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Derivationally related words, such as "runner" and "running", exhibitsemantic differences which also elicit different visual scenarios. In thispaper, we ask whether Vision and Language (V\&L) models capture suchdistinctions at the morphological level, using a a new methodology and dataset.We compare the results from V\&L models to human judgements and find thatmodels' predictions differ from those of human participants, in particulardisplaying a grammatical bias. We further investigate whether the human-modelmisalignment is related to model architecture. Our methodology, developed onone specific morphological contrast, can be further extended for testing modelson capturing other nuanced language features.

The Scenario Refiner: Grounding subjects in images at the morphological level