You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: In this work, we injectthe discovered patternsby either fixing or masking the attention weights prior to the softmax function.For fixed attention weights, the attention logits in the scaled-dot-product attention is replaced with a fixed (possibly input dependent) matrix such that:\textrm{FixAttn}(V,X)=\sigma(F^{(P)}(X))V(2)where \sigma is the softmax operation, V is the value vectors, and F(X)\in[0,1] computes a binary matrix from the input sequence X based on the predicated P for the specific pattern. Similarly, a pattern can also be injectedby casting a mask over the attention weights computed from the key and query vectors, as:\textrm{MaskAttn}(Q,K,V,X)=\sigma(M^{(P)}(X)+QK^{T})V(3)where M(X)\in[0,-\infty) computes the desired behaviour in the same fashion as F(X), and is added to the attention logits to approximate the multiplication of the attention distribution by a weight. Although the two methods are very similar with respect to the improvement they contribute (see §4), masking allows more flexibility and is generally used for patterns with a large number of applicable tokens, while fixing is more rigid and better suited for a small number of applicable tokens.

Question: How does using the fixed attention approach affect performance differently when compared to the masked attention approach?
There is no discussion on how the performance difference is brought about.