scholarly journals Distinct but cooperating brain networks supporting semantic cognition

2021 ◽  
Author(s):  
JeYoung Jung ◽  
Matthew A Lambon Ralph
Keyword(s):  
Semantic Processing ◽  
Semantic Network ◽  
Brain Networks ◽  
Semantic Networks ◽  
Task Demands ◽  
Matching Task ◽  
Semantic Cognition ◽  
Frontoparietal Network ◽  
The Neural Networks ◽  
General Networks

Semantic cognition is a complex brain function involving multiple processes from sensory systems, semantic systems, to domain-general cognitive systems, reflecting its multifaceted nature. However, it remain unclear how these systems cooperate with each other to achieve effective semantic cognition. Here, we investigated the neural networks involved in semantic cognition using independent component analysis (ICA). We used a semantic judgement task and a pattern matching task as a control task with two levels of difficulty to disentangle task-specific networks from domain-general networks and to delineate task-specific involvement of these networks. ICA revealed that semantic processing recruited two task-specific networks (semantic network [SN] and extended semantic network [ESN]) as well as domain general networks including the frontoparietal network (FPN) and default mode network (DMN). Specifically, two distinct semantic networks were differently modulated by task difficulty. The SN was coupled with the extended semantic network and FPN but decoupled with the DMN, whereas the ESN was synchronised with the FPN and DMN. Furthermore, the degree of decoupling between the SN and DMN was associated with semantic performance. Our findings suggest that human higher cognition is achieved by the neural dynamics of brain networks, serving distinct and shared cognitive functions depending on task demands.

scholarly journals Validated measures of semantic knowledge and semantic control: Normative data from young and older adults for more than 300 semantic judgements

2021 ◽  
Author(s):  
Wei Wu ◽  
Paul Hoffman
Keyword(s):  
Normative Data ◽  
Semantic Processing ◽  
Feature Matching ◽  
Cognitive Architecture ◽  
Later Life ◽  
Semantic Knowledge ◽  
Matching Task ◽  
Large Set ◽  
Semantic Cognition ◽  
Control Processes

Recent studies suggest that knowledge representations and control processes are the two key components underpinning semantic cognition, and are also crucial indicators of the shifting cognitive architecture of semantics in later life. Although there are many standardized assessments that provide measures of the quantity of semantic knowledge participants possess, normative data for tasks that probe semantic control processes are not yet available. Here, we present normative data from more than 200 young and older participants on a large set of stimuli in two semantic tasks, which probe controlled semantic processing (feature-matching task) and semantic knowledge (synonym judgement task). We verify the validity of our norms by replicating established age- and psycholinguistic-property-related effects on semantic cognition. Specifically, we find that older people have more detailed semantic knowledge than young people but have less effective semantic control processes. We also obtain expected effects of word frequency and inter-item competition on performance. Parametrically varied difficulty levels are defined for half of the stimuli based on participants’ behavioral performance, allowing future studies to produce customized sets of experimental stimuli based on our norms. We provide all stimuli, data and code used for analysis, in the hope that they are useful to other researchers.

scholarly journals Age-related changes in the neural networks supporting semantic cognition: A meta-analysis of 47 functional neuroimaging studies

2017 ◽  
Author(s):  
Paul Hoffman ◽  
Alexa M. Morcom
Keyword(s):  
Young People ◽  
Semantic Processing ◽  
Functional Neuroimaging ◽  
Semantic Network ◽  
Meta Analysis ◽  
Later Life ◽  
Cognitive Domain ◽  
Neural Basis ◽  
Semantic Cognition ◽  
Age Related

AbstractSemantic cognition is central to understanding of language and the world and, unlike many cognitive domains, is thought to show little age-related decline. We investigated age-related differences in the neural basis of this critical cognitive domain by performing an activation likelihood estimation (ALE) meta-analysis of functional neuroimaging studies comparing young and older people. On average, young people outperformed their older counterparts during semantic tasks. Overall, both age groups activated similar left-lateralised regions. However, older adults displayed less activation than young people in some elements of the typical left-hemisphere semantic network, including inferior prefrontal, posterior temporal and inferior parietal cortex. They also showed greater activation in right frontal and parietal regions, particularly those held to be involved in domain-general controlled processing, and principally when they performed more poorly than the young. Thus, semantic processing in later life is associated with a shift from semantic-specific to domain-general neural resources, consistent with the theory of neural dedifferentiation, and a performance-related reduction in prefrontal lateralisation, which may reflect a response to increased task demands.

scholarly journals Modelling the structure and dynamics of semantic processing

2018 ◽  
Author(s):  
Armand S. Rotaru ◽  
Gabriella Vigliocco ◽  
Stefan L. Frank
Keyword(s):  
Semantic Processing ◽  
Lexical Processing ◽  
Response Times ◽  
Semantic Network ◽  
Semantic Networks ◽  
Semantic Activation ◽  
Structure And Dynamics ◽  
Connectionist Modeling ◽  
Semantic Decision ◽  
Activation Profile

The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co-occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure.

scholarly journals Is the Letter ‘t’ in the Word ‘gourmet’? Disruption in Task-Evoked Connectivity Networks in Adults with Impaired Literacy Skills

NeuroSci ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 75-94
Author(s):  
Kulpreet Cheema ◽  
William E. Hodgetts ◽  
Jacqueline Cummine
Keyword(s):  
Developmental Disorders ◽  
Brain Networks ◽  
Literacy Skills ◽  
Resting State Networks ◽  
Skilled Readers ◽  
University Of Alberta ◽  
Domain Specific ◽  
Probe Task ◽  
The University ◽  
General Networks

Much work has been done to characterize domain-specific brain networks associated with reading, but very little work has been done with respect to spelling. Our aim was to characterize domain-specific spelling networks (SpNs) and domain-general resting state networks (RSNs) in adults with and without literacy impairments. Skilled and impaired adults were recruited from the University of Alberta. Participants completed three conditions of an in-scanner spelling task called a letter probe task (LPT). We found highly connected SpNs for both groups of individuals, albeit comparatively more connections for skilled (50) vs. impaired (43) readers. Notably, the SpNs did not correlate with spelling behaviour for either group. We also found relationships between SpNs and RSNs for both groups of individuals, this time with comparatively fewer connections for skilled (36) vs. impaired (53) readers. Finally, the RSNs did predict spelling performance in a limited manner for the skilled readers. These results advance our understanding of brain networks associated with spelling and add to the growing body of literature that describes the important and intricate connections between domain-specific networks and domain-general networks (i.e., resting states) in individuals with and without developmental disorders.

scholarly journals Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

2021 ◽  
Vol 11 (14) ◽  
pp. 6368
Author(s):  
Fátima A. Saiz ◽  
Garazi Alfaro ◽  
Iñigo Barandiaran ◽  
Manuel Graña
Keyword(s):  
Ad Hoc ◽  
Data Augmentation ◽  
Semantic Network ◽  
Semantic Networks ◽  
Stereo Image ◽  
Generative Adversarial Networks ◽  
Specific Class ◽  
Adversarial Networks ◽  
Augmentation Techniques ◽  
Image Acquisition System

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

scholarly journals Rapid learning of minimally different words in five- to six-year-old children: effects of acoustic salience and hearing impairment

2015 ◽  
Vol 43 (2) ◽  
pp. 310-337 ◽  
Author(s):  
MARCEL R. GIEZEN ◽  
PAOLA ESCUDERO ◽  
ANNE E. BAKER
Keyword(s):  
Cochlear Implant ◽  
Hearing Impairment ◽  
Normal Hearing ◽  
Task Demands ◽  
Deaf Children ◽  
Matching Task ◽  
Object Matching ◽  
Rapid Learning ◽  
Minimal Pairs

AbstractThis study investigates the role of acoustic salience and hearing impairment in learning phonologically minimal pairs. Picture-matching and object-matching tasks were used to investigate the learning of consonant and vowel minimal pairs in five- to six-year-old deaf children with a cochlear implant (CI), and children of the same age with normal hearing (NH). In both tasks, the CI children showed clear difficulties with learning minimal pairs. The NH children also showed some difficulties, however, particularly in the picture-matching task. Vowel minimal pairs were learned more successfully than consonant minimal pairs, particularly in the object-matching task. These results suggest that the ability to encode phonetic detail in novel words is not fully developed at age six and is affected by task demands and acoustic salience. CI children experience persistent difficulties with accurately mapping sound contrasts to novel meanings, but seem to benefit from the relative acoustic salience of vowel sounds.

Semantic Network Language Generation based on a Semantic Networks Serialization Grammar

2010 ◽  
Vol 13 (3) ◽  
pp. 307-341 ◽  
Author(s):  
Yintang Dai ◽  
Shiyong Zhang ◽  
Jidong Chen ◽  
Tianyuan Chen ◽  
Wei Zhang
Keyword(s):  
Semantic Network ◽  
Semantic Networks ◽  
Language Generation ◽  
Network Language

scholarly journals Semantic Processing Impairment in Patients with Temporal Lobe Epilepsy

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Amanda G. Jaimes-Bautista ◽  
Mario Rodríguez-Camacho ◽  
Iris E. Martínez-Juárez ◽  
Yaneth Rodríguez-Agudelo
Keyword(s):  
Episodic Memory ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Semantic Processing ◽  
Functional Neuroimaging ◽  
Declarative Memory ◽  
Semantic Networks ◽  
N400 Effect ◽  
Functional Reorganization ◽  
Electrophysiological Studies

The impairment in episodic memory system is the best-known cognitive deficit in patients with temporal lobe epilepsy (TLE). Recent studies have shown evidence of semantic disorders, but they have been less studied than episodic memory. The semantic dysfunction in TLE has various cognitive manifestations, such as the presence of language disorders characterized by defects in naming, verbal fluency, or remote semantic information retrieval, which affects the ability of patients to interact with their surroundings. This paper is a review of recent research about the consequences of TLE on semantic processing, considering neuropsychological, electrophysiological, and neuroimaging findings, as well as the functional role of the hippocampus in semantic processing. The evidence from these studies shows disturbance of semantic memory in patients with TLE and supports the theory of declarative memory of the hippocampus. Functional neuroimaging studies show an inefficient compensatory functional reorganization of semantic networks and electrophysiological studies show a lack of N400 effect that could indicate that the deficit in semantic processing in patients with TLE could be due to a failure in the mechanisms of automatic access to lexicon.

scholarly journals What executive function network is that? An image-based meta-analysis of network labels

2020 ◽  
Author(s):  
Suzanne T. Witt ◽  
Helene van Ettinger-Veenstra ◽  
Taylor Salo ◽  
Michael C. Riedel ◽  
Angela R. Laird
Keyword(s):  
Neural Networks ◽  
Executive Function ◽  
Meta Analysis ◽  
Control Network ◽  
Attention Network ◽  
Frontoparietal Network ◽  
Current State ◽  
The Neural Networks ◽  
Task Positive Network ◽  
Spatial Topography

AbstractThe current state of label conventions used to describe brain networks related to executive functions is highly inconsistent, leading to confusion among researchers regarding network labels. Visually similar networks are referred to by different labels, yet these same labels are used to distinguish networks within studies. We performed a literature review of fMRI studies and identified nine frequently-used labels that are used to describe topographically or functionally similar neural networks: central executive network (CEN), cognitive control network (CCN), dorsal attention network (DAN), executive control network (ECN), executive network (EN), frontoparietal network (FPN), working memory network (WMN), task positive network (TPN), and ventral attention network (VAN). Our aim was to meta-analytically determine consistency of network topography within and across these labels. We hypothesized finding considerable overlap in the spatial topography among the neural networks associated with these labels. An image-based meta-analysis was performed on 166 individual statistical maps (SPMs) received from authors of 72 papers listed on PubMed. Our results indicated that there was very little consistency in the SPMs labeled with a given network name. We identified four clusters of SPMs representing four spatially distinct executive function networks. We provide recommendations regarding label nomenclature and propose that authors looking to assign labels to executive function networks adopt this template set for labeling networks.

scholarly journals Perceptual and Semantic Processing in Cognitive Robots

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2216
Author(s):  
Syed Tanweer Shah Bukhari ◽  
Wajahat Mahmood Qazi
Keyword(s):  
Visual Perception ◽  
Cognitive Processes ◽  
Semantic Processing ◽  
Similarity Index ◽  
Semantic Networks ◽  
Human Robot Interaction ◽  
Conceptual Graphs ◽  
Average Precision ◽  
Robot Interaction ◽  
Cognitive Robots

The challenge in human–robot interaction is to build an agent that can act upon human implicit statements, where the agent is instructed to execute tasks without explicit utterance. Understanding what to do under such scenarios requires the agent to have the capability to process object grounding and affordance learning from acquired knowledge. Affordance has been the driving force for agents to construct relationships between objects, their effects, and actions, whereas grounding is effective in the understanding of spatial maps of objects present in the environment. The main contribution of this paper is to propose a methodology for the extension of object affordance and grounding, the Bloom-based cognitive cycle, and the formulation of perceptual semantics for the context-based human–robot interaction. In this study, we implemented YOLOv3 to formulate visual perception and LSTM to identify the level of the cognitive cycle, as cognitive processes synchronized in the cognitive cycle. In addition, we used semantic networks and conceptual graphs as a method to represent knowledge in various dimensions related to the cognitive cycle. The visual perception showed average precision of 0.78, an average recall of 0.87, and an average F1 score of 0.80, indicating an improvement in the generation of semantic networks and conceptual graphs. The similarity index used for the lingual and visual association showed promising results and improves the overall experience of human–robot interaction.

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