Numerical Information Research Articles

Adult education and training for the development of problem-solving skills

Introduction. Currently, the presence of employees with developed basic skills of working with textual and numerical information can no longer explain how effectively a person functions in society and the professional sphere. This is due to the rapid transformation of the innovative economy and the renewal of production technologies and business processes. Therefore, the key basic skills of the adult population have been supplemented with one more cognitive skill – problem solving. Aim. The present research aimed to present a portrait of a person with a developed problem-solving skill on the basis of sociological data, and to identify which types of employee training could most effectively lead to his/her development during participation in educational activities. Materials and methods. The study was based on theoretical methods of literature analysis, which made it possible to describe the categorical field and to systematize existing scientific approaches. The empirical foundation for this research was built on the data of OECD-PIAAC studies in Russia (2013). The study sample consisted of 3,877 people employed in various sectors of economic activity. To process the results obtained, the authors used the methods of mathematical statistics (descriptive statistics, one-factor analysis of variance, regression analysis method). Results. It was discovered that even prolonged employer-provided training does not lead to development of problem-solving skills, but still provides an increase in salary for some workers. Moreover, training which is not directly connected to the professional field and not initiated by the employer would lead to increase in problem-solving skills in a technology-rich environment. In this study, it is debated that the most significant increase in problem solving skills was brought by informal on-the-job training. It was shown that problem-solving skills provide significant increase in labour productivity in the production fields and intellectual labour fields (education and science). At the same time, problem-solving skills barely provide any increase in labour productivity in the routine fields, because such type of jobs does not involve behavioural variability, operational problem solving, and constant cooperation with other people. Scientific novelty. For the first time in the Russian academic literature, the authors conceptualised the concept of problem-solving skills and clarified which types of training significantly increase the contribution to the development of human capital of Russian workers. Based on the results of the study the authors presented a sociological portrait of a Russian worker with a high and low level of problem-solving skills. The revealed patterns allowed the authors to form an idea of the system of organisational conditions of enterprises that ensure maximum involvement of employees in the constant updating of their knowledge and skills, and contribute to the effective use of the problems-solving skill in the process of labour activity. Practical significance. This article might be of interest for the specialists in sphere of adult education and lifelong learning, HR-specialists and small and medium-sized enterprise managers in order to improve the efficiency of the recruitment process and to adopt more efficient employer-provided training systems.

Long‐term arid and hot climate during earlyMid‐Cretaceousindicate by unvaried Cheirolepidiaceae‐dominant palynoflora from the Liupanshan Basin, China

The Mid‐Cretaceous was characterized by long‐term and stabilized high temperatures confirmed by numerous palaeoclimatic information from marine sedimentary archives. However, few terrestrial records have yet been employed to verify the terrestrial humidity under this long‐term hot climate. The Liupanshan Basin, located in central China during the Cretaceous contains thick Aptian‐Albian sediments. Here, we reconstructed a long‐term palaeovegetational record during late Aptian to Albian from the Huoshizhai Section and Sikouzi Section, Liupanshan Basin. Five palynological assemblages were defined from the bottom to top of these strata, and a total of 124 types of palynomorphs including 47 spore species and 77 gymnosperm pollen species were recognized in the palynological assemblages. Sporopollen analysis revealed that the spore‐pollen spectra of Huoshizhai Section were dominated byPerinopollenitesand Bisaccate pollen. In Sikouzi section, theClassopolliscontent dominate in all samples (average 63.8%), with a few other pollen, including conifer bisaccates types,Perinopollenites,Jiaohepollis,Ephedripites,Cycadopitesand a few spores. The sporopollen assemblage reflected that the flora of the central Asia continent gradually changed from Taxodiacean‐dominated to Cheirolepidiaceae‐dominated in the late Aptian and the Albian, which further indicates that the central Asia continent palaeoclimate gradually changed from a wet and cold climate to a long‐term dry and hot climate with small fluctuation. In addition, theClassopolliscontent also presented a slightly increased trend from late Aptian to late Albian, which probably indicated a gradually intensified hot and dry climate. The thermophilic floral changes and warmer/cooler ratio of the Sporomorph Ecogroup Model at the middle Madonghshan Formation also shows slightly decreased trends which correspond to the global short‐term cooling events at the Aptian‐Albian transition.

The effect of numeric information about the likelihood of receiving a false negative or false positive result on people’s attitudes towards colorectal cancer screening using faecal immunochemical testing (FIT)

ObjectiveTo examine the impact of numeric risk information about false negative (FN) and false positive (FP) rates in fecal immunochemical testing (FIT) on attitudes towards screening. Methods102 people aged 45–55, living in the UK, read 6 hypothetical vignettes presented online about the use of FIT kits to detect colorectal cancer, in which information about FN and FP rates was systematically varied. ResultsNumeric FN risk information reduced people’s interest in screening, perception of screening effectiveness and lowered trust in screening compared to verbal FN information. Verbal FN information reduced perceptions of screening effectiveness and trust compared to no FN information. People with high subjective numeracy reported lower trust in screening following the provision of numeric FN information but numeracy did not moderate any other associations. FP information did not affect attitudes towards FIT testing. ConclusionNumeric FN risk information decreased people’s perceptions of screening effectiveness and trust in the results of screening. While it influenced people’s interest in screening, the effect was small. Practice implicationsNumeric FN information has a small effect on interest in screening and could promote informed decision making without affecting screening uptake.

A comparative study of machine learning methods for classifying ERP scalp distribution

Objective. Machine learning (ML) methods are used in different fields for classification and regression purposes with different applications. These methods are also used with various non-invasive brain signals, including Electroencephalography (EEG) signals to detect some patterns in the brain signals. ML methods are considered critical tools for EEG analysis since could overcome some of the limitations in the traditional methods of EEG analysis such as Event-related potentials (ERPs) analysis. The goal of this paper was to apply ML classification methods on ERP scalp distribution to investigate the performance of these methods in identifying numerical information carried in different finger-numeral configurations (FNCs). FNCs in their three forms of montring, counting, and non-canonical counting are used for communication, counting, and doing arithmetic across the world between children and even adults. Studies have shown the relationship between perceptual and semantic processing of FNCs, and neural differences in visually identifying different types of FNCs. Approach. A publicly available 32-channel EEG dataset recorded for 38 participants while they were shown a picture of an FNC (i.e., three categories and four numbers of 1,2,3, and 4) was used. EEG data were pre-processed and ERP scalp distribution of different FNCs was classified across time by six ML methods, including support vector machine, linear discriminant analysis, naïve Bayes, decision tree, K-nearest neighbor, and neural network. The classification was conducted in two conditions: classifying all FNCs together (i.e., 12 classes) and classifying FNCs of each category separately (i.e., 4 classes). Results. The support vector machine had the highest classification accuracy for both conditions. For classifying all FNCs together, the K-nearest neighbor was the next in line; however, the neural network could retrieve numerical information from the FNCs for category-specific classification. Significance. The significance of this study is in exploring the application of multiple ML methods in recognizing numerical information contained in ERP scalp distribution of different finger-numeral configurations.

Analyzing and supporting mental representations and strategies in solving Bayesian problems.

Solving Bayesian problems poses many challenges, such as identifying relevant numerical information, classifying, and translating it into mathematical formula language, and forming a mental representation. This triggers research on how to support the solving of Bayesian problems. The facilitating effect of using numerical data in frequency format instead of probabilities is well documented, as is the facilitating effect of given visualizations of statistical data. The present study not only compares the visualizations of the 2 × 2 table and the unit square, but also focuses on the results obtained from the self-creation of these visualizations by the participants. Since it has not yet been investigated whether the better correspondence between external and internal visualization also has an effect on cognitive load when solving Bayesian tasks, passive and active cognitive load are additionally measured. Due to the analog character and the proportional representation of the numerical information by the unit square, it is assumed that the passive cognitive load is lower when using the unit square as visualization than when using the 2 × 2 table. The opposite is true for active cognitive load.

A Right-Hand Side Function Surrogate Model-Based Method for the Black-Box Dynamic Optimization Problem

Abstract When solving the black-box dynamic optimization problem (BDOP) in the sophisticated dynamic system, the finite difference technique requires significant computational efforts on numerous expensive system simulations to provide approximate numerical Jacobian information for the gradient-based optimizer. To save computational budget, this work introduces a BDOP solving framework based on the right-hand side (RHS) function surrogate model (RHSFSM), in which the RHS derivative functions of the state equation are approximated by the surrogate models, and the Jacobian information is provided by inexpensive estimations of RHSFSM rather than the original time-consuming system evaluations. Meanwhile, the sampling strategies applicable to the construction of RHSFSM are classified into three categories: direct, indirect, and hybrid sampling strategy, and the properties of these strategies are analyzed and compared. Furthermore, to assist the RHSFSM-based BDOP solving framework search for the optimum efficiently, a novel dynamic hybrid sampling strategy is proposed to update RHSFSM sequentially. Finally, two dynamic optimization examples and a co-design example of a horizontal axis wind turbine illustrate that the RHSFSM-based BDOP solving framework integrated with the proposed dynamic hybrid sampling strategy not only solves the BDOP efficiently but also achieves the optimal solution robustly and reliably compared to other sampling strategies.

Revisiting the Links Between Numeracy and Decision Making: Replication Registered Report of Peters et al. (2006) With an Extension Examining Confidence

Numeracy is individuals’ capacity to understand and process basic probability and numerical information required to make decisions. We conducted a Replication Registered Report of Peters et al. (2006) examining numeracy as a predictor of positive-negative framing effect (Study 1), frequency-percentage effect (Study 2), ratio effect (Study 3), and bets effect (Study 4). With an online US American Amazon Mechanical Turk sample (N = 860), our replication using the target’s dichotomizing of the numeracy measure found support for the original findings regarding interactions between numeracy and three decision-making effects. Numeracy was associated with weaker framing effect (η2p = 0.01, 90% CI [0.00, 0.02]), weaker ratio bias (Cramer’s V = 0.17, 95% CI [0.10, 0.24]), and stronger bets effect (η2p = 0.02, 90% CI [0.01, 0.04]), yet we found no support for the frequency-percentage effect (η2p = 0.00, 90% CI [0.00, 0.01]). However, we found support for associations with all four studies when treating numeracy as a continuous variable. We extended the replication to examine confidence, yet the results were mixed with support found for only three conditions (Study 1 positive framing condition: r = -0.11, 95% CI [-0.20, -0.02]; Study 3: r = 0.15, 95% CI [0.08, 0.21]; Study 4 no-loss bet condition: r = 0.10, 95% CI [0.01, 0.20]), suggesting a much weaker and more complex relationship than anticipated. Materials, data, and code are available on: https://osf.io/4hjck/.

Light‐Induced Thin‐Film Transfer Processes Based on Phase Transition of GeSbTe and ITO Sacrificial Layers

AbstractIn the upcoming ubiquitous era, wearable/flexible electronics are spotlighted to get various types of numerous information in real time. Several researchers have investigated flexible materials, and developed diverse thin‐film transfer methods. However, there are some limitations of the intrinsic material unstabilities, and low production yield. Here, light‐induced thin‐film transfer methods are reported by using new transfer mechanism of phase transition in sacrificial materials. Lift‐off conditions with high‐energy laser are delicately optimized by theoretical calculations and experiments to minimize mechanical/thermal damages of the upper thin‐film devices. The selected sacrificial materials of GeSbTe (GST) and indium tin oxide (ITO) with the 300 nm thickness are delaminated from a transparent and rigid glass substrate by irradiating the excimer laser to the surface of the sacrificial layer. The laser‐based exfoliation mechanism of GST and ITO films are comprehended by various material surface analyses. Eventually, flexible oxide thin‐film transistors (TFTs) are successfully demonstrated through light‐induced exfoliation process, showing the usability of the developed transfer technique to future practical applications.

Impact of social media messages on perceived risk and severity of adverse effects and regret of HPV vaccination: The moderating role of decision-making preference.

10543 Background: The HPV vaccine has proven to be a highly effective and safe public health intervention for reducing the risk of various types of cancer. Despite its efficacy and safety, misinformation and negative opinions about the vaccine on social media can lead to negative emotions such as regret among those who have received it. This study investigated the impact of different content of social media messages on perceived risk and severity of adverse effects and regret of HPV vaccination. Methods: An experiment was designed to expose participants to messages posted on social media that manipulated the number of Vaccine Adverse Effects and the appeal used (narrative vs. statistics). The study measured the effect of the message on decisional regret and also assessed preferred level of involvement with medical decision-making. The online study was implemented among college students enrolled in a Midwestern or a South Texas university. We used structural equation modeling (SEM) to systematically test the relationships between the variables of interest. Results: A total of 721 participants were exposed to the experimental materials and completed the questionnaire. The results of the SEM (with overall good fitness of RMSEA = 0.015, CMIN/df = 1.172) suggest that messages that contained more numeric information (standardized regression weights [Std Reg Wght = 0.161]) and more adverse effects of the HPV vaccine (Std Reg Wght = 0.076) led to a higher perception of the risk of vaccine adverse effects. The severity of adverse effects was significantly associated with the number of side effects in the message (Std Reg Wght = 0.147). This increase in perceived risk (Std Reg Wght = 0.182) and severity of side effects of the vaccine (Std Reg Wght = 0.163) seem to increase decisional regret. However, this relationship was moderated by individuals’ preferences for making their own medical decisions. Individuals with a higher preference to make their own health decisions were less likely to experience decisional regret (Std Reg Wght = -0.238). Conclusions: Messages on social media can exacerbate negative emotions and lead to hesitation in following future medical advice. Encouraging individuals to play a more active role in their medical decision-making appears to reduce this negative emotion and increase their confidence in health decisions.

DeepTrip: A Deep Learning Model for the Individual Next Trip Prediction With Arbitrary Prediction Times

The increasing availability of travel trajectory data allows for a better understanding of travel behavior. In the individual mobility analysis, the problem of next trip prediction assumes a central role and is beneficial for applications such as personalized services and mobility management. This paper addresses the next trip prediction problem with arbitrary prediction times (the time when the prediction is made). This problem has not been studied adequately in the literature and it is important for applications driven by system events, such as proactive travel recommendations under disruptions or crowding in transport systems. It predicts an individual’s next trips given their historical trip sequences and the prediction time. We formulate the next trip prediction problem as on-board and off-board predictions depending on an individual’s travel status (i.e. on-board/off-board). Using historical/real-time travel trajectories, a DeepTrip model is proposed based on a trip sequence-to-sequence deep learning structure coupled with an attention mechanism. A novel overlapped embedding method is proposed to represent continuous travel attributes capturing simultaneously the categorical and numerical feature information. We also develop a random-sampling training algorithm to learn the impact of the prediction time. The model is validated using trip data in urban rails. The results show that DeepTrip outperforms statistical-based models by more than 10% in terms of accuracy and other deep learning models by 2%-3%. The impact analysis shows that different representations are appropriate for the two prediction cases (on-board/off-board), and the prediction performance does not monotonically improve as the prediction time approaches the next trip.

A Modified Hybrid Objective Model to Calculate the Weights of Cause and Effect Criteria in a System: DEMATEL and DEVELOPED SWARA (D-DS) Based Model

Abstract Criteria weighting is a widely used and also an important feature of multi criteria decision making problems specially in engineering, computer science and management investigations. In particular in many studies related to complex systems there would be usually two main groups of cause and effect criteria. In this research it is intended to make an hybrid objective model comprising DEMATEL and SWARA techniques to assign classified weights to the subgroup of cause and effect criteria. As a main goal, the proposed hybrid model in this presented paper can afford to assign greater values for criteria who belong to cause group. In this regard we apply the objective information which derived from the parameters of (R, equal to sum of direct and indirect influence of a criteria), (R/C, named as net influence power of a criteria) and (R-C, named as net effect of a criteria) related to the final total influence matrix T in DEMATEL methodology. The main contribution in this work lies in utilizing the SWARA methodology and making us of its revision where the relatively Comparative Importance Sj , applied in SWARA technique is reconfigured by some aggregation operators including max, Einstein and Hamacher operators for obtaining more uniformed weights of cause and effect criteria relatively to SWARA basic methodology. Finally results shows that the (R/C) and (R-C)would transfer more clear and refined data and numeric information achieving better and highly reliable weights of criteria categorized into two groups of cause and effect group.

When do consumers favor overly precise information about investment returns?

Consumers are often shown investment returns with high levels of precision, which could lead them to misunderstand the inherent uncertainty. We test whether consumers are drawn to precision-that is offset the uncertainty in investment decisions by over-relying on precise numerical information. Five incentivized experiments compared decisions when expected growth is presented in precise forecasts as opposed to ranges. Consumers are more likely to prefer and invest more in precise forecasts when they are evaluated jointly with ranges and when the range features a potential loss. Under these circumstances, precise forecasts give consumers more confidence to invest. This effect holds when consumers are told investment returns are uncertain. On the other hand, experiencing discrepancies between expected and actual growth dissipates the preference for precise forecasts. We identify conditions under which consumers are more likely to favor precise forecasts and how this could be avoided if necessary. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

E-Tourism Information Literacy and Its Role in Driving Tourist Satisfaction With Online Travel Information: A Qualitative Comparative Analysis

As numerous travel information is provided on digital platforms, information literacy is becoming increasingly important for tourists to address travel-related problems and ensure a smooth tour experience. This study conceptualizes e-tourism information literacy (eTIL) based on previous researches and develops a scale to measure this construct following a systematic approach, from which, 14 items were generated to measure four underlying dimensions of eTIL: information knowledge, information skills, information awareness, and information ethics. Additionally, to examine the interplay between eTIL, information quality, and system quality, fuzzy-set qualitative comparative analysis (fsQCA) is employed on 399 samples to obtain equifinal configurations that lead to tourist satisfaction with online travel information (TSOTI). The results revealed two configurations leading to high TSOTI and three configurations leading to low TSOTI. This study contributes theoretically by constructing the concept of eTIL and explaining its role in driving TSOTI.

Readability and beyond - Health literacy and numeracy and COVID-19 communications in early childhood education: Are we communicating effectively?

ObjectiveAnalyse the linguistic and numerical complexity of COVID-19-related health information communicated from Australian national and state governments and health agencies to national and local early childhood education (ECE) settings. MethodsPublicly available health information (n = 630) was collected from Australian national and state governments and health agencies, and ECE agencies and service providers. A purposive sample of documents (n = 33) from 2020 to 2021 was analysed inductively and deductively combining readability, health numeracy and linguistic analyses and focusing on the most frequent actionable health advice topics. ResultsCOVID-19 health advice most frequently related to hygiene, distancing and exclusion. Readability scores in 79% (n = 23) of documents were above the recommended grade 6 reading level for the public. Advice was delivered using direct linguistic strategies (n = 288), indirect strategies (n = 73), and frequent mitigating hedges (n = 142). Most numerical concepts were relatively simple, but lacked elaborative features (e.g., analogies) and/or required subjective interpretation. ConclusionCOVID-19 health advice available to the ECE sector included linguistic and numerical information open to mis/interpretation making it difficult to understand and implement. Practice ImplicationsCombining readability scores with measures of linguistic and numerical complexity offers a more holistic approach to assessing accessibility of health advice and improving health literacy among its recipients.

Emotion recognition from unimodal to multimodal analysis: A review

The omnipresence of numerous information sources in our daily life brings up new alternatives for emotion recognition in several domains including e-health, e-learning, robotics, and e-commerce. Due to the variety of data, the research area of multimodal machine learning poses special problems for computer scientists; how did the field of emotion recognition progress in each modality and what are the most common strategies for recognizing emotions? What part does deep learning play in this? What is multimodality? How did it progress? What are the methods of information fusion? What are the most used datasets in each modality and in multimodal recognition? We can understand and compare the various methods by answering these questions.

Radiomics in colorectal cancer patients.

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

Sensory substitution can improve decision-making

New technologies are often considered direct competitors to humans in the realm of decision-making. This paper explores a novel approach to augmenting human decision-making through technology. Specifically, drawing on the brain's unique ability to learn from sensory experiences, we introduce sensory substitution, the encoding of information in an alternative sensory modality, as a method to improve decision-making. In a within-subject design (N = 48), we show that translating numerical information into sensory experiences (i.e., tactile stimulation administered to a person's body) results in higher decision accuracy in a multiple-cue learning task. Response time analyses, participants' self-reports, and cognitive modeling all suggest that the benefits afforded by sensory substitution are the result of a shift from explicit rule abstraction to configural learning. That is, rather than deliberately inferring decision rules, participants develop intuitive, perceptual strategies to accurately predict outcomes. Together, our findings suggest that sensory substitution could enhance decision-making by training “gut instincts” rather than deliberate decision-making skills.

Digital Twin of Experience for Human–Robot Collaboration Through Virtual Reality

The keyword “human digital twin” has received considerable attention in recent years, and information technology has been developed in healthcare and sports training systems to guide human behavior to a better state. In contrast, from optimizing the production and maintenance processes of industrial products, which is the origin of the term “digital twin,” intelligent robot systems can be interpreted as a mainstream of digital twin. In other words, assistive robots that support humans in their daily lives and improve their life behavior require the integration of human digital twin and conventional object digital twin. However, integrating these two digital twins is not easy from the viewpoint of system integration. In addition, it is necessary to encourage humans to change their behavior to provide users with subjective and immersive experiences rather than simply displaying numerical information. This study reviews the current status and limitations of these digital twin technologies and proposes the concept of a virtual reality (VR) digital twin that integrates digital twins and VR toward assistive robotic systems. This will expand the experience of both humans and robots and open the way to the realization of robots that can better support our daily lives.

Analisis Bentuk Lagu “Bhuana Santhi” Karya I Komang Darmayuda

Purpose: This article is an analysis of the form of the song Bhuana Santhi by Komang Darmayuda. Bhuana Shanti performed by students of SMPN 1 Denpasar as Indonesia's representative in the 2014 international level vocal group (Celesta) has succeeded in making Indonesia proud as the best performer in the 2014 international vocal group event. The purpose of writing this article is to describe the form of the song Bhuana Shanti by Komang Darmayuda. Komang Darmayuda is the sole informant in writing this article. Method: This article focuses on the form of the song Bhuana Shanti by Komang Darmayuda in order to obtain the musical elements contained therein (melody, harmony/chord, and dynamics). Result and Discussion: Before obtaining the forms and musical elements contained in Bhuana Shanti's songs, the structure is first described, namely motifs, sentences, and periods. The musical elements that have been obtained through the process of analyzing structure and form will explain the beauty contained in the Bhuana Shanti song. Data in the form of numeric notation, audio, and information on the performance of the Bhuana Shanti song were obtained through direct observation and interviews with Komang Darmayuda as the composer of Bhuana Shanti. Analysis of the form of music is the most basic effort to find out the beauty contained in a piece of music. Implication: By writing this article, it is hoped that it can provide insight or material for consideration for future composers representing Indonesia in the international level vocal group (Celesta) event.

Elicitation of Content Layout Preferences in Virtual 3D Spaces Based on a Free Layout Creation Task

Three-dimensional virtual reality (VR) environments, whether operating on desktop platforms or immersive screens, have been recognized for enabling novel and extremely engaging methods of interacting with digital content across various fields of application. Studies conducted over the past several years have also consistently suggested that utilizing 3D in contrast to 2D interfaces can lead to enhancements in multiple performance dimensions. These enhancements encompass better understanding and retention of information, increased capacity for inventive and efficient collaboration, and the ability to execute workflows that integrate numerous information sources more quickly. At the same time, how digital content such as documents, audio–visual content and web browsers are integrated into 3D spaces is often decided by the creators of the spaces based on either aesthetic considerations, or on a case-by-case basis depending on the workflow. In this paper, we present the results of an experiment we conducted to better understand how users prefer to arrange digital content in their 3D environments, depending on the subject matter, the format of the content (e.g., text-based, image, or audio–visual) and the 3D objects within the space. The results of the experiment presented in the paper can help inform future 3D VR design methodologies and may also provide support for automated content arrangement solutions.