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

The 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.

Knowledge-driven perceptual organization reshapes information sampling via eye movements

Humans constantly move their eyes to explore the environment and obtain information. Competing theories of gaze guidance consider the factors driving eye movements within a dichotomy between low-level visual features and high-level object representations. However, recent developments in object perception indicate a complex and intricate relationship between features and objects. Specifically, image-independent object-knowledge can generate objecthood by dynamically reconfiguring how feature space is carved up by the visual system. Here, we adopt this emerging perspective of object perception, moving away from the simplifying dichotomy between features and objects in explanations of gaze guidance. We recorded eye movements in response to stimuli that appear as meaningless patches on initial viewing but are experienced as coherent objects once relevant object-knowledge has been acquired. We demonstrate that gaze guidance differs substantially depending on whether observers experienced the same stimuli as meaningless patches or organised them into object representations. In particular, fixations on identical images became object-centred, less dispersed, and more consistent across observers once exposed to relevant prior object-knowledge. Observers' gaze behaviour also indicated a shift from exploratory information-sampling to a strategy of extracting information mainly from selected, object-related image areas. These effects were evident from the first fixations on the image. Importantly, however, eye-movements were not fully determined by object representations but were best explained by a simple model that integrates image-computable features and high-level, knowledge-dependent object representations. Overall, the results show how information sampling via eye-movements in humans is guided by a dynamic interaction between image-computable features and knowledge-driven perceptual organisation.

Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs

The paper introduces a method for adaptive deductive synthesis of state models, of complex objects, with multilevel variable structures. The method makes it possible to predict the state of objects using the data coming from them. The data from the objects are collected with sensors installed on them. Multilevel knowledge graphs (KG) are used to describe the observed objects. The new adaptive synthesis method develops previously proposed inductive and deductive synthesis methods, allowing the context to be taken into account when predicting the states of the monitored objects based on the data obtained from them. The article proposes the algorithm for the suggested method and presents its computational complexity analysis. The software system, based on the proposed method, and the algorithm for multilevel adaptive synthesis of the object models developed, are described in the article. The effectiveness of the proposed method is shown in the results from modeling the states of telecommunication networks of cable television operators.

The nationality benefit: Long-term memory associations enhance visual working memory for color-shape conjunctions

Visual working memory (VWM) is typically found to be severely limited in capacity, but this limitation may be ameliorated by providing familiar objects that are associated with knowledge stored in long-term memory. However, comparing meaningful and meaningless stimuli usually entails a confound, because different types of objects also tend to vary in terms of their inherent perceptual complexity. The current study therefore aimed to dissociate stimulus complexity from object meaning in VWM. To this end, identical stimuli – namely, simple color-shape conjunctions – were presented, which either resembled meaningful configurations (“real” European flags), or which were rearranged to form perceptually identical but meaningless (“fake”) flags. The results revealed complexity estimates for “real” and “fake” flags to be higher than for unicolor baseline stimuli. However, VWM capacity for real flags was comparable to the unicolor baseline stimuli (and substantially higher than for fake flags). This shows that relatively complex, yet meaningful “real” flags reveal a VWM capacity that is comparable to rather simple, unicolored memory items. Moreover, this “nationality” benefit was related to individual flag recognition performance, thus showing that VWM depends on object knowledge.

THEORETICAL DIMENSION OF INFORMATION-DIGITAL ECONOMY: INFORMATION-DIGITAL PROCESSES AND THEIR ATTRIBUTES

Cognitive activity and its types (sensory-emotional, empirical-abstract, theoretical-abstract, applied and integral-synthetic) are presented as a contradictory unity of discretizing and cretinizing components. The accompanying information-digital discretizing activity and the accompanying information-digital cretizing activity are characterized as components of the accompanying information-digital activity, their separate attributes, and also results are the basic kinds of discrete-digital materialized derivative information products. The main serial-parallel technical, technological and technical-technological processes of processing accompanying information-digital activity are considered: i) transformation of a discrete materialized derivative information product into a discrete-digital materialized derivative information product by digitization in an analog-to-digital converter; ii) own production of discrete-digital materialized derivative information products with the use of discrete-digital electronic computer; iii) conversion of a discrete-digital materialized derivative information product into an analog materialized derivative information product in a digital-to-analog converter. The composition of the knowledge-information chain of intermediate links between the real object and its final-surface designation is determined: “Real object – knowledge product – knowledge-information product – knowledge-concept product – primary information product – derivative information product – materialized derivative information product – discrete materialized derivative information product – discrete-digital materialized derivative information product.”Four types of information-digital economy are highlighted. Its core, or information-digital economy of the I kind, is represented exclusively by the accompanying information-digital activity, its types and attributes. Information-digital economy of the II kind includes information-digital economy of the I kind and production of attributes of accompanying information-digital activity both within the information economy, and outside it. Information-digital economy of the II kind together with information and non-information economic activity, the attributes of which are discrete-digital materialized derivative information products, forms the information-digital economy of the III kind. Finally, in the information-digital economy of the IV kind, along with the information-digital economy of the III kind, the components of all types of economic activity are presented, in which at least one of the attributes of the accompanying information-digital activity is used.

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

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.

The “Idea of Human” Literary Category: Epistemological Potential

The “idea of human” (“type of attitude to the world”) is considered as a relevant category of the conceptual apparatus of the modern science of literature. The aim of the work is to analyze the theoretical and methodological potential of this category on the basis of large typological units of the literary process, marked with the concepts of “historical and literary era”, “artistic and cognitive cycle”, “literary direction”, “big style”, “artistic method”. The research used the methods of a typological and complex study of literary works, which in the synthesis of literary criticism and philosophy determine the strategy of searches in the field of theoretical and methodological content of the “idea of human” category as the foundation of the literary and philosophical anthropology of cultural and historical eras. The historical and genetic links between the worldview aesthetic principles and the artistic practice of literary trends are problematized. The logic of the research reveals the concept “object – knowledge”, fundamental for epistemology, in the aspects of the structuring of the knowledge of the methodological semantics of the “idea of human” category and of the functioning of the definitions “generalized idea of human”, “type of attitude to the world”, “concept of human and reality”, “whole of human”, “human as a value”. The article shows that the “idea of human” as a philosophical and aesthetic interpretation of the nature and essence of human at a certain stage in the development of artistic consciousness, worked out by the whole culture (R.R. Moskvina, G.V. Mokronosov) and defining integrity and logical consistency of the artistic system, is a synergistically functional semantic core of the historical and cultural era, and this core contains the dialectical potential of “negation of the negation”. As a variable, the historical “idea of human”, in the perspective of the stage development of artistic consciousness, undergoes dramatic changes and is realized in the logic of the successive change of historical and cultural epochs and their philosophical paradigms, in the constant alternation of “realistic” and “mystical”, materialistic and idealistic methods of cognition and images of human and the world (D.I. Chizhevsky, A.M. Panchenko, and others). The conclusions are substantiated that the successive development of literary trends, creative methods and their axiological systems is conditioned by the dynamics of “types of attitude to the world”; that the functioning of the “idea of human” category in literary discourse is focused on argumentation of the ontological nature of fiction, on the identification of philosophical and aesthetic principles that determine the systematic nature and the successive change of artistic and cognitive cycles; that the evolution of the “idea of human” within the framework of one artistic and cognitive cycle is fixed by the dynamics of genre systems since, in the correlations of method, genre and style, “the idea of human” acts as a factor in genre formation.

Geometric property-based convolutional neural network for indoor object detection

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.