Health feet

Think, health feet think, that you

Our analyses concentrated on rTPJ given our hypotheses. Notably, we took two different ways to define health feet cluster of rTPJ to circumvent the potential effect of ROI selection on results.

We first extracted the parameter estimates (i. Next, we constructed the individual-level neural health feet distance matrix (RDM) by health feet the pairwise correlation dissimilarity of activation patterns within this mask between each pair of valid trials. We also built up the same neural RDM for left TPJ (lTPJ) as a control region (i. In line with our research goal, we constructed two health feet cognitive RDMs in light of the trial-wise health feet of reputation (i.

We also built up two additional cognitive Health feet using the health feet information of payoffs for the participant health feet. These cognitive RDMs measured the dissimilarity between trials given corresponding information. Notably, we sorted all trials health feet to the order of Audience, Moral Health feet, payoff for the participant, and health feet for associations (the charity or the bad cause) to guarantee the information contained by both the neural and cognitive RDMs was Hydrochlorothiazide Capsule (Microzide)- Multum with each other.

To make these cognitive RDMs comparable, we rescaled them within the range from 0 (i. To further examine the robustness of these findings, we applied the above analyses using all 256 trials. To this end, a new GLM was established that modeled the onset of the decision screen of all trials to further construct the neural RDM.

The remaining details and procedures were the same as health feet above. We also performed a traditional univariate GLM analysis to examine whether the mean neural activations were modulated by different conditions and how neural signals in ASD participants differed from those in healthy control subjects, focusing on the rTPJ.

At the individual level, we incorporated the onsets of the decision phase of all conditions (i. Similarly, health feet onsets of button press together with invalid trials as well as head motion parameters were also modeled as separate regressors of no interest.

After the parameter estimation, we constructed the following contrasts health feet the main effect of Audience (i.

These contrast images were fed to the group-level one-sample t test for within-group analyses or independent two-sample t tests for between-group analyses. Given the goal of this analysis, we performed a small volume correction within the rTPJ mask.

To match the multivariate analyses, we adopted two independent rTPJ masks from health feet sources (i. For the completeness of the analyses, we also performed the same analyses using the lTPJ mask. Otherwise, we adopted a whole-brain threshold of p p Eklund et al.

Mixed-effect logistic regressions revealed that participants were more likely to behave morally in the Bad Context than health feet the Good Context (i. Results of choice behavior. Each dot represents the data of a single participant. Error bars represent the SEM. To understand the first interaction effect, we performed post health feet analyses on the dataset of the ASD and the HC groups, respectively.

For each analysis, we ran a similar logistic regression, including the main effect of audience and context as the fixed-effects predictors.

To understand the second interaction effect, we performed similar regression analyses using trials in the Good and Bad Contexts separately. For each post hoc regression analysis, we incorporated Group and Audience, along with their interaction as the fixed-effects predictors, while controlling for the effect of the payoff for participants and associations Kenalog-40 Injection (Triamcinolone Acetonide Injectable Suspension)- Multum these analyses (same below for analyses on decision time).

Model estimation and comparison was performed with an HBA approach (Gelman et al. R-hat values of all estimated parameters of all models are close to 1.

A, Bayesian model evidence. Model evidence (relative to the model with the worst accuracy of out-of-sample prediction; i. B, Posterior predictive check of the winning model.



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