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1. An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering

   https://doi.org/10.1109/IRI.2019.00058  

   Kafle, Sabin; de Silva, Nisansa; Dou, Dejing (July 2019, Proceedings of IEEE 20th International Conference on Information Reuse and Integration for Data Science)

   Question Answering (QA) requires understanding queries expressed in natural languages and relevant information content to provide an answer. For closed-world QAs, information access is by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems. 

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2. Traces of Our Past: The Social Representation of the Physical World

   https://doi.org/10.1177/09637214241268145  

   Jara-Ettinger, Julian; Schachner, Adena (October 2024, Current Directions in Psychological Science)

   How do humans build and navigate their complex social world? Standard theoretical frameworks often attribute this success to a foundational capacity to analyze other people’s appearance and behavior to make inferences about their unobservable mental states. Here we argue that this picture is incomplete. Human behavior leaves traces in our physical environment that reveal our presence, our goals, and even our beliefs and knowledge. A new body of research shows that, from early in life, humans easily detect these traces—sometimes spontaneously—and readily extract social information from the physical world. From the features and placement of inanimate objects, people make inferences about past events and how people have shaped the physical world. This capacity develops early and helps explain how people have such a rich understanding of others: by drawing not only on how others act but also on the environments they have shaped. Overall, social cognition is crucial not only to our reasoning about people and actions but also to our everyday reasoning about the inanimate world. 

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3. Cultural stereotypes and sense of belonging contribute to gender gaps in STEM.

   Master, Allison; Meltzoff, Andrew N. (January 2020, International journal of gender science and technology)

   There is a need to help more students succeed in science, technology, engineering, and mathematics (STEM) education, with particular interest in reducing current gender gaps in motivation and participation. We propose a new theoretical model, the STEreotypes, Motivation, and Outcomes (STEMO) developmental model, to account for and integrate recent data emerging in social and developmental psychology. Based on this model, we synthesize research suggesting that social factors, such as stereotypes and self-representations about “belonging,” are powerful contributors to observed gender differences in STEM interest and academic outcomes. The review has four parts. First, we examine how cultural stereotypes specific to STEM contribute to gender gaps by negatively impacting interest and academic outcomes. Second, we review the central role of the self-representations affected by those stereotypes, including the particular importance of a sense of belonging. Third, we discuss various interventions that buffer against stereotypes and enhance a sense of belonging to reduce gender gaps in STEM interest and academic outcomes. Finally, we suggest theory-driven directions for future research. By organizing the research in this way, our review and theoretical analysis clarify key factors contributing to current gender gaps in STEM and mechanisms by which psychological interventions can help address STEM gender gaps. 

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4. Mental models use common neural spatial structure for spatial and abstract content

   https://doi.org/10.1038/s42003-019-0740-8  

   Alfred, Katherine L.; Connolly, Andrew C.; Cetron, Joshua S.; Kraemer, David J. M. (January 2020, Communications Biology)

   Abstract Mental models provide a cognitive framework allowing for spatially organizing information while reasoning about the world. However, transitive reasoning studies often rely on perception of stimuli that contain visible spatial features, allowing the possibility that associated neural representations are specific to inherently spatial content. Here, we test the hypothesis that neural representations of mental models generated through transitive reasoning rely on a frontoparietal network irrespective of the spatial nature of the stimulus content. Content within three models ranges from expressly visuospatial to abstract. All mental models participants generated were based on inferred relationships never directly observed. Here, using multivariate representational similarity analysis, we show that patterns representative of mental models were revealed in both superior parietal lobule and anterior prefrontal cortex and converged across stimulus types. These results support the conclusion that, independent of content, transitive reasoning using mental models relies on neural mechanisms associated with spatial cognition. 

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5. Social-ecological models with social hierarchy and spatial structure applied to small-scale fisheries

   https://doi.org/10.1007/s12080-024-00594-4  

   Wulfing, Sophie; White, Easton R (December 2024, Theoretical Ecology)

   Socio-ecological models combine ecological systems with human social dynamics in order to better understand human interactions with the environment. To model human behavior, replicator dynamics can be used to model how societal influence and financial costs can change opinions about resource extraction. Previous research on replicator dynamics has shown how evolving opinions on conservation can change how humans interact with their environment and therefore change population dynamics of the harvested species. However, social-ecological models often assume that human societies are homogeneous with no social structure. Building on previous work on social-ecological models, we develop a two-patch socio-ecological model with social hierarchy in order to study the interactions between spatial dynamics and social inequity. We found that fish movement between patches is a major driver of model dynamics, especially when the two patches exhibit different social equality and fishing practices. Further, we found that the societal influence between groups of harvesters was essential to ensuring stable fishery dynamics. Next, we developed a case study of two independently managed fisheries that were connected by fish movement where one human group fishes sustainably while another was over-harvests, resulting in a fishery collapse of both patches. We also found that because in this model, the influence of one human patch on another only communicates the amount of each catch and no fishing strategies were employed, increased social influence decreased the sustainability of the fishery. The findings of this study indicate the importance of including spatial components to socio- ecological models and highlights the importance of understanding species’ movements when making conservation decisions. Further, we demonstrate how incorporating fishing methods from outside sources can result in higher stability of the harvested population, demonstrating the need for effective communication across management regimes. 

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