Although formal thought disorder (FTD) has been for long a clinical label in the assessment of some psychiatric disorders, in particular of schizophrenia, it remains a source of controversy, mostly because it is hard to say what exactly the “formal” in FTD refers to. We see anomalous processing of terminological knowledge, a core construct of human knowledge in general, behind FTD symptoms and we approach this anomaly from a strictly formal perspective. More specifically, we present here a symbolic computational model (...) of storage in, and activation of, a human semantic network, or semantic memory, whose core element is logical form; this is normalized by description logic (DL), namely by CL, a DL-based language – Conception Language – designed to formalize conceptualization from the viewpoint of individual cognitive agency. In this model, disruptions in the rule-based implementation of the logical form account for the apparently semantic anomalies symptomatic of FTD, which are detected by means of a CL-based algorithmic assessment. (shrink)

Genera, typically hand-in-hand with their branching species, are essential elements of vocabulary-based information constructs, in particular scientific taxonomies. Should they also feature in formal ontologies, the highest of such constructs? I argue in this article that the answer is “Yes” and that the question posed in its title also has a Yes-answer: The way medieval ontologists sliced up the world into genera does matter to formal ontology. More specifically, the way Dietrich of Freiberg, a Latin scholastic, conceived and applied strictly (...) generic criteria to slice up the world into its entities can provide some guidelines to the field of formal ontology with respect to not only its contents, but also its scope. In particular, Dietrich's information criterion plays here a central role. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 189-208, January 2022.

Parent and preschool teacher ratings of the 10 noun categories of MacArthur-Bates Communication Development Inventory were used to study expressive language in 2–4-year-old children with autism spectrum disorder across the home and preschool context. There was no significant difference in the total number of words the children said in the two contexts, but the children said significantly more words in the noun categories “Furniture and rooms” and “People” at home. Only one third of the words the children said were said (...) both at home and in the preschool, while the other two thirds were said only at home or only in preschool. This suggests that what words the children use across contexts differ substantially and that their vocabulary is larger than it seems when measured only in one context. This novel study highlights the importance of assessing the language in children with ASD in multiple contexts in order to better measure their vocabulary and to design appropriate language interventions. (shrink)

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on (...) shared and non-shared features. Feature distinctiveness predicted order of acquisition across all measures: Words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non-shared features and did not include shared features. This indicates that shared features may play less of a role in early word learning than non-shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects' distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning. (shrink)

We compared the ability of three different contextual models of lexical semantic memory and of a simple associative model to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on (...) an associative network successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts, and contextual representations may be used to process information about more abstract concepts. (shrink)

The organization of our knowledge about the world into an interconnected network of concepts linked by relations profoundly impacts many facets of cognition, including attention, memory retrieval, reasoning, and learning. It is therefore crucial to understand how organized semantic representations are acquired. The present experiment investigated the contributions of readily observable environmental statistical regularities to semantic organization in childhood. Specifically, we investigated whether co‐occurrence regularities with which entities or their labels more reliably occur together than with others (a) contribute to (...) relations between concepts independently and (b) contribute to relations between concepts belonging to the same taxonomic category. Using child‐directed speech corpora to estimate reliable co‐occurrences between labels for familiar items, we constructed triads consisting of a target, a related distractor, and an unrelated distractor in which targets and related distractors consistently co‐occurred (e.g., sock‐foot), belonged to the same taxonomic category (e.g., sock‐coat), or both (e.g., sock‐shoe). We used an implicit, eye‐gaze measure of relations between concepts based on the degree to which children (N = 72, age 4–7 years) looked at related versus unrelated distractors when asked to look for a target. The results indicated that co‐occurrence both independently contributes to relations between concepts and contributes to relations between concepts belonging to the same taxonomic category. These findings suggest that sensitivity to the regularity with which different entities co‐occur in children's environments shapes the organization of semantic knowledge during development. Implications for theoretical accounts and empirical investigations of semantic organization are discussed. (shrink)

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on (...) shared and non‐shared features. Feature distinctiveness predicted order of acquisition across all measures: Words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non‐shared features (object dissimilarity) and did not include shared features (object similarity). This indicates that shared features may play less of a role in early word learning than non‐shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects' distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning. (shrink)

Various aspects of semantic features drive early vocabulary development, but less is known about how the global and local structure of the overall semantic feature space influences language acquisition. A feature network of English words was constructed from a large database of adult feature production norms such that edges in the network represented feature distances between words (i.e., Manhattan distances of probability distributions of features elicited for each pair of words). A word's global feature distinctiveness is measured with respect to (...) all other words in the network and a word's local feature distinctiveness is measured relative to words in sub‐networks derived from clustering analyses. This paper investigates how feature distinctiveness of individual words at local and global scales of the network influences language acquisition. Regression analyses indicate that global feature distinctiveness was associated with earlier age of acquisition ratings, and was a stronger predictor of age of acquisition than local feature distinctiveness. These results suggest that the global structure of the semantic feature network could play an important role in language acquisition, whereby globally distinctive concepts help to structure vocabulary development over the lifespan. (shrink)