  In human semantic cognition, proper names (names which refer to individual
entities) are harder to learn and retrieve than common nouns. This seems to be
the case for machine learning algorithms too, but the linguistic and
distributional reasons for this behaviour have not been investigated in depth
so far. To tackle this issue, we show that the semantic distinction between
proper names and common nouns is reflected in their linguistic distributions by
employing an original task for distributional semantics, the Doppelg\"anger
test, an extensive set of models, and a new dataset, the Novel Aficionados
dataset. The results indicate that the distributional representations of
different individual entities are less clearly distinguishable from each other
than those of common nouns, an outcome which intriguingly mirrors human
cognition.
