scholarly journals Recruitment of representational components of the semantic system distinguishes successful from unsuccessful access to complex factual knowledge

2021 ◽  
Author(s):  
Silvia Ubaldi ◽  
Giuseppe Rabini ◽  
Scott L Fairhall
Keyword(s):  
Control Systems ◽  
Semantic Representation ◽  
Region Of Interest ◽  
Relevant Information ◽  
Semantic Knowledge ◽  
Spatial Knowledge ◽  
Factual Knowledge ◽  
Whole Brain ◽  
Knowledge Domains ◽  
Semantic Access

Our ability to effectively retrieve complex semantic knowledge meaningfully impacts our daily lives, yet the interactions between semantic control and semantic representational systems that underly successful access and transient failures in access remain only partially understood. In this fMRI study, we contrast activation during successful semantic access, unsuccessful semantic access due to transient access-failures (i.e., "tip-of-the-tongue", "feeling-of-knowing"), and trials where the semantic knowledge was not possessed. Twenty-four participants were presented 240 trivia-based questions relating to person, place, object or scholastic knowledge-domains. Whole brain analyses of the recall event indicate comparable recruitment of prefrontal semantic control systems during successful and unsuccessful semantic access and greater activation in representational systems in successful access. Region-of-interest analysis of domain-selective areas showed that successful access was generally associated with increased responses for both preferred and non-preferred stimuli, with the exception of place-selective regions (PPA, TOS and RSC). Both whole brain and Region-of-interest analysis showed the particular recruitment of place-selective regions during unsuccessful attempts at semantic access, for all stimulus domains. Collectively, these results suggest that prefrontal semantic control systems and classical spatial-knowledge-selective regions work together to locate relevant information and that access to complex knowledge results in a broadening of semantic representation to include regions selective for other knowledge domains.

scholarly journals Which animal is big? Aging-related effects on the controlled retrieval of information

2021 ◽  
Author(s):  
Ettore Ambrosini ◽  
Francesca Peressotti ◽  
Marisa Gennari ◽  
Silvia Benavides ◽  
Maria Montefinese
Keyword(s):  
Older Adults ◽  
Semantic Information ◽  
Semantic Representation ◽  
Reaction Times ◽  
Relevant Information ◽  
Semantic Knowledge ◽  
Verification Task ◽  
Target Feature ◽  
Use Of Knowledge ◽  
Semantic Significance

The efficient use of knowledge requires semantic control processes to retrieve context-relevant information. So far, it is well established that semantic knowledge, as measured with vocabulary tests, do not decline in aging. Yet, it is still unclear if controlled retrieval -the context-driven retrieval of very specific aspects of semantic knowledge- declines in aging, following the same fate of other forms of cognitive control. Here, we tackled this issue by comparing the performance of younger and older native Italian speakers during a semantic feature verification task. To manipulate the control demands, we parametrically varied the semantic significance, a measure of the salience of the target feature for the cue concept. As compared to their young counterparts, older adults showed a greater performance disruption (in terms of reaction times) as the significance value of the target feature decreased. This result suggests that older people have difficulties in regulating the activation within semantic representation, such that they fail to handle non-dominant (or weakly activated) yet task-relevant semantic information.

The Alerting and Orienting Systems of Attention Are Modified by Cannabis Dependence

2021 ◽  
Vol 27 (6) ◽  
pp. 520-532
Author(s):  
Ivett E. Ortega-Mora ◽  
Ulises Caballero-Sánchez ◽  
Talía V. Román-López ◽  
Cintia B. Rosas-Escobar ◽  
Mónica Méndez-Díaz ◽  
...  
Keyword(s):  
Selective Attention ◽  
Control Systems ◽  
Executive Control ◽  
Age Of Onset ◽  
Relevant Information ◽  
Cannabis Use ◽  
Network Systems ◽  
Attention Network ◽  
Cannabis Dependence ◽  
Alerting System

AbstractAttention allows us to select relevant information from the background. Although several studies have described that cannabis use induces deleterious effects on attention, it remains unclear if cannabis dependence affects the attention network systems differently.Objectives:To evaluate whether customary consumption of cannabis or cannabis dependence impacts the alerting, orienting, and executive control systems in young adults; to find out whether it is related to tobacco or alcohol dependence and if cannabis use characteristics are associated with the attention network systems.Method:One-hundred and fifty-four healthy adults and 102 cannabis users performed the Attention Network Test (ANT) to evaluate the alerting, orienting, and executive control systems.Results:Cannabis use enhanced the alerting system but decreased the orienting system. Moreover, those effects seem to be associated with cannabis dependence. Out of all the cannabis-using variables, only the age of onset of cannabis use significantly predicted the efficiency of the orienting and executive control systems.Conclusion:Cannabis dependence favors tonic alertness but reduces selective attention ability; earlier use of cannabis worsens the efficiency of selective attention and resolution of conflicts.

scholarly journals Digitizing the Non-Digital: Creating a Global Context for Events, Artifacts, Ideas, and Information

2006 ◽  
Vol 25 (2) ◽  
pp. 95 ◽  
Author(s):  
Deborah L. MacPherson
Keyword(s):  
Open Source ◽  
Spatial Databases ◽  
Digital Rights Management ◽  
Relevant Information ◽  
Demonstration Project ◽  
Specific Data ◽  
Global Context ◽  
Digital Rights ◽  
Knowledge Domains ◽  
Rights Management

This paper discusses some of the problems associated with search and digital-rights management in the emerging age of interconnectivity. An open-source system called Context Driven Topologies (CDT) is proposed to create one global context of geography, knowledge domains, and Internet addresses, using centralized spatial databases, geometry, and maps. The same concept can be described by different words, the same image can be interpreted a thousand ways by every viewer, but mathematics is a set of rules to ensure that certain relationships or sequences will be precisely regenerated. Therefore, unlike most of today’s digital records, CDTs are based on mathematics first, images second, words last. The aim is to permanently link the highest quality events, artifacts, ideas, and information into one record documenting the quickest paths to the most relevant information for specific data, users, and tasks. A model demonstration project using CDT to organize, search, and place information in new contexts while protecting the authors’ intent is also introduced.

scholarly journals From survey to semantic representation for Cultural Heritage: the 3D modeling of recurring architectural elements

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 98
Author(s):  
Valeria Croce ◽  
Gabriella Caroti ◽  
Andrea Piemonte ◽  
Marco Giorgio Bevilacqua
Keyword(s):  
Cultural Heritage ◽  
Semantic Representation ◽  
Laser Scanner ◽  
3D Models ◽  
Semantic Knowledge ◽  
Documentary Sources ◽  
Building Components ◽  
Integrated Programs ◽  
The Creation ◽  
Historical Sites

The digitization of Cultural Heritage paves the way for new approaches to surveying and restitution of historical sites. With a view to the management of integrated programs of documentation and conservation, the research is now focusing on the creation of information systems where to link the digital representation of a building to semantic knowledge. With reference to the emblematic case study of the Calci Charterhouse, also known as Pisa Charterhouse, this contribution illustrates an approach to be followed in the transition from 3D survey information, derived from laser scanner and photogrammetric techniques, to the creation of semantically enriched 3D models. The proposed approach is based on the recognition -segmentation and classification- of elements on the original raw point cloud, and on the manual mapping of NURBS elements on it. For this shape recognition process, reference to architectural treatises and vocabularies of classical architecture is a key step. The created building components are finally imported in a H-BIM environment, where they are enriched with semantic information related to historical knowledge, documentary sources and restoration activities.

Semantic Representation and Naming in Young Children

2002 ◽  
Vol 45 (2) ◽  
pp. 332-346 ◽  
Author(s):  
Karla K. McGregor ◽  
Rena M. Friedman ◽  
Renée M. Reilly ◽  
Robyn M. Newman
Keyword(s):  
Picture Naming ◽  
Semantic Representation ◽  
A Priori ◽  
Visual Complexity ◽  
Semantic Knowledge ◽  
Object Representations ◽  
Semantic Representations ◽  
Dynamic Nature ◽  
Retrieval Failure ◽  
Age Appropriate

Children's semantic representations and semantic naming errors were the focus of this study. In Experiment 1, 25 normally developing children (mean age=5 years 4 months) named, drew, and defined 20 age-appropriate objects. The results suggested that functional and physical properties are core aspects of object representations in the semantic lexicon and that these representations are often organized and accessed according to a taxonomic hierarchy. Results of a new procedure, comparative picture naming/picture drawing, suggested that the degree of knowledge in the semantic lexicon makes words more or less vulner-able to retrieval failure. Most semantic naming errors were associated with limited semantic knowledge, manifested as either lexical gaps or fragile representations. Comparison of definitions for correctly named and semantically misnamed objects provided converging evidence for this conclusion. In Experiment 2, involving 16 normally developing children (mean age=5 years 5 months), the comparative picture naming/picture drawing results were replicated with a stimulus set that allowed a priori matching of the visual complexity of items drawn from correct and semantic error pools. Discussion focuses on the dynamic nature of semantic representations and the relation between semantic representation and naming during a period of slow mapping. The value of comparative picture naming/ picture drawing as a new method for exploring children's semantic representa-tions is emphasized.

Semantic Cognition: Semantic Memory and Semantic Control

2021 ◽  
Author(s):  
Elizabeth Jefferies ◽  
Xiuyi Wang
Keyword(s):  
Semantic Memory ◽  
Semantic Processing ◽  
Inferior Frontal Gyrus ◽  
Semantic Knowledge ◽  
Human Cognition ◽  
Factual Knowledge ◽  
Conceptual Representations ◽  
Semantic Cognition ◽  
Control Processes ◽  
Functional Features

Semantic processing is a defining feature of human cognition, central not only to language, but also to object recognition, the generation of appropriate actions, and the capacity to use knowledge in reasoning, planning, and problem-solving. Semantic memory refers to our repository of conceptual or factual knowledge about the world. This semantic knowledge base is typically viewed as including “general knowledge” as well as schematic representations of objects and events distilled from multiple experiences and retrieved independently from their original spatial or temporal context. Semantic cognition refers to our ability to flexibly use this knowledge to produce appropriate thoughts and behaviors. Semantic cognition includes at least two interactive components: a long-term store of semantic knowledge and semantic control processes, each supported by a different network. Conceptual representations are organized according to the semantic relationships between items, with different theories proposing different key organizational principles, including sensory versus functional features, domain-specific theory, embodied distributed concepts, and hub-and-spoke theory, in which distributed features are integrated within a heteromodal hub in the anterior temporal lobes. The activity within the network for semantic representation must often be controlled to ensure that the system generates representations and inferences that are suited to the immediate task or context. Semantic control is thought to include both controlled retrieval processes, in which knowledge relevant to the goal or context is accessed in a top-down manner when automatic retrieval is insufficient for the task, and post-retrieval selection to resolve competition between simultaneously active representations. Control of semantic retrieval is supported by a strongly left-lateralized brain network, which partially overlaps with the bilateral network that supports domain-general control, but extends beyond these sites to include regions not typically associated with executive control, including anterior inferior frontal gyrus and posterior middle temporal gyrus. The interaction of semantic control processes with conceptual representations allows meaningful thoughts and behavior to emerge, even when the context requires non-dominant features of the concept to be brought to the fore.

Business Network Modelling

2010 ◽  
Vol 1 (4) ◽  
pp. 77-91 ◽  
Author(s):  
Alexander Smirnov ◽  
Nikolay Shilov
Keyword(s):  
Web Services ◽  
World Economy ◽  
Relevant Information ◽  
Business Networks ◽  
Business Network ◽  
Network Modelling ◽  
Context Management ◽  
Knowledge Domains ◽  
Technological Level

Business networks have appeared as a reaction to changes taking place in the world economy and logistic networks can be considered as examples of such networks. The approach proposed in the paper is based on the idea to represent the business network members with services provided by them, and to achieve interoperability via application of the SOA standards. The approach is based on usage of such technologies as Web services, ontology, and context management. Web services enable interoperability at the technological level. Ontologies are used for description of knowledge domains and enable interoperability at the level of semantics. The purpose of the context is to represent only relevant information from the large amount of the information and the application of the approach is demonstrated on the case study from the area of dynamic logistics. The considered problem takes into account a continuously changing problem environment and requires nearly real-time solving.

Accessing Knowledge, Information and Resources for Planning and Spatial Decision Support

2012 ◽  
Vol 1 (1) ◽  
pp. 90-97
Author(s):  
Naicong Li
Keyword(s):  
Decision Support ◽  
Conceptual Framework ◽  
Planning Process ◽  
Relevant Information ◽  
Spatial Decision Support ◽  
Knowledge Domains ◽  
Problem Types ◽  
Standard Set ◽  
Planning Problems ◽  
Knowledge Portal

To help synthesize and systematically organize the information, knowledge and resources for spatial decision support (SDS), and to help researchers and practitioners engaged in an actual planning process find relevant information and resources for solving their specific planning problems, the SDS Consortium and University of Redlands have developed a conceptual framework for SDS and a collection of SDS resources, hosted on the SDS Knowledge Portal. The conceptual framework includes a set of defined, inter-connected concepts pertaining to planning and spatial decision support, such as planning and decision problem types, application domains, knowledge domains and planning process including phases and steps. This conceptual framework is further used to organize a representative set of SDS resources, such as planning process workflows, methods, tools and models, data sources, case studies, literature, and so forth. The SDS Knowledge Portal facilitates learning of SDS and accessing SDS resources, promotes semantic clarity by adopting a common vocabulary for the user community, and promotes interoperability among SDS resources by using a standard set of concepts to define and classify these resources.

scholarly journals Listening to speech and non-speech sounds activates phonological and semantic knowledge differently

2020 ◽  
Vol 73 (8) ◽  
pp. 1135-1149 ◽  
Author(s):  
James Bartolotti ◽  
Scott R Schroeder ◽  
Sayuri Hayakawa ◽  
Sirada Rochanavibhata ◽  
Peiyao Chen ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Semantic Information ◽  
Semantic Knowledge ◽  
Phonological Information ◽  
Opposite Pattern ◽  
Semantic Access ◽  
Word Forms ◽  
Time Courses ◽  
The Mind ◽  
Phonological Activation

How does the mind process linguistic and non-linguistic sounds? The current study assessed the different ways that spoken words (e.g., “dog”) and characteristic sounds (e.g., <barking>) provide access to phonological information (e.g., word-form of “dog”) and semantic information (e.g., knowledge that a dog is associated with a leash). Using an eye-tracking paradigm, we found that listening to words prompted rapid phonological activation, which was then followed by semantic access. The opposite pattern emerged for sounds, with early semantic access followed by later retrieval of phonological information. Despite differences in the time courses of conceptual access, both words and sounds elicited robust activation of phonological and semantic knowledge. These findings inform models of auditory processing by revealing the pathways between speech and non-speech input and their corresponding word forms and concepts, which influence the speed, magnitude, and duration of linguistic and nonlinguistic activation.

scholarly journals Robust Deep Co-Saliency Detection with Group Semantic

2019 ◽  
Vol 33 ◽  
pp. 8917-8924 ◽  
Author(s):  
Chong Wang ◽  
Zheng-Jun Zha ◽  
Dong Liu ◽  
Hongtao Xie
Keyword(s):  
Category Learning ◽  
Visual Cues ◽  
Large Scale ◽  
Semantic Representation ◽  
Saliency Detection ◽  
Spatial Location ◽  
Semantic Knowledge ◽  
Visual Features ◽  
Feature Maps ◽  
Semantic Level

High-level semantic knowledge in addition to low-level visual cues is essentially crucial for co-saliency detection. This paper proposes a novel end-to-end deep learning approach for robust co-saliency detection by simultaneously learning highlevel group-wise semantic representation as well as deep visual features of a given image group. The inter-image interaction at semantic-level as well as the complementarity between group semantics and visual features are exploited to boost the inferring of co-salient regions. Specifically, the proposed approach consists of a co-category learning branch and a co-saliency detection branch. While the former is proposed to learn group-wise semantic vector using co-category association of an image group as supervision, the latter is to infer precise co-salient maps based on the ensemble of group semantic knowledge and deep visual cues. The group semantic vector is broadcasted to each spatial location of multi-scale visual feature maps and is used as a top-down semantic guidance for boosting the bottom-up inferring of co-saliency. The co-category learning and co-saliency detection branches are jointly optimized in a multi-task learning manner, further improving the robustness of the approach. Moreover, we construct a new large-scale co-saliency dataset COCO-SEG to facilitate research of co-saliency detection. Extensive experimental results on COCO-SEG and a widely used benchmark Cosal2015 have demonstrated the superiority of the proposed approach as compared to the state-of-the-art methods.

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