Understanding definition of NP
Here is an example that will hopefully make things clearer. Consider the problem 3COL, which is the set of all graphs that can be legally colored using 3 colors. It is conjectured that no polynomial time algorithm can determine whether a graph belongs to 3COL (i.e., can be legally colored using 3 colors). However, given a 3-coloring of the graph, it is easy to check whether it is legal (that is, you can do it in polynomial time). In this case:


$L$ is 3COL.
$x$ is a graph.
$t$ is a 3-coloring of $x$.
$M$ is a machine that checks that $t$ is a valid 3-coloring of $x$.


If a graph $x$ is 3-colorable, then some witness $t$, namely a valid 3-coloring, will convince $M$ that $x$ is in 3COL. Conversely, if a graph $x$ is not 3-colorable, then no 3-coloring $t$ would be valid, and so $M$ will always reject given inputs $x$ and $t$ (for any $t$). This shows that 3COL is in NP.

What happens if $t$ is the same for all $x$? In this case we can hard-code $t$ into the algorithm, and do away with the witness. Under such a definition we get P rather than NP.

What if $t$ is allowed to depend only on the size of $x$? The corresponding class is known as P/poly, and is important in computational complexity. It is more generally known as the class of (languages accepted by) polysize circuits. If you find this interesting, many advanced textbooks discuss this topic.