The demographic characteristics, clinical, and laboratory findings of the patients are given in Table 1.

The patients were middle-aged with an average age of 41.4 years and with a slight predominance of the male gender.

In the evaluation of the creatinine and GFR lev- els, we observed that the patients did not develop end- stage renal disease but had overt proteinuria.


Table 2 shows the comparison of the patients based on the presence of fibrosis.

The elastography measurements in the transverse sections revealed a kidney stiffness of 12.83 ± 9.80 kPa in the fibrosis group and 7.76 ± 5.16 kPa in the non-fibrosis group (n ¼ 51), exhibiting a signifi- cant difference between the groups (p ¼ .046).

The whole cohort had a mean total histological score of 4.49 ± 3.3, a glomerular score of 2.64 ± 1.85, a tubular score of 1.45 ± 1.48, and a vascular score of 0.22 ± 0.25.


The total histological score was found to be positively correlated with age (r ¼ 0.428, p ¼ .001), creatinine level (r ¼ 0.759, p < .001), global sclerosis percentage (r ¼ 0.874, p < .001), and proteinuria (r ¼ 0.316, p ¼ .017), whereas it showed a negative correlation with GFR (r ¼ -0.537, p < .001).

No correlation was observed between the transverse elastography measurements and the total histological score (r ¼ 0.006, p ¼ .967) (Table 3).


We also performed a ROC curve analysis and calcu- lated the threshold value for the diagnosis of fibrosis as 6.50 kPa.

Accordingly, we calculated the relevant values for the sensitivity (50%), specificity (62%), positive pre- dictive value (13%), and negative predictive value (91%).


The prevalence of end-stage chronic kidney disease is increasing all around the world [3].

Although kidney length is a simple parameter that shows kidney func- tions, it has been recently discovered that cortical thick- ness measurement has a stronger relationship with kidney functions [12,13].

Hoi et al. detected a strong correlation between cortical thickness and kidney func- tion [14].


The fibrotic process, which progresses due to the formation of interstitial fibrosis or glomerulosclero- sis, accelerates the progression to chronic kidney dis- ease.

Despite its invasive nature and complications, kidney biopsy is still the gold standard in detecting fibrosis, since recently studied ideal noninvasive markers, such as elastography, is limited [15].


In their study, Moon et al. [ 16] evaluated the tissue stiffness by elastography, and the kidney dimensions, resistive index, and echogenicity by ultrasonography.

The authors divided the 38 kidneys of 19 rabbits into three subgroups and found a correlation between the degree of fibrosis and renal tissue stiffness, however, they observed no difference among the groups in terms of resistive index and other sonographic parameters.

Unlike this study, Dalkiran et al. recommended the use of the resistive index in the differentiation of atrophy and hypoplasia in their study of 63 patients [17].

In another study of 202 patients with chronic renal failure, Hanamura et al. showed that the resistive index increased in cases of glomerulosclerosis, arteriolosclero- sis, and tubulointerstitial damage [18].


Although the SSI elastography method is the most ideal elastographic method, different results have been reported in differ- ent studies.

Derieppe et al. investigated the relationship between the cortical tissue elasticity determined by the SSI elastography method and the histopathological development of fibrosis in the kidney in their experi- mental study on a mouse model and detected a rela- tionship between impaired kidney function and increased tissue elasticity [19].

Contrary to this finding, Desvignes et al. found no relationship between elasti- city and fibrosis in their study of 95 children and ado- lescents, 31 of whom were transplant recipients [20].


In their study of 177 patients, Jenkins et al. found a significant relationship between the global and seg- mental sclerosis percentages and interstitial fibrosis, and between the global and segmental sclerosis per- centages and serum creatinine level [21].

Turgutalp et al. found a correlation between interstitial fibrosis and elastography in a study involving 30 patients with IgA nephropathy and 32 healthy controls [10].

Jenkins et al. 's findings are similar to those of our study [21].

In our study, we observed that the elastography trans- verse measurement value was significantly different in the group with fibrosis than the group without fibrosis.


In a study of 76 patients with chronic kidney disease, Guanghe et al. measured tissue elasticity quantitatively with the ARFI (acoustic radiation force impulse) imaging technique and found a significant difference between the groups with and without fibrosis [22].

Our study comprised of patients with proteinuria and a kidney biopsy justification with a mean GFR greater than 60, which differs from previous studies owing to the lack of patients with end-stage chronic kidney disease and extensive fibrosis.


In another study by Iyama et al., the authors found no correlation between kidney elasticity and interstitial fibrosis and between kidney elasticity and glomerular sclerosis [23].

Similar to our study, Hu et al. found a correlation between elastography meas- urements and histological score, and between elastog- raphy measurements and creatinine level in their study on 162 chronic kidney disease patients and 32 healthy patients [11].

In another study on 75 patients with chronic kidney disease, Leong et al. found that a kidney tissue stiffness above 5.81% kPa was associated with glomerular sclerosis, tubular atrophy, and interstitial fibrosis [24].


Because of the high negative predictive value, we suggest that elastography should mainly be used as an exclusion test for the presence of fibrosis.

However, we want to point out that because of the low number of patients with fibrosis our results should be confirmed in larger groups.

Indeed, the most important limitation of our study was the small number of patients in the fibrosis group, since patients with proteinuria with new onset of chronic kidney disease or developing acute kidney disease rather than the end-stage chronic kidney disease group were included in the study.

Despite these limitations, a significant relationship was observed between elastography measurements and fibrosis.

If we included the patients with end-stage chronic kidney disease in whom fibrosis is more prevalent, the signifi- cant relationship could have been established more strongly.

Further studies with more chronic kidney dis- ease patients are needed.


In conclusion, we found a significant relationship between the kidney shear wave elastography measure- ments and the fibrosis detected in kidney biopsy.

The prevalence of chronic kidney disease and consequent end-stage chronic kidney disease is increasing gradually and has become an important public health problem due to its increasing prevalence and morbidity.

Since fibrosis is an important risk factor for the progression of chronic kidney disease, we recommend kidney elastog- raphy as a key noninvasive method to evaluate fibrosis.


Dear Editor,


We read with great interest the recently published article by Islamoglu MS et al. [ 1] entitled ‘Kidney tissue elastography and interstitial fibrosis observed in kidney biopsy’.

The study aimed to investigate the relationship between tissue stiffness detected in shear wave elastography (SWE) and interstitial fibrosis observed in kidney biopsy.

The authors propose a significant relationship between the SWE measurements and interstitial fibrosis.

Although we appreciate the authors for their hard work on the construction of a study to enhance our understanding of the possible association of renal fibrosis with renal stiffness derived from SWE, some methodological notes should be pointed out to make this study more reasonable and practical, at least theoretically.


First, the authors pointed out that the SWE procedure was performed using a device (Aixplorer; SuperSonic Imagine, Aix-en-Provence, France).

However, to our knowledge, the images (Figures 1 and 2) [1] presented in this study were not derived from Aix-en-Provence Ultrasound system [2].

The authors need to clarify the elastography system used as the accuracy of the results could be affected by different elastic imaging systems [3].


Second, the SWE values were obtained from the inferior pole of the kidney in this study.

However, due to the nature of renal anisotropy, the difference of elastic measurements between renal poles and mid-region was significant, which was related to the angle between the ultrasound beam and the cortex [4].

The success rate of measurements in the renal poles was low compared with measurements in the mid-portion of the renal parenchyma and suggested that the renal poles should be avoided during measurements to ensure good reproducibility [5].


Third, in this study, no effective measures were taken to control confounding factors during SWE procedure.

Patients need to be instructed to hold their breath in order to minimize motion artifacts when taking elastic measurements.

Additionally, patients were requested to empty their bladder prior to SWE examination to avoid the influence induced from the filling renal pelvis.


Due to the lack of rigorous methodology, the conclusion of this study may not be reliable in fact.

Recently, a study conducted by Chen Z et al. had demonstrated that the SWE derived elastic values reduced as pathology grade of renal fibrosis or grade of vessel wall thickening progresses in patients with CKD, which may be attributed to renal hypo-perfusion rather than tubulo-interstitial fibrosis progression [2].

Another study performed by Guven AT et al. also shown that magnetic resonance elastography-derived stiffness values were lower in patients with chronic injury [6].

The authors need to further explain why their findings were inconsistent with the above studies.


It’s because of the nature of renal anisotropy which may affect the application of SWE in the evaluation of renal fibrosis, a more scientific and rigorous experimental protocol needs to be conducted to unveil the true relationship between SWE-derived stiffness and renal fibrosis.


We read the letter of Chen et al. with interest and we thank the authors for pointing out an inadvertent error in our manuscript [1].

We noticed that we have sent the wrong figures which belong to our another study on elastography.

Therefore, we are sending you the figures that we have taken with the Aixplorer (SuperSonic Imagin, Aix-en-Provence, France) brand device which we used in our study [2].

No changes are needed in the figure legends.


Chen et al. made comments about the methodology of our manuscript and pointed out different results from the literature.

Elastography is a new tool and standardization of methodology will add to its clinical applications.

Our study and this letter at least raised attention to this issue.

We think that constructive criticism raised by the authors will be a guide for further studies on this subject.


Nicotine replacement therapy (NRT) helps reduce the nicotine withdrawal symptoms experienced by people when they quit smoking.

However, no NRT product currently available matches the nicotine concentrations and rapid delivery achieved with smoking.

In contrast, contemporary nicotine e-cigarettes enable users to obtain nicotine as rapidly, and potentially in as high concen- trations, as that from cigarettes.

1 E-cigarettes, mostly with nicotine, are used by smokers in many countries as an aid to quit smoking.

2 To date, seven clinical trials 3-9 (total N=8222; range n=68 to 6006) have investigated the efficacy and effectiveness of e-cigarettes for smoking cessation and overall suggest a net benefit.

However, only four trials (N=7849; n=300 to 6006) had 6-month abstinence as a primary outcome 3,4,8,9 and only two trials (total N=6892) 8,9 tested a second-generation e-cigarette (capable of delivering nicotine more effectively than first-generation devices).


56 www.thelancet.com/respiratory Vol 8 January 2020 lished protocol describes procedures in detail.

11 In brief, people were eligible if they were living in New Zealand, were aged 18 years or older, smoked tobacco (amount not specified), were motivated to quit in the next 2 weeks, were able to provide verbal consent, were prepared to use any of the trial treatments, and had access to a telephone.

We excluded pregnant or breastfeeding women, people who had used an e-cigarette for smoking cessation for more than 1 week anytime in the past year, people currently using smoking cessation medication, people enrolled in another cessation programme or study, or who self-reported a history of severe allergies, poorly controlled asthma, or a cardiovascular event in the 2 weeks before enrolment.

Only one participant per household was permitted.


We recruited participants using national media advertising and contacted them via text message or phone after registration of interest online.

Potential participants were called by a researcher to obtain verbal consent and undertake screening.

The Northern A Health and Disability Ethics Committee approved the study (15/NTA/123) and the Standing Committee on Therapeutic Trials approved the use of nicotine e-cigarettes.


We did a parallel group, community-based, pragmatic, three-arm, randomised trial in New Zealand.

The pub- Research in context


The Cochrane review of e-cigarettes for smoking cessation sought studies published between 2004 and January, 2016.

Two trials (N=953) were identified that measured smoking abstinence rates after 6 months or more, and both tested ad libitum use of first-generation e-cigarettes.

Combined data from the trials showed that using a nicotine e-cigarette significantly increased smoking abstinence at 6 months compared with using a nicotine-free e-cigarette, although absolute quit rates were low (9% vs 4%).

No serious adverse events were reported.

Confidence in the findings was low given the "small number of trials, low event rates and wide confidence intervals around the estimates".


We sought to identify e-cigarette trials that measured smoking abstinence rates at 6 months or more, published between Jan 1, 2016, and June 30, 2019.

We searched MEDLINE, Embase, and PsycINFO using the same search terms as the Cochrane Review (e-cig$ OR electr$ cigar$ OR electronic nicotine OR [vape or vaper or vapers or vaping]) plus "Randomized Controlled Trial" (Publication Type).

We identified 48 articles, of which two were trials.


The first trial (N=54 US companies, 6006 employees and their spouses) tested the effectiveness of four smoking cessation interventions (involving cessation medications, nicotine e-cigarettes, and financial rewards) against usual care (behavioural support).

The verified continuous smoking abstinence rate at 6 months in the usual care plus e-cigarette group was higher, but not significantly different, to that observed in the usual care plus cessation medication group (1·0% vs 0·5%) or usual care alone (1·0% vs 0·1%).


The second trial, done within the UK national stop smoking services, tested the effectiveness of second-generation e-cigarettes plus moderate level face-to-face behavioural support for smoking cessation (N=886).

Self-reported continuous smoking abstinence rates at 6 months were higher in the 18 mg nicotine e-cigarette group (35·4%) than in those allocated 3 months of nicotine replacement therapy (NRT) (25·1%).


Our study adds to the scarce trial evidence base on e-cigarettes as a smoking cessation tool and, to our knowledge, is the first to investigate the effectiveness and safety of combining nicotine patches and second-generation e-cigarettes (with and without nicotine) on smoking abstinence.

We did the trial in a country with strong tobacco control measures, where advertising is restricted, use of e-cigarettes is low, and there was a ban on the sale of nicotine e-cigarettes.

The findings may therefore be generalisable to countries with similar policy backgrounds.

For example, Canada, Costa Rica, Ecuador, Japan, and Mexico had similar advertising restrictions on e-cigarettes at the time of the study and Australia, Canada, Costa Rica, Jamaica, Japan, Malaysia, Mexico, and Switzerland prohibited the sale of nicotine e-cigarettes.

The research questions addressed in our trial have informed tobacco control policy being developed by the New Zealand Ministry of Health.


All five published trials on e-cigarettes for smoking cessation are pragmatic in design, suggesting that under real-world conditions, e-cigarettes (with and without nicotine) could help some people quit smoking but the tobacco control policy environment within which they are available might affect cessation rates.

E-cigarettes should be offered as one of the many smoking cessation aids available to people wanting to quit, but do not appear to be a solution for all.

People using e-cigarettes should be encouraged to fully switch away from tobacco to e-cigarettes, with the aim of eventually also stopping vaping (if possible) given the lack of any long-term safety data for these devices.

Future e-cigarette trials should consider carefully which comparator intervention(s) to use given the high likelihood of differential loss to follow-up (or withdrawal) when only usual care or NRT is offered.


The study statistician (VP) prepared the computer- generated randomisation sequence.

Eligible participants were randomly allocated by computer to patches, patches plus a nicotine e-cigarette, or patches plus a nicotine-free e-cigarette in a 1:4:4 ratio using stratified block ran- domisation (block size of nine).

An unequal allocation ratio was used in an effort to minimise loss of power from withdrawal or cross-over.

Randomisation was stratified by ethnicity: Māori (who comprise 15% of the NZ popu lation 17 and among whom 37% were current smokers in 2015 compared with 16% of the general population) 12 versus non-Māori.

Participants and researchers collecting outcome data were masked to the nicotine content of the e-liquid.


Moderate-intensity behavioural support was available for all participants immediately after randomisation, then once a week for 6 weeks.

This support con sisted of 10-15 min of withdrawal-oriented behavioural support 18 and advice on using their allocated treatment, delivered proactively over the phone by researchers who had received standardised training in delivery of such support, including motivational interviewing principles.


Participants received a 14-week supply of their allo cated treatment (provided at no cost and couriered in one shipment).

A 21 mg, 24 h nicotine patch (Habitrol) was selected because it is the standard patch strength used in New Zealand.

The choice of e-cigarette type and brand (plus e-liquid, nicotine strength, and flavours) was informed by our consultation with New Zealand e-cigarette retailers.


We selected a second-generation eVOD (Kangertech, Shenzhen GuangDong, China) starter kit, containing two batteries, two refillable tanks, one charging kit, one carry case, and five (2·2 mL, 1·8 OHM) atomisers.

Participants had a choice of one of two tobacco e-liquid flavours-one often preferred by those who usually smoke roll-your-own tobacco and one usually preferred by those who smoke factory-made cigarettes.

Irrespective of the type of tobacco used by the participant at baseline, they could choose the e-liquid flavour they wanted.


The flavours selected for the trial were those recommended by the vaping retailer NZVAPOR (www.nzvapor.com) based on their experience with new e-cigarette users and unpublished sales data.

The e-liquid had a 60:40 PG to VG ratio, a masked nicotine content of 0 mg/mL or 18 mg/mL, and was provided in childproof 30 mL brown bottles (four bottles per participant).

Independent testing of each batch was done by NicoTar (Roswell Park Cancer Institute, Buffalo, NY, USA).

We considered variability of plus or minus 10% nicotine concentration as acceptable.

Participants were free to seek out new e-cigarettes and e-liquids if they wished, reflecting the real-world nature of the trial.


We advised participants to start using one patch per day, 2 weeks before their quit-date; although pre-cessation use of NRT is not mentioned as a treatment option in the New Zealand smoking cessation guidelines (unless as part of a cut-down to quit strategy), 19 trial evidence indicates this approach significantly increases long-term quitting success.

20 During this period, participants randomly assigned to an e-cigarette were advised to use the device as and when necessary or desired, and in accordance with the manufacturer's written instructions, to become familiar with its use.

3,21,22 In this way, the time-period of pre-cessation use of treatment products was matched.

Written and online video instructions on how to assemble and use an e-cigarette were also provided via a weblink hosted by NZVAPOR, plus participants were advised that they could call NZVAPOR for advice on how to assemble the device, add the e-liquid, and use and maintain the device (reflecting the support offered by the New Zealand vaping community for new e-cigarette users).

The above material is available on request from the authors.


Participants were instructed to stop smoking from their quit date and continue with their allocated treat- ment for 12 weeks (one patch per day, ad libitum use of the e-cigarette), irrespective of any lapses to smoking.

8-12 weeks of NRT treatment is standard care in New Zealand.

19 Participants who had not quit at the end of their time in the trial were referred to publicly funded smoking cessation services.

No participant payments were made to encourage retention.


All data (except verification of quit status) were collected by telephone, in calls separate to the behavioural support calls.

The published trial protocol 11 presents a tabulated list of outcomes and definitions.

In summary, baseline data included date of birth, gender, self-reported ethnicity, years Articles www.thelancet.com/respiratory Vol 8 January 2020 57 of schooling, self-reported height and weight, smoking history (daily or non-daily smoker, cigarettes smoked per day by daily smokers, age started smoking, years smoked, type of tobacco smoked, quit attempts in the past 12 months and method[s] used), cigarette dependence (measured using the Fagerström Test of Cigarette Dependence, which is on a scale of 1-10, with scores >5 indicating high cigarette dependence and ≤5 indicating low cigarette dependence), 23 concomitant medi cation, motivation to quit (measured on a 5-point Likert scale, for which 1=very low motivation and 5=very high motivation), tobacco withdrawal symptoms and urge to smoke, 24 urge to vape, household smoking and e-cigarette use, exposure to other e-cigarette users, smoke-free home and car policies, self- reported frequency of shortness of breath and cough (measured on a 5-point Likert scale, for which 1=not at all and 5=all the time), self-reported asthma, chronic obstructive pulmonary disease (COPD), and current and history of mental health problems.


The primary outcome was continuous smoking absti- nence 6 months after the agreed quit date (self-reported abstinence since quit date, allowing five or fewer cigarettes in total).

25 Abstinence was verified by a researcher or community-based cessation provider using standardised exhaled carbon monoxide (CO) measurement with a Bedfont Smokerlyzer (Bedfont Scientific Ltd, Kent, UK), with a reading of 9 ppm or lower signifying abstinence.

25 Secondary outcomes assessed at quit date, 1, 3, 6, and 12 months after the agreed quit date were continuous abstinence (CO-verified at 12 months); 7-day point prevalence abstinence (no cigarettes, not a single puff, in the previous 7 days); 25 relapse (time to returning to regular smoking of cigarettes: five cigarettes in the past 7 days); self-reported treatment adherence; tobacco withdrawal symptoms and urge to smoke; 24 urge to vape; self-reported weight; concomitant medication; treatment cross-over; use of other smoking cessation support or medication; continued use of allocated treatment past 14 weeks; changes in shortness of breath, cough, asthma, COPD, and mental health problems; belief in ability to quit and remain tobacco-free; smoking identity (whether those allocated e-cigarettes considered themselves a smoker; a smoker still trying to quit; an ex-smoker; an ex-vapor; a vapor trying to quit smoking; a vapor trying to quit vaping; a vapor, other; or none of the above); and views on their allocated treat- ment for smoking cessation and whether they would recommend it to other smokers who want to quit.

In people still smoking at each follow-up call, outcomes were date returned to daily smoking (for daily smokers); number of cigarettes smoked per day (or when smoking for non-daily smokers); reduction in cigarettes smoked per day; and proportion who reduced the number of cigarettes smoked per day (or when smoking for non-daily smokers) by at least 50%.

Participants allocated e-cigarettes were asked about their urge to vape; whether they changed devices or e-liquid, or both; whether they accessed any e-cigarette support or met anyone at least once a week that currently used an e-cigarette; and dual use (daily use of both their allocated e-cigarette and cigarettes).


We recorded prespecified self-reported adverse effects from product use and serious adverse events.

Serious adverse events were allocated International Classification of Diseases 10th edition Australian Modification codes and were classified by a physician (CB) as non-serious or serious (death, life-threatening, led to hospitalisation, otherwise medically important [ie, that might not be immediately life threatening or result in death or hospitalisation but might jeopardise the participant or might require medical or surgical intervention to prevent one of the other outcomes]).


A sample size of 1809 (804 in both e-cigarette groups and 201 in the patches only group) conferred 90% power, with a two-sided p=0·05 to detect an absolute difference of 8% in 6-month quit rates between the patches plus nicotine e-cigarette group and the patches plus nicotine-free e-cigarette group (1:1 ratio), and a 15% difference between the patches plus nicotine e-cigarette group and the patches only group (4:1 ratio).

11 A 20% loss to follow-up was assumed.

3 The assumed 6-month quit rate of 16% in the patch group was the average observed in the 2012 Cochrane review 16 for nicotine patches versus placebo or no NRT control.

The assumed 6-month quit rate of 23% for the patches plus nicotine-free e-cigarette group was based on our trial of NRT combined with very low nicotine cigarettes.

26 The estimated quit rate of 31% for the patches plus nicotine e-cigarette group was observed in a trial comparing patches plus nicotine spray to patches plus placebo spray.

27 For primary analyses, we used the intention-to-treat (ITT) approach whereby participants with unknown smoking status were deemed to be smoking.


The vali- dated primary outcome relates to people who self-reported quitting, could be located, and for whom verification of smoke-free status was confirmed.

However, locating people to verify quit status proved difficult given the geographically-dispersed trial population.

Therefore, we calculated a post-hoc, extrapolated, validated abstinence rate in which the proportion of people who were located and confirmed as smoke-free was applied to all people who self-reported being smoke-free.

We calculated quit rates, relative risks (RR), risk differences (RD), number needed to treat (NNT), and 95% CIs for both compari- sons.

We compared treatment groups using χ² tests.

We did adjusted analyses using log-binomial regression and adjusted for the stratification factor (ethnicity).


In prespecified subgroup analyses, we assessed the consistency of effects for the primary outcome for ethnicity, age, sex, education, level of cigarette depen dence, motivation to quit, and e-liquid batch using tests for heterogeneity.

We did post-hoc subgroup analyses for Māori and non-Māori women.

Per-protocol analysis excluded participants with missing 6-month data and major protocol Articles www.thelancet.com/respiratory Vol 8 January 2020 violations.

Additional per-protocol analysis also excluded non-adherent participants, with adherence defined as use of both patches and the e-cigarettes (if allocated) at both one and three months.

We also did complete case analysis.


Sensitivity analyses addressed the effect of varying cutoffs for CO mea surements (ie, at ≤3 ppm, ≤5 ppm, and ≤8 ppm).

27 We assessed change from baseline in the number of cigarettes per day (for non-abstainers) and weight and body-mass index over time using repeated measures mixed models with a compound symmetry covariance structure adjusted for baseline value.

We used a Mann-Whitney U test to compare data that were non- normally distributed.

We used Kaplan-Meier curves, the log-rank test, and Cox proportional hazards regression analysis to measure time-to-first-lapse from quit date.

We analysed serious adverse events by treatment group, and calculated an incidence rate ratio (IRR) for each comparison.

We used SAS (version 9.3) and R for analysis, guided by a prespecified plan.

This trial is registered with ClinicalTrials.gov, NCT02521662.


The sponsor of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.

The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.


Between March 17, 2016, and Nov 30, 2017, 1623 people were screened, of whom 1124 (69%) were eligible and randomly assigned to the patches only group (n=125), the patches plus nicotine e-cigarette group (n=500), or the patches plus nicotine-free e-cigarette group (n=499; figure).

This sample size was less than planned, as the trial ran out of time and funding to continue recruitment due to delays in sourcing nicotine e-liquid and slower than anticipated recruitment.


Last follow-up occurred on Aug 31, 2018.

In the patches only group, 42 (34%) of 125 participants were lost to follow-up at 6 months (those with a low level of education and high cigarette dependence were more likely to be lost to follow-up; appendix p 22).

In comparison, approximately 30% (152 of 500 and 155 of 499) of participants in each of the two e-cigarette groups were lost to follow-up at 6 months.

Participants in the patches plus nicotine e-cigarette group were more likely to be lost to follow-up if they were Māori, were younger than 40 years, or had a low level of education (appendix p 22).

Participants in the patches plus nicotine-free e-cigarette group were more likely to be lost to follow-up if they were Māori (appendix p 22).


In the patches only group, 20 (16%) participants with- drew, seven immediately post-randomisation (six citing they did not want to be in the patches only group, and one gave no reason).

In comparison, nine (2%) partici- pants withdrew in the patches plus nicotine e-cigarette group, two immediately post-randomisation (one citing they didn't like the products and one providing no reason), and seven (1%) participants withdrew in the patches plus nicotine-free e-cigarette group, one immediately post- randomisation (citing they had changed their mind).


In March 2018, 4 months after the last person was randomly assigned, and 5 months before the last follow- up call, the New Zealand Ministry of Health allowed nicotine e-cigarettes to be advertised and sold to adult New Zealanders.

Before this date, participants could only legally access nicotine e-liquid via the trial or by Baseline characteristics were balanced between the three treatment groups (table 1).

Participants were pre- dominantly daily smokers (99%) with a high level of cigarette dependence and motivation to quit.

A high pro- portion of participants were women (768 [68%]; table 1), Māori (451 [40%]), and Māori women (342 [30%]).

Overall, 18% of participants reported at least one current mental health problem, 10% reported asthma, and 3% reported COPD (table 1).

Additional baseline variables are presented in table 1.


Verified continuous abstinence at 6 months after the agreed quit date was significantly higher in the patches plus nicotine e-cigarette group (35 [7%] of 500) than in the patches plus nicotine-free e-cigarette group (20 [4%] of 499; RR 1·75 [95% CI 1·02-2·98]; p=0·038; RD 2·99 [95% CI 0·17-5·81]; NNT 33·4 [95% CI 17·2-596·1; table 2).

Verified, extrapolated, continuous abstinence rates at 6-months post-quit date were similar to self-reported 6-month continuous abstinence rates.

Complete case and per-protocol analyses, and sensitivity analyses around varying CO concentrations, concurred with ITT analysis (table 2).

In participants who meet the study definition for treatment adherence, quit rates remained in favour of the patches plus nicotine e-cigarette com bination, but did not reach statistical significance (table 2).


In prespecified subgroup analyses we found no significant differences in quit rates nor for post-hoc subgroup analyses comparing quit rates in Māori and non-Māori women (appendix p 9).

However, quit rates were significantly higher in the patches plus nicotine e-cigarette group for non-Māori, participants younger than 40 years, non-Māori women, and in those with a low level of cigarette dependence.


Secondary self-reported cessation outcomes at 1, 3, and 6 months after the agreed quit date were consistent with the primary outcome (table 2).

In those still smoking, no difference between groups, at any timepoint, was noted for the change from baseline in the mean number of cigarettes smoked per day (appendix p 23).

However, a higher proportion in the patches plus nicotine e-cigarette group We found no significant difference in 6-month verified continuous abstinence rates between the patches plus nico- tine e-cigarette group (35 [7%] of 500) and the patches only group (3 [2%] of 125; RR 2·92 [95% CI 0·91-9·33]; p=0·05; RD 4·60 [1·11-8·09]; NNT 21·7 [12·4-90·3]; table 3).

Veri- fied, extrapolated, continuous smoking abstinence at 6 months post-quit date was statistically in favour of the patches plus nicotine e-cigarette group (table 3).

Complete case and per-protocol analyses found similar findings to the ITT analysis (table 3).

However, self-reported secondary cessation out comes were significantly higher at 1, 3, and 6 months after the agreed quit date in favour of the patches plus nicotine e-cigarette group (table 3).

We did not do prespecified subgroup analyses for this comparison due to a small number of participants who quit.

In those still smoking, no difference was noted between the two groups at any timepoint for the change from baseline in the mean number of cigarettes smoked per day (appendix p 26).

However, the proportion of people reducing the number of cigarettes per day by more than 50% was signifi cantly higher (at all timepoints) in the patches plus nicotine e-ciga - rette group than in the patches only group (appendix p 26).


The median time to relapse back to smoking was 5-6 months, with no statistical difference noted for the two comparisons (appendix p 13).

Almost 70% of participants allocated to e-cigarettes correctly identified the nicotine content of their e-liquid (appendix p 27).

Most participants with asthma, COPD, or mental health problems at baseline reported that their condition re- mained the same or improved over time (appendix p 5).

The prevalence of self-reported side-effects of treatment was low (appendix p 28).

Data for other secondary and post-hoc outcomes can be found in the appendix (pp 1-32).


18 serious adverse events occurred in 16 people in the patches plus nicotine e-cigarette group compared with 27 events in 22 people in the patches plus nicotine-free e-cigarette group (IRR 0·66 [95% CI 0·36-1·20]; p=0·18), and four events in three people in the patches only group (0·86 [0·29-2·53]; p=0·78; table 5; appendix p 24-25).

No serious adverse events were treatment related.


This effectiveness trial found that a combination of nicotine patches plus a nicotine e-cigarette was superior to patches plus a nicotine-free e-cigarette for 6 month smoking abstinence among dependent smokers moti vated to quit.


For the patches plus nicotine e-cigarette versus patches plus nicotine-free e-cigarette comparison, as well as for the patches plus nicotine e-cigarette versus patches only comparison, self-reported, self-reported quit rates at all timepoints were in favour of patches plus a nicotine e-cigarette, so the fact that the comparison of patches plus a nicotine e-cigarette versus patches alone did not reach significance is likely to be a result of the smaller than anticipated sample size.


Overall, the observed absolute 6-month quit rates in the e-cigarette groups were low and similar to rates reported in the first trials 28 of e-cigarettes for smoking cessation that used first-generation devices with minimal behavioural support.

3,4 However, quit rates at 1 months and 3 months in the e-cigarette groups in the current study were almost twice those reported in two early trials.


The safety findings are consistent with previous e-cigarette trials and cohort study findings.

9,29 Despite a higher proportion of par ticipants in the patches only group using the patch (compared with patch use in the other treatment groups), 6-month self-reported and verified absolute quit rates for this group were low, but similar to those observed in trials of over-the-counter NRT.


Our study is the second largest e-cigarette trial to date with a primary outcome of at least 6-month smoking abstinence.


To our knowledge, it is also the first large trial testing the effectiveness of a second-generation e-cigarette for smoking cessation within the general population and with the selection of e-cigarette and e-liquid based Sixth, the use of patches differed to what participants might have been used to in the past, in that we started product use 2 weeks before the target quit date.


This change did not affect adherence (because product use at quit date was very high), but might limit the generalisable of the findings to policy environments where precessation use of NRT is not supported.


Finally, serious adverse events were self-reported and similar to those reported in previous e-cigarette 3-7,9 and NRT trials.

16 However, the sample size was not sufficient to assess uncommon serious adverse events, nor was follow- up long enough to identify serious adverse events with a long lead-time.


In summary, when looking at continuous abstinence from smoking, provision of patches plus a nicotine e-cigarette resulted in three to seven more smokers per 100 quitting long-term (depending on the analyses done) than with patches plus a nicotine-free e-cigarette.

The smaller than anticipated sample size meant the study was not sensitive enough to pick up a definitive finding for the second comparison, although analyses suggest combination nicotine therapy-ie, use of a slow release nicotine patch, together with a faster-acting oral nicotine product (in this case a nicotine e-cigarette)-could result in five to ten more smokers per 100 quitting long-term than with mono therapy (ie, nicotine patches alone).

Our findings are consistent with the current findings of the Cochrane review of e-cigarettes for smoking cessation 29 and contribute to the growing body of evidence from randomised trials on the efficacy, effectiveness and safety of e-cigarettes for smoking cessation.


NW, VP, GL, ML, and CB conceived the original idea for the trial, and sought and obtained funding.

All authors contributed to the writing Articles www.thelancet.com/respiratory Vol 8 January 2020 of the study protocol.

VP did all data analyses.

The paper was written by NW, with input from all co-authors.

NW is guarantor for this paper.

All authors read and approved the final manuscript.


NW, CB, MV, GL, ML, and VP report grants from the Health Research Council of New Zealand, during the conduct of the study.

NW, CB, MV, and VP report grants from Pfizer, outside of the submitted work.

GL chairs the organisation End Smoking New Zealand, which advocates for harm reduction approaches to tobacco control.

E-cigarettes were purchased from a New Zealand e-cigarette online retailer (NZVAPOR, https://www.nzvapor.com/), e-liquid was purchased from Nicopharm, Australia (https://www.nicopharm.com.au/), and nicotine patches were supplied by the New Zealand Government via their contract with Novartis (Sydney, Australia).

NZVAPOR also provided, at no cost to participants, on-line and phone support regarding use of the e-cigarettes.

Neither NZVAPOR nor Nicopharm have links with the tobacco industry.

None of the above parties had any role in the design, conduct, analysis, or interpretation of the trial findings, or writing of this publication.


Requests for de-identified individual participant data or study documents will be considered where the proposed use aligns with public good purposes, does not conflict with other requests or planned use by the trial steering committee, and the requestor is willing to sign a data access agreement.

Contact is through the corresponding author.


On Dec 12, 2019, our Twitter-based University of Toronto Respirology and Sleep Journal Club discussed Walker and colleagues’ pragmatic, randomised, parallel-arm trial designed to look at the effectiveness of combining nicotine patches with an e-cigarette (with and without nicotine) on 6-month smoking abstinence [1].

Our discussions highlighted the challenges of studying populations of smokers in general, given the habitual nature of the exposure, challenges related to follow-up, and a need for reliable and valid objective measures of smoking abstinence.


With regard to the primary outcome of smoking abstinence at 6 months, the risk difference observed was only 2.99 (95% CI 0.17–5.81; p=0.038) between the patches with nicotine e-cigarette group compared with patches with nicotine-free e-cigarette group, and 4.6 (1.11–8.09; p=0.05) when compared with patch monotherapy.

Besides reaffirming the superiority of combination smoking cessation therapy over monotherapy, which has already been shown [2][3], this study establishes the importance of the use of nicotine in e-cigarettes to optimise smoking cessation success.


Although the results presented are significant, the effect size in terms of abstinence is quite modest.

It might have been underestimated owing to substantial crossover among participants who used e-cigarettes outside of the study protocol, but if so, the true magnitude of effect is unclear.

Furthermore, the findings are not readily generalisable given the multitude of e-cigarette brands and delivery systems available worldwide with probably different amounts of nicotine content from that used in this trial.

Other study limitations we discussed included the sizable loss of follow-up, which is unsurprising in this context.


However, 70% of those allocated to the e-cigarette groups were able to correctly determine the nicotine content of their e-liquid, raising the question of whether true masking can be achieved in e-cigarette trials.

Notably, exhaled carbon monoxide measurement was used to verify smoking cessation only at 6 months, but not at earlier timepoints, calling into question whether continuous abstinence was truly identified.

Objective confirmation is crucial in determining smoking cessation efficacy owing to the well-known bias associated with self-reported abstinence.


Although evidence exists that nicotine-free e-cigarettes help in reducing cravings and increase quit rates in smokers [4], it would seem preferable to establish the efficacy of nicotine e-cigarettes for smoking cessation before doing a pragmatic trial, as was done for other smoking cessation interventions [2][5].

This pragmatic study reaffirms that e-cigarettes can function as nicotine replacement therapy, but did not evaluate whether e-cigarettes are superior to established forms of nicotine replacement that do not expose the respiratory tract to potential toxins with uncertain long-term health consequences.


A key question for clinicians is whether the putative advantages of e-cigarettes, including higher nicotine content, faster nicotine delivery to the brain and similarity to the act of smoking a cigarette, actually make a difference to smoking cessation rates.

Efficacy trials comparing e-cigarettes with other on-demand nicotine replacement modalities (eg, gum) are therefore needed to answer this.


The Lancet Respiratory Medicine published a letter by Nermin Diab and colleagues (2020) [1] about our three-arm trial investigating the effectiveness and safety of nicotine patches combined with e-cigarettes (with and without nicotine) for smoking cessation [2].

We wish to address two points made by the authors of the letter.


First, they say, '...it would seem preferable to establish the efficacy of nicotine e-cigarettes before doing a pragmatic trial' [1].

We would suggest that both efficacy and effectiveness trials are important, but they serve very different purposes and provide very different information.

Both designs have pros and cons—the choice of design relates to the research question of interest, is context specific, and one does not need to occur before the other.

Efficacy trials are highly controlled (both in patient population and conduct) and their findings are therefore not translatable to the real world.

In comparison, trials on the effectiveness of e-cigarettes have the most relevance for decision making by health-care policy makers and health-care providers.

Such trials tend to have a usual care comparator, but can be expensive to do as they often require a large sample size and long-term follow-up.

It is important when reading effectiveness trials to consider the context of the trial (ie, the country in which it was done, and the country’s policy environment).

In New Zealand, a country with a smoke-free 2025 goal and the urgent need for policy relevant information about the effect of e-cigarettes on quitting, our effectiveness trial design was the most appropriate design to answer the policy-based research question.


When our trial findings are considered in their entirety, one can see that they are consistent across different time points, measures of cessation, and with most of the sensitivity analyses supporting the conclusions.

We acknowledge the authors’ comments about the desirability for more frequent verification of smoking status.

However, New Zealand is a small country (of approximately 5 million people) with little public research funding available.

It was not feasible or affordable to do more extensive follow-up of our trial participants, as they were geographically dispersed throughout the entire country and we had only around US$400,000 to run a three-year trial.

Our chosen measurements of abstinence are those recommended by the Society for Research on Nicotine and Tobacco for pragmatic trials [3].

In New Zealand, we do not have access to large pools of research funding, so must maximise our use of every dollar.

Efficacy trials of little relevance to the population using e-cigarettes are a poor use of our time and money.


Second, they say, '...the findings are not readily generalisable given the multitude of e-cigarette brands and delivery systems available worldwide and probably different amounts of nicotine content from that used in the trial' [1].

We agree, but no efficacy or effectiveness trial of e-cigarettes will ever be fully generalisable.

Nor is it possible to design a trial where every type of e-cigarette ever designed (and nicotine concentration) can be evaluated.

However, there are other pathways to inference than those of drug evaluation.

In this case we have created generalisable evidence as proof of principle that nicotine-containing e-cigarettes combined with nicotine patches will increase quit rates.

We tested the type of e-cigarette (and nicotine concentration) that was the most popular amongst naive e-cigarette users in New Zealand at the time the trial was done.

Newer devices are known to deliver nicotine more efficiently and effectively, so one can assume quit rates would be higher.


reflection interact to produce decisions in social dilem- mas (Rand, Greene & Nowak, 2012).

More specifically, Rand and colleagues argued that a cognitive conflict ex- ists between intuition and reflection (deliberation), such that "intuition supports cooperation . . . and . . .

reflec- tion can undermine these cooperative impulses" (p. 427).

As a theoretical explanation, Rand, Peysakhovich et al. ( 2014) stated that cooperation is typically advantageous in the context of daily life interactions, and therefore leads to the formation of cooperative intuitions.

These intu- itions are generalized to other contexts, such as social dilemma experiments, resulting in spontaneous coopera- tive impulses in these situations as well.

However, co- operation might not be advantageous at all in these new contexts, and reflection may override the initial coopera- tive impulse, adapting behavior towards the context spe- cific optimum (defection).


Empirical studies designed to test the prediction of intu- itive cooperation can be grouped into two categories: ex- perimental manipulations designed to influence the degree to which people rely on intuition versus reflection in their decision, and correlational studies focusing on the rela- tionship between process characteristics (such as response times) and decisions in social dilemmas.


Studies using experimental manipulations follow the idea that manipulations designed to foster the influence of intuition should increase cooperation, whereas manipula- 510 https://doi.org/10.1017/S1930297500006392 Published online by Cambridge University Press 511 tions designed to foster the influence of reflection should decrease cooperation.


In line with this reasoning, ask- ing participants to write about a situation where intuitive decision making had led them in the right direction in- creased contributions in a public goods game compared to a condition where participants had to write about a situ- ation where careful reasoning worked out well (Rand et al., 2012, Studies 8 & 9).


In addition, participants con- tributed more to a public good when they were forced to decide quickly (i.e., under time pressure) than when they were instructed to reflect and to decide slowly (Rand et al., 2012, Studies 6 & 7).


However, in another series of experi- ments no effect of time pressure on cooperation was found (Tinghög et al., 2013), leading to an ongoing controversy about the stability and potential moderators of this ef- fect (Rand, Greene & Nowak, 2013; Rand & Kraft-Todd, 2014; Rand, Newman & Wurzbacher, in press; Rand, Peysakhovich et al., 2014; Verkoeijen & Bouwmeester, 2014).


One possible caveat concerning time pressure may be that, rather than increasing the influence of intuition, time pressure may lead to an increase in guessing 1 or a de- crease in the amount of information searched (from givens or from memory)-neither of which necessarily foster co- operation.


In addition to experimental manipulations, research in- vestigating the link between intuition and cooperation has also examined the process characteristics of decisions (Rand et al., 2012).

These process characteristics are taken as indicators for the degree to which a decision was in- tuitive.

If the hypothesis put forward by Rand and col- leagues holds, there should be a positive relationship be- tween cooperation and indicators of intuition.

So far, stud- ies have focused on one specific attribute of the process, namely speed.

2 The basic assumption is that intuitive re- sponses are faster than reflective responses (Kahneman, 2011).


In line with their hypothesis that cooperation is in- tuitive, Rand and colleagues (2012) report that faster deci- sions were more cooperative than slower decisions across several studies.

For example, response time negatively predicted contributions to a public good (Studies 1 & 5).

Similarly, response time was negatively associated with the probability of cooperative choices in different variants of the prisoner's dilemma game (Studies 2-4).


Evidence on the relationship between response time and cooperation is, however, also not consistent in the litera- ture.

In line with Rand et al. ( 2012), several other stud- 1 Rand, Peysakhovich et al. ( 2014) however suggest that the effect of time pressure on cooperation in Rand et al. ( 2012) cannot be explained by more random behavior in the time pressure condition, as the average contribution in this condition is not closer to 50% (which should result from random behavior) compared with the time delay condition.

2 Recently, text analyses of open ended survey responses have also been used to investigate the relationship between intuition and coopera- tion in social dilemmas (Rand, Kraft-Todd & Gruber, 2014; Roberts et al., 2014).


ies reported a negative relationship between response time and cooperation in public goods games (Lotito, Migheli & Ortona, 2013; Nielsen, Tyran & Wengström, 2014).

Yet, the opposite pattern was found in a dictator game in which faster participants made more egoistic choices (Pi- ovesan & Wengström, 2009).

A more complex relation- ship between response time and cooperation was found in studies assessing the social value orientation of par- ticipants, where individualists had shorter response times than both cooperators and competitors (Dehue, McClin- tock & Liebrand, 1993; Fiedler, Glöckner, Nicklisch & Dickert, 2013; Liebrand & McClintock, 1988).

In their re- cent working paper, Evans, Dillon, and Rand (2014) find evidence for an inverted-U relationship between response time and cooperation in both the public goods game and the prisoner's dilemma game in two studies and a meta- analysis.

They conclude that "self-paced reaction times should not be treated as a direct proxy for the degree of reflection [vs. intuition], as other psychological vari- ables may also influence decision speed" (p. 29, brackets added).

Indeed, although it is commonly assumed that in- tuitive processes are fast, short response times could well be produced by mechanisms other than intuition (e.g., very limited information search or pure guessing).


Part of the difficulty, we conjecture, lies in the reliance on theoretical constructs that are insufficiently specified on the level of testable cognitive processes (Glöckner & Wit- teman, 2010).

Specifically, it is difficult to test a dissocia- tion between intuitive and deliberative processing because these are rather vague labels (Gigerenzer, 1998) that have been understood to mean quite different things (Keren & Schul, 2009).

For example, some have implied that intuition drives the use of heuristics (Gigerenzer, 2007; Kahneman, 2003)-and thus strategies for effort reduc- tion (Shah & Oppenheimer, 2008)-whereas others equate intuition with effortless integration of large amounts of information (Betsch & Glöckner, 2010; Hogarth, 2001; Seligman & Kahana, 2009) and thus quite the opposite of what effort-reducing heuristics do (Hilbig, Scholl & Pohl, 2010).

Also, in-depth analyses imply that there may be some gradual differences rather than distinct modes of thinking (Horstmann, Ahlgrimm & Glöckner, 2009).

Fi- nally, even though intuitions are often understood to be fast, one cannot assert that fast processes are intuitive and it has also been argued that some intuition can be no- tably slow and time-consuming (Dijksterhuis & Nordgren, 2006).

In summary, the intuitive-reflective-dichotomy makes it difficult to test the underlying theoretical idea of Rand et al. ( 2012).


Thus, rather than trying to test a dissociation between intuition and deliberation per se, it seems prudent to rely on specific process measures that can be used to test the proposed relationship.

That is, one can focus on process variables for which the assumptions made by Rand et al. https://doi.org/10.1017/S1930297500006392 Published online by Cambridge University Press 512 (2012) make clear-cut predictions, independent of the la- bels intuition and deliberation.


One such process charac- teristic is the cognitive conflict involved in making a de- cision.

If cooperation is the "effortless", "spontaneous", "initial" or "intuitive" response (Rand et al., 2012), de- fection should entail more cognitive conflict than cooper- ation.


To measure cognitive conflict one can employ a re- cently proposed and promising method: the analysis of re- sponse dynamics.

Response dynamics provide researchers with implicit measures of the growth of a preference lead- ing up to the decision (Koop & Johnson, 2011; Spivey & Dale, 2006).

From this method, measures for cognitive conflict can be derived.


In the following, we will briefly introduce the logic of response dynamics and report an ex- periment applying this type of measure to social dilemma choices.


The basic response dynamics paradigm for computerized experiments involves the continuous tracking of mouse movements while participants choose between options that are spatially separated on a screen (also referred to as mouse-tracking; Freeman & Ambady, 2010).

The as- sumption behind the paradigm is that cognitive processing is continuously revealed in motor movements (Spivey & Dale, 2006).

More specifically, it is assumed that mouse movements reveal the tentative commitments to the differ- ent choice options during the decision process (Freeman, Dale & Farmer, 2011).

In that sense, an option that is strongly considered at one point during the decision pro- cess exerts a "pull" of the mouse movement in the direc- tion of this option.

Therefore, the mouse movement as a whole (the so-called response trajectory) and different measures derived from it can be used to draw inferences about the cognitive processes that take place during the formation of the decision (Koop & Johnson, 2011).


One property of the response trajectory that is of par- ticular interest for our purposes is its curvature.

The basic idea is that the more seriously an ultimately non-chosen option is considered during the decision process, the more will the trajectory deviate in direction of this option.

In other words, the degree of curvature represents the spa- tial attraction towards the non-chosen option (Freeman & Ambady, 2010; Spivey & Dale, 2006), which can be taken as a proxy of the overall degree of cognitive conflict.

An early experiment by Spivey, Grosjean, and Knoblich (2005) validates this interpretation.

Participants were re- peatedly asked via headphones to click on one of two im- ages displayed on the screen (e.g., "Click the candy.").

The requested object ("candy") was paired with a distrac- tor that was either phonologically similar (e.g., "candle") or phonologically dissimilar (e.g., "jacket").

As expected, response trajectories were more curved towards the dis- tractor if the distractor was phonologically similar, sug- gesting that the increased curvature was caused by the increasing attraction of the distractor in this condition.


More recently, a validation study of the response dynam- ics paradigm was also provided for the area of preferential choice (Koop & Johnson, 2013, Experiment 1).

In a series of trials, participants had to indicate which of two images they preferred.

Importantly, images were selected from a well-normed set of stimuli (International Affective Pic- ture System; Lang, Bradley & Cuthbert, 2008), and the a priori difference in pleasantness of the pictures in a pair was varied continuously.

As expected, the curvature of the response trajectories increased with increasing similarity in pleasantness, validating the interpretation that curvature reveals the competition between choice options for the do- main of preferential choice.


Both behavioral and neurophysiological evidence has corroborated the assumptions behind the response dynam- ics approach (Spivey, 2007).

Following the pioneering work by Spivey et al. ( 2005), the paradigm has been suc- cessfully applied to a variety of cognitive domains, includ- ing language, social cognition, and learning (see Freeman et al., 2011, for a review).

More recently, response dy- namics have also been used to study judgment and de- cision making processes (Dshemuchadse, Scherbaum & Goschke, 2013; Koop, 2013; Koop & Johnson, 2011, 2013).

However, to the best of our knowledge, no study has considered response dynamics as process indicators in social dilemma decision making.


The experiment reported below takes advantage of the response dynamics paradigm to test a hypothesis derived from the theoretical framework by Rand et al. ( 2012) that considers cooperation as the spontaneous response in so- cial dilemmas.

Building on the previously outlined theo- retical and empirical arguments, we expect a main effect of decision (cooperation vs. defection) on cognitive con- flict (as indicated by the curvature of mouse trajectories) in social dilemmas.

More specifically, defection should entail more cognitive conflict (greater curvature) than co- operation.


In their studies, Rand et al. ( 2012) observed that the no- tion of intuitive cooperation appears to hold for some indi- viduals, whereas both intuition and reflection lead to little cooperation for other individuals.

Consequently, Rand et al. have stressed the importance of identifying individual difference factors that might moderate the effect.

Indeed, it has been emphasized that one should not only test for effects on the aggregate but also consider to what extent an effect holds per individual-both in judgment and de- cision making research and experimental psychology in general (Baron, 2010).

Optimally, then, such interindivid- https://doi.org/10.1017/S1930297500006392 Published online by Cambridge University Press 513 ual variation in the effect could also be explained by some moderating factor.

Therefore, an additional goal was to investigate whether there are between-participant differ- ences in the hypothesized relation between decision and cognitive conflict, and if so, to explain these through mod- erating factors.


Regarding the relationship of intuition and coopera- tion, two moderating factors have been uncovered so far, namely (a) the cooperativeness of interaction partners in daily life and (b) prior experience with laboratory exper- iments.

Faster responses were found to be positively re- lated to cooperation only for individuals who reported having many cooperative daily-life interaction partners (Rand et al., 2012).

Besides, time pressure increased co- operation only for individuals who had little prior expe- rience with laboratory experiments (Rand, Peysakhovich et al., 2014) and were thus arguably less often exposed to situations in which defection is positively reinforced.

An additional study that assessed both factors in combi- nation even showed evidence for an interaction, namely, that time pressure increased cooperation only when par- ticipants both had little prior experience with economic games and reported to have mainly cooperative interaction partners (Rand & Kraft-Todd, 2014).


Although these specific factors fit nicely with the rea- soning of Rand, Peysakhovich et al. ( 2014), they refer more to prior experiences than to individual differences in the sense of basic, dispositional tendencies such as person- ality traits.

However, it is straightforward to predict that personality traits reflecting cooperativeness and pro-social tendencies should explain the difference in cognitive con- flict when people defect versus cooperate.

Disposition- ally cooperative individuals should be particularly inclined to cooperate spontaneously and their decisions should be characterized by particularly strong conflict whenever they defect.

In other words, especially for highly cooperative individuals a large positive difference in cognitive conflict between defection and cooperation should be expected.

In turn, the overall hypothesized effect (that cooperation yields less conflict) may diminish or even reverse for dis- positionally uncooperative individuals.


One personality trait that specifically reflects disposi- tional cooperativeness is the Honesty-Humility (HH) fac- tor of the HEXACO personality model (Ashton & Lee, 2007; Ashton, Lee & de Vries, 2014).

HH is defined as the "tendency to be fair and genuine in dealing with oth- ers, in the sense of cooperating with others even when one might exploit them without suffering retaliation" (Ash- ton & Lee, 2007, p. 156).

In line with this definition, HH has been found to predict cooperation in a variety of paradigms from behavioral economics ranging from the dictator game (Hilbig & Zettler, 2009; Hilbig, Zettler, Leist & Heydasch, 2013) and social values (Ackermann, Fleiß & Murphy, in press; Hilbig, Glöckner & Zettler, Building on the theoretical definition of HH and corre- sponding empirical findings, HH should predict between- participant variation in the hypothesized effect of deci- sion on cognitive conflict, that is, function as a modera- tor.

Specifically, individuals high in HH should be partic- ularly inclined to cooperate spontaneously and should find it more difficult to counteract this impulse when ultimately deciding to defect.

In other words, the difference in con- flict between defection and cooperation decisions should be larger for individuals high in HH.

For their counterparts low in HH, this difference should be considerably smaller or it might even reverse.


The hypotheses outlined above were tested in a fully in- centivized lab experiment, where participants played sim- ple two-person social dilemma games with the two options cooperation and defection.

Depending on the combina- tion of players' choices, the individual payoff varied fol- lowing a specific payoff structure.

( See Table 1 for the general structure.) If both players cooperated, both re- ceived the same payoff called reward (R).

Similarly, if both players defected, both received an inferior payoff called punishment (P).

If one player cooperated but the other de- fected, the cooperative player received the sucker's payoff (S) while the defecting player received the payoff called temptation (T).

By modifying the order of the different payoffs, different types of social dilemmas were created such that different motivations for cooperation versus de- fection existed.


To increase variation in cooperation rates, three differ- ent types of social dilemma games were implemented.

First, the prisoner's dilemma game (PDG; Rapoport & Chammah, 1965) was used (T > R > P > S), in which https://doi.org/10.1017/S1930297500006392 Published online by Cambridge University Press 514 two mechanisms-temptation (T > R) and fear (P > S)- motivate defection.

Second, the chicken game (Rapoport & Chammah, 1966) was employed (T > R > S > P), where temptation still motivates defection (T > R), but fear is no longer present (P < S).

Third, the stag hunt game (Gächter, 2004) was used (R > T = P > S), where fear motivates de- fection (P > S), but temptation is no longer present (T < R).

All three game types have in common the previously out- lined characteristics of a social dilemma (Van Lange et al., 2013).

That is, in each game, there is a non-cooperative option that is (at least sometimes) more attractive for the individual than the cooperative option.

However, if both individuals decide to pursue this non-cooperative option, they end up worse off than if they had both cooperated (R > P for all three game types).


For the experiment, we constructed five variants of each of the three previously described game types.

Within each game type, the cooperation index (Rapoport & Chammah, 1965; Vlaev & Chater, 2006) was held constant and only the specific payoff values were varied, meaning that the relative attractiveness of cooperation versus defection did not differ across variants.

All payoff matrices used in the current experiment can be found in Appendix A.


Basic personality factors and the HH dimension in par- ticular were assessed using the German version (Mosha- gen, Hilbig & Zettler, 2014) of the 60-item HEXACO- Personality-Inventory-Revised (HEXACO-60; Ashton & Lee, 2009; for further information on the inventory and all items see http://hexaco.org).

To maintain the structure of the inventory, all 60 items-including those pertaining to personality factors not of interest here-were assessed and answered on a scale ranging from 1 (strongly disagree) to 5 (strongly agree).

However, in the analysis only the individual scores on the HH scale (i.e., individual means across the 10 items corresponding to this factor) were con- sidered.

3 The internal consistency and descriptive statis- tics of this scale (Cronbach's α = .78, M = 3.31, SD = 515 able responding (e.g., Moshagen, Hilbig & Musch, 2011).

Whether the cooperative choice option was presented left (as "Option A") or right (as "Option B") was counterbal- anced across participants to avoid presentation order ef- fects (Hehman, Stolier & Freeman, in press).

In addition to the choice and response time, the mouse trajectory (i.e., the x-, y-coordinates of the cursor) was recorded at a rate of 100 Hz (higher than or comparable to other mouse- tracking applications; Freeman & Ambady, 2010; Koop & Johnson, 2011).

Participants were not told that their mouse movements were recorded nor were they given spe- cial instructions regarding the movements of the mouse (Koop & Johnson, 2011, 2013).


Participants were recruited from a local participant pool.

In total, 116 individuals participated in the experiment.

Their age ranged from 18 to 36 years (M = 22.5, SD = 4.0), 79 were female, and 107 were students from various disciplines.

On average, participants earned 2.56 C (ap- prox. 3.50 USD) in the social dilemma game which lasted for about fifteen minutes.

The obtained sample size yields high statistical power (1−β = .94) to detect a small effect (d = 0.30) in a paired t-test given an α error probability of .05 (Faul, Erdfelder, Lang & Buchner, 2007).


To assess whether the variability in participants' coopera- tion/defection decisions was sufficient, we first computed the individual cooperation rate per participant across all games.

On average, participants cooperated in 58% of their decisions (SD = 19%).

Inspection of the individ- ual cooperation rates revealed that 115 participants (99% of participants) chose each option (cooperation and defec- tion) in at least one of the fifteen rounds.

The remaining participant always cooperated and was excluded from all further analyses.

6 In line with the notion that two mech- anisms foster defection in the PDG but only one mecha- nism does so in each of the other games, participants co- operated less in the PDG (25%) than in the other games, as confirmed by a generalized linear-mixed model using a binomial link function, 7 z = -18.63, p < .001.

In addition, the probability of cooperation was higher in the stag hunt game (86%) than in the chicken game (63%), z = 9.12, p < .001.


Next, we tested the first hypothesis that defection en- tails more cognitive conflict than cooperation.

As a first descriptive indicator of conflict, we inspected the average response trajectories.

Following previous studies employ- 516 ), we mapped all trajectories to one side, time-normalized trajectories into 101 time bins, 8 and aggregated them first within and then across participants separately for the dif- ferent decisions.

The resulting average trajectories for cooperation and defection are depicted in Figure 2.

In line with the hypothesis, trajectories of defection deci- sions were characterized by a greater curvature and thus greater attraction towards the non-chosen option (cooper- ation) than vice versa.


To statistically test this curvature pattern, we calculated the maximum deviation (MD, in pixels) for each single trajectory.

The MD is defined as the maximum perpen- dicular deviation between the actual trajectory and its ide- alized trajectory, which is the straight line connecting the trajectory's starting point and end point (Freeman & Am- bady, 2010; McKinstry, Dale & Spivey, 2008).

MD serves as an index of the maximum attraction towards the non- chosen option (Freeman & Ambady, 2010), and thus as an indicator of the maximum conflict during the decision.

Whereas the following analyses use the MD towards the non-chosen option as dependent variable, Appendix B re- ports additional analyses based on other measures.


Given the skewness of the MD distribution, we square root transformed the MD values and used these values for all following analyses (but replicated all results using the 8 Potential initial phases without mouse movements were removed be- fore time-normalization.

0 5 10 15 20 cooperate defect Decision Square root (maximum deviation) untransformed MD values, see Appendix B).


Next, we av- eraged the MD per decision and participant (Wojnowicz, Ferguson, Dale & Spivey, 2009).


As can be seen in Figure 3, the mean MD was higher when participants defected (M = 15.14, Mdn = 14.33, SD = 6.03) than when they cooper- ated (M = 12.92, Mdn = 12.31, SD = 4.98).

This difference was statistically significant in a paired t-test, t(114) = 3.40, p < .001, d = 0.32, hence confirming statistically the ob- served differences in the average response trajectories.


9 Furthermore, we examined whether the effect of deci- sion was robust across the different game types.


For each game type, the qualitative pattern was as expected, as the mean MD was higher for defection than cooperation (see Table 2, 428.30) = 0.16, p = .85.


Hence, defection decisions entailed more conflict than cooperation decisions regardless of the game type and thereby regardless of differences in the specific structure of the payoff matrix.


Moreover, it is noteworthy that even in the game encompassing the lowest coopera- tion rates (25% cooperation in PDG) the mean MD for de- fection was significantly higher than for cooperation, F(1, 89.31) = 6.54, p = .01.


Thus, even though defection was predominant on the choice level, cooperation still entailed less cognitive conflict than defection.


To investigate between-participant differences in the ef- fect of decision on cognitive conflict, we computed the dif- ference of mean MD between defection and cooperation per participant.

Directly replicating our previous analy- sis, the mean difference of 2.22 was significantly greater than zero, t(114) = 3.40, p < .001, d = 0.32.

However, as indicated by a SD of 6.99, there was also considerable variation in the size of the effect.

Specifically, although the difference was positive for the majority of participants (63%), a substantial proportion of participants showed the reversed difference (37%).


To account for this variation across individuals we tested the hypothesized moderating factors as outlined above.

First, we examined whether the overall coop- erativeness of participants in the current study (as indi- cated by their individual proportion of cooperative choices across all trials) predicted the MD difference between co- operation and defection, and, as a more distal factor, we asked whether the individual HH scores predicted the MD difference.

In line with expectations, both predictors sig- nificantly (positively) predicted the MD differences in sep- arate correlational analyses (r = .46, t(113) = 5.51, p < .001, for cooperativeness, and r = .20, t(113) = 2.18, p = Honesty-Humility (centered on mean) Square root (maximum deviation) cooperate defect .03, for HH).

As displayed in Figure 4, the relationship between mean MD and HH was as hypothesized: For in- dividuals high in HH, defection yielded more conflict than cooperation; for their counterparts low in HH there was no such difference or even a tendency for the reversed effect.


Finally, we asked whether the effect of HH was actually driven by the situational cooperativeness of participants in the current study.

To this end, we conducted a mediation analysis following the steps proposed by Baron and Kenny (1986).

As reported above, HH predicted the difference score, β = 0.20, t(113) = 2.18, p = .03.

In addition, HH also predicted cooperativeness, β = 0.34, t(113) = 3.79, p < .001.

When including both HH and cooperativeness in a regression to predict the difference score, only cooper- ativeness was a significant predictor, β = 0.44, t(112) = 4.98, p < .001, whereas HH was no longer significant, β = 0.05, t(112) = 0.58, p = .56-thus confirming a full medi- ation.

Correspondingly, the indirect effect of HH was sta- tistically significant, Sobel's Z = 2.98, p = .003.

11 That is, the situational cooperativeness of participants in the cur- rent study mediated the effect of HH on the difference in cognitive conflict between defection and cooperation.

In summary, these analyses help explain the substantial vari- ation in the main effect across individuals.


In the current experiment, we tested a hypothesis derived from the recently proposed assumption that cooperation is intuitive in social dilemmas, whereas defection requires effortful deliberation (Rand et al., 2012).

To this end, we presented a first application of the response dynam- ics paradigm (Koop & Johnson, 2011) to social dilem- mas.


Specifically, given that intuition and reflection are inherently difficult to tease apart because they represent a rather vague dichotomy, we tested a more specific impli- cation: that defection should entail more cognitive con- flict than cooperation as indicated by participants' mouse movements during the decision process.


In line with this hypothesis, response trajectories were more curved (to- wards the non-chosen option) when individuals defected than when they cooperated.

In other words, the coopera- tive option exerted more "pull" on mouse movements in case of defection than the non-cooperative option (defec- tion) did in case of cooperation.


This effect was robust across different types of social dilemmas and occurred even in the prisoner's dilemma where defection was pre- dominant on the choice level.


The effect of more con- flict in defection held for the majority of individuals, but there was also notable interindividual variation in the ef- fect.


This interindividual variation could be accounted for by individuals' situational cooperativeness which, in turn, was driven by their dispositional cooperativeness in terms of the basic personality factor Honesty-Humility as speci- fied in the HEXACO model (Ashton & Lee, 2007).


The current experiment extends previous research that has primarily focused on response times to examine the relationship between intuition and cooperation.

Specifi- cally, one main argument for the hypothesis that cooper- ation is intuitive stems from correlational studies demon- strating that shorter response times are associated with in- creased cooperation (Rand et al., 2012).

However, such a negative relationship between response times and coop- eration has not been consistently found in the literature, and recently it was argued that response times should not be taken as a direct indicator of intuitive versus reflective processing (Evans et al., 2014).

The current experiment provides a solution for this problem by considering an al- ternative process variable-cognitive conflict-for which specific predictions can be derived based on the hypoth- esis that cooperation is the "spontaneous", "initial", "ef- fortless" response in social dilemmas while defection re- quires effortful deliberation (Rand et al., 2012).

Indeed, cooperation decisions entailed less conflict.

This is also in line with growing evidence that prosocial behavior in general may stem from intuitive, reflexive, and automatic processes (see Zaki & Mitchell, 2013, for a short sum- mary).


Moreover, the current experiment further specifies ex- tant findings on interindividual variation in how much of a spontaneous, initial response cooperation tends to be (Rand, Peysakhovich et al., 2014).

While previous stud- ies have identified individual differences in terms of prior experiences as moderators (Rand et al., 2012; Rand & Kraft-Todd, 2014; Rand, Peysakhovich et al., 2014), we demonstrated the moderating influence of a basic person- ality trait.

The HH factor of the HEXACO personality model has recently been identified as the basic trait driving active cooperativeness in various domains (for overviews see Ashton et al., 2014; Hilbig et al., 2014).

Correspond- ingly, we found that particularly decisions of individuals high in HH were characterized by more cognitive conflict when defecting than when cooperating.

Furthermore, the effect of HH was mediated by the situational cooperative- ness of participants in the current study.


Finally, the current experiment demonstrates the use- fulness of the response dynamics paradigm for investigat- ing complex decision making tasks.

Whereas earlier stud- ies used the response dynamics paradigm primarily to in- vestigate basic cognitive tasks with objectively correct re- sponse options, the paradigm has recently been extended to study complex decision making tasks that also include preferences (Koop & Johnson, 2011).

In these studies, re- sponse dynamics have offered novel insights and availed tests for which existing response output data (i.e., choices and response times) were not sufficient-in areas such as risky decision making (Koop & Johnson, 2013, Experi- ment 2), moral decision making (Koop, 2013), and in- tertemporal choice (Dshemuchadse et al., 2013).

We add to this recent development and extend the application of response dynamics to social dilemma decision making.


Nevertheless, the application of the response dynam- ics paradigm to more complex tasks without objectively correct response options also bears some methodological challenges.

Previous research with basic cognitive tasks usually involved a correct or desired response option and the primary comparison dimension of the response tra- jectories could be experimentally manipulated (e.g., the phonological similarity of the distractor in the experiment by Spivey et al., 2005).

By contrast, the current experi- ment involved preferences, and the final choice (coopera- tion vs. defection) constituted the comparison dimension of interest.


This implies a loss of experimental control over the comparison dimension, because the frequency of each response type depended entirely on the participant.

This dependence could result in missing cells and other analytical challenges (Koop & Johnson, 2013).

To address this problem, we decided to not only rely on the classic social dilemma, the PDG, in which cooperation rates are typically low, but to also implement two additional social dilemmas that should render cooperation more likely.

This modification was successful, as observations for both re- sponses were obtained for all participants but one.

Nev- https://doi.org/10.1017/S1930297500006392 Published online by Cambridge University Press 519 ertheless, the comparison dimension of the response tra- jectories in our experiment was an observed predictor, not under full experimental control.


The amount of information that people needed to take into account when making their decision was also consid- erably larger in the current experiment than in previous applications of response dynamics.

Nevertheless, in line with previous applications of response dynamics we re- frained from instructing participants to move the mouse in a specific way (Koop & Johnson, 2011, 2013).

As a con- sequence, several response trajectories in our dataset con- tained analysis-irrelevant mouse movements (e.g., "read- ing" movements).

We accounted for this increased level of noise by performing the analyses both with the unfiltered dataset and after excluding trials with irrelevant mouse movements.

Importantly, the results remained stable re- gardless of the criterion used for excluding trials (see Ap- pendix B).

Future studies could find procedural solutions to reduce the noise in the mouse movements, for exam- ple, by simplifying the task or by modifying the temporal order in which the task information is presented-similar to solutions used in other decision making applications of response dynamics (Dshemuchadse et al., 2013; Koop, 2013).


Finally, although the different curvatures of response trajectories for cooperation and defection decisions sup- port the idea that cooperation is the spontaneous and less conflicting response in social dilemmas, they do not nec- essarily imply that there are actually two distinct sys- tems (intuition and reflection)-as assumed by Rand et al. ( 2012)-that interact to produce decisions in social dilemmas (Horstmann et al., 2009).

Instead, these re- sponse trajectory differences could also be explained by a single system assuming functional differences in the ev- idence accumulation process (see Dshemuchadse et al., 2013, for a similar argument in intertemporal choice).

Fu- ture studies should address this problem, following Koop and Johnson (2011, 2013), who emphasized the potential of response dynamics for distinguishing between different model classes.

A particularly promising approach is a re- cent study combining response dynamics and eye tracking that tested quantitative predictions derived from a simple evidence accumulation model for risky decision making (Koop & Johnson, 2013, Experiment 3).

Future studies could use a similar approach to discriminate between dif- ferent model classes and test their specific predictions for social dilemmas.


Kieslich and Hilbig (2014) employ a mouse-tracking technique to measure decision conflict in social dilemmas.

They report that defectors exhibit more conflict than do cooperators.

They infer that cooperation thus is the reflexive, default behavior.

We argue, however, that their analysis fails to discriminate between reflexive versus cognitively controlled behavioral responses.

This is because cognitive conflict can emanate from resisting impulse successfully—or unsuccessfully.


Kieslich and Hilbig (2014) use mouse-tracking to measure cognitive conflict in three different economic games—the prisoner’s dilemma, the chicken game, and the stag hunt game—each characterized by a social dilemma.

They report that defection is associated with higher degrees of conflict than is cooperation, and they reason that this result is consistent with their prediction that the reflexive, “default” behavior is to cooperate [1].

We argue, however, that their specific result—and their paradigm, more generally—fails to discriminate between reflexive versus cognitively controlled behavioral responses.


Kieslich and Hilbig (2014) base their prediction on results arising from a controversial research program by Rand and colleagues (see e.g., Rand et al., 2012; Rand et al., 2014) [2][3].

The message in this program is succinctly conveyed in the title of the first paper, “Spontaneous giving and calculated greed” (Rand et al., 2012) [2].

However, as Kieslich and Hilbig (2014) note, the empirical pattern is contested (e.g., Tinghög et al., 2013; Lohse et al., 2014; Verkoeijen & Bouwmeester, 2014) [4][5][6], and a recent re-examination of Rand et al. ( 2012) and Rand et al. ( 2014) reveals that the data sets contain no meaningful evidence for the notion that giving is “spontaneous” and greed “calculated” (Myrseth & Wollbrant, 2015) [7].

Efforts to shed further light on the cognitive processes behind the decision to defect or cooperate are thus well placed.


Adopting the stated assumption that cooperation represents the reflexive, default behavior, Kieslich and Hilbig (2014) formulate the following prediction: cooperation should be associated with less cognitive conflict than should defection.

Their line of reasoning is predicated on two tacit assumptions: (1) efforts to resist behavioral impulse involve more conflict than would no such efforts; and (2) efforts to resist impulse are successful.

The former is unproblematic, but the latter is clearly not.

Sometimes individuals succeed at self-control; other times they fail.

Thus, cognitive conflict may emanate both from the successful and unsuccessful resistance to impulse.

If we knew that conflict associated with successful self-control were stronger than that associated with self-control failure, then we could derive the prediction put forth by Kieslich and Hilbig (2014).

However, we do not know this, and the alternative possibility—that unsuccessful resistance to impulse is associated with more conflict—seems equally plausible.

In that event, and on the assumption that cooperation is the impulsive response, one might predict that cooperation is associated with more cognitive conflict [8].

The authors’ prediction, therefore, does not follow from their stated assumption; it might well be the opposite.


That said, Kieslich and Hilbig (2014) add to the literature by applying a measure of cognitive conflict hitherto not introduced in the study of social dilemmas. And they find that those who defect exhibit more conflict than do those who cooperate.

Might we work the other way, and deduce from this which behavioral response—to defect or to cooperate—represents the default? Unfortunately, we cannot.

Observing that defectors exhibit more conflict could mean (a) that cooperation is the default, and that successful self-control entails more conflict than does unsuccessful self-control, but it could also mean (b) that defection is the default, and self-control failure entails more conflict than does successful self-control.

Neither possibility seems profoundly more plausible than the other.

A visualization of possible interpretations of differences in conflict might prove instructive.


Myrseth and Fishbach (2009) present a two-stage model of self-control (see Figure 1 for an adaptation), which distinguishes between identification of self-control conflict, and the exercise of self-control [9].

This framework has recently been applied to the study of pro-social behavior in economic games (e.g., Martinsson et al., 2012; Kocher et al., 2013; Martinsson et al., 2014) [10][11][12], and it highlights when we would expect individuals to experience cognitive conflict.

Crucially, when individuals fail to identify a self-control conflict, they will experience none, and will follow their impulse.

However, they may also follow their impulse after having seen the conflict but failed in their efforts to resist.

Hence, an observed behavior could be the result of three distinct cognitive scenarios: (1) absence of conflict identification, accompanied by no experience of conflict; (2) conflict identification, conflict experience, and successful self-control; or, (3) conflict identification, conflict experience, and failed resistance.

Merely observing, then, that one of two behaviors is associated with more conflict, does not allow us to determine which is driven by impulse.


One might also consider whether the conclusions drawn by Kieslich and Hilbig (2014) are justified by the moderator, the Honesty-Humility factor of the HEXACO personality model (Ashton & Lee, 2007; Ashton et al., 2014) [13][14].

Unfortunately, they are not. Again Kieslich and Hilbig (2014) base their reasoning on an unnecessarily restrictive assumption: “Dispositionally cooperative individuals should be particularly inclined to cooperate spontaneously and their decisions should be characterized by particularly strong conflict whenever they defect” (p. 513).

How do we know that disposition here captures spontaneous inclination as opposed to deliberative goal pursuit? Might it not be equally plausible that dispositionally cooperative individuals are characterized by the same spontaneous inclinations as others, but differ in their deliberate pursuit of abstract goals? This second interpretation would also allow for the pattern observed by Kieslich and Hilbig, but it would change the meaning: we might observe among dispositionally cooperative individuals a stronger difference in cognitive conflict between defectors and cooperators because dispositionally cooperative individuals more likely would identify the self-control conflict between cooperation and defection.

Higher observed levels of conflict associated with defection would then mean that self-control failure involves more conflict than does successful self-control.


Although Kieslich and Hilbig fail to discriminate between default versus cognitively controlled behavioral responses, they do provide evidence against four out of a total of six possible cognitive scenarios.

As illustrated in Table 1, there might have been no difference in conflict between cooperators and defectors, and cooperation could have been associated with more conflict; the latter possibility could have resulted from cooperation as the default and self-control failure yielding more conflict than successful self-control, or from defection as the default and successful self-control yielding more conflict than self-control failure.

As for assigning merit to the two possibilities that both involve higher conflict associated with defection, we would have to determine how to interpret the moderator—whether it captures dispositional spontaneous inclination or deliberative goal-pursuit.

If it captures the former, then we could conclude that the default is to cooperate (among the dispositionally cooperative) and that more conflict is associated with successful self-control in efforts to defect. But if it measures the latter, then we would conclude that the default is to defect, and that more conflict is associated with failed resistance to the impulse to defect.


We conclude that the predictions formulated by Kieslich and Hilbig (2014) are premature, as is the inference that their empirical results “support the idea that cooperation is the spontaneous and less conflicting response in social dilemmas” (p. 519).

While their results indeed have narrowed the possibility set, they do not allow us to discriminate between the two possible reflexive, default behaviors.

Future research might clarify the meaning of cognitive conflict in economic games.


Myrseth and Wollbrant (2015) offer an alternative theoretical explanation for our finding that defection entails more cognitive conflict than cooperation [1].

Although we completely agree that different theoretical explanations for a result are possible, we maintain that the theoretical approach we tested (Rand et al., 2014) is parsimonious and falsifiable, excluding certain plausible results a priori [2].

By comparison, the alternative framework proposed by Myrseth and Wollbrant requires several debatable assumptions to account for our findings, rendering it the more complex theory.

Besides, their framework as a whole could have accounted for any possible finding in our experiment, making it impossible to falsify it with our data.

We thus conclude that the notion by Rand et al.—that there is a spontaneous disposition to cooperate—has more empirical content while requiring fewer assumptions.


In their comment on our original article [1], Myrseth and Wollbrant (2015, M&W in what follows) raise an important general issue concerning the conclusiveness of empirical findings.

Whereas their arguments do not question that the methodology and analyses presented in our article indeed test (and support) the hypothesis that defection entails more cognitive conflict than cooperation, M&W offer an alternative explanation for the empirical pattern based on their own theoretical framework.

They argue that different assumptions within their framework—assumptions that run contrary to the theoretical assumptions underlying our hypothesis—could predict the same empirical pattern.

Here we will very briefly reiterate our line of reasoning and then discuss M&W’s alternative theoretical framework.


From recent theoretical arguments by Rand and colleagues that cooperation is the effortless and spontaneous response in social dilemmas [3][2], we derived the prediction that defection should entail more cognitive conflict—as measured by the curvature of response trajectories—than cooperation.

Our findings mirrored the hypothesis, showing that the attraction of the non-chosen option was stronger when people defected—which held particularly for individuals with a stronger dispositional tendency to make cooperative choices.

More importantly, the theoretical framework by Rand et al. could not have accounted for the opposite pattern (more conflict for cooperative choices) or a null effect (equivalent conflict for defection and cooperation).

As such, the hypothesis derived from Rand et al. ’s framework is specific and was strictly tested in the sense that it could have been falsified.


However, as is well known from the philosophy of science, the fact that empirical data are in line with a theory (even though it could have failed) is merely necessary but not sufficient for the “truth” of said theory [4]—simply because it is usually possible to generate various theoretical explanations for the same finding.

Note that we meant to imply this in the discussion of our original article in stating that “although the different curvatures of response trajectories for cooperation and defection decisions support the idea that cooperation is the spontaneous and less conflicting response in social dilemmas, they do not necessarily imply that there are actually two distinct systems (intuition and reflection)—as assumed by Rand et al. ( 2012)—that interact to produce decisions in social dilemmas” [1].

Whereas M&W’s selective citation of part of this sentence lets our reasoning appear to be inductive, it was clearly not our intention to make conclusive inferences regarding the “truth” of the theoretical framework by Rand et al. We merely sought to test a specific prediction derived from it and found support (in that the empirical data was in line with—and hence did not falsify—the prediction).

Nonetheless, other theoretical models may just as well allow for the observed empirical pattern to occur and thus be viable alternatives.