Semantic memory: A review of methods, models, and current challenges
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Semantic memory: A review of methods, models, and current challenges Abhilasha A. Kumar 1
# The Psychonomic Society, Inc. 2020
Abstract Adult semantic memory has been traditionally conceptualized as a relatively static memory system that consists of knowledge about the world, concepts, and symbols. Considerable work in the past few decades has challenged this static view of semantic memory, and instead proposed a more fluid and flexible system that is sensitive to context, task demands, and perceptual and sensorimotor information from the environment. This paper (1) reviews traditional and modern computational models of semantic memory, within the umbrella of network (free association-based), feature (property generation norms-based), and distributional semantic (natural language corpora-based) models, (2) discusses the contribution of these models to important debates in the literature regarding knowledge representation (localist vs. distributed representations) and learning (error-free/Hebbian learning vs. error-driven/predictive learning), and (3) evaluates how modern computational models (neural network, retrievalbased, and topic models) are revisiting the traditional “static” conceptualization of semantic memory and tackling important challenges in semantic modeling such as addressing temporal, contextual, and attentional influences, as well as incorporating grounding and compositionality into semantic representations. The review also identifies new challenges regarding the abundance and availability of data, the generalization of semantic models to other languages, and the role of social interaction and collaboration in language learning and development. The concluding section advocates the need for integrating representational accounts of semantic memory with process-based accounts of cognitive behavior, as well as the need for explicit comparisons of computational models to human baselines in semantic tasks to adequately assess their psychological plausibility as models of human semantic memory. Keywords Semantic memory . Distributional semantic models . Semantic networks . Neural networks . Language models
Introduction What does it mean to know what an ostrich is? The question of how meaning is represented and organized by the human brain has been at the forefront of explorations in philosophy, psychology, linguistics, and computer science for centuries. Does knowing the meaning of an ostrich involve having a prototypical representation of an ostrich that has been created by averaging over multiple exposures to individual ostriches? Or does it instead involve extracting particular features that are characteristic of an ostrich (e.g., it is big, it is a bird, it does not fly, etc.) that are acquired via experience, and stored and
* Abhilasha A. Kumar [email protected] 1
Department of Psychological and Brain Sciences, Washington University in St. Louis, Campus Box 1125, One Brookings Drive, St. Louis, MO 63130, USA
activated upon encountering an ostrich? Further, is this knowledge stored throug
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