Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to (...) a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information. (shrink)

As embodied theories of cognition are increasingly formalized and tested, care must be taken to make informed assumptions regarding the nature of concepts and representations. In this study, we outline three reasons why one cannot, in effect, represent the same concept twice. First, online perception affects offline representation: Current representational content depends on how ongoing demands direct attention to modality-specific systems. Second, language is a fundamental facilitator of offline representation: Bootstrapping and shortcuts within the computationally cheaper linguistic system continuously modify (...) representational content. Third, time itself is a source of representational change: As the content of underlying concepts shifts with the accumulation of direct and vicarious experience, so too does the content of representations that draw upon these concepts. We discuss the ramifications of these principles for research into both human and synthetic cognitive systems. (shrink)

Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in (...) sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task. (shrink)

Recent theories of cognition have argued that embodied experience is important for conceptual processing. Embodiment can be contrasted with linguistic factors such as the typical order in which words appear in language. Here, we report four experiments that investigated the conditions under which embodiment and linguistic factors determine performance. Participants made speeded judgments about whether pairs of words or pictures were semantically related or had an iconic relationship. The embodiment factor was operationalized as the degree to which stimulus pairs were (...) presented in the spatial configurations in which they usually occur (i.e., an iconic configuration, e.g., attic presented above basement). The linguistic factor was operationalized as the frequency of the stimulus pairs in language. The embodiment factor predicted error rates and response time better for pictures, whereas the linguistic factor predicted error rates and response time better for words. These findings were modified by task, with the embodiment factor being strongest in iconicity judgments for pictures and the linguistic factor being strongest in semantic judgments for words. Both factors predicted error rates and response time for both semantic and iconicity judgments. These findings support the view that conceptual processing is both linguistic and embodied, with a bias for the embodiment or the linguistic factor depending on the nature of the task and the stimuli. (shrink)

Previous studies have shown that object properties are processed faster when they follow properties from the same perceptual modality than properties from different modalities. These findings suggest that language activates sensorimotor processes, which, according to those studies, can only be explained by a modal account of cognition. The current paper shows how a statistical linguistic approach of word co-occurrences can also reliably predict the category of perceptual modality a word belongs to (auditory, olfactory–gustatory, visual–haptic), even though the statistical linguistic approach (...) is less precise than the modal approach (auditory, gustatory, haptic, olfactory, visual). Moreover, the statistical linguistic approach is compared with the modal embodied approach in an experiment in which participants verify properties that share or shift modalities. Response times suggest that fast responses can best be explained by the linguistic account, whereas slower responses can best be explained by the embodied account. These results provide further evidence for the theory that conceptual processing is both linguistic and embodied, whereby less precise linguistic processes precede precise simulation processes. (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)