Table 1 Comparative evaluation of model performances for dataset 1: likelihoods, frequencies, PEP (protected exceedance probabilities), and posterior group parameters

From: Expectation violations signal goals in novel human communication

Model

Log likelihood

Model frequency

PEP

τ

ε

λ

γ

α

Surprise model

−60.06

0.80

1.00

6.93

0.39

0.10

2.14

1.84

State model

−70.38

0.16

0.00

2.90

0.39

–

–

1.11

Movement model

−96.57

0.05

0.00

1.92

0.62

0.15

1.50

–

  1. Model: refers to the three tested computational models: surprise, state, and movement models. Log likelihood: represents the log-transformed probability of the data given the model, where higher values indicate a better fit of the model to the observed data. Model frequency: reflects the proportion of subjects for which a given model is most likely the best fit, with values closer to 1 indicating higher generalizability across the sample population. PEP: is the probability that a model is more likely than any other model, while also considering the possibility that no single model is a good fit for the data population. Posterior group parameters (τ, ε, λ, γ, α): estimated group values for parameters: \(\varepsilon\) (discount rate for planning horizon), \(\lambda\) (backward movement),\(\,\gamma\) (forward and left/right movement), \(\alpha\) (slope of State prior), and \(\tau\) (choice stochasticity).
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