Cognitive scientists have long used distributional semantic representations of categories. The predominant approach uses distributional representations of category‐denoting nouns, such as “city” for the category city. We propose a novel scheme that represents categories as prototypes over representations of names of its members, such as “Barcelona,” “Mumbai,” and “Wuhan” for the category city. This name‐based representation empirically outperforms the noun‐based representation on two experiments (modeling human judgments of category relatedness and predicting category membership) with particular improvements for ambiguous nouns. We (...) discuss the model complexity of both classes of models and argue that the name‐based model has superior explanatory potential with regard to concept acquisition. (shrink)

The recent trend in cognitive robotics experiments on language learning, symbol grounding, and related issues necessarily entails a reduction of sensorimotor aspects from those provided by a human body to those that can be realized in machines, limiting robotic models of symbol grounding in this respect. Here, we argue that there is a need for modeling work in this domain to explicitly take into account the richer human embodiment even for concrete concepts that prima facie relate merely to simple actions, (...) and illustrate this using distributional methods from computational linguistics which allow us to investigate grounding of concepts based on their actual usage. We also argue that these techniques have applications in theories and models of grounding, particularly in machine implementations thereof. Similarly, considering the grounding of concepts in human terms may be of benefit to future work in computational linguistics, in particular in going beyond “grounding” concepts in the textual modality alone. Overall, we highlight the overall potential for a mutually beneficial relationship between the two fields. (shrink)

Logical metonymy resolution (begin a book begin reading a book or begin writing a book) has traditionally been explained either through complex lexical entries (qualia structures) or through the integration of the implicit event via post-lexical access to world knowledge. We propose that recent work within the words-as-cues paradigm can provide a more dynamic model of logical metonymy, accounting for early and dynamic integration of complex event information depending on previous contextual cues (agent and patient). We first present a self-paced (...) reading experiment on German subordinate sentences, where metonymic sentences and their paraphrased version differ only in the presence or absence of the clause-final target verb (Der Konditor begann die Glasur Der Konditor begann, die Glasur aufzutragen/The baker began the icing The baker began spreading the icing). Longer reading times at the target verb position in a high-typicality condition (baker + icing spread ) compared to a low-typicality (but still plausible) condition (child + icing spread) suggest that we make use of knowledge activated by lexical cues to build expectations about events. The early and dynamic integration of event knowledge in metonymy interpretation is bolstered by further evidence from a second experiment using the probe recognition paradigm. Presenting covert events as probes following a high-typicality or a low-typicality metonymic sentence (Der Konditor begann die Glasur AUFTRAGEN/The baker began the icing SPREAD), we obtain an analogous effect of typicality at 100 ms interstimulus interval. (shrink)