Markov chain Monte Carlo (MCMC) sampling was used to estimate the parameters (i.e.,
kC,
kP, and
kS). We used a 1,000,000 iterations as a burn-in period. Before starting the iteration, we randomly selected start-point values for
kC,
kP, and
kS. For example, in our codes,
kC = [3, 3], corresponding to 45 and 135 dots in the flow field, and
kP = [3, 3], corresponding to ±30° and ±10° preheadings,
kS = 1. The start-point values did not affect the final results. From the second iteration, we added a random number (Δ) selected from the standard normal distribution to the parameter values of preiteration. We compared the log-likelihood posterior (
LLPc) of the current iteration with the
LLPc − 1 of the pre–first iteration (
c is the index of iternation). For each dot number condition, each participant's
\(LL{P}^{\prime}\) was given by
\begin{eqnarray}
LL{P}^{\prime} &=& \mathop \sum \limits_i^2 \mathop \sum \limits_{i,j}^{i,3} \log \left( {P(M|{\theta }_{{C}_i}S = 0,{\theta }_{{P}_{i,j}})} \right) \nonumber \\
&& +\, \log \left( {P\left( {{\theta }_{{C}_i}|S = 0,{\theta }_{{P}_{i,j}}} \right)} \right),
\end{eqnarray}
in which
\({\theta }_{{C}_i}\) indicates two current headings (±20°);
i,
j indicates three preheading conditions under one current heading (e.g., as
i is 1, meaning that the current heading is −20°); and the corresponding preheading conditions include three cases (
j = 1, 2, 3): −10°, −30°, and no preheading conditions. We then summarized the
\(LL{P}^{\prime}\) of 18 participants and two dot-number conditions, given by
\begin{equation*}LLP = \mathop \sum \limits_s^{18} \mathop \sum \limits_k^2 LL{P}^{\prime}_{s,k},\end{equation*}
in which,
s represents the
sth participants, and
k represents the
kth dot-number condition. If the
LLPc of the current iteration was larger than the
LLPc − 1 of the pre–first iteration, the parameter values of the current iteration were kept; if not, one random number (ε) was generated. If ε > 0.5, then the parameter values of the current iteration were kept; if ε ≤ 0.5, the parameter values of the current iteration were equal to the parameter values of the preiteration. The same procedure was repeated a million times. The final values of
kC,
kP, and
kS were the mean of the 18,000 iterations selected from the 100,001
st iteration to the end with step 50.
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