The more you deal with this situation, the more clear the problem at its core is going to become. With one solid example and some time spent looking at it you'll probably go "aha!" and it'll click.
In theory the problem is usually described as a situation where pixels in the immediate and neighboring area of a t-vert are shaded based off of separate and sometimes different inputs (the normal at the t-vert versus the normals of neighboring verts). You can exaggerate the problem as an illustration by setting the t-vert's normal to something very different than the neighboring verts' normals (ex. very different than their average).
In practice though, aside from corner cases you're usually dealing with smooth gradations of normals among vertices, so the problem is more subtle. I view the problem in a different way because of this: as a sample data propagation issue. The situation causes an interpolation across samples that doesn't propagate the sample data across the surface in a homogeneous way. In your example, the t-vert light sample input isn't being propagated upward, only left/right/down. That's one reason that t-vertices are problematic, they represent discontinuities in a mesh's network that lead to issues like this.
You can visualize it in your mind by picturing light values at each of the normal points on the surface and then thinking of what the resultant colors would be across the faces for given light locations. Using your example but with a smoother gradation of normals, for the top face you'd see one long linear interpolation of color. For the bottom two faces though, you'd see two linear interpolations of color driven by the t-vertex normal. Depending on the light angle, the t-vertex normal can pick up different amounts of light than the neighboring normals. This will drive apart the color interpolations above and below it, and you'll see a shading seam.
To illustrate with your example, I'd use one color only, adjust the normals so they form a more even distribution of relative angle (something like the set I'll throw in below), and then view it using different light locations (especially ones close to the t-vertex).
top left normal: [-1, 1, 1]
top right normal: [1, 1, 1]
middle left normal: [-1, 0, 1]
t-vert normal: [0, 0, 1]
middle right normal: [1, 0, 1]
bottom left normal: [-1, -1, 1]
bottom middle normal: [0, -1, 1]
bottom right normal: [1, -1, 1]
Because this is an issue driven by uneven propagation of sampled data--and propagation is what interpolation does--similar anomalies occur with other interpolation techniques too (like Phong shading) by the way.