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Modern language models and vector-symbolic architectures show that vector-based models are capable of handling the compositional, structured, and symbolic properties required for human concepts. Vectors are also able to handle key phenomena from the psychology, including computation of features and similarities, reasoning about relations and analogies, and representation of theories. Language models show how vector representation of word semantics and sentences can interface between concepts and language, as seen in definitional theories of concepts or ad hoc concepts. The idea of Church encoding, from logic, allows us to understand how meaning can arise in vector-based or symbolic systems. By combining these recent computational results with classic findings in psychology, vector-based models provide a compelling account of human conceptual representation. For decades, cognitive scientists have debated what kind of representation might characterize human concepts. Whatever the format of the representation, it must allow for the computation of varied properties, including similarities, features, categories, definitions, and relations. It must also support the development of theories, ad hoc categories, and knowledge of procedures. Here, we discuss why vector-based representations provide a compelling account that can meet all these needs while being plausibly encoded into neural architectures. This view has become especially promising with recent advances in both large language models and vector symbolic architectures. These innovations show how vectors can handle many properties traditionally thought to be out of reach for neural models, including compositionality, definitions, structures, and symbolic computational processes. [ABSTRACT FROM AUTHOR]

The scope of human learning and development poses a radical challenge for cognitive science. We propose that developmental theories can address this challenge by adopting perspectives from computer science. Many of our best models treat learning as analogous to computer programming because symbolic programs provide the most compelling account of sophisticated mental representations. We specifically propose that children's learning is analogous to a particular style of programming called hacking, making code better along many dimensions through an open-ended set of goals and activities. By contrast to existing theories, which depend primarily on local search and simple metrics, this view highlights the many features of good mental representations and the multiple complementary processes children use to create them. • Programs provide our best general-purpose representations for human knowledge, inference, and planning; human learning is thus increasingly modeled as program induction, learning programs from data. • Many formal models of learning as program induction reduce to a stochastic search for concise descriptions of data. Actual human programmers and learners are significantly more complex, using many processes to optimize complex and frequently changing objectives. • The goals and activities of hacking, making code better along many dimensions through an open-ended and internally motivated set of goals and activities, are helping to inspire better models of human learning and cognitive development. [ABSTRACT FROM AUTHOR]

Recent evidence suggests that cognitive pressures associated with language acquisition and use could affect the organization of the lexicon. On one hand, consistent with noisy channel models of language (e.g., Levy, 2008), the phonological distance between wordforms should be maximized to avoid perceptual confusability (a pressure for dispersion). On the other hand, a lexicon with high phonological regularity would be simpler to learn, remember and produce (e.g., Monaghan et al., 2011) (a pressure for clumpiness). Here we investigate wordform similarity in the lexicon, using measures of word distance (e.g., phonological neighborhood density) to ask whether there is evidence for dispersion or clumpiness of wordforms in the lexicon. We develop a novel method to compare lexicons to phonotactically-controlled baselines that provide a null hypothesis for how clumpy or sparse wordforms would be as the result of only phonotactics. Results for four languages, Dutch, English, German and French, show that the space of monomorphemic wordforms is clumpier than what would be expected by the best chance model according to a wide variety of measures: minimal pairs, average Levenshtein distance and several network properties. This suggests a fundamental drive for regularity in the lexicon that conflicts with the pressure for words to be as phonologically distinct as possible. [ABSTRACT FROM AUTHOR]