Table 2. List of statistical tests performed.
| Figure | Test name | Purpose | Arguments |
|---|---|---|---|
| Figure 4A | Mann-Kendall test (pymannkendall package in Python) | To determine a monotonic upward trend in the success rate of all CLNF mice before the rule change (from day 1 to day 10). | Normalized success rate per mouse before the rule change (N=40). |
| To determine a monotonic upward trend in the success rate of CLNF rule-change mice after the rule change (day 11 to day 19). | Normalized success rate per mouse after the rule change (N=17). | ||
| Figure 4A | RM-ANOVA (Pingouin package in Python) | Test the significance of the success rate increase before the rule change in the CLNF No-rule-change group (day 1 to day 10). | Normalized success rate per mouse in No-rule-change mice before the rule change (N=23). |
| Test the significance of the success rate increase after the rule change in the CLNF Rule-change group (day 11 to day 19). | Normalized success rate per mouse in Rule-change mice after the rule change (N=17). | ||
| Figure 4A | Two-way RM-ANOVA (Pingouin package in Python) | Test significance of day and group on the CLNF success rate before rule change (day 1 to day 10). | Normalized success rate per mouse in Rule-change and No-rule-change mice before rule change, along with group labels. |
| Figure 4A | Mann-Whitney U-test with Benjamini-Hochberg correction (pymannkendall package in Python) | Test the difference in the distribution of CLNF success rates between: day 1 no-rule-change and day 3 no-rule-change day 10 No-rule change and day 10 Rule change day 10 rule change and day 11 rule change day 10 rule change and day 19 rule change. |
Normalized success rate per mouse in Rule-change and No-rule-change mice before rule change, along with group labels. |
| Figure 4B | RM-ANOVA (Pingouin package in Python) | Test the significance of the success rate increase before the rule change in the CLMF Rule-change group (day 1 to day 4). Test the significance of the success rate increase after the rule change in the CLMF Rule-change group (from day 5 to day 10). Test the significance of the success rate increase in the CLMF no-rule-change group (day 1 to day 10). Test significance of success rate increase in CLMF No-feedback group (day 1 to day 10). |
Normalized success rate per mouse in Rule-change and No-rule-change mice before rule change, along with group labels. |
| Figure 5C | Permutation test (MMD function in hyppo package) | Multivariate two-sample test to compare ROI1 vs ROI2 bivariate distribution on day 9 and day 19. | Bivariate distribution of ROI1 and ROI2 on day 9 and day 19. |
| Figure 9C | Mann-Whitney, corrected for multiple comparisons with Benjamini-Hochberg (pymannkendall package in Python) | Test the significance of the difference in ΔF/F0 peak distribution between. Day 1 and day 4 in cortical OBL of CLMF rule-change mice. Day 4 and day 5 in cortical OBL of CLMF rule-change mice. Day 1 and day 4 in cortical FLL of CLMF rule-change mice. Day 4 and day 5 in cortical FLL of CLMF rule-change mice Day 1 and day 4 in cortical V1L of CLMF rule-change mice. Day 4 and day 5 in cortical V1L of CLMF rule-change mice. |
ΔF/F0 peak values in named cortical regions on each day for all mice along with their group labels. |
| Figure 10B, Figure 10C | Two-way RM-ANOVA, Bonferroni corrected for multiple comparisons (Pingouin package in Python) | Test significant changes in seed pixel correlations over days and between trial and rest conditions for CLMF No-rule-change mice (Figure 10B) and for CLMF No-feedback mice (Figure 10C). | Pairwise seed pixel correlation values for all combinations of cortical seed locations over days for each mouse, along with group labels. |
| Figure 4—figure supplement 1A | Mann-Whitney U-test with Benjamini-Hochberg correction (pymannkendall package in Python) | Test the difference in the distribution of CLNF success rates between. Day 1 male and day 3 male. Day 4 male and day 4 female. Day 10 female and day 11 female. |
Normalized success rate per mouse along with their sex labels. |
| Figure 4—figure supplement 1B | Mann-Whitney U-test with Benjamini-Hochberg correction (pymannkendall package in Python) | Test the difference in the distribution of CLNF success rates between. Day 1 single ROI and day 3 single ROI Day 10 single ROI and day 10 dual ROI. |
Normalized success rate per mouse along with their ROI-type of the experiment. |