A bird is a warm-blooded organism with circulatory lungs." How close did I come?
So if I removed the lungs of chicken, you would no longer consider it a bird? Or if I surgically modified some other creature (e.g. a pig) to have circulatory lungs, you would consider this to be a bird?
This kind of argument is why it is pretty difficult to come up with a comprehensive set of features for a broad category like 'bird'. Often the best you can do is produce a set of examples demonstrating the category. Humans are pretty good at such pattern recognition from a set of data.
Like a lot of things, it is hard to define, but you know it when you see it :-)
So if I removed the lungs of chicken, you would no longer consider it a bird? Or if I surgically modified some other creature (e.g. a pig) to have circulatory lungs, you would consider this to be a bird?
Different people's concepts of "bird" agree on most real-world examples, but I see no reason why they should agree on all conceivable hypothetical examples, so the task of "defining" a word is futile.
Warrigal gave a good recognition algorithm: it inspects a small subset of properties and gives an answer that accords with our judgment in most real-world cases. That's about as far as one can or should go when "defining" something outside of mathematics.
The classical understanding of categories centers on necessary and sufficient properties. If a thing has X, Y, and Z, we say that it belongs to class A; if it lacks them, we say that it does not. This is the model of how humans construct and recognize categories that philosophers have held since the days of Aristotle.
Cognitive scientists found that the reality isn't that simple.
Human categorization is not a neat and precise process. When asked to explain the necessary features of, say, a bird, people cannot. When confronted with collections of stimuli and asked to determine which represent examples of 'birds', people find it easy to accept or reject things that have all or none of the properties they associate with that concept; when shown entities that share some but not all of the critical properties, people spend much more time trying to decide, and their decisions are tentative. Their responses simply aren't compatible with binary models.
Concepts are associational structures. They do not divide the world clearly into two parts. Not all of their features are logically necessary. 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. The stronger the total activation, the more clearly the stimulus can be said to embody the concept.
Does this sound familiar? It should for us - we have the benefit of hindsight. We can recognize that pattern - it's how neural networks function. Or to put it another way, it's how neurons work.
But wait! There's more!
Try applying that model to virtually every empirical fact we've acquired regarding how people produce their conclusions. For example, our beliefs about how seriously we should take a hypothetical problem scenario depend not on a rigorous statistical analysis, but a combination of how vividly we feel about the scenario and how frequently it appears in our memory. People are convinced not only by the logical structure of an argument but the traits of the entities presenting it and the specific way in which the arguments are made. And so on, and so forth.
Most human behavior derives directly from the behavior of the associational structures in our minds.
To put it another way: what we call 'thinking' doesn't involve rational thought. It's *feeling*. People ponder an issue, then respond in the way that they feel stands out the most from the sea of associations.
Consider the implications for a while.