Semantic Knowledge Use in Discourse: Influence of Age

Enhanced generalization and specialization of brain representations of semantic knowledge in healthy aging

Aging is often associated with a decrease in cognitive capacities. However, semantic memory appears relatively well preserved in healthy aging. Both behavioral and neuroimaging studies support the view that changes in brain networks contribute to this preservation of semantic cognition. However, little is known about the role of healthy aging in the brain representation of semantic categories. Here we used pattern classification analyses and computational models to examine the neural representations of living and non-living word concepts. The results demonstrate that brain representations of animacy in healthy aging exhibit increased similarity across categories, even across different task contexts. This pattern of results aligns with the neural dedifferentiation hypothesis that proposes that aging is associated with decreased specificity in brain activity patterns and less efficient neural resource allocation. However, the loss in neural specificity for different categories was accompanied by increased dissimilarity of item-based conceptual representations within each category. Taken together, the age-related patterns of increased generalization and specialization in the brain representations of semantic knowledge may reflect a compensatory mechanism that enables a more efficient coding scheme characterized by both compression and sparsity, thereby helping to optimize the limited neural resources and maintain semantic processing in the healthy aging brain.

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issues, such as contagious diseases and terrorism, that pose severe financial and human risks. These problems sporadically arise in various locales, and current smart city frameworks lack the capability to autonomously identify and address these issues. The challenge intensifies especially when trying to recognize and respond to unprecedented problems. The primary objective of this research is to predict potential urban issues and support their resolution proactively. To achieve this, our system makes use of semantic reasoning to understand the ongoing situations within the city. In this process, the 5W1H principles serve as inference rules, guiding the extraction and consolidation of context. Firstly, utilizing domain-specific annotation templates, we craft a semantic graph by amalgamating information from various sources available in the city, such as municipal public data and IoT platforms. Subsequently, the system autonomously infers and accumulates contexts of situations occurring in the city using 5W1H-based reasoning. As a result, the accumulated contexts allow for inferring potential urban problems by identifying repeated disruptions in city services at specific times or locations and establishing connections among them. The main contribution of this paper lies in proposing a comprehensive conceptual model for the suggested system and presenting actual implementation cases and applicable use cases. These contributions facilitate awareness among city administrators and citizens within a smart city regarding potential problem-prone areas or times, thereby aiding in the preemptive identification and mitigation of urban challenges.

Category norms with a cross-sectional sample of adults in the United States: Consideration of cohort, age, and historical effects on semantic categories

This paper describes normative data for newly collected exemplar responses to 70 semantic categories described in previous norming studies (Battig & Montague, Journal of Experimental Psychology, 80(3, pt.2): 1-46, 1969; Van Overschelde, Rawson, & Dunlosky, Journal of Memory and Language, 50(3): 289-335, 2004; Yoon et al., Psychology and Aging, 19(3), 379-393, 2004). These categories were presented to 246 young (18-39 years), middle (40-59 years), and older (60 years and older) English-speaking adults living in the United States who were asked to generate as many category exemplars as possible for each of the 70 categories. In order to understand differences in normative responses, we analyzed these responses a) between age groups within the current sample and b) in comparison to three previously published sets of norms. Experimental studies using such norms typically assume invariance of normative likelihoods across age and historical time. We replicate previous findings such that exemplar frequency correlations suggest moderate stability in generated category members between age groups and cohorts for many, but not all, categories. Further, analyses of rank-order correlations highlight that the traditional measure of typicality may not capture all aspects of typicality, namely that for some categories there is high consistency in the frequency of exemplars across age groups and/or norms, but the ordering of those exemplars differs significantly. We include a cluster analysis to aid in grouping categories based on relative stability across time, cohort, and age groups. These results emphasize the importance of maintaining and updating age-differentiated category norms.