I am a bit confused on how to best interpret my model coefficients as the estimates seem to be all in the opposite direction to what the data suggests, or what I see when I plot conditional_effects() in brms.

My experiment split users into two conditions, then all users saw profiles belonging to three groups (so 2bw x 3ws); they responded on a 10-point Likert-type scale (1-10).

Below is the code I ran:

```
#use all cores and make STAN run faster
options(mc.cores = parallel::detectCores())
#set seed for reproducible results
set.seed(1410)
#set appropriate contrasts for factors with more than 2 levels.
options(contrasts = c('contr.orthonorm', 'contr.poly'))
#Make sure model understands which are factors
SEacc$Participant <-factor(SEacc$Participant)
SEacc$Original = factor(SEacc$Original)
SEacc$Condition = factor(SEacc$Condition)
SEacc$Trial = factor(SEacc$Trial)
#Likert ordinal data
SEacc$Confidence = factor(SEacc$Confidence, ordered = TRUE)
#priors set to extract BF per parameter [weakly informative][revised using Prior check]
my_conf_priors <- prior(normal(0, 5), class = "b") + prior(normal(0, 5), class = "Intercept")
m1.conf <- brm(Confidence ~ Condition*Original + (1| Participant),
data = SEacc,
family =cumulative("probit"),
warmup = 2000, iter = 40000,
save_pars = save_pars(all = TRUE),
inits = 0,
prior = my_conf_priors)
```

Output:

```
print(m1.conf) #for model details
Family: cumulative
Links: mu = probit; disc = identity
Formula: Confidence ~ Condition * Original + (1 | Participant)
Data: SEacc (Number of observations: 3480)
Samples: 4 chains, each with iter = 40000; warmup = 2000; thin = 1;
total post-warmup samples = 152000
Group-Level Effects:
~Participant (Number of levels: 116)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 1.27 0.09 1.10 1.46 1.00 9410 19817
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1] -3.93 0.18 -4.28 -3.59 1.00 10152 26559
Intercept[2] -3.49 0.15 -3.78 -3.20 1.00 7336 18463
Intercept[3] -2.85 0.13 -3.11 -2.60 1.00 5872 13803
Intercept[4] -2.21 0.12 -2.46 -1.97 1.00 5468 12586
Intercept[5] -1.55 0.12 -1.79 -1.31 1.00 5207 11713
Intercept[6] -1.01 0.12 -1.25 -0.78 1.00 5046 11427
Intercept[7] -0.29 0.12 -0.53 -0.06 1.00 4972 11139
Intercept[8] 0.56 0.12 0.32 0.80 1.00 5003 10871
Intercept[9] 1.36 0.12 1.11 1.60 1.00 5113 11144
Condition1 -0.18 0.17 -0.51 0.15 1.00 4419 10190
Original1 -0.29 0.03 -0.35 -0.22 1.00 127445 110978
Original2 0.04 0.03 -0.03 0.10 1.00 122714 110192
Condition1:Original1 -0.02 0.04 -0.11 0.06 1.00 130721 111146
Condition1:Original2 -0.00 0.04 -0.09 0.09 1.00 132923 111827
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc 1.00 0.00 1.00 1.00 1.00 152000 152000
Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
```

Based on the raw data, and the marginal means, I interpret that Original1 should be positive (i.e., the SD in the Good profile is higher on the latent variable than in the Bad profile [reference category]) as the figure shows the probability of users selecting ratings of 8-10 is higher. Yet, based on 10.1177/2515245918823199 I understand I should read it as a decrease in overall confidence ratings.

What am I interpreting incorrectly here?

Thank you in advance.