Matlab

Data is presented as mean ± SD. Statistical analysis was performed in SPSS Statistics (IBM version 23) and MATLAB. Group comparison was done using the Kruskal-Wallis H test, with post-hoc pairwise comparison of statistically significant results using Dunn's procedure with a Bonferroni correction for multiple comparisons. Correlation was done using Spearman Rank Correlation as the test for normality was not fulfilled. Statistical significance was set at 95%.

The data are processed by MS Excel, Origin 8.0, MATLAB, and IBM SPSS 25.0. Multiple comparisons are performed with the LSD method. The equations of retention curves of the γ-PGA-treated soil are fitted by RETC code. Both the retention curves and the measured amount of cumulative infiltration are entered into the HYDRUS-1D model. The inversed solution in HYDRUS-1D and the Levenberg–Marquardt optimization method are used to optimize the soil hydraulic parameters. The corresponding soil hydraulic parameters of the soil mixed with different γ-PGA content are presented in Table 3. The objective function is defined as the sum of squared residuals (SSQ). The correspondence between simulated and observed data are evaluated by correlation coefficient (R²) at p = 0.05, and the root mean square error (RMSE).

Data analysis was conducted using Matlab and SPSS (IBM Corporation). Data visualisation was done using Python using Jupyter notebooks in conjunction with a number of libraries including numpy, scipy, matplotlib, seaborn, and pandas.

The one-trial-back stay-switch analysis is the most widely used method for characterizing behavior on the two-step task (Daw et al., 2011 (link); Akam, Costa, & Dayan, 2015 (link); Wunderlich et al., 2012 (link)). This method quantifies the tendency of a participant to repeat the choice made on the last trial or switch to the other choice, as a function of the outcome and transition on the previous trial. We considered four possible outcomes: Common-Rewarded (CR), Rare-Rewarded (RR), Common-Unrewarded (CU), and Rare-Unrewarded (RU). Model-based and model-free indices were computed from the stay probabilities following each outcome according to:
$\begin{array}{rcl}\text{MF}& =& (P(\text{stay}|CR)+P(\text{stay}|RR))-(P(\text{stay}|CU)\\ & & +P(\text{stay}|RU)),\end{array}$ $\begin{array}{rcl}\text{MB}& =& (P(\text{stay}|CR)+P(\text{stay}|RU))-(P(\text{stay}|CU)\\ & & +P(\text{stay}|RR))\mathrm{.}\end{array}$
We also examined whether hunger modulated other measures of simple reinforcement learning. We found no effect of hunger on changes in model-free control for second-stage choices or for action-specific, stimulus-irrelevant choices at the first-stage (Fig. S1).
Statistical analyses were implemented in MATLAB and SPSS (IBM Corp. Released 2019. IBM SPSS statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.). We report the effect size with Cohen’s d and ${\eta }_p^2$ and report the 95% confidence interval (CI) of the difference between groups.

Statistical analysis was performed using SPSS (IBM v22, North Castle, NY, USA) and MATLAB. To evaluate the ANN effect, the performance of the original SDI was compared with the one random ANN regression–derived SDI. The Pearson correlation coefficient, mean absolute error (MAE), and area under the curve (AUC) for the EACL were computed and considered the gold standard. The receiver operating characteristic (ROC) curve was calculated to obtain the AUC, which is often used in medical fields during diagnosis of disease. The binary threshold used to distinguish between anaesthesia and consciousness was set to 65 (Johansen & Sebel, 2000 ). The parametric paired Student’s t-test was then used to assess the statistical significance. To prove the capability of the EANN-derived SDI to measure DoA, its relationship with EACL was analysed. Furthermore, the commonly used BIS was used as a reference. The same significance test was also undertaken between the two indices, thus demonstrating a solid and convincing result.