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. Author manuscript; available in PMC: 2022 Nov 1.
Published in final edited form as: Biol Psychol. 2021 Oct 18;166:108209. doi: 10.1016/j.biopsycho.2021.108209

Culture-related differences in the neural processing of probability during mixed lottery value-based decision-making

Chun-Yi Lee a, Chi-Chuan Chen a, Ross W Mair b, Angela Gutchess c,d, Joshua Oon Soo Goh a,e,f,g,*
PMCID: PMC8643324  NIHMSID: NIHMS1753589  PMID: 34673148

Abstract

This study evaluated how differences in economic risk-taking in Westerners and East Asians reflect cultural differences in the analytic or holistic processing of probabilistic outcomes during value-based decisions. Twenty-seven Americans (US) and 51 Taiwanese (TW) young adults completed a functional magnetic resonance imaging (fMRI) Lottery Choice Task (LCT) experiment. Participants accepted or rejected stakes with varying probabilities of winning or losing different magnitudes of points. TW participants accepted more stakes when win probabilities were > 0.50, whereas US participants reduced their acceptance rates of winning stakes more discriminately as win probabilities decreased. Both groups rejected losing stakes (win probabilities < 0.50) with similar frequency. Critically, ventromedial prefrontal cortex (VMPFC) responses correspondingly showed greater discrimination between win probability conditions in US than TW groups. Our findings highlight a neurocognitive mechanism in the VMPFC for how cultural differences in distinguishing between probabilistic reward outcomes shape neural computations of risk and prospects.

Keywords: Culture, Decision-Making, FMRI

1. Introduction

When deciding between financial choices, East Asians are more likely than Westerners to accept riskier options and expect greater returns (Fan & Xiao, 2005; Hsee & Weber, 1999). Such examples of differential value-based decision behaviors suggest that a person’s cultural background influences their perceptions of risk and value despite receiving the same value-related information (Bontempo, Bottom, & Weber, 1997; Levinson & Peng, 2006). In light of increasing international cross-communication and exchange, it is critical to understand how culture influences the subjective processing of value-related information (Douglas & Wildavsky, 1982; Guiso, Sapienza, & Zingales, 2006; Zheng, El Ghoul, Guedhami, & Kwok, 2012; Zingales, 2015), particularly when outcomes are uncertain or probabilistic (Kahneman & Tversky, 1979; Tversky & Kahneman, 1974). Here, we sought to distill candidate psychological mechanisms underlying culture-related differences in probabilistic value-based choice behaviors by evaluating associated neural responses during the decision process. To this end, we applied a mixed lottery decision functional magnetic resonance imaging (fMRI) experiment validated in previous studies (Chuang, Su, & Goh, 2020; Goh et al., 2016; Su et al. 2018) and measured neural responses in Westerners and East Asians as they deliberated differential probabilistic outcomes for their choice decisions.

Differential risk-taking between East Asians and Westerners has been attributed to distinct treatment of probabilistic information in persons from these culture groups (Phillips & Wright, 1977; Wright & Phillips, 1980; Yates, Lee, Shinotsuka, Patalano, & Sieck, 1998). Interestingly, many studies have already demonstrated culture-related differences even at the basic level of perceptual processing and attention to visual stimuli in Westerners and East Asians (Goh, Li, Tu, & Dallaire-Théroux, 2020; Goh & Park, 2009; Markus & Kitayama, 1991; Nisbett, 2003; Nisbett & Miyamoto, 2005; Nisbett, Peng, Choi, & Norenzayan, 2001). Specifically, Westerners evince more analytic cognitive styles that emphasize the perception of physical features discriminating between different items. By contrast, East Asians engage more holistic cognitive styles and perceive different items associatively as parts of a common context. Generalizing these notions to the processing of uncertain outcomes during decision-making, we hypothesized that analytic cognition in Westerners should involve more discriminative predictions about different subsequent probabilistic states. Moreover, holistic cognition in East Asians should involve less distinctive treatment of cues signaling different outcome probabilities – i.e., outcomes are viewed as binary contexts of either gain or loss. In support, Westerners have been shown to use a range of probabilistic responses whereas East Asians use more yes/no responses when answering questions about facts or the possibility of event occurrences, despite no group differences in mathematical ability or concepts about probability (Phillips & Wright, 1977; Wright & Phillips, 1980). In addition, Taiwanese East Asians show less discrimination than Westerners of differential information that probabilistically predicted a fictional disease (Yates et al., 1998).

We also consider an alternative perspective involving the greater emphasis on individualism in Western culture in contrast to collectivism in East Asian culture (Hofstede, 1983; Nisbett, 2003; Triandis, 1995). In this view, greater risk-taking in East Asians than Westerners is mediated by a more interdependent social structure in the former and a more independent social mode in the latter (Fan & Xiao, 2005; Hsee & Weber, 1999). Specifically, East Asians might engage in more risk-taking economic decisions because they perceive themselves as having financial aid buffers provided by their social connections resulting in a sort of cushioning effect. By contrast, Western society regards financial resources more as a personal responsibility so that financial support through social connections is not expected. This in turn might provide less psychological security for one’s financial situation, enhancing attention to differential losing and gaining of resources, and discouraging risk-taking. Overall, whereas the first perspective suggests that culturally distinct cognitive styles drive differential discriminative predictions of probabilistic outcome states in Western and East Asian decision-making, the second perspective appeals to a social buffering proxy that licenses behavioral prospecting in East Asians more than Westerners.

In this present study, we assessed risk-taking behavior in Westerners (from the United States of America, US) and East Asians (from Taiwan, TW) using economic-type decision scenarios in the Lottery Choice Task (LCT). In the LCT, participants choose to accept or reject stakes with different probabilities of winning (and simultaneously losing) different magnitudes of points, which are explicitly presented. Critically, we were interested in whether the cultural differences in LCT decision behavior evinced differences in probabilistic processing and whether they were also associated with differences in social support. When processing mixed lottery stakes ranging from likely losses to likely gains, we reasoned that less discriminative treatment of probabilistic information in East Asians than Westerners, as reviewed above, should result in greater risk-taking under gains as well as losses in the former reflecting holistic and analytic processing styles, respectively. Moreover, greater discrimination of different probabilities in Westerners than East Asians should correspond with greater modulation of neural responses involved in deliberating different predictive representations of probabilistic states about outcome values during the LCT. As such, there should be more distinctive neural responses in Westerners than East Asians across different probability stake conditions in reward processing areas including the ventromedial prefrontal cortex (VMPFC) and the striatum (Goh et al., 2016; Haber & Knutson, 2010; Knutson, Taylor, Kaufman, Peterson, & Glover, 2005; Kuhnen & Knutson, 2005; Su et al., 2018). In addition, the social support perspective suggests that more risk-taking decision behaviors in East Asians should be associated with a greater sense of support from their social circles compared to their Western counterparts. If so, we expect VMPFC and striatal responses to differential reward conditions should also be modulated by the differential degree of social support in East Asians relative to Westerners.

2. Methods

2.1. Participants

A total of 78 right-handed young adults with no history of neurological or psychiatric diseases participated in this cross-cultural fMRI study. Fifty-one participants were TW young adults (mean age (SD) = 23.3 (2.32); age range = 20–29; 26 female and 25 male) who were recruited from National Taiwan University (NTU) and the Taipei city area in Taiwan. Twenty-seven participants were US young adults (mean age (SD) = 21.4 (3.51); age range = 18–29; 15 female and 12 male) who were recruited from Brandeis University and the greater Boston area in the United States of America. TW and US participants were all native to their own country and had not lived outside their respective countries at least within the last two years prior to participation. All participants provided written informed consent before beginning the study, which was approved by the Brandeis University Institutional Review Board and NTU Hospital Research Ethics Committee. Participants were all remunerated for their time in the study. Data collection of this present sample began in mid-2019. Due to the COVID-19 pandemic, data collection at the US site was halted in March 2020, hence the somewhat unbalanced samples across sites. To address this, group comparisons below were conducted using Welch’s t-test where possible given its robustness against unequal sample sizes (Derrick, Toher, & White, 2016; Ruxton, 2006). In addition, we evaluated statistical results using bootstrap sampling with 10,000 iterations of n = 27 each for the TW and US groups where possible. Finally, we also applied corresponding analyses using a fixed subset of TW participants (n = 27) that were matched with the US participants by age and sex.

2.2. Personal value and social support assessments

In addition to the LCT fMRI experiment, participants also completed the Schwartz Values Survey (SVS; Schwartz, 1992, 1994; Schwartz & Sagiv, 1995;) and the Singelis Individualism and Collectivism Scale (IND-COL; Singelis, 1994) to assess their personal values associated with individualism and collectivism as a means to validate the cultural backgrounds of our participant sample. The SVS consists of 57 word items for which participants rated the importance of the personal values described by each word on a 9-point scale. In this present study, we derived each participants’ personal value scores for Hedonism and Security based on their SVS responses (see Supplementary Methods) as these were most relevant to our LCT value-based decision-making task (see Chuang et al., 2020). We additionally used SVS items to define a Social Image sub-value (Supplementary Methods) in our subsequent evaluation of the possibility that TW participants reported lower social support from their networks (see below Results 3.1) because of their cultural value of maintaining public impression and not burdening others (Han, 2016; Hwang, 1987; Kim, Sherman, & Taylor, 2008; Sherman, Kim, & Taylor, 2009; Taylor et al. 2004). The IND-COL scale consists of 30 questions probing how participants relate to the self, others, and society. Participants rated their agreement with each question on a 7-point scale. Mean ratings for half of the questions indexed a participant’s degree of independence while the other half indexed their interdependence. Participants also completed the Social Support Questionnaire (SSQ; Sarason, Levine, Basham, & Sarason, 1983) which afforded the number of persons from which each participant reported they could receive social support for general (total), financial, and emotional needs, as well as the mean satisfaction level (based on a 6-point scale) with the support they had for each of these needs. All participants completed the above assessments on a separate day from the MRI session but within one month.

2.3. fMRI lottery choice task behavioral paradigm and data analysis

There were 180 trials in the LCT applied in this study (Fig. 1A). Trials each consisted of a choice and an outcome phase and were distributed equally across four scanning runs. During the choice phase, participants were presented with text stimuli depicting stakes with varying magnitudes, M, of points (low (ML): 1–12; middle (MM): 50–61; high (MH): 99–110) that were fully crossed with varying levels of probabilities, P, of winning (low-low (PLL): 4–15%; middle-low (PML): 24–35%; middle-middle (PMM): 44–55%; middle-high (PMH): 64–75%; high-high (PHH): 84–95%). Probabilities of winning were simultaneously the reverse probabilities (1 - P) of losing the same stake magnitudes. In sum, there were 15 choice conditions each with 12 trials, with variable expected values (EV) such that EV = (P × M) + ((1-P) × (-M)). Table S1 shows the ranges and details of P, M, and EVs of trials across the 15 choice conditions. Positive EVs denoted winning stakes, such as when a trial had a middle-high P of winning low M (e.g. 75% chance of 8 points; PMHML). Negative EVs denoted losing stakes, for example when a trial has low-low P of winning high M (e.g. 5% chance of a 100 points outcome, which is also 95% chance of a −100 points outcome; PLLMH). Note that although choice conditions are here described as discretized conditions with ranges of P and M (and EV), participants perceived P and M as continuously distributed across trials. The discretization facilitated pseudo-randomized trial order such that no choice conditions were repeated three times consecutively. Participants were instructed to accumulate as many points as possible over the entire LCT experiment by indicating whether they accepted or rejected the stakes presented in each trial using assigned button presses. Choice stimuli remained on screen for a full 4 s within which responses were made, following which the outcome was shown.

Fig. 1.

Fig. 1.

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A. Sample trial stimuli for the Lottery Choice Task (LCT) in which participants viewed a sample stake during the choice phase and decided to accept or decline the stake. In the example, 87 points with a 71% probability of winning (simultaneously a 29% probability of losing the same magnitude) is the presented stake. After participants make their choice, an inter-trial interval (ITI) fixation is presented followed by the outcome feedback. Accepted stakes result in either gain or loss feedback with the outcome displayed as the top number (a gain of 87 points in the example) and the accumulated points displayed as the bottom number. Outcomes for declined stakes are also shown but in parentheses with no change in accumulated points. B. Mean acceptance rates across the different condition levels of probability from LL to HH (see Methods) of Taiwanese (TW) and American (US) participants. TW showed higher acceptances than US for MH and MM conditions. Error bars show standard error of the mean. The ranges of win probability for each probability condition are shown below the condition labels in parentheses. * indicate significant planned one-tailed group differences between the TW and US group at p(unc.) <0.05 following a significant probability × group interaction effect (see Results).

During the 2 s outcome phase, participants saw text stimuli with the top number indicating the gained (positive), lost (negative), or missed (also positive or negative but in parentheses) outcome points for that trial, and the bottom number indicating the currently accumulated points. Outcomes comprised four feedback conditions including accept and gain (AG), accept and loss (AL), reject and gain (RG), reject and loss (RL). Outcomes in each trial were predetermined according to the actual stakes with the limitation that no choice condition outcome could have only winning or only losing outcomes. Outcomes for trials in which participants failed to respond within the 4 s choice phase (null responses) resulted in an outcome of zero points as well as a reminder to respond on time. Choice and outcome phases were separated from each other by fixation inter-stimulus intervals (ISI) jittered between 1 and 5 s with mean 3 s 10 s fixation screens also preceded and concluded each run to facilitate rest baseline neural responses. Participants underwent a practice version of the task before scanning to ensure that they fully understood the task goal and were familiar with the meaning and range of probabilities and magnitudes. To ensure comparable testing across sites, laboratory personnel traveled to both experimental sites to aid initial protocol set up with additional coordination via online video calls to facilitate comparability. J.O.S.G and A.G. have extensive experience in collaborating and conducting cross-site fMRI studies.

Behavioral analyses of responses across the 10 choice conditions were conducted using R 3.6.0 (www.r-project.org). Acceptance rates (AR) for each of the 15 choice conditions were computed as the proportion of accepted trials out of all responded trials per condition. Mean ARs and response time (RT) of each participant were fed into repeated measures ANOVA to evaluate effects of group (TW, US), probability (PLL, PML, PMM, PMH, PHH), magnitude (ML, MM, MH), and their interactions. Specific planned contrasts based on our experimental hypotheses were applied using Welch’s t-tests to qualify significant interactions in the above ANOVA analysis. Significance for unplanned post-hoc behavioral pairwise comparisons were set at p < 0.05 with false discovery rate (FDR) adjustment for multiple comparisons where appropriate (Benjamini, 2010). Note that because our hypotheses in this study pertained to cultural differences in the processing of probability, we focused on effects of probability and group, and did not evaluate effects of magnitude in behavioral as well as brain data. Nevertheless, as reported in the results below, the effects of magnitude on decision behaviors and brain responses were not significantly modulated by culture groups.

2.4. Brain imaging acquisition protocol

MRI data were acquired using identical 3T Siemens MAGNETOM Prisma systems (Erlangen, Germany) with 64-channel head coils located at the Imaging Center for Integrated Body, Mind and Culture Research, National Taiwan University, Taipei, Taiwan, and the Center for Brain Science, Neuroimaging facility, Harvard University, Cambridge, MA, USA. Signal comparability between the two sites was assessed in calibration scans involving the same four participants (not in this present study) who were extensively scanned at these two sites (Chen, Li, Mair, Gutchess, & Goh, 2020). Briefly, the calibration results revealed that the effects of site on functional neural response estimates were minimal relative to the effects of individual differences between participants and tasks (visual and motor engagement). Moreover, effects of site were restricted largely to the primary visual area, which we attribute to higher mean light intensity of the stimuli projection system at the US than the TW site. We note a similar site effect in the visual area for a comparison between two other different Siemens MRI systems in a previous study involving the same visual and motor tasks (Sutton et al. 2008).

In this present study, for each participant, 676 functional volumes were acquired in each of four LCT fMRI runs using an echo-planar sequence that employed multiband RF pulses and Simultaneous Multi-Slice (SMS) acquisition (Feinberg et al., 2010; Moeller et al., 2010; Setsompop et al. 2012; Xu et al., 2013). The SMS technique allowed the acquisition of 8 slices simultaneously, increasing temporal resolution considerably over conventional techniques. 64 total slices were acquired at repetition time (TR) = 650 ms, echo time (TE) = 34.80 ms, flip-angle (FA) = 50°, field of view (FOV) = 220 × 220 mm, and 2.3 × 2.3 × 2.3 mm voxel resolution. Slices were aligned with a 20° tilt (upward at the anterior location) relative to the anterior-posterior commissural plane and positioned for whole-brain coverage. The SMS-EPI acquisition used the CMRR-MB pulse sequence from the University of Minnesota.

A T2 image coplanar to the functional images was also acquired for co-registration, with 64 axial slices, voxel size 1.0 × 1.0 × 2.3 mm, FOV = 256 × 256 mm, TR = 11170.0 ms, TE = 60 ms, and FA = 150°. A high resolution T1-weight magnetization-prepared rapid gradient echo image (multi-echo MPRAGE: van der Kouwe, Benner, Salat, & Fischl, 2008) was acquired for normalization to standard space with 176 sagittal slices, voxel size 1.0 × 1.0 × 1.0 mm, FOV = 256 × 256 mm, TR = 2530.0 ms, short TE = 1.69 ms, long TE = 7.27 ms, and FA = 7°. Dual gradient-echo field maps were also acquired for distortion correction and better spatial registration of the functional images with TR = 282.0 ms, short TE = 4.12 ms, long TE = 6.58 ms, FA = 55°m, FOV = 220 × 220 mm, axial slices = 64, voxel size 2.3 × 2.3 × 2.3 mm.

2.5. LCT fMRI whole-brain data preprocessing and analysis

Brain images were first converted from the DICOM to the NIfTI format using dcm2nii in MRIcron (https://www.nitrc.org/projects/mricron/). Preprocessing and analysis of functional brain imaging data were then conducted using SPM12 (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging, UK). For each participant, field maps were first coregistered to the functional images. A voxel displacement map (VDM) was then calculated from the coregistered phase and magnitude field maps and used in the realignment and unwarping of functional images for head motion as well as distortion correction. Bias fields were generated using segmentation of the mean unwarped images for each run and used to correct for signal inhomogeneity in functional images followed by slice-time corrections. The coplanar T2 anatomical image was coregistered to the first functional volume. The T1 image was then coregistered to the coregistered T2 image and then spatially normalized to the Montreal Neurological Institute (MNI) template space. Functional volumes were spatially normalized to MNI space using the resulting deformation parameters with resampling to 2 × 2 × 2 mm voxels. Spatial smoothing was then applied using a 3D 8 mm full-width at half-maximum (FWHM) Gaussian kernel.

For each participant, preprocessed functional images were submitted to first-level general linear model (GLM) analysis which included, for each run, delta regressors (0 s durations) for the onsets of the 15 discrete choice phase conditions (PLLML, PMLML, PMMML, PMHML, PHHML,PLLMM, PMLMM, PMMMM, PMHMM, PHHMM, PLLMH, PMLMH, PMMMH, PMHMH, PHHMH) and four outcome phase conditions (AG, AL, RG, RL) that were convolved with the canonical hemodynamic response function (HRF), six motion covariate parameters and a constant. These 26 regressors were replicated across the four functional runs. Whole-brain voxel-wise neural response estimates to the 15 choice conditions were then obtained using first-level contrasts averaging regression coefficients of the model across the four runs, accordingly. Because the focus of this study is on cultural differences in the processing of probability information for decisions during the choice phase, outcome phase responses are not evaluated here.

A second-level whole-brain voxel-wise analysis was then conducted on participant first-level choice phase neural responses estimates. This analyses applied a factorial model with probability (5 levels) and magnitude (3 levels) as within-subjects independent variables, group (US, TW) as the between-subjects independent variable, and age and sex as covariates. To identify brain areas showing group differences in neural processing across different probability conditions during the LCT, we contrasted US and TW mean neural responses for the PHH, PMH, PMM, and PML conditions relative to the PLL condition as the baseline response for each group, collapsed across magnitude levels. The PLL condition was included as the baseline in group contrasts in order to remove possible neural estimates effects due to site system differences. We considered this condition as a valid baseline because we expected it to be the least salient (involving least value and deliberation) in the LCT and there were no group differences in behavioral ARs (see Results) for this condition. Significance threshold for whole-brain contrasts was a cluster-level family wise error (FWE) rate of p < 0.05 using 3dClustSim in AFNI version 21.0.11 (Cox, Chen, Glen, Reynolds, & Taylor, 2017; Forman et al. 1995). We set a primary voxel threshold of p < 0.001 (uncorrected) which yielded a cluster size >180.

2.6. fMRI functional ROI analysis

Follow-up of second-level whole-brain neural response contrasts were conducted using functional ROIs. Functional ROIs were defined as contiguous voxels within an 8 mm sphere around peak contrast voxels showing significant effects from the whole-brain contrasts above. Neural response estimates for the 15 discrete choice conditions were first extracted from identified ROIs for each individual participant. Extracted neural responses estimates were then used to visualize and further validate specific effects not immediately obvious from the above whole-brain contrasts.

3. Results

3.1. Differential personal values and social support between TW and US groups

Participant demographics and summaries of ratings for their personal values and social support are listed in Table 1 for TW and US groups along with results of planned group comparisons. Note that sample sizes were different for some assessments due to missing participant data. Table S2 lists the results for the same analyses applied on a subset of the TW sample (n = 27) matched by age and sex to the US sample (see Methods) to further address the issue of sample size differences between the TW and US groups. Whereas TW and US rated Hedonism similarly, Security was rated higher in TW than US participants (t(50) = 2.01, p(unc.) = 0.025, bootstrap 95% CI [0.026, 1.029]), as expected. In addition, interdependence was higher in the TW than the US group (t(48) = 2.13, p(unc.) = 0.019, bootstrap 95% CI [0.042, 0.664]) whereas independence was higher in the US than the TW group (t(44) = −1.92, p(unc.) = 0.031, although bootstrap 95% CI was [−0.639, 0.008]).

Table 1.

Means and standard deviations (SD) of participant demographics and ratings for sub-variables in the Schwartz Value Survey (SVS), Social Support Questionnaire (SSQ), and the Individualism-Collectivism Scale (IND-COL) for Taiwanese (TW; n = 51, 26 F) and Americans (US; n = 27, 15 F) in our sample. Note that sample sizes for SVS, SSQ, and IND-COL differed due to some missing participant data. P-values are all for one-tailed t-tests unless otherwise indicated. T-values are planned comparisons based on Welch’s t-test and bootstrap 95% CI are reported for the mean differences of TW - US contrasts obtained from 10,000 samplings of n = 27 of each group.

Variable Sub-variable TW US Group comparison
mean SD mean SD T DF p (unc.) Bootstrap 95% CI
AGE 23.3 2.32 21.4 3.51 2.44 38 0.019d [0.327, 3.224]
EDUCATION 16.8 1.89 15.4 2.29 2.57 45 0.014d [0.331, 2.289]
SVSa
Hedonism 4.63 1.49 4.76 1.39 −0.38 54 0.354 [−0.795, 0.555]
Security 5.09 1.07 4.57 1.08 2.01 50 0.025 [0.026, 1.029]
Social Image 4.84 1.34 4.22 0.90 2.40 70 0.010 [0.114, 1.121]
IND-COLb
Independence 4.47 0.62 4.80 0.73 −1.92 44 0.031 [−0.639, 0.008]
Interdependence 4.89 0.65 4.55 0.69 2.13 48 0.019 [0.042, 0.664]
SSQc
Total support network size 3.26 1.51 4.99 2.11 −3.54 33 0.001 [−2.680, −0.799]
   Inner network size 1.38 0.90 2.36 0.94 −4.24 41 <0.001 [−1.437, −0.541]
   Outer network size 1.86 1.17 2.54 2.11 −1.44 28 0.081 [−1.614, 0.181]
   Satisfaction 4.96 0.81 5.32 0.68 −1.94 50 0.029 [−0.724, −0.004]
Financial support network size 3.18 1.65 3.91 2.41 −1.32 32 0.098 [−1.848, 0.319]
   Inner network size 2.08 0.97 2.35 1.03 −1.05 41 0.149 [−0.769, 0.213]
   Outer network size 1.10 1.39 1.57 2.19 −0.94 30 0.179 [−1.448, 0.440]
   Satisfaction 5.14 0.88 5.26 1.01 −0.49 38 0.312 [−0.572, 0.380]
Emotional support network size 3.27 1.59 5.14 2.14 −3.76 34 0.001 [−2.824, −0.929]
   Inner network size 1.27 0.94 2.37 0.99 −4.45 41 <0.001 [−1.569, −0.623]
   Outer network size 0.43 0.62 0.84 0.78 −2.25 35 0.016 [−0.774, −0.059]
   Satisfaction 4.93 0.87 5.32 0.70 −2.05 52 0.023 [−0.759, −0.017]
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a

SVS sample size: Taiwan [n = 50, 25 F), USA (n = 26, 15 F).

b

IND-COL sample size: Taiwan (n = 50, 25 F), USA (n = 26, 15 F).

c

SSQ sample size: Taiwan (n = 50, 25 F), USA (n = 23, 13 F).

d

Two-tailed.

Surprisingly, social support network sizes were smaller for the TW than the US group for total (t(33) = −3.54, p(unc.) = 0.001, bootstrap 95% CI [−2.680, −0.799]) and emotional (t(34) = −3.76, p(unc.) = 0.001, bootstrap 95% CI [−2.824, −0.929]) support with no difference for financial support (t(32) = −1.32, n.s., bootstrap 95% CI [−1.848, 0.319]). In addition, the degree of satisfaction was higher in the US than the TW group for the total (t(50) = −1.94, p(unc.) = 0.029, bootstrap 95% CI [−0.724, −0.004]) and emotional (t(52) = −2.05, p (unc.) = 0.023, bootstrap 95% [−0.759, −0.017]) networks. This counter-intuitive result was inconsistent with previous findings (Fan & Xiao, 2005; Hsee & Weber, 1999). As such, we considered finer definitions of social networks sizes in the SSQ responses involving distinguishing between support from parents, siblings, or partners (inner support) compared to relatives, friends, non-family members (outer support). Despite this, social support network sizes were still smaller for the TW than the US group for total (inner, t(41) = −4.24, p(unc.) < 0.001, bootstrap 95% CI [−1.437, −0.541]) and emotional (inner, t (41) = −4.45, p(unc.) <0.001, bootstrap 95% CI [−1.569, −0.623]; outer, t(35) = −2.55, p(unc.) = 0.016, bootstrap 95% CI [−0.774, −0.059]) support with no differences for financial support.

We further evaluated whether TW participants reported lower social support because of their value for social image (see Methods 2.2, Supplementary Methods). As expected, TW participants gave higher ratings for social image than US participants (Table 1; t(70) = 2.40, p(unc.) = 0.010, bootstrap 95% CI [0.114, 1.121]). Importantly, in post-hoc analyses, higher social image ratings correlated with higher security (r = 0.55, p(FDR) < 0.001, bootstrap 95% CI [0.386, 0.721]), and interdependence (r = 0.46, p(FDR) < 0.001, bootstrap 95% CI [0.309, 0.611]) ratings, and lower network sizes for financial (r = −0.31, p (FDR) = 0.256, bootstrap 95% CI [−0.518, −0.119]) and outer financial (r = −0.26, p(FDR) = 0.04, bootstrap 95% CI [−0.489, −0.079]) support from others. Other associations between the SSQ, SVS, and Singelis responses are shown in Supplementary Results. Overall, these findings show that there is a stronger preference to maintain social image in TW than US participants that might have mitigated reported levels of social support.

3.2. Higher risk-taking in TW than US groups in lottery choice decisions

Fig. 1 B shows the acceptance rates (AR) across the five probability levels in the LCT for the TW and US groups. A repeated measures ANOVA revealed significant main effects of probability (F(4,1140) = 1244, p(unc.) <0.001), magnitude (F(2,1140) = 4.51, p(unc.) = 0.011), and group (F(1,1140 = 11.6, p(unc.) = 0.001), as well as probability × magnitude (F(8, 1140) = 2.69, p(unc.) = 0.006) and probability × group (F(4,1140) = 5.86, p(unc.) <0.001) interaction effects (see Table S3 for bootstrap and matched sample ANOVA). There were no significant interaction effects of magnitude with group on AR. Critically, more detailed planned pairwise comparisons showed that the probability × group interaction was driven by significantly higher ARs in TW than US participants for the PMH (t(37.4) = 1.90, p(unc.) = 0.032, one-tailed, bootstrap 95% CI [0.005, 0.165]; matched sample: t(37.8) = 2.25, p(unc.) = 0.015) and PMM (t(54.3) = 1.81, p(unc.) = 0.038, one-tailed, albeit bootstrap 95% CI [−0.011, 0.219]; matched sample: t(51.7) = 1.51, p(unc.) = 0.068) conditions with no significant differences in the other conditions. Planned comparisons of AR differences from a within-group perspective (Table S4) revealed the reduction of ARs were significantly lower in the US than the TW group from PHH to PMH (t(35.9) = −1.87, p(unc.) = 0.035, bootstrap 95% CI [−0.158, −0.005]) and from PHH to PMM (t(53.8) = −1.77, p(unc.) = 0.041, although the bootstrap 95% CI [−0.209, 0.008] and the matched sample comparison did not reach significance). In addition, there was a greater reduction of ARs from PMM to PLL in the TW than US group (t(58.5) = −2.18, p(unc.) = 0.017, bootstrap 95% CI [−0.224, −0.015]) with no group or condition differences in ARs between PML and PLL conditions.

To further validate that higher ARs in TW for the above probability conditions stemmed from a tendency to less discriminately accept winning stakes in this group, we examined ARs across a finer resolution of the probability conditions. Specifically, we discretized the continuous trial-wise probability stimuli into 10 levels (Table S5). As expected, this finer distinction of probability levels consistently yielded trends indicating higher ARs in TW than US generally when win probabilities were greater than 0.5 with the differences being significant at P7 (t(40.1) = 1.92, p(unc.) = 0.031, one-tailed, bootstrap 95% CI [0.005, 0.201]; matched sample: t(40.7) = 2.35, p(unc.) = 0.011, one-tailed) and P8 (t (33.8) = 1.63, p(unc.) = 0.056, one-tailed, albeit bootstrap 95% CI [−0.003, 0.123]; matched sample: t(33) = 1.79, p(unc.) = 0.041, one-tailed) (Fig. S1). When win probabilities dropped below 0.5, both groups similarly reduced their ARs below the chance level. In sum, whereas US participants visibly reduced their ARs as probability of winning decreased, albeit in winning stakes, TW participants tended to maintain higher levels of acceptance that reduced at a slower rate as a function of reducing win probability. In addition, both groups showed similar ARs for losing stakes where win probabilities were less than 0.5. Finally, we examined but found no significant correlations between the LCT ARs with the SSQ responses, with the strongest association being a negative one between the AR of PHH and total social support network size (r = −0.36, n.s.). This result suggests that social support had minimal direct influence on participant LCT decision behaviors.

3.3. Differential VMPFC responses to win probability in TW and US

Whole-brain neural responses to each of the probability conditions are shown for the TW (Fig. S2) and US (Fig. S3) groups separately. Critically, we found only the VMPFC (MNI coordinates 4, 20, −22) showing significantly greater neural response to the PMH relative to the PLL baseline condition in the TW than US group (Figs. 2A, see S4 for matched samples), in line with group differences in the behavioral AR for the PMH condition. Note, the use of PLL as the baseline condition in brain analyses removes potential site-related differences not to do with LCT-related processing that might affect neural response estimate comparisons (see Methods). Neural response estimates for the 15 choice conditions in the VMPFC ROI were extracted and submitted to a repeated measures ANOVA (Fig. 2B; see Table S6 for matched and bootstrap samples results). This analysis revealed significant main effects of group (F(1,1140) = 4.44, p = 0.035) and probability (4,1140) = 9.64, p < 0.001), and a group × probability interaction (F (4,1140) = 3.95, p = 0.003). There were no significant effects of magnitude. Select planned pairwise comparisons following up the significant interaction further revealed that neural responses were significantly lower for the PMM than PLL conditions in the US group (t(51.0) = 2.38, p(unc.) = 0.010, one-tailed, bootstrap 95% CI [0.406, 4.204]) and, critically, there was a greater reduction in VMPFC response from PHH to PMH in the US than the TW group (t(52.2) = −2.61, p(unc.) = 0.006, one-tailed, bootstrap 95% CI [−3.563, −0.555]) (Table S7). In general, the trends support more distinctive VMPFC responses to different probability conditions in the US than TW group characterized by more reduced neural responses for the Americans during the PMH and PMM relative to other conditions, which corresponded with group differences in behavioral ARs to these conditions above. Finally, we did not find significant associations between the VMPFC ROI neural response estimates to probability conditions and the SSQ responses, with the strongest association being a negative one between PHH and financial support network size (r = −0.22, n.s.).

Fig. 2.

Fig. 2.

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A. Sagittal view of ventromedial prefrontal cortex (VMPFC) in which Taiwanese (TW) showed significantly higher neural activity contrast than Americans (US) in the MH relative to the LL probability conditions. Specifically, MH and LL condition responses were similar in the TW group but more suppressed for MH than LL conditions in the US group (see Results for details on other pairwise comparisons). This statistical activation map was thresholded at whole-brain cluster-wise p (FWE) < 0.05. B. Mean neural response estimates for TW and US participants across probability levels were extracted from the VMPFC region-of-interest (ROI) identified from the whole-brain contrasts above. Error bars represent standard error of the mean. Black line overlays reflect linear trends of the responses as a visualization aid. FWE: Family-Wise Error rate adjustment for multiple comparisons.

4. Discussion

Accurate processing of the probabilities of external physical or abstract events is critical for how we interact with our environment. Moreover, under circumstances when information about such event outcome probabilities is explicit, it might be expected that human beings should engage similar behaviors in response to the same sensed information. Our findings show that this is not always the case and that a person’s cultural background influences their decision behaviors pertaining to probabilistic information. Specifically, whereas US participants reduced their acceptances of winning stakes as information indicated that the likelihood of winning was reduced, TW participants maintained relatively higher acceptance rates until the probability of winning was less than 0.5. This finding is consistent with previous studies showing greater discrimination of different levels of probability in Westerners and more binary yes/no categorical decision responses in East Asians (Bontempo et al., 1997; Phillips & Wright, 1977; Wright & Phillips, 1980; Yates et al., 1998). Importantly, it is unlikely that such cultural differences in decision behavior about probabilistic information stems from differences in mathematical ability or understanding to do with the concept of probability. Participants in our study were all at least at the undergraduate level of education or above. Rather, we suggest that the same culture-related differential perceptual and attentional bias to discriminatively or associatively process visual information in Westerners and East Asians (Goh & Park, 2009; Goh et al., 2020; Nisbett, 2003; Nisbett et al., 2001), respectively, are in operation for probabilistic information as well. That is, analytic processing to discriminate items in the perceptual system in Westerners also biases Westerners to discriminate between items in the decision system – in this case, predictions about differential probabilities of outcomes. Similarly, holistic processing to associate and bind different items in East Asian perceptual processing also applies a bias to form associative links between different outcome probabilities.

Our finding of the VMPFC as the key brain area to show such cultural differences in neural responses to differential probability information in the LCT supports the above proposal. Neural activity in the VMPFC has been suggested to code for differential subjective value (Bartra, McGuire, & Kable, 2013; Clithero & Rangel, 2014; O’Doherty, 2011; Salomon, Botvinik-Nezer, Oren, & Schonberg, 2020; Westbrook, Lamichhane, & Braver, 2019). Indeed, the VMPFC is a central target of midbrain dopamine neurons, which are highly sensitive to different predictive probabilistic levels of reward (Fiorillo, Tobler, & Schultz, 2003; Tobler, Fiorillo, & Schultz, 2005). The VMPFC is also critical for the comparison of different or conflicting reward signals in the stimuli (Basten, Biele, Heekeren, & Fiebach, 2010; Krönke et al., 2020; Lim, O’Doherty, & Rangel, 2011). Disruption of VMPFC activity compromises the ability to make normative choices (Baumgartner, Knoch, Hotz, Eisenegger, & Fehr, 2011; Camille, Griffiths, Vo, Fellows, & Kable, 2011). Interestingly, greater orientation for independence has been associated with larger VMPFC volumes as well (Wang, Peng, Chechlacz, Humphreys, & Sui, 2017). These findings together point to the role of the VMPFC in ranking different predictive outcomes that are subjectively relevant to the task at hand. Thus, greater modulation of VMPFC responses across differential reward prediction conditions in Westerners than East Asians reflects a neural locus for culture-related cognitive style differences in the neural representation of probability information during subjective value processing. Moreover, we note that Westerners responded to reduced win probabilities by suppressing VMPFC responses relative to higher win probabilities and also losing stakes. Such selective VMPFC suppression might indicate a cultural bias in Westerners, but not East Asians, to reduce the subjective value of these stakes otherwise indicated by the stimuli information – hence a reduction in acceptance rates for such conditions in Westerners.

While the above behavioral and neural functional differences between Westerners and East Asians in our sample applied to winning stakes, there were minimal differences for losing stakes (stakes for which the probabilities of winning were less than 0.5). Prospect theory highlights the operation of an additional loss aversion parameter that accentuates the magnitude of stakes when framed as losses compared to when the same stake value is framed as a gain (Kahneman & Tversky, 1979). As a result, the seminal finding is that for the same outcome expected value, people make more rejections of losing outcomes (more risk-taking) than they accept winning outcomes (more risk-averse). Replicating this finding in Westerners, we found that ARs for conditions where win probabilities (P) were <0.5 were all as low as the lowest win probability (or highest losing probability; PLL) whereas ARs for conditions where P > 0.5 were significantly lower than for the highest win probability (PHH). That the two culture groups showed minimal behavioral and neural response differences for losing stake conditions suggest that loss aversion in such situations might be less amenable to culture-related influences. Nevertheless, while loss aversion in East Asians is hinted at in the similarity of response for losing stakes with Westerners, we note its effect was somewhat altered in East Asians given their ARs for different winning stakes were reduced relative to Westerners (i.e., East Asians evinced more risk-taking under gains instead). Thus, our findings point to how culture-related differences in the neurocognitive processing of probabilistic outcomes modulates the degree to which loss aversion might be observed when comparing decision behaviors under gains and losses in a given culture group.

We note the lack of cultural differences in the striatum, which we hypothesized should reflect cultural differences in value as well. Along with the VMPFC, the striatum is also subserved by midbrain dopamine neurons and has been centrally implicated in the processing probabilistic subjective reward (Fiorillo et al., 2003; Tobler et al., 2005). Thus, the observation of the operation of cultural differences in the VMPFC but not the striatum might distinguish the content of the neural computations towards subject value in these two brain areas. We suggest that the VMPFC is further upstream in the decision computational process relative to the striatum such that culture-related biases in the treatment of differential predictive probabilities are resolved in the VMPFC before subsequent selection of actions is passed to the striatum. Once the action has been selected, there might be minimal cultural differences in the anticipatory processing of actions predicted to yield rewards. Further studies mitigating the ability of participants from different cultural backgrounds to apply their decisions or restricting their agency for choices are needed to confirm this hypothesis.

Our findings provide limited support for the cushion hypothesis, which considers that greater risk-taking in East Asians reflects an emboldening from a greater sense of security due to the interdependent social support structure in their culture (Fan & Xiao, 2005; Hsee & Weber, 1999). Critically, although East Asians in our sample generally evinced typical emphasis of collectivistic over individualistic personal values compared to Westerners who showed the reverse case, they reported lower social support than Westerners. Moreover, we did not find associations between SSQ variables and LCT-related behavior or VMPFC activity. Interestingly, we found that the value for Social Image in our East Asian sample might have mitigated them to report lower social support network sizes. Thus, it is unlikely that the higher risk-taking behavior for winning stakes in East Asians than Westerners in our sample and the accompanying VMPFC responses reflected the contribution of a sense of security in their social support, at least not directly. Indeed, in our previous study investigating neural loci associated with individual differences in personal value for security using the same LCT paradigm, the neural responses implicated did not include the VMPFC but rather the inferior frontal/amygdala and precuneus areas (Chuang et al., 2020). Taking these together, we consider that while social support might induce a sense of security that plays a role in an individual’s decision processing, our findings suggest that its mechanisms are distinct from those to do with culture-related differences in processing probabilistic information, particularly those involved in the LCT. Overall, how social cushioning specifically modulates value-based decision-making requires further validation.

With globalization, the frequency of interactions between persons from different cultural backgrounds will increase along with the complexity of these interactions. The efficacy of communicating messages in such cross-cultural interactions depends on whether the sender and receiver both have the same standards for coding and decoding the transmitted information. When these standards differ, then some metric is helpful to convert between the two. We suggest that the ranking of probabilistic outcomes is a standard mechanism operating in the VMPFC for coding the relative values of items for a person. Indeed, these value rankings differ across persons so that we cannot assume that what is of a given value to us is of equal value to others – a topic of much economic research. In this light, our findings provide a candidate neurocognitive mechanism that tracks how information about different probabilities of reward outcomes (ranging from certain gains, to uncertainty, to certain losses) are translated to differential subjective values in persons with East Asian and Western, holistic vs. analytic, cognitive cultural backgrounds. Future studies might continue to investigate how such VMPFC responses that govern differential biases in prospective behaviors are acquired over the lifespan.

Supplementary Material

Supplementals_Lee

Acknowledgements

We thank Danielle Schwartz and Krystal Leger for assistance in recruiting and testing research participants. The research was carried out in part at the Harvard Center for Brain Science and involved the use of instrumentation supported by the NIH Shared Instrumentation Grant Program - grant number S10OD020039. We acknowledge the University of Minnesota Center for Magnetic Resonance Research for use of the multiband-EPI pulse sequences.

Funding

This work was supported by Taiwan Ministry Science and Technology grant numbers 107–2410-H-002–124 and 110–2321-B-006–004, and the National Institute on Aging, USA, grant number R01AG061886.

Footnotes

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.biopsycho.2021.108209.

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