That’s a classic sliding‑window pattern. Think of each fourth as a stride in a convolutional layer, pulling the same filter over the input but offset. If the stride stays fixed, the layer isn’t really learning a new feature, just re‑sampling it. To really capture new patterns, you’d need to vary the weights or add non‑linearity between passes.