The sample was composed of one hundred and twenty-one healthy participants, whose ages ranged from 18 to 33 years old (Xage= 21.7; SD = 2.8). They were recruited through media advertisements, and all of them completed an online survey on their financial habits. Participants were eligible if they had normal or corrected-to-normal vision and above 18 years old. The exclusion criteria were: 1) any illness or mental disorders suggesting possible difficulty in completing the different tasks and 2) current use of psychotropic medications for psychiatric symptoms with a concurrent dependence on other substances (cocaine, heroin, alcohol, etc.).
This study was approved by the Ethics Committee for Research in Humans of the University of Granada (Spain) (Approval code: 717) and was conducted in accordance with the Declaration of Helsinki. All participants signed written informed consent.
The UPPS Impulsive Behavior Scale 19was utilized, however we used theSpanish version 20. The UPPS is a 45-item self-report questionnaire based on a four-factor model of personality traits thought to be associated with behavioral patterns of impulsivity. The determining factors consist of urgency, lack of premeditation, lack of perseverance, and sensation seeking. Participants respond using a Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree). Although results are generally interpreted at the subscale level, a total UPPS score was also computed. The 1-month test/re-test reliability of total UPPS scores in this sample was .87 (BPD r = .85; DD r = .89).
The Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ; Torrubia et al., 2001). The 48-item SPSRQ measures sensitivities to punishment and reward. The Sensitivity to Punishment (SP) scale measures individual differences in functions dependent upon the BIS. Items on the Sensitivity to Punishment (SP) scale relate to a passive avoidance in general situations that involve the possibility of aversive consequences, and also to a heightened sense of worry or the cognitive processes produced by the threat of punishment or failure. The Sensitivity to Reward (SR) scale assesses individual differences in Gray's BAS dimension. Items on this scale refer to obtaining possible rewarding stimuli such as money, sex partners, social events, power, and sensation. Reliability of the SPSRQ was found to be satisfactory with alphas of 0.75 (SR) and 0.83 21.
Trust and risk games.
In order to investigate the factors associated with decision-making in digital contexts, we implemented an adaptation of the trust and risk games. Specifically, participants played 12 trials of each game in a pseudo-randomized order, for a total of 24 trials. At the beginning of each trial, participants watched a four second message on the screen explaining the game they were about to play (risk or trust). Immediately afterwards, they had eight seconds to decide how much to transfer and then press the corresponding button.
Participants played the role of “investor”. To increase the credibility of the game, participants were told that their possible “trustee” was another randomly selected participant in the experiment. In each trial, participants received an initial endowment of 12 points, of which participants could give either 0, 4, 8 or 12 points to the trustee. The trustee had the option to return any amount between zero and their total available amount to the investor. Participants made their selections by pressing one of the four buttons we provided, indicating the number of points to transfer. Subsequently, a new screen appeared for eight seconds informing the participants that the administrators were in the process of making their decision. Finally, each trial ended with a fixation crossover screen with a variable duration of 10-12 seconds.
Participants were faced with the same choices as in the trust game. However, in this game the “investors” were paired with a random computer mechanism in the role of “trustee”, rather than with another participant in the experiment. Therefore, participants knew that a computer would determine whether or not to return points based on the probability distribution generated by the trustees' decisions in the trust game. Thus, participants would face the same possibility of having their points returned as in the trust game, however the risk game eliminates the influence of interpersonal expectations such as trust, betrayal or reciprocity.
After the first 12 rounds of both risk and trust games, participants received information on the number of times their partner (person or computer) had returned their points. At the end of the experiment, participants were informed about the total number of points they scored in each trial. The central difference between trust and risk games is that while in the risk game the investor's potential risk depends on a random mechanism, while in the trust game it relies on the uncertainty about a social interaction with a real person in the role of trustee.
Experimental evidence shows that individuals have an aversion to being betrayed 16. Therefore, in our experiment, investors displaying a lower risk-taking tendency in the trust game as opposed to the risk game, indicates what we interpret as “betrayal aversion”. Based on this, the amount of points transferred was collected on a trial-by-trial basis. The average amounts sent in the trust and risk games were calculated, respectively. In addition, to further examine the phenomenon of betrayal aversion, we compared the points transferred in each game and then calculated a betrayal aversion score by subtracting the amount transferred in the risk game from the amount transferred in the trust game.
An online survey was designed to ask the general population about their financial habits. The main structure of the survey followed that of the Survey of Consumer Payment Choice conducted by the Federal Reserve Bank of Boston. However, our survey incorporated comprehensive information on consumers' digital preferences. We included information on a set of factors that, based on the theoretical underpinnings of technology acceptance, explain the adoption and use of digital channels (e.g., perceived usefulness, cost, complexity, convenience, and risk). All survey participants were offered the opportunity to participate in a second session in which some of the tests were administered. Specifically, the task was a computer version of the trust and risk game. At the end of the experiment, subjects would receive a payment based on a publicly announced exchange rate of 25 points = 1 euro. The protocol described follows the structure described in a previous study 18.
Imaging data acquisition and preprocessing
Neuroimaging data acquisition and preprocessing corresponds to the protocol described in our previous study 18. In order to acquire brain measurements, we used a 3 T Magnetom Tim Trio scanner supplied by Siemens Medical Solutions (Erlangen, Germany) equipped with a 32-channel receive-only head coil. We acquired T2*-weighted echo-planar imaging (EPI) sequences. The following parameters were used: Repetition time (TR): 2,000 ms; echo time (TE): 25 ms; flip angle: 80º; field of view (FOV): 238 mm; number of slices: 35; voxel size: 3.5 × 3.5 × 3.5 mm; gap: 0.7 mm; number of volumes: 390 and 410 for the trust and video tasks, respectively.
All images were axial and parellel to the AC-PC plane37. Both a sagittal three-dimensional T1-weighted image and a diffusion tensor imaging sequence were obtained. The parameters provided to obtain the images were: For the TR: 2,300 ms for the 3D image; 3.1 ms for TE; 9º flip angle; 256 mm FOV; 208 slices; 0.8 × 0.8 × 0.8 mm voxel dimension. For DTI acquisition: TR: 9,400 ms; TE: 88 ms; FOV: 256 mm; 72 slices; 2.0 × 2.0 × 2.0 voxel dimension; 30 volumes with diffusion weighting (b = 1,000 s/mm2) and one volume without diffusion weighting (b = 0 s/mm2)37.
All images were inspected for correct processing. T1 image processing was conducted using the recon-all automated processing pipeline in Freesurfer (version 6.0). Cortical and subcortical volumes were automatically calculated based in the Destrieux atlas38 and the subcortical Freesurfer parcellation39. The software used to obtain the functional images was the Statistical Parametric Mapping (SPM12) which is available to the [neuro]imaging community through the University College London (Welcome Department of Cognitive Neurology https://www.fil.ion.ucl.ac.uk/spm/). Preprocessing with SPM12 includes realignment to the first image of the time series, co-registration to the structural image of each participant, unwarping, slice-timing correction, outlier detection, and normalization to an EPI template in the Montreal Neurobiological Institute (MNI) space.
We presented sociodemographic data as percentages and frequencies. Firstly, a Multiple Lineal Regression was conducted to identify the strongest predictors of risk-taking behavior (trust in traditional financial transactions, trust in digital transactions and impulsivity dimensions). In addition, cluster analysis has been used to group different psychological traits according to the betrayal aversion cluster (reward and punishment sensitivity scale and impulsive personality traits).
Finally, in order to analyze the causal relationship between the influence of psychological variables and risk and betrayal aversion, we dichotomized the betrayal aversion score into two categories high aversion (n=66; 68% females) and low aversion (n=53; 62% males) as a function of the overall mean. We statistically compared cortical thickness values between low-aversion and high-aversion participants. A two-sample t-test was performed to compare whether there are differences in cortical and subcortical volumes between the groups. All statistical analyses were carried out using SPSS 25.0.