Title: Conceptual Behavior and Human-Likeness in Vision-and-Language Models. Abstract: Learning conceptual structures requires acquiring knowledge of how members of a class share a set of semantic properties. The challenge is that some properties are more efficiently learned by perceptual experience (e.g., an image of a dog that shows its texture, shape and color) while others benefit from language input  (e.g., “a dog is a mammal”). Unimodal machine learning systems, as opposed to human brains, are therefore fundamentally limited in this respect. In contrast, systems integrating multimodal information should be able to learn a more human-like representational space since they can leverage both types of complementary sources of information. Multimodal neural network models offer a unique opportunity to test this hypothesis. We evaluate this proposal through a series of experiments on architecturally diverse vision-and-language networks trained on massive caption image datasets. We introduce an analytic framework that characterizes the semantic information behind the discrimination of concepts (i.e., lexicalized categories) through image-text matching tasks and representational similarity. We further compare how this discrimination (i.e., the model’s “conceptual behavior”) differs from that of humans and unimodal networks, and to what extent it depends on the multimodal encoder mechanism. Our results suggest promising avenues to align human and machine representational invariants via multimodal inputs.