Symbol Interdependency in Symbolic and Embodied Cognition

By Max M. Louwerse. Topics in Cognitive Science, 2011

321 citations recorded by the source corpus.

Subfields: Cultural Studies, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/1e507a62327dce8f747b1cc2a094d2040eed120b

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Citations in the corpus

Understanding Natural Language Understanding Systems. A Critical Analysis (2023, background): “…distributional learning as a knowledge induction mechanism depends on the fact that language has evolved to communicate our experiences about the world and encodes relevant aspects of such experiences in its structures, as argued by the Symbol Interdependence Hypothesis proposed by Louwerse (2011).”
Multimodal Machine Learning: A Survey and Taxonomy (2017, background): “Human semantic knowledge relies heavily on perceptual information [131] and”
Multimodal Distributional Semantics (2013, background): “…DSMs not being grounded or embodied are exaggerated, because they overlook the fact that the patterns of linguistic co-occurrence exploited by DSMs reflect semantic knowledge we acquired through perception, so that linguistic and perceptual information are strongly correlated (Louwerse, 2011).”
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research (2016, background): “In contrast, there is abundant evidence that human meaning representation is grounded in physical reality and sensorimotor experience [9, 10, 11, 12, 13].”
A Roadmap Towards Machine Intelligence (2015, background): “ch of the latter can be conveyed through linguistic means. For example, we can use language to talk about what we perceive with our senses, or to give instructions on how to operate in the world (see Louwerse, 2011, among others, for evidence that language encodes many perceptual aspects of our know”
Distributional Semantics in Technicolor (2012, background): “These models (as well as virtually all work in computational lexical semantics) rely on verbal information only, while human semantic knowledge also relies on non-verbal experience and representation (Louwerse, 2011), crucially on the information gathered through perception.”
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics (2014, background): “mantic space models extract meanings solely from linguistic data, even though we know that human semantic knowledge relies heavily on perceptual information (Louwerse, 2011).”
Distributional Models of Word Meaning (2018, unknown): “The contribution of linguistic experience vis-à-vis other kinds of nonlinguistic inputs in shaping concepts is an empirical question that is widely debated in cognitive science (Dove 2014, Louwerse 2011, Vigliocco et al. 2009).”
Taste and smell words form an affectively loaded and emotionally flexible part of the English lexicon (2016, unknown): “However, given the fact that the embodied relations reflected in language can influence linguistic processing (Louwerse, 2011; Louwerse & Connell, 2011), the structural and usage differences analyzed here have implications for language processing as well.”
Linguistic Distributional Knowledge and Sensorimotor Grounding both Contribute to Semantic Category Production (2020, methodology): “These findings support linguistic–simulation theories of the conceptual system (Barsalou et al., 2008; Connell & Lynott, 2014b; Louwerse, 2011) as the basis for conceptual processing during categorization.”
Thinking in Words: Language as an Embodied Medium of Thought (2014, background): “The success of these models demonstrates the informational richness of external language (Louwerse, 2011).”