interpretation of 'input' variables & input in Bayes optimal parameter estimation

[PotatoLee0]

Hi,
I’m a quite newbie in TAPAS, and I’m trying fit 3-level binary HGF model to my own data.

Here is the description of the task I used :
Social evaluation task(Button et al., 2014)

• Subjects meet character in computer screens, and two words(positive words and negative words) are presented, which mean the possible character’s opinion towards the subjects or stranger. The subjects’ goal is to choose(predict) a right word at each trial that corresponding to character’s opinion. After subjects choose a word, then the character provides feedback(incorrect/correct).

• All subjects are exposed to 6 conditions, 2(Self-evaluated condition vs. Stranger-evaluated condition) x 3(like vs. dislike vs. neutral) conditions. ‘Like condition’ means that if the subjects choose positive words, then the character provides ‘correct’ feedback for with probability of 80%. (20%, 50% for dislike and neutral conditions, respectively.)

I have some questions about the interpretation about ‘input’ variables here and Bayes optimal parameter,

1. Is it right to interpret input variables in this task to be ‘the state the subject confirm from each trial, not a feedback(correct/incorrect)’
   e.g. If a subject chose positive word(response, coded as 1) and the character said ‘incorrect’, then the input is positive opinion of character towards subject/stranger(can be coded as 1), and not a feedback(incorrect feedback might be coded as 0)?

2. Suppose that the interpretation of the input variable(in question 1) is right, I wonder what should be ‘u’ in the codes that calculate Bayes optimal parameter. Should it be the columns of all ‘inputs’ of all subjects?

est = tapas_fitModel([], u, 'tapas_hgf_binary_config', 'tapas_bayes_optimal_binary_config');

Then how the codes differentiate each participants’ input? (As the subjects were assigned to 3 blocks differing in contingency(20%, 50%, 80%) and the blocks were presented in random orders, each subject has different inputs data) Or is it unnecessary to differentiate them?

3. Can I get a brief explanation about what is ‘Bayes optimal parameter’ and what the codes(tapas_bayes_optimal_binary_config) do? Or recommendation about some articles dealing with it?