Why are the results of the two methods for "seeing positive value only" different?

Hi afni experts,
The results of the two methods to see positive value only are very different although the same threshold is used:
1.Use the button “Pos?”.
2. Change the chooser from “Pos & Neg” to “Neg only” on the popup menu called by right-clicking on the label at the top of the threshold slider.
Why? And what is the relationship between these two methods? If I want to see negative value only, do I have another method except the second method mentioned above?
Thank you in advance!

For negative only, the best way is the popup menu and choose “Neg Only”. Please note that this menu applies to the threshold value (“Thr” sub-brick). That is, if you have “Neg Only” chosen, then only voxels with threshold values that are negative and below the minus of the slider value will get color.

The color they get is from the “Olay” sub-brick. In some cases, that might be positive. (It will not be if the OLay and Thr volumes are beta coefficient and corresponding t-statistic.)

The “Pos” button turns off displaying colors for negative OLay sub-brick values. That is somewhat different that turning off colors for negative Thr sub-brick values, as the “Pos Only” popup menu item does.

Thank you!
The OLay and Thr volumes are really beta coefficients and corresponding t-statistic in my case. I found the results (voxels survived) would become exactly same if the p-value threshold in the “Pos button” condition was set to two times bigger than that in the “Pos Only” popup condition. That is, if the p-value threshold is set to 0.05 in the “Pos button” condition, the p-value threshold in the “Pos Only” popup condition must be set to 0.025 to get the same survived voxels. If the p-value threshold was set equally in these two conditions, the results are definitely different. Does that difference come from the difference between one tail or two tails t-test, or something else?

When you switch the threshold slider to be ‘Pos only’ (or ‘Neg only’), the program “knows” that it is now a 1-sided test, so it adjusts the p-value of a t-statistic. That is what you are seeing.