The recognition of features produces an activation, the strength of which depends not only on the degree to which the feature is present but a weighting factor. When the sum of the activations crosses a threshold, the concept becomes active and the stimulus is said to belong to that category.

This is also how linear classifiers in machine learning work, and many other statistical classifiers just replace "sum" with "something else" (support vector machines etc). On pattern recognition problems like "does this image contain a tree?" or "will this person return their loan?" they far outperform human-tuned decision trees, which classify by asking a series of yes/no questions. That's the nature of the complex sensory information we have to process, and it's not surprising that our brains work like that.