Participants and procedures

Twenty-five children between the ages of 8 and 14 years (mean 10.9 years old; 14 boys) and their mothers were enrolled in the study. Children were instructed to fast for 3 h before the experiment to ensure moderate and similar levels of hunger. After obtaining informed consent and assent, children provided separate taste (four-point scale; very bad–very good) and health (very unhealthy–very healthy) ratings, as well as their overall preference (five-point scale; strongly dislike–strongly like) ratings for 60 different food items that varied on taste and health attributes outside the scanner (Fig. 1a). Next, while undergoing fMRI scans, children made a series of choices for each food item shown on the screen in two different experimental conditions that were randomly presented (Fig. 1b). In the ‘own choice’ condition, children were asked to make their own food decisions based on how much they wanted to eat that food. In the ‘mom’s choice’ condition, children were asked to estimate what their moms would choose for them to eat. In both conditions (120 choices total), children entered their decision values by using a four-button response pad (strong no, no, yes and strong yes). For a better understanding of our data, descriptive statistics and pairwise correlations of behavioural ratings are reported in Supplementary Table 1.

Figure 1: Experimental tasks. (a) Children completed taste (four-point scale: very bad–very good), health (four-point scale: very unhealthy–very healthy) and overall preference (five-point scale: strongly dislike–strongly like) ratings for 60 different food items before fMRI scans. All food images used are in the public domain.). All error bars denote s.e. The order of taste and health ratings was counterbalanced across subjects. (b) During fMRI scans, children made food decisions in own choice and mom’s choice conditions (four-point scale: strong no–strong yes). In mom’s choice condition, children were asked to guess their mom’s food choices for them. Own choice and mom’s choice blocks were randomly presented. Full size image

Behavioural results

Before the fMRI data, we first examined how children incorporate taste and health values into their own choices and their projected mom’s choices as well. We predicted that children would make their own food decisions primarily using taste values rather than health values. We fitted a linear regression model of taste and health ratings on children’s decisions separately for each experimental condition. In this regression model, two rating predictor variables were simultaneously entered. Then, we performed t-tests with estimated regression coefficients for the group-level analyses. As hypothesized, only taste ratings (mean β=0.61, t 24 =14.40, P<0.001), and not health ratings (mean β=0.04, t 24 =1.11, P=0.28), significantly predicted children’s own food decisions. However, both taste ratings (mean β=0.15, t 24 =2.57, P<0.05) and health ratings (mean β=0.50, t 24 =10.22, P<0.001) significantly predicted the children’s projected mom’s food decisions for them (Fig. 2a). This result suggests that children do not utilize health information for their own food choices, despite possessing the knowledge of nutritional health values. However, they predict that their mothers will use health information when making food choices for them. None of the taste and health rating beta-weights showed a significant correlation with age, pubertal development or body mass index (BMI) z-scores in our sample (correlation coefficients were checked separately at P<0.05).

Figure 2: Children’s food decision. (a) Children’s own food choices were solely predicted by taste ratings, whereas the projected mom’s food choices were predicted by both taste and health ratings. (b) Children’s own food choices were predicted by both own preferences (from behavioural ratings) and the projected mom’s choices (estimated from fMRI mom’s choice condition). All error bars denote s.e. *P<.05; ***P<0.001. Full size image

For the food choice data acquired during fMRI scans, we first compared the reaction times between children’s own choice and mom’s choice conditions. Not surprisingly, children made their own choices significantly quicker than mom’s choices (own choice mean response time (RT)=1.63 s, s.d.=0.24; mom’s choice mean RT=1.76 s, s.d.=0.76; t 24 =5.05, P<0.001), suggesting that controlled decision-making procedures (that is, the projected mom’s choices) require additional cognitive resources. Also, a significant positive correlation between children’s own and mom’s choice RTs was observed across participants (r=0.90, P<0.001). Next, we checked the percentages of children’s healthy decisions separately for each decision condition. Healthy decisions were defined by ‘yes’ or ‘strong yes’ decisions for healthy food items (based on children’s subjective health ratings outside the scanner) and ‘no’ or ‘strong no’ decisions for unhealthy food items (based on children’s subjective health ratings outside the scanner). Not surprisingly, children made relatively less healthy choices for their own decision trials (M=48.4%, s.d.=15.6%) compared with the projected mom’s decision trials (M=76.9%, s.d.=13.0%; t 24 =9.13, P<0.001). Similarly, we examined the percentages of children’s tasty decisions. Tasty decisions were defined by ‘yes’ or ‘strong yes’ decisions for tasty food items (based on children’s subjective taste ratings outside the scanner) and ‘no’ or ‘strong no’ decisions for not-tasty food items (based on children’s subjective taste ratings outside the scanner). As expected, children made relatively more tasty choices for their own decision trials (M=85.5%, s.d.=6.5%) compared with the projected mom’s decision trials (M=59.9%, s.d.=15.2%; t 24 =8.94, P<0.001). Next, we checked the percentage of children’s self-regulated decisions separately for each decision condition. The self-regulated decisions were operationally defined by ‘yes’ or ‘strong yes’ decisions for healthy but not-tasty food items (based on children’s subjective taste and health ratings outside the scanner) and ‘no’ or ‘strong no’ decisions for tasty but unhealthy food items (based on children’s subjective taste and health ratings outside the scanner). Again, we found that children made relatively less self-regulated food choices for own decision trials (M=17.1%, s.d.=15.6%) compared with the projected mom’s decision trials (M=67.6%, s.d.=22.8%; t 24 =11.69, P<0.001). None of the percentages of healthy decisions, tasty decisions and self-regulated decisions was significantly correlated with age, puberty development or BMI z-scores. However, the children with higher self-control scores from a self-report questionnaire showed fewer self-regulated decision ratio differences between the two conditions (the projected mom’s choices–own choices; r=−0.45, P<0.05), suggesting that the decision context had less impact on the children with higher self-regulation ability. Similarly, the children who showed smaller decision time differences (mom’s choices–own choices) made fewer self-regulated decisions in the own choice condition (r=−0.45, P<0.05).

Most importantly, we hypothesized that children who did not incorporate health values into their own decisions would still utilize the projected moms’ food choices for them when they made their own food decisions. In other words, we hypothesized that children’s food decisions would be determined by both the projected mom’s choices, as well as their own preferences. To test this main hypothesis of the computational model of children’s food decisions, we fitted a linear regression model on the children’s own food decision data (inside the scanner). For each food item choice, the children’s own preferences (from overall liking ratings outside the scanner) and the children’s projected mom’s choices for them (from the mom’s choice data inside the scanner) were simultaneously entered into the regression model to predict children’s own food decisions. Interestingly, the projected mom’s choices (mean β=0.18, t 24 =2.79, P<0.05), as well as children’s own preferences (mean β=0.36, t 24 =9.12, P<0.001), significantly predicted children’s own food decisions (Fig. 2b), suggesting that both decision variables uniquely explain children’s own food decisions after controlling for each other. Across participants, both beta-weights were not correlated with age, pubertal development or BMI z-scores.

To further establish the robustness of our findings of the projected mom’s choices in predicting children’s own food decisions, we performed an additional confirmatory regression model on children’s own choices that included additional health and taste ratings, as well as the child’s own preferences and the projected mom’s choices. To avoid a potential co-linearity issue due to high correlations between children’s own preferences and taste ratings (r=0.70, P<0.01), additional taste and health rating predictors were orthogonalized in respect to both children’s own preference and the projected mom’s choices in this regression model. Again, the beta-weights of the projected mom’s choices were statistically significant (mean β=0.24, t 24 =3.99, P<0.001), even after controlling for taste and health ratings (mean β=0.34, t 24 =9.13, P<0.001; mean β=−0.06, t 24 =−3.02, P<0.01), as well as their own preferences (mean β=0.44, t 24 =10.40, P<0.001). Interestingly, the health ratings showed a significant negative beta-weight (that is, higher healthiness predicts ‘not to eat’ decisions) after controlling for the projected mom’s choices in this model. This finding corresponded to a significant zero-order correlation between children’s own preferences and health ratings (r=−0.17, P<0.05). Furthermore, the projected mom’s choices still significantly predicted children’s own food decisions even when we conducted similar regression models separately for healthy and unhealthy food items (mean β=0.28, t 24 =3.89, P<0.005; mean β=0.25, t 24 =3.09, P<0.01). The child’s health ratings were not significant in the models. This indicates the projected mom’s choices uniquely explained the children’s food decisions that could not be simply substituted by children’s healthy ratings. Overall, children’s choice data strongly support our computational decision model in which both the projected mom’s choices and children’s own preferences determine the children’s own food decisions.

fMRI results

We examined fMRI data to identify brain regions that encode these two decision variables representing children’s own preferences and their projected mom’s choices, which were both found to significantly predict children’s behavioural food decisions. Similar to the behavioural regression model, the parametric regressors of children’s own food preferences and the projected mom’s choices were simultaneously entered into the general linear model (GLM) of fMRI data along with other regressors of non-interest. Consistent with our hypotheses of the neural model of children’s food decisions, when children made their own food choices, brain activity in the vmPFC positively correlated with own preferences, and brain activity in the left dlPFC positively correlated with the projected mom’s choices (P<0.05 corrected; Fig. 3a). Even though our main goal was to confirm these two critical decision variables at the time of children’s own choices with fMRI data, we further explored the neural correlates of these variables at the time of the projected mom’s choices. Interestingly, when children estimated their mom’s food choices for them, the same left dlPFC area showed a positive correlation with the projected mom’s choices, but the vmPFC area did not show a significant correlation (P<0.05 corrected; Fig. 3b and Table 1). The lack of the vmPFC activity that encoded children’s own preference at the time of the projected mom’s choice trials could be explained by the incentive structure of our task (that is, no motivational reason to encode expected rewards, because no food item was selected from mom’s trials and given to children). However, the overlapping dlPFC activity suggests that children might use identical neural mechanisms to compute the projected mom’s choices in both decision conditions. In addition, we inspected two event indicator regressors to explore potential systematic task differences (for example, motivational or cognitive demand differences; self versus others) between the children’s own choices and the projected mom’s choices. However, although one might expect differences in the dorsomedial prefrontal cortex (PFC) or temporo-parietal junction (TPJ) that often associated with reflected self-appraisals or social perception18,19, we did not observe any statistically significant difference between the two event indicator regressors at our whole-brain threshold (P<0.05 corrected; Supplementary Fig. 1).

Figure 3: vmPFC and dlPFC activations at the time of choices. (a) In own choice trials, activity in vmPFC positively correlated with children’s own preference ratings and activity in left dlPFC positively correlated with the projected mom’s choices. (b) In mom’s choice trials, activity in left dlPFC positively correlated with the projected mom’s choices. All images were thresholded at P<0.05 corrected. Full size image

Table 1 Brain regions correlated with children’s own preferences and projected mom’s choices in the food decision task (GLM-1). Full size table

Next, we further explored how the neural correlates of two decision-related signals changed over the course of decision time in the vmPFC and left dlPFC regions by constructing the time-series beta-weight (effect size) plots of the parametric regressors (Fig. 4). As described in the methods, we conducted GLMs with a finite impulse response (FIR) basis functions for 7 repetition time (TR)s (∼18 s) from the onset of stimuli. At the time of children’s own choices, the vmPFC activity that correlated with own preferences showed a significant effect (t 24 =2.20, P<0.05) after 2 TRs from the onset of stimuli (∼5 s), whereas the left dlPFC activity that correlated with the projected mom’s choices showed a significant effect (t 24 =2.28, P<0.05) after 3 TRs (∼7.5 s). Interestingly, at the time of the projected mom’s choices, the left dlPFC activity revealed a significant effect (t 24 =2.10, P<0.05) after 2 TRs (∼5 s). None of other time points showed a statistically significant effect. Even though there is an inherent limitation of the temporal resolution of fMRI blood oxygenation level-dependent (BOLD) data, the delayed effect of the dlPFC signal relative to the vmPFC signal we observed at the time of children’s own choices suggests a potential temporal difference of integrating two decision-related signal inputs into children’s food decisions.

Figure 4: vmPFC and dlPFC ROI time series. (a) ROI time series of the beta-weights of parametric regressors for child’s own food preferences and the projected mom’s choices in my choice trials. (b) ROI time series of the beta-weights of parametric regressors for child’s own food preferences and the projected mom’s choices in mom’s choice trials. TR=2.53 s. The grey box represents a visual aid for the approximate decision period, adjusted for the fMRI haemodynamic response lag. All error bars denote s.e. *P<0.05 (one-sample t-tests against zero). Full size image

We also ran the GLM-2 to confirm that the left dlPFC activity observed in the previous GLM-1 represent the projected mom’s choices, not health values. In the GLM-2, the parametric regressor of children’s health ratings from the behavioural task was entered instead of the projected mom’s choices along with other regressors of non-interest. As before, brain activity in the vmPFC positively correlated with children’s own preferences at the time of children’s own choices (P<0.05 corrected), but not at the time of the projected mom’s choices (Table 2). Most importantly, the left dlPFC region did not show a significant correlation with health ratings at our predetermined whole-brain threshold (P<0.05 corrected), suggesting the dlPFC signals were better represented by the projected mom’s choices rather than the health attributes. The robustness of our findings that children brain encodes the projected mom’s choice at the time of own choices was further supported by supplementary analyses that included GLM-S1 (Supplementary Note 1) with only taste and healthy ratings as predictors (Supplementary Table 2) and GLM-S2 (Supplementary Note 2) with all four ratings as predictors (Supplementary Table 3).

Table 2 Brain regions correlated with children’s own preferences and health ratings (GLM-2). Full size table

Finally, we investigated the task-related functional connectivity between the vmPFC and left dlPFC brain areas by performing a psychophysiology interaction (PPI) analysis. We used the left dlPFC as a seed region, as it showed significant activations during both choice conditions. The left dlPFC region revealed significant negative functional connectivity with the vmPFC during own choice trials (t=−2.50, P<0.05, Fig. 5), suggesting an inhibitory relationship between the children’s own preference values and the projected mom’s decision values at the time of children’s own food decisions. Not surprisingly, the left dlPFC and vmPFC regions showed no significant functional connectivity during the projected mom’s choice trials, suggesting these two regions significantly interact only when children made their own choices. We further postulated that if the inhibitory functional interaction between the dlPFC and vmPFC modulates children’s own food choices, its connectivity strength would be correlated with children’s body mass or their ability to exercise self-control. To test this possibility, we performed correlational analyses across subjects. Stronger inhibitory functional connectivity between the dlPFC and vmPFC was significantly associated with higher BMI z-scores (r=−0.41, P<0.05) and lower self-control scale scores (r=0.42, P<0.05). The correlation between BMI z-scores and self-control scores was not significant (r=−0.31, P=0.14). In our results, children with excessive body mass or low self-control scores showed stronger inhibitory functional connectivity between the left dlPFC and vmPFC regions at the time of their own food choices.