In order to interact with objects in our environment, we rely on an understanding of the actions that can be performed on them, and the extent to which they rely or have an effect on the propertiesof the object. This knowledge is called the object "affordance". We propose an approach for creating an embedding of objects in an affordance space , in which each dimension corresponds to an aspect of meaning shared by many actions, using text corpora. This  embedding makes it possible to predict which verbs will be applicable to a given object, as captured in human judgments of affordance. We show that the dimensions learned are interpretable, and that they correspond to patterns of interaction with objects . Finally , we show that they can be used to predict other dimensions of object representation that have been shown to underpin human judgments of object similarity  .
In order to interact with objects in our environment, humans rely on an understanding of the actions that can be performed on them, as well as their properties. When considering concrete motor actions, this knowledge has been called the object affordance. Can this notion be generalized to any type of interaction that one can have with an object? In this paper we introduce a method to represent objects in a space where each dimension corresponds to a broad mode of interaction, based on verb selectional preferences in text corpora. This object embedding makes it possible to predict human judgments of verb applicability to objects better than a variety of alternative approaches. Furthermore , we show that the dimensions in this space can be used to predict categorical and functional dimensions in a state-of-the-art mental representation of objects, derived solely from human judgements of object similarity . These results suggest that interaction knowledge accounts for a large part of mental representations of objects .
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and the extent to which they rely or have an effect on the propertiesof the object. This knowledge is
as well as their properties. When considering concrete motor actions, this knowledge has been
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"affordance". We propose an approach for creating an embedding of objects in an affordance space , in which
affordance. Can this notion be generalized to any type of interaction that one can have with an object? In this paper we introduce a method to represent objects in a space where
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an aspect of meaning shared by many actions, using
a broad mode of interaction, based on verb selectional preferences in
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object
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which verbs will be applicable to a given object, as captured in
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affordance. We show that the dimensions learned are interpretable, and that they correspond to patterns of interaction with objects . Finally
verb applicability to objects better than a variety of alternative approaches. Furthermore
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the dimensions in this space
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other dimensions of object representation that have been shown to underpin human judgments
categorical and functional dimensions in a state-of-the-art mental representation of objects, derived solely from human judgements
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. These results suggest that interaction knowledge accounts for a large part of mental representations of objects