scholarly journals THINGS-EEG: Human electroencephalography recordings for 1,854 concepts presented in rapid serial visual presentation streams

2021 ◽  
Author(s):  
Tijl Grootswagers ◽  
Ivy Zhou ◽  
Amanda K Robinson ◽  
Martin N Hebart ◽  
Thomas A Carlson
Keyword(s):  
Large Scale ◽  
Large Body ◽  
Visual Presentation ◽  
Semantic Knowledge ◽  
Image Databases ◽  
Visual Object ◽  
Neural Basis ◽  
Object Knowledge ◽  
Large Stimulus ◽  
Stimulus Set

The neural basis of object recognition and semantic knowledge have been the focus of a large body of research but given the high dimensionality of object space, it is challenging to develop an overarching theory on how brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. Traditional image databases are based on manually selected object concepts and often single images per concept. In contrast, 'big data' stimulus sets typically consist of images that can vary significantly in quality and may be biased in content. To address this issue, recent work developed THINGS: a large stimulus set of 1,854 object concepts and 26,107 associated images. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to all concepts and 22,248 images in the THINGS stimulus set. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.

scholarly journals Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Tijl Grootswagers ◽  
Ivy Zhou ◽  
Amanda K. Robinson ◽  
Martin N. Hebart ◽  
Thomas A. Carlson
Keyword(s):  
Large Scale ◽  
Visual Presentation ◽  
Semantic Knowledge ◽  
Human Vision ◽  
Visual Object ◽  
Neural Basis ◽  
Object Knowledge ◽  
Object Processing ◽  
Eeg Recordings ◽  
The Brain

AbstractThe neural basis of object recognition and semantic knowledge has been extensively studied but the high dimensionality of object space makes it challenging to develop overarching theories on how the brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to 1,854 object concepts and 22,248 images in the THINGS stimulus set, a manually curated and high-quality image database that was specifically designed for studying human vision. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.

scholarly journals Executive Semantic Processing Is Underpinned by a Large-scale Neural Network: Revealing the Contribution of Left Prefrontal, Posterior Temporal, and Parietal Cortex to Controlled Retrieval and Selection Using TMS

2012 ◽  
Vol 24 (1) ◽  
pp. 133-147 ◽  
Author(s):  
Carin Whitney ◽  
Marie Kirk ◽  
Jamie O'Sullivan ◽  
Matthew A. Lambon Ralph ◽  
Elizabeth Jefferies
Keyword(s):  
Semantic Processing ◽  
Large Scale ◽  
Semantic Representation ◽  
Inferior Frontal Gyrus ◽  
Semantic Knowledge ◽  
Middle Temporal Gyrus ◽  
Neural Basis ◽  
Healthy Human ◽  
Left Posterior ◽  
Domain Dependence

To understand the meanings of words and objects, we need to have knowledge about these items themselves plus executive mechanisms that compute and manipulate semantic information in a task-appropriate way. The neural basis for semantic control remains controversial. Neuroimaging studies have focused on the role of the left inferior frontal gyrus (LIFG), whereas neuropsychological research suggests that damage to a widely distributed network elicits impairments of semantic control. There is also debate about the relationship between semantic and executive control more widely. We used TMS in healthy human volunteers to create “virtual lesions” in structures typically damaged in patients with semantic control deficits: LIFG, left posterior middle temporal gyrus (pMTG), and intraparietal sulcus (IPS). The influence of TMS on tasks varying in semantic and nonsemantic control demands was examined for each region within this hypothesized network to gain insights into (i) their functional specialization (i.e., involvement in semantic representation, controlled retrieval, or selection) and (ii) their domain dependence (i.e., semantic or cognitive control). The results revealed that LIFG and pMTG jointly support both the controlled retrieval and selection of semantic knowledge. IPS specifically participates in semantic selection and responds to manipulations of nonsemantic control demands. These observations are consistent with a large-scale semantic control network, as predicted by lesion data, that draws on semantic-specific (LIFG and pMTG) and domain-independent executive components (IPS).

scholarly journals Attending to Entities for Better Text Understanding

2020 ◽  
Vol 34 (05) ◽  
pp. 7554-7561
Author(s):  
Pengxiang Cheng ◽  
Katrin Erk
Keyword(s):  
Large Scale ◽  
Human Performance ◽  
State Of The Art ◽  
Syntactic Structure ◽  
Semantic Knowledge ◽  
Training Data ◽  
Language Models ◽  
Long Distance ◽  
Future Directions ◽  
Text Understanding

Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al. 2017), and in a range of end tasks, such models have achieved state-of-the-art results, approaching human performance. This clearly demonstrates the power of the stacked self-attention architecture when paired with a sufficient number of layers and a large amount of pre-training data. However, on tasks that require complex and long-distance reasoning where surface-level cues are not enough, there is still a large gap between the pre-trained models and human performance. Strubell et al. (2018) recently showed that it is possible to inject knowledge of syntactic structure into a model through supervised self-attention. We conjecture that a similar injection of semantic knowledge, in particular, coreference information, into an existing model would improve performance on such complex problems. On the LAMBADA (Paperno et al. 2016) task, we show that a model trained from scratch with coreference as auxiliary supervision for self-attention outperforms the largest GPT-2 model, setting the new state-of-the-art, while only containing a tiny fraction of parameters compared to GPT-2. We also conduct a thorough analysis of different variants of model architectures and supervision configurations, suggesting future directions on applying similar techniques to other problems.

A Semantic-Driven Adaptive Architecture for Large Scale P2P Networks

2010 ◽  
Vol 2 (4) ◽  
pp. 12-30 ◽  
Author(s):  
Athena Eftychiou ◽  
Bogdan Vrusias ◽  
Nick Antonopoulos
Keyword(s):  
Large Scale ◽  
Network Formation ◽  
Collective Intelligence ◽  
Semantic Knowledge ◽  
Resource Discovery ◽  
Online Information ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Adaptive Architecture

The increasing amount of online information demands effective, scalable, and accurate mechanisms to manage and search this information. Distributed semantic-enabled architectures, which enforce semantic web technologies for resource discovery, could satisfy these requirements. In this paper, a semantic-driven adaptive architecture is presented, which improves existing resource discovery processes. The P2P network is organised in a two-layered super-peer architecture. The network formation of super-peers is a conceptual representation of the network’s knowledge, shaped from the information provided by the nodes using collective intelligence methods. The authors focus on the creation of a dynamic hierarchical semantic-driven P2P topology using the network’s collective intelligence. The unmanageable amounts of data are transformed into a repository of semantic knowledge, transforming the network into an ontology of conceptually related entities of information collected from the resources located by peers. Appropriate experiments have been undertaken through a case study by simulating the proposed architecture and evaluating results.

scholarly journals OntCheck: An Ontology-Driven Static Correctness Checking Tool for Component-Based Models

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xi Lin ◽  
Hehua Zhang ◽  
Ming Gu
Keyword(s):  
Large Scale ◽  
Description Logic ◽  
Early Stage ◽  
Semantic Knowledge ◽  
Unified Framework ◽  
Domain Specific ◽  
Scale Models ◽  
Correctness Checking ◽  
Static Checking

Component-based models are widely used for embedded systems. The models consist of components with input and output ports linked to each other. However, mismatched links or assumptions among components may cause many failures, especially for large scale models. Binding semantic knowledge into models can enable domain-specific checking and help expose modeling errors in the early stage. Ontology is known as the formalization of semantic knowledge. In this paper we propose an ontology-driven tool for static correctness checking of domain-specific errors. two kinds of important static checking, semantic type and domain-restrcted rules, are fulfilled in a unified framework. We first propose a formal way to precisely describe the checking requirements by ontology and then separately check them by a lattice-based constraint solver and a description logic reasoner. Compared with other static checking methods, the ontology-based method we proposed is model-externally configurable and thus flexible and adaptable to the changes of requirements. The case study demonstrates the effectiveness of our method.

Simulation of Fast Retrieval Method for Large-Scale Image Database

2014 ◽  
Vol 556-562 ◽  
pp. 4959-4962
Author(s):  
Sai Qiao
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Great Increase ◽  
Texture Features ◽  
Image Database ◽  
Image Databases ◽  
Matching Method ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Database Constraints

The traditional database information retrieval method is achieved by retrieving simple corresponding association of the attributes, which has the necessary requirement that image only have a single characteristic, with increasing complexity of image, it is difficult to process further feature extraction for the image, resulting in great increase of time consumed by large-scale image database retrieval. A fast retrieval method for large-scale image databases is proposed. Texture features are extracted in the database to support retrieval in database. Constraints matching method is introduced, in large-scale image database, referring to the texture features of image in the database to complete the target retrieval. The experimental results show that the proposed algorithm applied in the large-scale image database retrieval, augments retrieval speed, thereby improves the performance of large-scale image database.

Indirect inputs to ventral temporal cortex of monkey: the influence of unit activity of alerting auditory input, interhemispheric subcortical visual input, reward, and the behavioral response

1993 ◽  
Vol 70 (6) ◽  
pp. 2215-2225 ◽  
Author(s):  
J. L. Ringo ◽  
S. G. O'Neill
Keyword(s):  
Unit Activity ◽  
Behavioral Response ◽  
Temporal Cortex ◽  
Warning Signal ◽  
Visual Presentation ◽  
Visual Input ◽  
The Other ◽  
Similar Response ◽  
Neural Basis ◽  
Split Brain

1. This study examined nonvisual and indirect inputs to 1,021 single units recorded in inferotemporal and parahippocampal cortex of behaving macaques. 2. To better isolate these influences, a fully split-brain, split-chiasm preparation was used. Extracellular single-unit activity was recorded while the ipsilateral eye was covered. During the recordings the monkeys worked on a visual discrimination task that consisted of a series of presentations of single images. 3. When the interval between presentations was varied randomly (usually between 4 and 15 s) about one-quarter of these cells responded to an alerting tone sounded 500 ms before the onset of the visual image. That this response is due to the warning value of the tone was shown by finding that an identical tone sounded at the end of each trial produced no response from these cells. Use of an exchange between pairs of light-emitting diodes as a warning signal (one turned on as the other was turned off, also 500 ms before the visual stimulus onset) produced a similar response in many units. This indicates a subcortical route for the alerting signal. In most cases, warning responses were inhibitory, often delayed with respect to the warning signal occurrence to more nearly match the image arrival time. 4. Surprisingly, and despite the monkeys' confirmed split-brain status, occasional cells (approximately 2%) showed a response from a visual presentation limited to the other hemisphere. Although this subcortical visual input was far weaker than direct visual input, it was nonetheless statistically reliable. Importantly, the indirect input was stimulus specific and could form the neural basis for a limited interhemispheric visual transfer of the sort seen in human split-brain patients. 5. Also rarely, cells showed activity time locked to the animal's behavioral response.

User-opinion mining for mobile library apps in China: exploring user improvement needs

2019 ◽  
Vol 37 (3) ◽  
pp. 325-337 ◽  
Author(s):  
Haichen Zhou ◽  
Dejun Zheng ◽  
Yongming Li ◽  
Junwei Shen
Keyword(s):  
Quantitative Research ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Visual Presentation ◽  
User Needs ◽  
Content Type ◽  
Expert Opinions ◽  
Different Types ◽  
Questionnaire Surveys

Purpose To further provide some insight into mobile library (m-library) applications (apps) user needs and help libraries or app providers improve the service quality, the purpose of this paper is to explore all the types of user improvement needs and to discover which need is the most important based on user results. Design/methodology/approach Data were collected from more than 27,000 m-library app users from 16 provinces and autonomous regions in China. Text analysis using latent Dirichlet allocation and Word2Vec was carried out by text preprocessing. Furthermore, a visual presentation was conducted through pyLDAvis and word cloud. Finally, combined with expert opinions, the results were summarized to find the different types of needs. Findings There are three different types of needs for improvement: needs of function, needs of technology and needs of experience. These types can be further divided into six subtypes: richness of function, feasibility of function, easiness of technology, stableness of technology, optimization of experience and customization of experience. Besides the richness of function, the feasibility of function has received the most attention from users. Originality/value Most studies on m-library user needs have only focused on a method of quantitative research based on questionnaire surveys. This study, however, is the first to apply text mining methods for large-scale user opinion texts, which place more focus on user needs and inspire libraries and app providers to further improve their services.

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