  Humans judge the similarity of two objects not just based on their visual
appearance but also based on their semantic relatedness. However, it remains
unclear how humans learn about semantic relationships between objects and
categories. One important source of semantic knowledge is that semantically
related objects frequently co-occur in the same context. For instance, forks
and plates are perceived as similar, at least in part, because they are often
experienced together in a ``kitchen" or ``eating'' context. Here, we
investigate whether a bio-inspired learning principle exploiting such
co-occurrence statistics suffices to learn a semantically structured object
representation {\em de novo} from raw visual or combined visual and linguistic
input. To this end, we simulate temporal sequences of visual experience by
binding together short video clips of real-world scenes showing objects in
different contexts. A bio-inspired neural network model aligns close-in-time
visual representations while also aligning visual and category label
representations to simulate visuo-language alignment. Our results show that our
model clusters object representations based on their context, e.g. kitchen or
bedroom, in particular in high-level layers of the network, akin to humans. In
contrast, lower-level layers tend to better reflect object identity or
category. To achieve this, the model exploits two distinct strategies: the
visuo-language alignment ensures that different objects of the same category
are represented similarly, whereas the temporal alignment leverages that
objects from the same context are frequently seen in succession to make their
representations more similar. Overall, our work suggests temporal and
visuo-language alignment as plausible computational principles for explaining
the origins of certain forms of semantic knowledge in humans.
