Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations (...) of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children’s lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources. (shrink)

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research (...) exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. (shrink)

The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We (...) test the model on a variety of different datasets from grounded cognition experiments and demonstrate that this diverse set of results can be explained as perceptual simulation (cf. Barsalou, Simmons, Barbey, & Wilson, 2003) within a global memory model. (shrink)