Histogram and point distributions of the individual-level fitted parameters derived from the computation model (Probabilistic reversal learning model).

(A) Our model was able to recapitulate the real data well. The real (Q1 –Q3) and simulated (simQ1 –simQ3) Q values generated by the model for each trial across all participants for each different symbol. (B) All parameters were recovered very well. Correlation matrix showing the Pearson correlations between the real (X axis) and recovered (Y axis) parameter. (C) The 5-parameter model produced equivalent to better BIC values compares to the 3-parameter core model. In these plots, blue dots below the line indicate better fit than the reference model (model 3) and above the line indicate the reference model fits better. Correlation comparisons between the BIC values for each alternative model (named in the facet title) and the core 3-parameter model (X axis); reference lines on each plot indicate +/- 6 and +/- 10 BIC values. Models 4 and 5 were not significantly different in individual BIC values from Model 3 (χ2(2) = 2.13, p = 0.345). (DOCX)</p

Top Model Average of Variables Associated with decision temperature (τ).

All regression estimates are extracted from Model J2c in the analysis code. wSI was not included in the final top model and therefore excluded from this table. (DOCX)</p

The relationship between decision temperature, attributions, and social task parameters.

(A) Spearman correlations between decision temperature and mean attributions observed summed across 20 trials for each participant. (B) Permutation analysis of the relationship between decision temperature, and computational model-based parameters from the winning model and pre-existing paranoia. The grey distribution represents the null distribution following random sampling of the population for each Spearman pairwise correlation. The true Spearman correlations of each social parameters against tau are depicted for each parameter. Only the strength of prior beliefs over harmful intent (pHI0; ρ = 0.16, ppermuted ~ 0), uncertainty over partner policies (uπ; ρ = 0.09, ppermuted = 0.015), and paranoia (ρ = 0.16, ppermuted ~ 0) were associated with decision temperature. Red lines denote that the observed correlation with tau is very unlikely due to chance (p 0.05).</p

Recovery analysis of the winning social model.

X = non-significant relationship. (DOCX)</p

Smoothed posterior density distributions of the individual-level fitted parameters derived from the hierarchical Bayesian fit (using CBM; modified repeated reversal Dictator Game).

(DOCX)</p

Associative Social Model Formalism.

(DOCX)</p

Social Model Comparison Statistics.

LL, BIC, and AIC figures are indicative of the summed log probability from the combination of harmful intent and self-interest estimates for each model fitted using Maximum-A-Priori techniques. Bold highlighting represents winning models in each class. (DOCX)</p

Network analysis between social parameters and paranoia from Barnby et al., 2020.

(A) Our nonparanormal network replicated results from Barnby et al., (2020). (B) Stability analysis demonstrated satisfactory case-dropping estimates. (C) Bootstrapped edge weights demonstrated satisfactory estimates. See S3 Table for all edge statistics in the network. (DOCX)</p

Behaviour of the participants in the probabilistic reasoning task.

Top panel: relationship of paranoia and ICAR total score with the proportion of correct cards chosen in each block. Bottom panel: Sum of each chosen card by paranoia and ICAR total score for each block. In Block 1, Card 1 was the optimal card to choose with an 80/20 probability of reward. In Block 2, Card 3 was the optimal card to choose, with 80/20 probability of reward. (DOCX)</p

Bootstrapped estimates for each edge in the replication network.

(DOCX)</p