013) United	Smoking university	M/F	NA	Treatment to reduce, abstain or quit	Education/information	Printed materials	NA	NA
		States of	students			smoking
		America
	Gilliam	United	High school students M/F	67	Combined intervention to reduce smoking	Education/information	Game	1 hour	1
	(2019)	States of				prevalence in a group of both smokers and
		America				non-smokers
	Ismail (2016) Malaysia	Smokers working in	M	99	Treatment to reduce, abstain or quit	Education/information,	Face to face (group),	21 days	1
			public offices			smoking	skill training, social	printed materials
							pledge, religious rulings
	Kim (2012)	United	Korean American	M/F	30	Treatment to reduce, abstain or quit	Education/information,	Face to face	8 weeks	8
		States of	smokers			smoking	skill training,	(individual), videos
		America					implementation of social
							support, counselling

Table 1 .

 1 Characteristics of included studies (continued).

	Study	Country	Population	Gender Number of	Objective of the intervention	Components of the	Mode of delivery of	Length	Number
					participants		intervention	the intervention		of
					analyzed					sessions
	Kim (2015)	United	Korean American	M/F	109	Treatment to reduce, abstain or quit	Education/information,	Face to face	8 weeks	8
		States of	smokers			smoking	skill training,	(individual), printed
		America					implementation of social	materials
							support, counselling
	Lakerveld	The	Adults at risk of type	M/F	536	Combined intervention to reduce smoking	Education/information,	Face to face	16 months 10
	(2008, 2011,	Netherlands	2 diabetes and/or			prevalence in a group of both smokers and	skill training, counselling	(individual),
	2012, 2013)		cardiovascular			non-smokers		telephone
			diseases
	Nazari (2013) Iran	High school students M	154	Combined intervention to reduce smoking	Education/information	Face to face (group),	NI	4
						prevalence in a group of both smokers and		printed materials,
						non-smokers		videos, games
	Nurumal	Malaysia	Never-smoking	M/F	140	Prevention to maintain non-smoking	Education/information,	Face to face (group),	1 month	3
	(2019)		elementary school				skill training	printed materials,
			students					videos, games
	Record	United	Smoking	M/F	283	Treatment to reduce, abstain or quit	Education/information	Printed materials	4 weeks	NA
	(2014, 2017;	States of	undergraduate			smoking
	2017; 2018)	America	university students
	Shi (2013,	United	Smokers in	M/F	169	Treatment to reduce, abstain or quit	Education/information	Face to face	5 minutes 1
	2014)	States of	preoperative			smoking		(individual)
		America	evaluation
	Zeidi (2013) Iran	Never-smoking high	M	150	Prevention to maintain non-smoking	Education/information,	Face to face (group),	NI	4
			school students				skill training, counselling	printed materials,
								game
	Zhao (2018,	China	High school students M/F	156	Combined intervention to reduce smoking	Education/information,	Face to face (group),	4 weeks	4
	2019)					prevalence in a group of both smokers and	skill training	game
						non-smokers

Table 2 .

 2 Impact of interventions on smoking behavior and theory of planned behavior variables.

		Significant impact of the intervention a
	Study	On smoking	On intention	On attitude	On subjective	On perceived
		behavior			norm	behavioral control
	Barati (2015)

Table 3 .

 3 Methodological quality synthesis of included studies.Note: NA = not applicable, a Confounders were not assessed for one-group studies, b Data collection methods were assessed for the measures of smoking behavior when reported or intention when smoking behavior was not reported.

	Study	Level of	Level of	Level of	Level of	Level for	Level of	GLOBAL
		evidence for	evidence for	evidence for	evidence for	evidence for	evidence for	RATING FOR
		selection bias	design	confounders a	blinding	data collection	withdrawals	THIS PAPER
						methods b	and drop-outs
	Barati (2015)	Moderate	Strong	Strong	Moderate	Weak	Strong	Moderate
	Cameron (2015)	Weak	Strong	Strong	Moderate	Moderate	Weak	Weak
	Chan (2008)	Strong	Strong	Strong	Weak	Moderate	Strong	Moderate
	Dehdari (2013)	Moderate	Strong	Strong	Moderate	Weak	Weak	Weak
	Epton (2013, 2014,	Weak	Strong	Strong	Moderate	Moderate	Moderate	Moderate
	2015)
	Fallin (2013)	Moderate	Moderate	NA	Strong	Strong	Moderate	Strong
	Gilliam (2019)	Weak	Moderate	NA	Weak	Weak	Strong	Weak
	Ismail (2016)	Moderate	Strong	Weak	Moderate	Weak	Moderate	Weak
	Kim (2012)	Weak	Strong	Weak	Moderate	Strong	Moderate	Weak
	Kim (2015)	Weak	Strong	Weak	Moderate	Strong	Moderate	Weak
	Lakerveld (2008,	Weak	Strong	Weak	Moderate	Moderate	Strong	Weak
	2011, 2012, 2013)
	Nazari (2013)	Moderate	Strong	Weak	Moderate	Moderate	Weak	Weak
	Nurumal (2019)	Strong	Strong	Strong	Weak	Strong	Strong	Moderate
	Record (2014,	Weak	Moderate	NA	Moderate	Weak	Weak	Weak
	2017, 2017, 2018)
	Shi (2013; 2014)	Strong	Strong	Strong	Moderate	Moderate	Moderate	Strong
	Zeidi (2013)	Moderate	Strong	Strong	Weak	Weak	Weak	Weak
	Zhao (2018, 2019) Moderate	Strong	Moderate	Moderate	Weak	Moderate	Moderate

Table 4 .

 4 Descriptive statistics for the items assessing theoretical implementation.

	Category	Item	Item title
		number

Table 4 .

 4 Descriptive statistics for the items assessing theoretical implementation (continued).

	Category	Item	Item title
		number

Note: NRR = no result reported, a Significant impact of the intervention on the variable is indicated when p < .05 was reported for the statistical test performed.


Jérémy Matias
email: jeremy.matias@uca.fr

Jean-Charles Quinton

Michèle Colomb

Marie Izaute

Laetitia Silvert

REWARD HISTORY MODULATES PERCEPTUAL LOAD EFFECTS

Keywords: Selective Attention, Distraction, Reward, Perceptual Load, Automaticity

The reward history of a stimulus can yield strong attentional selection biases. Indeed, attentional capture can be triggered by previously rewarded items which are neither salient nor relevant for the ongoing task, even when selection is clearly counter-productive to actually obtain the reward outcome. Therefore, value-driven attentional capture (VDAC) has been argued to be an automatic attention mechanism. Our study aimed at putting the VDAC automaticity directly to the test. For this purpose, the Load Theory offers a comprehensive framework where distraction is observed under low but not high perceptual load condition. Nevertheless, if VDAC is indeed automatic, distraction by reward-stimuli should be observed on both perceptual load conditions. We used a feature vs. conjunction discrimination of a go/no-go cue to manipulate perceptual load. As expected, our results revealed that perceptual load decreased interference produced by low-reward distractor. However, this effect was not significant for high-reward distractor, giving support to VDAC automaticity. We discussed our results in light of the Load Theory literature and we strongly encourage to consider reward history along with perceptual load in determining attentional capture.

INTRODUCTION

In face of a continuous stream of sensory inputs that exceed our limited information processing resources, selective attention allows the selection of some information for further processing while rejecting others [START_REF] Desimone | Neural mechanisms of selective visual attention[END_REF][START_REF] Hickey | Electrophysiological Indices of Target and Distractor Processing in Visual Search[END_REF]. Most of the time, we can intentionally pick out relevant information to achieve our current goal, a process referred to as top-down or "goal-driven" selection. However, we also sometimes experience that some stimuli (mainly physically salient stimuli in the environment, e.g., a sudden bright flash) capture our attention against our will, despite being completely irrelevant to the ongoing task (i.e., distractors), a process referred to as bottom-up or "stimulus-driven" selection. Goaland stimulus-driven selection are not mutually exclusive and, during the last decades, researchers invested a great amount of work in studying the interplay between these attentional processes (for reviews see: [START_REF] Corbetta | Control of goal-directed and stimulus-driven attention in the brain[END_REF][START_REF] Ruz | A review of attentional capture : On its automaticity and sensitivity to endogenous control[END_REF][START_REF] Theeuwes | Top-down and bottom-up control of visual selection[END_REF]. However, it has been recently pointed out that a host of selection biases may not be explained in the framework of this theoretical dichotomy [START_REF] Awh | Top-down versus bottom-up attentional control: A failed theoretical dichotomy[END_REF]. Notably, attention could also be drawn toward stimuli that are neither relevant for the task at hand nor physically salient but that have been previously paired with a reward outcome (for reviews see [START_REF] Anderson | The attention habit: How reward learning shapes attentional selection[END_REF][START_REF] Bourgeois | How motivation and reward learning modulate selective attention[END_REF][START_REF] Failing | Selection history: How reward modulates selectivity of visual attention[END_REF]. In this case, attentional capture would be solely triggered by the distractor value, due to its reward history [START_REF] Awh | Top-down versus bottom-up attentional control: A failed theoretical dichotomy[END_REF], an effect referred to as value-driven attentional capture (VDAC).

Evidence that reward history can constitute a separate class of priority signal in attentional selection, above and beyond top-down and bottom-up processes, comes from various experimental designs (e.g., [START_REF] Anderson | Value-driven attentional capture[END_REF][START_REF] Anderson | On the value-dependence of value-driven attentional capture[END_REF][START_REF] Bourgeois | How does reward compete with goal-directed and stimulus-driven shifts of attention? Cognition and Emotion[END_REF][START_REF] Bucker | Appetitive and aversive outcome associations modulate exogenous cueing[END_REF], 2017a[START_REF] Bucker | Pavlovian reward learning underlies value driven attentional capture[END_REF][START_REF] Failing | Oculomotor capture by stimuli that signal the availability of reward[END_REF][START_REF] Pelley | When goals conflict with values: Counterproductive attentional and oculomotor capture by reward-related stimuli[END_REF][START_REF] Munneke | Reward can modulate attentional capture, independent of top-down set[END_REF][START_REF] Munneke | Distractors associated with reward break through the focus of attention[END_REF][START_REF] Pearson | Cognitive control and counterproductive oculomotor capture by reward-related stimuli[END_REF][START_REF] Roper | Value-Driven Attentional Capture in Adolescence[END_REF]. Typically, those studies involve a visual search task of a target singleton (e.g., a diamond) among irrelevant shapes (e.g., circles). As a classic manipulation, one of the irrelevant shapes can appeared in a color that constitutes the signal of the potential reward that could be obtained in the current trial (e.g., [START_REF] Failing | Oculomotor capture by stimuli that signal the availability of reward[END_REF][START_REF] Failing | Don't let it distract you: how information about the availability of reward affects attentional selection[END_REF][START_REF] Pelley | When goals conflict with values: Counterproductive attentional and oculomotor capture by reward-related stimuli[END_REF][START_REF] Pearson | Cognitive control and counterproductive oculomotor capture by reward-related stimuli[END_REF]. However, the distractor should not be considered as relevant because reward is only obtained in case of fast (i.e., below a given threshold) and correct response to the target, or when the first saccade is initiated toward the target [START_REF] Failing | Oculomotor capture by stimuli that signal the availability of reward[END_REF][START_REF] Pearson | Cognitive control and counterproductive oculomotor capture by reward-related stimuli[END_REF].

Otherwise, reward is not obtained. In this way, a strategy wherein distractor selection occurs voluntarily would be always clearly counterproductive to obtain the reward (Le [START_REF] Pelley | When goals conflict with values: Counterproductive attentional and oculomotor capture by reward-related stimuli[END_REF]. Indeed, such strategy would delay the observer's response time, or the first saccade would be initiated toward the distractor and not toward the target, leading to reward omission.

Nevertheless, the presence of this irrelevant reward-signaling stimulus typically increases reaction times and error rates, or triggers oculomotor capture. Therefore, in this context, any reward distractor processing is aimed to result from (overt and/or covert) attentional capture. Some studies [START_REF] Failing | Oculomotor capture by stimuli that signal the availability of reward[END_REF][START_REF] Pearson | Cognitive control and counterproductive oculomotor capture by reward-related stimuli[END_REF] have reported that oculomotor capture by the reward distractor occurred especially for saccades with short latencies (i.e., triggered involuntarily). Furthermore, those effects have been observed even if the reward distractor was displayed among other colored shapes and thus appeared as a non-salient distractor (e.g., [START_REF] Failing | Don't let it distract you: how information about the availability of reward affects attentional selection[END_REF]. Finally, the VDAC increased with the magnitude of the reward associated with the distractor, high-reward distractors leading to stronger capture than low-reward distractors [START_REF] Failing | Don't let it distract you: how information about the availability of reward affects attentional selection[END_REF][START_REF] Pelley | When goals conflict with values: Counterproductive attentional and oculomotor capture by reward-related stimuli[END_REF]. There are therefore compelling evidence for a direct influence of reward history in the guidance of attention.

Considering that VDAC is observed when reward stimuli are irrelevant, non-salient and when their selection is undoubtedly detrimental to the goal of obtaining an immediate reward, VDAC has been argued to reflect an irrepressible, automatic attentional mechanism [START_REF] Anderson | Value-driven attentional capture[END_REF][START_REF] Failing | Oculomotor capture by stimuli that signal the availability of reward[END_REF][START_REF] Failing | Exogenous visual orienting by reward[END_REF][START_REF] Hickey | Reward Changes Salience in Human Vision via the Anterior Cingulate[END_REF]Le Pelley et al., 2015;[START_REF] Munneke | Reward can modulate attentional capture, independent of top-down set[END_REF][START_REF] Munneke | Distractors associated with reward break through the focus of attention[END_REF]. However, it has been proposed that the extent to which irrelevant distractors are processed does not depend only on the participants' expectancies or intentions to ignore them, but also on the processing demands of the current task. Notably, the Load Theory [START_REF] Lavie | Perceptual load as a necessary condition for selective attention[END_REF][START_REF] Lavie | Distracted and confused?: Selective attention under load[END_REF][START_REF] Lavie | Attention, Distraction, and Cognitive Control Under Load[END_REF][START_REF] Lavie | Perceptual load as a major determinant of the locus of selection in visual attention[END_REF] posits that one of the factors determining whether an irrelevant distractor will be successfully ignored or counterproductively processed is the level of perceptual load involved in the current task [START_REF] Lavie | Distracted and confused?: Selective attention under load[END_REF][START_REF] Lavie | Attention, Distraction, and Cognitive Control Under Load[END_REF][START_REF] Murphy | Twenty years of load theory-Where are we now, and where should we go next[END_REF]. Situations of low perceptual load (e.g., tasks involving small search set or simple perceptual discriminations) would leave spare capacities that would inevitably "spill-over" to the processing of distractors. Conversely, when the task imposes a high perceptual load (e.g., involves large search set sizes or complex perceptual discriminations), it could exhaust available perceptual resources in processing the relevant stimuli, thus preventing the processing of irrelevant information. Even if the Load Theory has been criticized on several grounds (e.g., [START_REF] Eltiti | Selective target processing: perceptual load or distractor salience?[END_REF][START_REF] Tsal | Diluting the burden of load: Perceptual load effects are simply dilution effects[END_REF], a wealth of evidence suggests that perceptual load is a major determinant of selective attention (see [START_REF] Murphy | Twenty years of load theory-Where are we now, and where should we go next[END_REF] for a review). Reduced distractor processing under high perceptual load condition has been reported in various kind of laboratory paradigms (for reviews see: [START_REF] Lavie | Distracted and confused?: Selective attention under load[END_REF][START_REF] Lavie | Attention, Distraction, and Cognitive Control Under Load[END_REF][START_REF] Murphy | Twenty years of load theory-Where are we now, and where should we go next[END_REF] as well as in ecological studies [START_REF] Marciano | Perceptual load in central and peripheral regions and its effects on driving performance: Advertizing billboards[END_REF], 2015;[START_REF] Murphy | Load theory behind the wheel; perceptual and cognitive load effects[END_REF]. Besides, perceptual load has been frequently manipulated to test whether a specific processing is automatic, the premise of these studies being that when attention is exhausted in high-load conditions, any interference by the distractor is an indication of automatic processing (see [START_REF] Benoni | Can automaticity be verified utilizing a perceptual load manipulation[END_REF] for a discussion on automaticity within the framework of the Load Theory).

Several studies have revealed that the perceptual load of the task at hand could influence differentially the processing of neutral, emotionally positive or negative (mostly expressive faces) to be-ignored stimuli (e.g., [START_REF] Gupta | Only irrelevant sad but not happy faces are inhibited under high perceptual load[END_REF][START_REF] Pessoa | Neural processing of emotional faces requires attention[END_REF][START_REF] Silvert | Influence of attentional demands on the 29 processing of emotional facial expressions in the amygdala[END_REF][START_REF] Srinivasan | Emotion-Attention Interactions in Recognition Memory for Distractor Faces[END_REF]. However, to our knowledge, only one previous study has specifically addressed the question of whether attentional capture by reward-distractors could occur independently of the current task load (Experiment 3 in [START_REF] Gupta | Distracted by Pleasure: Effects of Positive Versus Negative Valence on Emotional Capture Under Load[END_REF]. In this study, participants first performed a betting game in order to associate neutral faces with gain or loss outcomes (the association phase). Then, participants ran an unrewarded letter search task (the test phase). On each trial, target (X or N) and non-target letters were arranged around an imaginary circle with one of the faces of the association phase displayed at its center. Perceptual load was manipulated by varying the heterogeneity of the non-target letters (Os exclusively in the low load condition; H, K, W, M, Z in the high load condition). As expected, both gain-and loss-faces produced a strong distraction effect (i.e., RTs were longer on distractor faces presenttrials rather than absent-trials) in the low load condition. But remarkably, whereas no significant distraction effect was observed for loss-faces distractors in the high load condition, it remained significant for gain-faces distractors. [START_REF] Gupta | Distracted by Pleasure: Effects of Positive Versus Negative Valence on Emotional Capture Under Load[END_REF] therefore concluded that distractors associated with gain (i.e., reward) can capture attention even when the processing of relevant stimuli consumes all available resources. However, the distractor faces were presented at the center of the screen where the observer's attention was already deployed at the beginning of a trial. Consequently, the results do not speak to the guidance of attention by reward distractors: as acknowledged by the authors, the greater distraction effect observed for reward-vs. lossdistractors under high-perceptual load could rather reflect a more difficult attentional disengagement from reward-faces centrally displayed.

Our aim was therefore to investigate more thoroughly the impact of the perceptual load of the task at hand on the distractive effect of reward distractors. Perceptual load was manipulated by varying the processing requirement of a go/no-go cue centrally presented (for similar load manipulation see: [START_REF] Chen | Zooming in on the cause of the perceptual load effect in the go/no-go paradigm[END_REF][START_REF] Lavie | Perceptual load as a necessary condition for selective attention[END_REF]. In comparison with related studies (e.g., [START_REF] Gupta | Distracted by Pleasure: Effects of Positive Versus Negative Valence on Emotional Capture Under Load[END_REF][START_REF] Gupta | Only irrelevant sad but not happy faces are inhibited under high perceptual load[END_REF], such a manipulation of perceptual load has the advantage to involve strictly identical displays across conditions and therefore to discard any influence of distractor relative salience between low and high load [START_REF] Murphy | Twenty years of load theory-Where are we now, and where should we go next[END_REF]. The go (or no-go) cue was defined by a simple feature (e.g., any black cue) in the lowload condition and by a feature conjunction (e.g., a black circle cue) in the high-load condition.

On "go" trials, the participants had to determine the identity of a black target letter (i.e., H or S), while ignoring a more peripheral colored distractor letter (also a H or a S) whose color (red or green) signaled either high-or low-reward outcomes in case of fast and accurate response to the target [START_REF] Bucker | Appetitive and aversive outcome associations modulate exogenous cueing[END_REF][START_REF] Failing | Oculomotor capture by stimuli that signal the availability of reward[END_REF][START_REF] Pelley | When goals conflict with values: Counterproductive attentional and oculomotor capture by reward-related stimuli[END_REF][START_REF] Munneke | Reward can modulate attentional capture, independent of top-down set[END_REF][START_REF] Munneke | Distractors associated with reward break through the focus of attention[END_REF][START_REF] Pearson | Cognitive control and counterproductive oculomotor capture by reward-related stimuli[END_REF]. Importantly, as recommended by some authors [START_REF] Gaspelin | Slippage theory and the flanker paradigm: An early-selection account of selective attention failures[END_REF][START_REF] Lachter | Forty-five years after Broadbent (1958): Still no identification without attention[END_REF][START_REF] Theeuwes | Attentional control during visual search: the effect of irrelevant singletons[END_REF], attentional capture by the distractor was measured by the size of the compatibility effect, that is, by the benefit in target identification performance when the distractor letter was compatible (i.e., identical) versus incompatible (i.e., different) with the target identity. The rationale here is that if attention is captured by the distractor, its identity should influence RTs to identify the target with RTs in the compatible condition should be consistently faster than RTs in the incompatible condition.

Behavioral evidence of attentional capture is an important debate in our field (e.g., [START_REF] Folk | Dissociating compatibility effects and distractor costs in the additional singleton paradigm[END_REF] but compatibility effect might be considered as the most reliable measure (for comparisons between different measures and attentional capture paradigms see [START_REF] Becker | Irrelevant Singletons in Pop-Out Search: Attentional Capture or Filtering Costs[END_REF].

Since both high-and low-reward distractors are visually salient (colored), they should both capture attention in the low perceptual load condition. We should therefore observe a significant compatibility effect for both types of distractors in the low load condition. However, if attentional capture by high-reward distractors is indeed automatic [START_REF] Anderson | Value-driven attentional capture[END_REF][START_REF] Failing | Oculomotor capture by stimuli that signal the availability of reward[END_REF][START_REF] Failing | Exogenous visual orienting by reward[END_REF][START_REF] Gupta | Distracted by Pleasure: Effects of Positive Versus Negative Valence on Emotional Capture Under Load[END_REF][START_REF] Hickey | Reward Changes Salience in Human Vision via the Anterior Cingulate[END_REF][START_REF] Pelley | When goals conflict with values: Counterproductive attentional and oculomotor capture by reward-related stimuli[END_REF][START_REF] Munneke | Reward can modulate attentional capture, independent of top-down set[END_REF][START_REF] Munneke | Distractors associated with reward break through the focus of attention[END_REF] we should observe a significant interaction between perceptual load, reward-distractors and compatibility effects. Indeed, in the high-load condition, the compatibility effect should be significantly more reduced for low-than highreward distractors.

METHOD Participants

Thirty-two volunteers1 (29 females; M = 21.9, SD = 2.6) from Université Clermont Auvergne participated in the experiment for course credit after giving their written informed consent. All were right-handed, reported normal or corrected-to-normal visual acuity and normal vision color, and were naïve as to the purpose of the experiment.

Apparatus

Participants were tested individually in a quiet room with constant ambient illumination, in front of a 14-inch VGA monitor (1024 × 1280 resolution, 60 Hz) at a distance of approximately 50 cm. The presentation of the stimuli, timing operations and data collection were controlled by E-Prime 2.0 software (Psychology Software Tools, Pittsburgh, PA, United States).

Stimuli and procedure

The experimental setting was similar to the one used by [START_REF] Chen | Zooming in on the cause of the perceptual load effect in the go/no-go paradigm[END_REF] Experiment 2). Each trial (Figure 1a) began with a black fixation cross (0.57° width x 0.57° height, expressed in degrees of visual angle) displayed at the center of a white screen for 500, 600 or 700 ms (randomly determined at run time). Then the search display containing the go/nogo cue (0.46° x 0.46°), the target (0.57° x 0.8°) and the distractor (0.8° x 1°) was presented until participant's response or until 1500 ms had elapsed. The go/no-go cue could be either a full (i.e., black) or empty (i.e., white with black border) disc or square (Figure 1b). Target and distractor letters (H or S; uppercase, Calibri font) were horizontally aligned with the cue at the center of the screen and both letters always appeared together on the same side of the cue (with the target immediately next to the cue and the distractor further apart). The cue-target distance was 1.4° (center-to-center) and cue-distractor distance was 2.8°. The target was always displayed in black, the distractors appeared equally often in red (RGB: 255, 0, 0) or green (RGB: 0, 159, 0).

Figure 1a

Stimuli and time-course of a trial with a color distractor (red H) signaling a highreward outcome (+10 pts) in case of correct and fast identification of the target (S). 1b Go/Nogo cues were defined by a simple feature (i.e., color) in the low load condition, or by a feature conjunction (i.e., color and shape) in the high-load condition. Features associated with a Go or No-go cues were counterbalanced across the participants.

In the low-load condition, the "go" cues were a black disc or a black square for half of the participants and a white disc or a white square for the other half. In the high-load condition, the "go" cues were a black disc or a white square for a half of the participants, and a white disc or a black square for the other half. On "go" trials participants were instructed to report the target letter identity (H or S) by pressing "2" or "5" on the numeric keypad (counterbalanced across participants) with their right forefinger and middle finger respectively. On "no-go" trials, they had to press the space bar with their left hand. At the beginning of the experiment, the participants were explicitly informed of the association between the color of the distractors and the reward magnitude. For half of the participants, red distractors acted as high-reward distractors that signaled the possibility to win 10 points, green distractors acted as low-reward distractors signaling the possibility to win 1 point. For the other half, the color/reward association was reversed. A reward was obtained on "go" and "no-go" trials for correct responses that were faster than the participant's latency limit (see below). Errors or correct responses that were slower than the participant's latency limit were not rewarded, but the participant was nonetheless told how much he/she could have won. Hence, two feedbacks were displayed after the participant's response (or after the search display time had elapsed). The first feedback concerned the participant's accuracy and was displayed for 500 ms ("correct", "error" or "missed when the participant did not respond). Then, a second feedback, displayed for 750 ms, informed the participant of the number of points he/she had earned ("+10 pts"; "+1 pt"; "+0 pt") or missed ("+10 pts missed" or "+1 pt missed"). Finally, a blank screen separated two consecutive trials for 500 ms. The participants aimed at maximizing their score to obtain an additional 10€ gift card. For that purpose, they had to obtain at least 2000 points, but the threshold was announced only at the end of the session in order to maintain their motivation constant.

The experiment was split into 8 experimental blocks of 48 trials. Within each block, half of the trials contained a high-reward distractor and the other half, a low-reward distractor. Twothirds of trials were "go" trials and the remaining third was "no-go" trials, equally distributed across high-and low-reward distractors trials. For half of the trials, the distractor was compatible with the target (i.e., same letter) and incompatible (i.e., different letter) for the other half. Targets and distractors appeared equally often on each side of the cue. The order of trials within each block was random.

Half of the participants started with four consecutive blocks of the low perceptual load condition before taking part to four consecutive blocks of the high perceptual load condition, and the order was counterbalanced for the other half of the participants. A small break was given between each block. Each perceptual load condition began with a training block of 48 trials. The third quartile of correct responses latencies within each training block defined the participant's latency limit for each condition. If the participant's accuracy was below 75%, he/she had to repeat the training phase. The participants were informed of their latency limit and informed that for subsequent trials they would earn a reward only for correct responses that were faster than this limit.

RESULTS

Reaction times (RTs) for correct responses on "go" trials were analyzed. For each participant, RTs plus/minus 2.5 absolute deviations around the median [START_REF] Leys | Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median[END_REF] were discarded. This resulted in the loss of less than 3% of the data. Two participants were excluded from statistical analysis because of error rates above 40% (on overall trials or on no-go trials).

We performed a 2 x 2 x 2 ANOVA on RTs with Load (low-load; high-load), distractor We performed a 2 x 2 x 2 ANOVA on error rates with Load (low-load; high-load), distractor Compatibility (compatible; incompatible) and Reward distractor (low-reward; highreward) as within-subject factors. As classically observed, we found a significant main effect of Load [F(1, 29) = 60.5, p < .001, ƞ²p = .68], the error rate being higher in the high load (M = 19.8%; SE = 2.2) than in the low load condition (M = 7.4%; SE = 1.2). The interaction between Load and Compatibility [F(1, 29) = 3, p = .083, ƞ²p = .1] corresponded to a larger, but non-significant, difference between incompatible and compatible distractor under low-[Mincomp -comp = 1.5 %; SE = .8; F(1, 29) = 3.3, p = .08] than high-load [M = .8 %; SE = 1; F(1, 29) = .58, p = .45]. Confirming the effect on incentive motivation obtained for RTs, the main effect of Reward was also significant [F(1, 29) = 14.1, p < .001, ƞ²p = .33] with fewer errors when the high-reward distractor (M = 10.8 %; SE = 1.6) rather than the low-reward distractor (M = 16.3%; SE = 1.8) was present. Finally, the interaction between Load and Reward also reached significance [F(1, 29) = 7, p = .013, ƞ²p = .19]. The error rates difference between low-and high-reward distractor (Table 1) was significant in the high-[Mdiff = 9.4%; SE = 2.8; F(1, 29) = 11.5, p = .002, ƞ²p = .28] but not in the low-load condition [Mdiff = 1.6%; SE = 1;

F(1, 29) = 2

DISCUSSION

The present study investigated the influence of the processing demands of the task at hand on the extent to which irrelevant reward stimuli can capture attention. The current results revealed that the low-reward distractor interference (i.e., compatibility effect) observed in the low-load condition was significantly reduced in the high-load condition, replicating wellknown effects of load on salient but unrewarded stimuli (e.g., Experiment 2 in Chen & Cave, 2016 for a similar experimental design). In sharp contrast, the distractor interference produced by high-reward stimuli was not statistically different in the two load conditions. Therefore, it seems that attentional capture by high-reward distractors can survive strong tests of attention that involve task conditions of high perceptual load.

As mentioned above, one previous study [START_REF] Gupta | Distracted by Pleasure: Effects of Positive Versus Negative Valence on Emotional Capture Under Load[END_REF] already suggested that the processing of reward-distractors (and more generally positive valence stimuli) could modulate the effects of perceptual load. However, this study is not conclusive regarding attentional capture by reward-distractors (that is, involuntary allocation of spatial attention) because the distractors were presented at the center of the screen, where the observer's attention was already deployed at the beginning of a trial. As acknowledged by the authors themselves, the paradigm could not differentiate between the effects of reward on capturing attention and those on the latency of holding and captivating attention. In contrast, in the current study, the rewarddistractors were always presented peripherally to the central cue that had to be attended at first.

Moreover, response compatibility effects, rather than simple reaction times, were taken as an auxiliary measure for attentional capture [START_REF] Becker | Irrelevant Singletons in Pop-Out Search: Attentional Capture or Filtering Costs[END_REF][START_REF] Theeuwes | Attentional control during visual search: the effect of irrelevant singletons[END_REF].

Compatibility effect is often cited as the most reliable measure for detecting occurrences of attentional capture (e.g., [START_REF] Becker | Irrelevant Singletons in Pop-Out Search: Attentional Capture or Filtering Costs[END_REF][START_REF] Gaspelin | Slippage theory and the flanker paradigm: An early-selection account of selective attention failures[END_REF][START_REF] Lachter | Forty-five years after Broadbent (1958): Still no identification without attention[END_REF]. We are therefore confident that the present study constitute a more accurate evaluation of attentional capture by reward-distractors under load.

In the framework of the Load Theory [START_REF] Lavie | Distracted and confused?: Selective attention under load[END_REF][START_REF] Lavie | Attention, Distraction, and Cognitive Control Under Load[END_REF][START_REF] Murphy | Twenty years of load theory-Where are we now, and where should we go next[END_REF] the current results suggest that high-reward distractors have the ability to be processed and interfere

with the task at hand even when few, or maybe even no, attentional resources are available for irrelevant processes. As such, the present study provides new arguments in favor of VDAC automaticity: attentional capture by reward stimuli appears to be automatic in the stronger sense of the term, occurring in full independence from top-down factors, such as intentionality, volition, task instructions, but also attention (see [START_REF] Pessoa | To what extent are emotional visual stimuli processed without attention and awareness?[END_REF]. However, as already mentioned, the Load Theory has been criticized (see [START_REF] Murphy | Twenty years of load theory-Where are we now, and where should we go next[END_REF], for a review) and alternative interpretations of the results could be put forward. Mainly, it has been proposed that, in many previous studies in which load was manipulated by varying the number of items in the display, perceptual load may have affected interference not by influencing capacity, but rather by diluting the distractor among additional competing items [START_REF] Tsal | Diluting the burden of load: Perceptual load effects are simply dilution effects[END_REF] or by changing the relative saliency of the distractors [START_REF] Eltiti | Selective target processing: perceptual load or distractor salience?[END_REF]. Note that these criticisms cannot apply to the task we used here since the display was strictly identical across conditions and load was manipulated through the difficulty in discriminating a (central) go/no-go cue, a manipulation which does not affect the visual saliency of the (peripheral) distractors.

Still, we cannot completely exclude that the processing of high-reward distractors in the high-load condition relied on some residual resources that were not fully consumed by the load manipulation and that a much greater load would have abolished this processing. In fact, this criticism could be formulated against pretty much all the studies involving load manipulations to verify automaticity [START_REF] Benoni | Can automaticity be verified utilizing a perceptual load manipulation[END_REF]; but see [START_REF] Maquestiaux | Ideomotor compatibility enables automatic response selection[END_REF] for a recent demonstration that a visual stimulus can indeed be proceed automatically in the strongest sense) but it is certainly not at odds with the claim that the processing of reward distractors requires less resources than the processing of neutral ones. In that sense, VDAC would be "more automatic" than attentional capture by salient stimuli [START_REF] Moors | Automaticity: A Theoretical and Conceptual Analysis[END_REF]. Therefore, our study encourage Load Theory [START_REF] Lavie | Distracted and confused?: Selective attention under load[END_REF][START_REF] Lavie | Attention, Distraction, and Cognitive Control Under Load[END_REF][START_REF] Murphy | Twenty years of load theory-Where are we now, and where should we go next[END_REF] and related model [START_REF] Neokleous | Modeling the Effects of Perceptual Load: Saliency, Competitive Interactions, and Top-Down Biases[END_REF] to give greater consideration to reward history in order to provide a better comprehension of distractor processing under a large range of situations and to go beyond bottom-up, salience-based distraction.

Additionally, in this kind of task, the differential degree of distractor processing may also depend on how broadly/narrowly the attention is focused on the cue rather on the availability of the attentional resources [START_REF] Belopolsky | No capture outside the attentional window[END_REF][START_REF] Chen | Zooming in on the cause of the perceptual load effect in the go/no-go paradigm[END_REF][START_REF] Theeuwes | Top-down and bottom-up control of visual selection[END_REF]. More precisely, low (resp. high) perceptual load might rather widen (resp.

narrow) the attentional zoom (or attentional window) than consuming more or less perceptual resources. In this framework, in the high-load condition of the current study, low-reward distractors may have failed to capture attention because they stood outside the (narrow) attentional window of the observer. This has been previously observed in similar designs with unrewarded salient colored distractors (e.g., [START_REF] Theeuwes | Top-down search strategies cannot override attentional capture[END_REF]. Anyhow, our data indicate that, in contrast, high-reward salient distractors standing outside the attentional window could produce interference by capturing the observer's attention. Interestingly, it has been demonstrated that particularly salient stimuli, such as abrupt onsets, could break through the attentional window [START_REF] Gaspelin | Breaking through the attentional window: Capture by abrupt onsets versus color singletons[END_REF]. Therefore, the current results are consistent with the idea that reward associated stimuli are processed at a perceptual level as if they were more perceptually salient than unrewarded ones, or, in other words, that reward may boost their representation above and beyond their physical salience [START_REF] Anderson | On the relationship between value-driven and stimulusdriven attentional capture[END_REF]. They also fit well with previous findings showing that reward-associated stimuli can break through the inhibitory region around attentional focus (Wang et al., 2014).

One potential limitation of the current study is that the longest RTs and highest error rates were observed when no compatibility effects (i.e., no attentional capture) occurred (i.e., in the high load, low reward condition). This could indicate that attentional capture by the distractor may in fact facilitate the response to the target (in particular when the target and the distractor are compatible). Also, overall, the presence of high-rather than low-reward distractors was associated with faster target selection and improved accuracy. In prior studies investigating the effects of monetary reward on attention with visual search tasks, the differences between high-and low-reward distractors were classically in the opposite direction (e.g., [START_REF] Pelley | When goals conflict with values: Counterproductive attentional and oculomotor capture by reward-related stimuli[END_REF]. The current pattern of results could therefore cast some doubts on whether reward stimuli were actually processed as distractors. To reiterate, we reasoned that attending to reward-predictive distractors would, if anything, hinder participants' performance and hence the payoff they achieved. Indeed, the colored letters predicted whether a reward could be obtained, but it was the response to the black target letters that determined whether a reward was actually delivered. Therefore, the best available strategy was logically to try to ignore the reward-predictive distractors, and the data suggest that observers have actually adopted this strategy. Indeed, if they had deliberately chosen, as a global strategy, to attend to the rewardsignaling color, the compatibility effect should have been preserved for the low-reward distractor in the high-load condition too. The current pattern of results could alternatively be explained by the fact that, 1) the target appeared unpredictably on the left or right side of the fixation point (just as in visual search tasks) but (contrary to visual search tasks) the target and the distractor always appeared in immediate proximity, on the same side of the screen, and 2) spatial shift of attention could be trigger earlier in time by high reward stimuli [START_REF] Kiss | Reward priority of visual target singletons modulates event-related potential signatures of attentional selection[END_REF] even when irrelevant [START_REF] Donohue | The Rapid Capture of Attention by Rewarded Objects[END_REF]. Thus, the observer's attention might be more rapidly oriented toward the screen side containing the high-reward distractor and the target, leading to faster reaction times. Note also that improved accuracy in reporting the target in the presence of distractors associated with high-rather than low-reward has already been reported [START_REF] Anderson | Social reward shapes attentional biases[END_REF]. The authors had proposed that high-reward distractors might trigger greater recruitment of goal-directed mechanisms and enhanced incentive motivation to actually perform the task better and obtain the reward outcome. This view also fit a reinforcement learning framework (for a review in connection with psychology see: [START_REF] Sutton | Reinforcement learning: An introduction[END_REF], with participants maximizing the accumulated reward not only at the trial level, but also at the experiment level. After a few trials, participants may estimate the cost-benefit tradeoff to answer correctly and fast enough to steadily obtain the reward. Indeed, 10 low-reward trials were required to compensate for a single failed high-reward trial (1 vs. 10 points awarded).

Under low load, processing all information with limited effort is possible, leading to the observed compatibility effect, low response times and error rates, moderated by the reward level (high vs. low). Under high load, trying to be fast and accurate for every single trial will lead to fatigue, and therefore errors or misses on high-reward trials, thus reducing the cumulative reward. As a consequence, participants could adapt their behavior to maximize the accumulated reward at the experiment level. In this way, when a low-reward distractor captured attention (low-load) or when no capture occurred (high-load), participants might simply relax their attention, leading to the highest mean response times and error rates. In contrast, automatic attentional capture by high-reward distractor may promote a greater effort investment on those trials, leading to lower response times and error rates. As this strategy would be particularly relevant in situation of high-level fatigue, it could explain why high-reward distractors triggered lower response times and error rates particularly in the high-load condition. According to this strategy, it is possible that the high-reward distractor color was part of the observers' attentional control settings. Indeed, the contingent capture hypothesis [START_REF] Folk | Involuntary covert orienting is contingent on attentional control settings[END_REF][START_REF] Folk | The structure of attentional control: Contingent attentional capture by apparent motion, abrupt onset, and color[END_REF] claims that involuntary attentional shifts occur only where there is a contingency between the properties of the eliciting event and the properties required for task performance (but for one exception see [START_REF] Folk | Unexpected abrupt onsets can override a top-down set for color[END_REF]. Therefore, attentional capture by high-reward distractor could have survived perceptual load manipulation because, although irrelevant, they were contingent with task goals (maximizing reward without too much fatigue). This proposal is consistent with the results of a study by [START_REF] Belke | Top-down effects of semantic knowledge in visual search are modulated by cognitive but not perceptual load[END_REF], in which the interference produced by semantically related distractors (i.e., matching the observer's attentional set) was not modulated by perceptual load manipulations (but see discussion in [START_REF] Cosman | Perceptual load modulates attentional capture by abrupt onsets[END_REF]. Further research are now needed to determine more precisely how perceptual load and distractor contingency could interact with reward. Anyhow, this would not call into question the conclusion that the processing of high-reward stimuli persists in conditions in which the processing of low-reward ones is suppressed.

Furthermore, we argue that interactions between reward and load might find strong applied perspectives. For example, the magnitude of attentional bias arising from learned associations between visual stimuli and monetary reward has been linked to psychopathology (addiction, depression, social anxiety; see [START_REF] Anderson | The attention habit: How reward learning shapes attentional selection[END_REF]. Distraction is also identified as one of the determinants of car accidents with distractors standing from outside (e.g., advertising billboards: [START_REF] Belyusar | A field study on the effects of digital billboards on glance behavior during highway driving[END_REF][START_REF] Chattington | Investigating driver distraction: the effects of video and static advertising[END_REF][START_REF] Crundall | Attraction and distraction of attention with roadside advertisements[END_REF][START_REF] Decker | The impact of billboards on driver visual behavior: a systematic literature review[END_REF] or inside the vehicle (e.g., by irrelevant but driving-related information: [START_REF] Arexis | Attentional capture in driving displays[END_REF]or by "infotainment" systems: Strayer, 2015). However, those distractors, which can very often be regarded as rewarding, have usually been considered under the scope of bottom-up salience alone [START_REF] Bakiri | Distraction and driving: Results from a case-control responsibility study of traffic crash injured drivers interviewed at the emergency room[END_REF][START_REF] Crundall | Attraction and distraction of attention with roadside advertisements[END_REF][START_REF] Klauer | The Impact of Driver Inattention On Near Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data[END_REF]. The Load Theory certainly offers a relevant and fruitful framework when investigating distraction on driving simulator [START_REF] Marciano | Perceptual load in central and peripheral regions and its effects on driving performance: Advertizing billboards[END_REF], 2015;[START_REF] Murphy | Load theory behind the wheel; perceptual and cognitive load effects[END_REF]) but distractor's reward history has not been considered yet in those studies.

To conclude, the present study confirms the particular role of reward-related stimuli in attentional selection and the need to integrate reward history effects into attentional models [START_REF] Awh | Top-down versus bottom-up attentional control: A failed theoretical dichotomy[END_REF]. Our study and a handful of others suggest that attentional capture by rewarded or positive distractors could occur when few, or maybe even no, attentional resources are available for their processing (see Gupta, 2019 for a review). However, the question of the automaticity of attentional allocation to negative stimuli (which has generated much more studies so far) is still a matter of vigorous debate (see [START_REF] Victeur | Attentional allocation to task-irrelevant fearful[END_REF]. Besides, when considering "valued" stimuli altogether, it is still unclear whether it is the motivational saliency (i.e., high vs. low) or the valence (i.e., positive/reward vs. negative/punishment) of a stimulus which is the major determinant of attentional capture (e.g., [START_REF] Gupta | Priming by motivationally salient distractors produces hemispheric asymmetries in visual processing[END_REF]. Therefore, one could regret that current attentional models usually focus almost exclusively on either negative/threat-related or positive/rewarded stimuli, or put them together in the same "valuedriven" category [START_REF] Failing | Don't let it distract you: how information about the availability of reward affects attentional selection[END_REF][START_REF] Pourtois | Brain mechanisms for emotional influences on perception and attention: What is magic and what is not[END_REF][START_REF] Theeuwes | Visual Selection: Usually Fast and Automatic; Seldom Slow and Volitional[END_REF]. Thus, future research are certainly needed to contrast more precisely attentional effects produced by various types of "valued" distractors, and integrate them into a comprehensive model of the interactions between motivation, emotion and load in attentional capture.

  Compatibility (compatible; incompatible) and Reward distractor (low-reward; high-reward) as within-subject factors 2 . The main effect of Load was significant [F(1, 29) = 252.1, p < .001, ƞ²p = .9] with faster RTs in the low-load (M = 677 ms; SE = 16) than in the high-load condition (M = 867 ms; SE = 20), as usually observed. The main effect of Compatibility was significant [F(1, 29) = 11.6, p = .002, ƞ²p = .28] with slower RTs for incompatible (M = 778 ms; SE = 17) than compatible distractors (M = 766 ms; SE = 17). Importantly, the Load x Compatibility interaction [F(1, 29) = 5.1, p = .032, ƞ²p = .15] revealed successful load manipulation with larger compatibility effect under low-[Mincomp -comp = 22 ms; SE = 5; t(29) = 4.3, p < .001] than highload [M = 2 ms; SE = 6; t(29) < 1, p = .74]. The main effect of Reward was also statistically significant [F(1, 29) = 52, p < .001, ƞ²p = .64] with slower RTs for low-(M = 796 ms; SE = 18) than high-reward distractors (M = 748 ms; SE = 16). Moreover the Load x Reward interaction was also significant [F(1, 29) = 8.6, p = .007, ƞ²p = .29] showing a larger difference between reward distractors (low-minus high-reward) under high-(Mdiff = 65 ms; SE = 10) rather than low-load condition (M = 31 ms; SE = 7). The interaction between Reward and Compatibility did not reached significance [F(1, 29) = 1.2, p = .28] but, as we hypothesized, the interaction between Load, Reward and Compatibility did [F(1, 29) = 4.4, p = .046, ƞ²p = .13]. As shown on Figure 2, the compatibility effect in the low-reward distractor condition significantly decreased [F(1,29) = 8.3, p = .007, ƞ²p = .22] from low-(M = 26 ms; SE = 6) to high-load (M = -7 ms; SE = 7). However, in the high-reward distractor condition, the compatibility effect observed in low-(M = 17 ms; SE = 6) and high-load (M = 11 ms; SE = 8) did not differ significantly [F(1,29) = .4, p = .54].

Figure 2

 2 Figure 2 Compatibility Effect (in ms) for low-reward and high-reward distractors under low and high perceptual load. Error bars represent standard errors (SE). * p < .05, ** p < .01, ns: non-significant.

  .5, p = .13]. None of the other main effects and interactions reached significance [Fs(1, 29) < .25, ps > .62].

Table 1 .

 1 Error rates (%) and standard errors for each condition of Load and Reward distractor.

		Low-reward distractor	High-reward distractor
	Low-load (feature search)	8.2 (1.3)	6.6 (1.2)
	High-load (conjunction search)	24.5 (2.8)	15 (2.4)

The sample size was determined in order to be consistent and large enough to replicate the main effects and interactions reported in previous studies (e.g.,Gupta et al.,

2016). However, our study includes a three-way interaction that has not been previously tested in the literature. Therefore, we increased our sample size to minimize the risk of not observing a three-way interaction associated to a relatively small population effect size.

Controlling for color-reward association or bloc order as between-subject factors did not change the pattern of results. Thus those factors have been removed from the current analyze for clarity and simplicity of the reading. This reasoning was applied for RTs and error rates.