Different Types Of Information Research Articles

Identification, evolution and geological indications of solid bitumen in shales: A case study of the first member of Cretaceous Qingshankou Formation in Songliao Basin, NE China

On the basis of sorting out current understanding of solid bitumen (SB) in shales and taking organic-rich shales in the first member of the Cretaceous Qingshankou Formation in the Songliao Basin as an example, the definition, classification, occurrence and evolution path of SB are systemtically studied, and the indicative significance of SB reflectance (Rob) on maturity and its influence on the development of reservoir space are discussed and summarized. The results show that the difference of primary maceral types is primarily responsible for the different evolution paths of SB. Most of the pre-oil bitumen is in-situ SB with only a small amount being of migrated SB, while most of the post-oil bitumen and pyrobitumen are migrated SB. From the immature to early oil maturity stage, bituminite, vitrinite, and inertinite can be distinguished from SB based on their optical characteristics under reflected light, and alginite can be differentiated from SB by their fluorescence characteristics. Under scanning electron microscope, in-situ SB and migrated SB can be effectively identified. Rob increases linearly with increasing vitrinite reflectance (Ro), as a result of a decrease of aliphatic structure and the enhancement of aromatization of SB. Within the oil window three types of secondary pores may develop in SB, including modified mineral pores, devolatilization cracks and bubble holes. At a high maturity stage spongy pores may develop in pyrobitumen. Scanning electron microscopy combined with in-situ SEM-Raman spectroscopy can further reveal the structral information of different types of SB, thus providing crucial data for understanding for understanding OM migration paths, dynamics, and distances at micro-scale.

Trajectory of MRI and FDG‐PET biomarkers in Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) has a long preclinical phase, which is hypothesised to start with the accumulation of β‐amyloid (Aβ) plaques, followed by tau neurofibrillary tangles and neurodegeneration. Despite presenting quite different types of information, MRI‐derived volumetric measures and FDG‐PET‐derived metabolism are both commonly used as proxy of neurodegeneration in AD. The aim of this work is to assess the trajectory of both of these biomarkers in AD, relative to time to amyloid‐positivity.Method3289 MRI scans (682 participants) and 2472 FDG‐PET scans (1296 participants) were included from the ADNIGO, 1,2 and 3 cohorts. For MRI scans, total volume was calculated for a meta‐ROI consisting of entorhinal, inferior, middle temporal and fusiform gyri. For FDG‐PET, standardized uptake value ratios (SUVR) were calculated from 30‐60 min post injection for a meta‐ROI consisting of the angular, posterior cingulate and inferior temporal gyri, normalised to the pons. All metrices were transformed into z‐scores using the references values of the Aβ‐negative subjects. Age of amyloid‐positivity was determined by fitting a nonlinear mixed effects model to 3607 Centloid values corresponding to 1690 participants. Next, time to amyloid‐positivity was calculated for each scan. The relationship between time to amyloid‐positivity and (z‐scored) SUVR or cortical volume values was modelled using a non‐linear mixed effects model with spline function and trajectories from both modalities were compared.ResultsBaseline demographics can be found in Table 1. Figure 1 shows the trajectory of amyloid deposition that was used to determine age of amyloid‐positivity. Cortical volume and SUVR showed similar trajectories over time, with steeper decline in both measures after the event of amyloid‐positivity (Figure 2). The overlay of both biomarker trajectories shows that for the meta‐ROI, there is no difference in when these biomarkers become abnormal.ConclusionsThe present results suggest that measures of neurodegeneration from MRI and FDG‐PET scans corresponding to a meta‐ROI, follow very similar trajectories. Future work will investigate if this also holds true for smaller regions of interest.

Digital Code as an Object Graphic Design for Dissemination and Promotion of Brand

Summary The article reveals the features of the graphic language of coded information by identifying, systematizing, and combining QR-codes and AR-codes at the present stage of graphic design development. The analysis of coded information in different types of graphic design is presented: advertising, environmental, motion, print, packaging, set prop, web, mobile app, art, and illustration graphic design. The role of a graphic designer in the development of digital codes of various types for various fields of application is described. It is proved that the coded information is a trigger between the online and offline worlds. The article also reveals the importance of using coded information for the formation, development, and improvement of the brand.

Biased beliefs about immigration and economic concerns: Evidence from representative experiments

We investigate the causal relationship between biased beliefs about immigrants and the host country's attitudes towards immigration. Using representative survey experiments with over 7000 respondents, we find substantial biases in respondents' beliefs about the immigrant population in several domains. Exposure to different types of information about immigrants reduces concerns about negative effects of immigration on the welfare state. On the contrary, different types of information offset their effects on concerns about increased competition in the labor market. Using a data-driven approach to uncover systematic heterogeneity in treatment effects, we find that prior beliefs about immigration and their distribution in the host country's population are crucial for the effectiveness of information interventions. We conclude that different distributions of beliefs therefore require appropriately targeted information policies.

Study on the influence of the information diffusion and vaccination behavior on disease transmission in multiplex networks

To investigate the influence of information diffusion and vaccination behavior on disease transmission. In this paper, a coupling model is proposed to describe the co-evolution process of information diffusion, vaccination behavior and disease transmission in multiplex network. In the information layer, two types of information are diffused, including the positive information and the negative information, respectively. During the process of information diffusion, the influence of mass media on information diffusion is considered, due to the herd effect, which type of information is believed to depend on the information state of the surrounding neighbors based on the Heaviside step function. In the behavioral layer, the individuals with different types of information will choose different immune behavioral responses, which in turn will influence the disease transmission. The coupling model was analyzed by using the Micro Markov Chain Approach (MMCA) to obtain state transition equations and the prevalence thresholds for disease. By simulating the simulation experiments, it demonstrates that the information diffusion has no direct effect on disease prevalence thresholds, but affects the proportion of infected; vaccination behavior has a significant effect on disease prevalence thresholds and also affects the proportion of infected. The results of the study suggest that when the disease transmission is within the controllable range, the stronger the diffusion of positive information, the higher the vaccination rate, which has a more positive influence on inhibiting disease transmission; once out of control, the official will lose trust, which is not conducive to disease control.

A Statistical Analysis of Sentiment over Different Social Platforms on Drug Usage across High, Middle and Low-Income Countries

Social media serves as a platform for sharing information and connecting with others on various subjects, including healthcare and drugs. Analyzing drug sentiment from various social media like Twitter, reddit, quora etc are crucial for monitoring drug safety, identifying adverse reactions, and providing an early warning system for potential safety concerns, benefiting healthcare organizations and governments. This paper aims to study the opinions of people from all over the world by analyzing their messages, posts on social media. For this study 39,069 drug related message corpus were fetched making a comparison between the high income countries, and middle income and low income countries on the basis of drug consumption from 2021 to 2023. The dataset used for the study consisted of 41.63% text-corpus from high income countries out of which on an average from 2021 to 2023, 40.2% was found to have a positive sentiment. Whereas 34.65% of text-corpus are from middle-income countries out of which on an average 26.4% of was of a positive sentiment and 23.70% text-corpus are from low income countries with 23.6% having a positive sentiment. Furthermore, the primary factor for having such differences from people’s sentiment on drug consumption from high to low income countries includes Cultural and Social Norms, Legalization, availability etc therefore, In high-income countries, drug use is more socially accepted than in other regions. This proposed study gives an insight into people’s opinion on various drugs from different countries and regions. The results of this study attempted to understand how the public is responding to different types of information and to identify potential misinformation which can be used to formulate policies for existing and future drug prevention campaigns in order to improve public health and promote public education.

The microstructure of intra- and interpersonal coordination.

Movements are naturally composed of submovements, i.e. recurrent speed pulses (2-3 Hz), possibly reflecting intermittent feedback-based motor adjustments. In visuomotor (unimanual) synchronization tasks, partners alternate submovements over time, indicating mutual coregulation. However, it is unclear whether submovement coordination is organized differently between and within individuals. Indeed, different types of information may be variably exploited for intrapersonal and interpersonal coordination. Participants performed a series of bimanual tasks alone or in pairs, with or without visual feedback (solo task only). We analysed the relative timing of submovements between their own hands or between their own hands and those of their partner. Distinct coordinative structures emerged at the submovement level depending on the relevance of visual feedback. Specifically, the relative timing of submovements (between partners/effectors) shifts from alternation to simultaneity and a mixture of both when coordination is achieved using vision (interpersonal), proprioception/efference-copy only (intrapersonal, without vision) or all information sources (intrapersonal, with vision), respectively. These results suggest that submovement coordination represents a behavioural proxy for the adaptive weighting of different sources of information within action-perception loops. In sum, the microstructure of movement reveals common principles governing the dynamics of sensorimotor control to achieve both intra- and interpersonal coordination.

Multi-heterogeneous Data Synchronization Method for New Generation Substation Monitoring System

In principle, the new generation substation monitoring system no longer has duplicate data collection across safety partitions. To avoid information islands between nodes of different safety partitions and to realize efficient interconnection and interoperability of different types of information, this paper proposes a multivariate heterogeneous data synchronization method for the new generation substation monitoring system. Firstly, the overall framework of the structured data synchronization method, synchronization data encapsulation modeling, cache format construction, and subgroup and group packet processing are introduced; secondly, the overall architecture of the unstructured data synchronization method is introduced, including full data updating and difference data updating, and the latter improves the efficiency of data chunking and achieves the incremental synchronization process of the difference by the fast CDC method based on the identification of the local difference; finally, the test validation is carried out based on the actual data for testing and validation. The results show that the multivariate heterogeneous data synchronization methods are 100% correct, and the incremental difference synchronization saves more than 70% of the synchronization time.

Automatic detection of surface defects based on deep random chains

Defect detection is critical in production systems. The traditional methods are primarily manual, prohibiting its large-scale industrial application. The current deep learning methods usually require a large amount of data, which is challenging in some cases. This paper presents a novel deep learning method to detect a large variety of defects based on small datasets only. Specifically, the method is based on the deep random chain combined with the adaptive Faster R-CNN. The idea behind this method is to fuse both common and different types of information among candidate groups to each defect candidate, which thus improves the model’s generalization for small sample datasets with a wide variety of defects. Indeed, the deep random chains focus on learning the relationship among the pixels inside each defect, while many features are added to each defect using Faster R-CNN. Several experiments on industrial products demonstrate the merit of the proposed method for small sample datasets with yet a wide variety of defects.

Predictions and rewards affect decision-making but not subjective experience.

To survive, organisms constantly make decisions to avoid danger and maximize rewards in information-rich environments. As a result, decisions about sensory input are not only driven by sensory information but also by other factors, such as the expected rewards of a decision (known as the payoff matrix) or by information about temporal regularities in the environment (known as cognitive priors or predictions). However, it is unknown to what extent these different types of information affect subjective experience or whether they merely result in nonperceptual response criterion shifts. To investigate this question, we used three carefully matched manipulations that typically result in behavioral shifts in decision criteria: a visual illusion (Müller-Lyer condition), a punishment scheme (payoff condition), and a change in the ratio of relevant stimuli (base rate condition). To gauge shifts in subjective experience, we introduce a task in which participants not only make decisions about what they have just seen but are also asked to reproduce their experience of a target stimulus. Using Bayesian ordinal modeling, we show that each of these three manipulations affects the decision criterion as intended but that the visual illusion uniquely affects sensory experience as measured by reproduction. In a series of follow-up experiments, we use computational modeling to show that although the visual illusion results in a distinct drift-diffusion (DDM) parameter profile relative to nonsensory manipulations, reliance on DDM parameter estimates alone is not sufficient to ascertain whether a given manipulation is perceptual or nonperceptual.

Insight on HBIM for Conservation of Cultural Heritage: The Galleria dell’Accademia di Firenze

The application of Building Information Modeling (BIM) on historic constructions is investigated in this paper by discussing, as a representative case study, the Galleria dell’Accademia di Firenze (Italy), thus showing as this tool can be used effectively for the management and maintenance activities of a historic museum. In fact, while BIM is already well-known as a powerful tool for the design and management of new buildings, its development in the field of historical construction is currently growing and attracting increasing interest in the scientific community. This paper proposes, in particular, an Information Model (IM) aimed to collect the structural information to be subsequently employed for numerical modeling of the building, thus representing the link between the real world and the computational models. The possibility to handle different types of information to be used for the management, maintenance, and modeling of existing constructions, as shown in this paper, encourages the popularization of this approach as an effective method to support cultural heritage (CH) conservation.

Classification of patients with chronic disease by activation level using machine learning methods.

Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients needing more support from the care team. We define PAM levels 1 and 2 as low PAM and investigate the performance of eight machine learning methods (Logistic Regression, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosted Trees, Support Vector Machines, Decision Trees, Neural Networks) to classify patients. Primary data collected from adult patients (n=431) with Diabetes Mellitus (DM) or Hypertension (HT) attending Family Health Centers in Istanbul, Turkey, is used to test the methods. [Formula: see text] of patients in the dataset have a low PAM level. Classification performance with several feature sets was analyzed to understand the relative importance of different types of information and provide insights. The most important features are found as whether the patient performs self-monitoring, smoking and exercise habits, education, and socio-economic status. The best performance was achieved with the Logistic Regression algorithm, with Area Under the Curve (AUC)=0.72 with the best performing feature set. Alternative feature sets with similar prediction performance are also presented. The prediction performance was inferior with an automated feature selection method, supporting the importance of using domain knowledge in machine learning.

Understanding Consumer’s Willingness to Engage with Digital Reuse Systems

Reusable packaging systems can help tackle the global problem of plastic waste by keeping material in circulation. Furthermore, technology now makes it possible to trace material as it moves through the reuse process, which potentially confers benefits across the supply chain. However, research into these digital reuse systems remains limited, particularly from the perspective of consumers who are asked to use such systems. The current work involved a series of online studies designed to identify (i) the barriers and enablers to engaging with digital reuse systems, (ii) which are most strongly endorsed and predict willingness to engage, and (iii) what information about digital reuse systems influences consumers’ willingness to engage and their beliefs. Studies 1 (N = 40) and 2 (N = 300) found that, while people are typically willing to engage with digital reuse systems, some people have concerns over the tracking aspect of digital reuse systems, and this is associated with their willingness to engage. Study 3 (N = 690) found that providing consumers with different types of information about digital reuse systems (e.g., going into more vs. less detail) did not influence their willingness to engage with digital reuse systems or their beliefs about the systems. These findings have implications for how digital reuse systems should be implemented.

PAREI: A progressive approach for Web API recommendation by combining explicit and implicit information

Mashup is an application with specific functions by combining Web APIs that can provide services or data on the Internet, thus avoiding the behavior of repeatedly building wheels. Recommending suitable Web APIs in the vast number of Web APIs on the Internet for Mashup developers has become a challenging problem. Previous studies often fail to fully exploit and effectively synthesize various types of information between Web APIs and Mashups. This work proposes a Web API recommendation approach - PAREI by combining both explicit and implicit information to progressively optimize the recommendation results. First, PAREI uses the explicit structural information between Mashups and Web APIs to construct the Call Relationship Network (CRN). Second, PAREI calculates explicit semantic similarities between developer’s requirement and Mashups to obtain candidate Mashup nodes in CRN. Then PAREI further mines the implicit structural information between Mashups. A combined similarity score for each Mashup node is calculated. Finally, PAREI uses CRN to obtain candidate Web APIs related to candidate Mashup nodes, and integrates implicit semantic information of Web APIs with combined scores of corresponding Mashups, so as to obtain Top-K Web APIs. Comparison experiments show that PAREI has significantly improved the Recall, Precision, and MAP metrics compared with other approaches. Ablation experiments show that different types of information play various roles in Web API recommendation, and different combination modes have different effects on the recommendation results. This work constructs the PAREI model, which combines explicit and implicit information to obtain Web API recommendation results through a progressive strategy. According to the experiment results, we believe that the PAREI approach can help Mashup developers to find demanded Web APIs rapidly and accurately.

Optimal by the Order of Accuracy Cubature Formula for the Approximate Calculation of Triple Integrals from Fast Oscillating Functions in General View

Introduction. The rapid development of digital technologies encourages scientists to create new or improve existing mathematical models of technical processes. It is time to develop mathematical models with different types of data. In the tasks of digital signal and image processing, the approximate calculation of integrals from rapidly oscillating functions using new information operators makes it possible to build cubature formulas using different types of information: the values of functions on planes, lines and points can be used as data. The purpose is to present and investigate the optimal cubature formula for the approximate calculation of the triple integral from rapidly oscillating functions in the general form on the class of differential functions. Information about functions are traces on systems of mutually perpendicular planes. Results. The study of the problems of digital signal and image processing continued using the example of numerical integration of triple integrals from rapidly oscillating functions in the general form. The values of functions on systems of mutually perpendicular planes are using for constructed cubature formula. The main attention in the research focuses on obtaining the estimations of errors. Proposed cubature formula for the approximate calculation of the triple integral from rapidly oscillating functions in general view is optimal in order of accuracy on the class of differential functions. The conducted numerical experiment confirmed the theoretical results. Conclusions. The obtained results make it possible to build new and improve existing mathematical models of processes with different types of input information. New information operators are a powerful tool in the development of such models. Cubature formulas for the approximate calculation of integrals from rapidly oscillating functions of many variables have been created. Іn the construction of the formulas traces of the function on planes, lines, and points are used. Formulas in their construction use function traces on planes, lines, and points. In this work, a cubature formula for the approximate calculation of the triple integral from a rapidly oscillating function in the general form, which is optimal in order of accuracy, is constructed and investigated on the class of differentiable functions. A feature of the proposed formula is the use of values of functions on systems of mutually perpendicular planes as data. Keywords: integrals of rapidly oscillating functions of many variables, cubature formulas, new information operators, digital signal and image processing.

Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow

In the foreseeable future, a complex heterogeneous traffic environment will emerge as Connected and Autonomous Vehicles (CAVs) coexist with Human-driven Vehicles (HDVs). Consequently, understanding the impact of CAVs on car-following behavior and the operational characteristics of heterogeneous traffic flow becomes crucial before their widespread deployment. To tackle this challenge, this research proposes an improved car-following model based on the Intelligent Driver Model (IDM). The model incorporates the position, velocity, and acceleration information of both front and sub-front vehicles in the heterogeneous traffic flow. The impact of different types of information on the model's stability is verified through linear stability analysis while investigating the operational characteristics of traffic flow during vehicle start-up. Additionally, the car-following modes are classified based on the type of leading vehicle, and the corresponding following model is formulated. The results indicate that the improved model significantly improves traffic flow stability, particularly when considering acceleration information. Compared to the front vehicle, the influence of the sub-front vehicle on traffic flow stability is less significant, but their combined impact yields positive effects. Furthermore, the improved model reduces the start-up time of vehicles at signalized intersections by 7.9% and enables a smoother start-up process for vehicles. Moreover, CAVs can mitigate the impact of HDV's disturbances on the overall heterogeneous fleet operation by adjusting their spacing relative to the front vehicle. With an increasing penetration rate, the velocity fluctuation of the entire fleet decreases.

SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization

Issue Tracking Systems (ITS) often support commenting on software issues, which creates a space for discussions centered around bug fixes and improvements to the software. For Open Source Software (OSS) projects, issue discussions serve as a crucial collaboration mechanism for diverse stakeholders. However, these discussions can become lengthy and entangled, making it hard to find relevant information and make further contributions. In this work, we study the use of summarization to aid users in collaboratively making sense of OSS issue discussion threads. Through an empirical investigation, we reveal a complex picture of how summarization is used by issue users in practice as a strategy to help develop and manage their discussions. Grounded on the different objectives served by the summaries and the outcome of our formative study with OSS stakeholders, we identified a set of guidelines to inform the design of collaborative summarization tools for OSS issue discussions. We then developed SUMMIT, a tool that allows issue users to collectively construct summaries of different types of information discussed, as well as a set of comments representing continuous conversations within the thread. To alleviate the manual effort involved, SUMMIT uses techniques that automatically detect information types and summarize texts to facilitate the generation of these summaries. A lab user study indicates that, as the users of SUMMIT, OSS stakeholders adopted different strategies to acquire information on issue threads. Furthermore, different features of SUMMIT effectively lowered the perceived difficulty of locating information from issue threads and enabled the users to prioritize their effort. Overall, our findings demonstrated the potential of SUMMIT, and the corresponding design guidelines, in supporting users to acquire information from lengthy discussions in ITSs. Our work sheds light on key design considerations and features when exploring crowd-based and machine-learning-enabled instruments for asynchronous collaboration on complex tasks such as OSS development.

Laminar neural dynamics of auditory evoked responses: Computational modeling of local field potentials in auditory cortex of non-human primates

Evoked neural responses to sensory stimuli have been extensively investigated in humans and animal models both to enhance our understanding of brain function and to aid in clinical diagnosis of neurological and neuropsychiatric conditions. Recording and imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFPs), and calcium imaging provide complementary information about different aspects of brain activity at different spatial and temporal scales. Modeling and simulations provide a way to integrate these different types of information to clarify underlying neural mechanisms.In this study, we aimed to shed light on the neural dynamics underlying auditory evoked responses by fitting a rate-based model to LFPs recorded via multi-contact electrodes which simultaneously sampled neural activity across cortical laminae. Recordings included neural population responses to best-frequency (BF) and non-BF tones at four representative sites in primary auditory cortex (A1) of awake monkeys. The model considered major neural populations of excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SOM) neurons across layers 2/3, 4, and 5/6. Unknown parameters, including the connection strength between the populations, were fitted to the data. Our results revealed similar population dynamics, fitted model parameters, predicted equivalent current dipoles (ECD), tuning curves, and lateral inhibition profiles across recording sites and animals, in spite of quite different extracellular current distributions. We found that PV firing rates were higher in BF than in non-BF responses, mainly due to different strengths of tonotopic thalamic input, whereas SOM firing rates were higher in non-BF than in BF responses due to lateral inhibition.In conclusion, we demonstrate the feasibility of the model-fitting approach in identifying the contributions of cell-type specific population activity to stimulus-evoked LFPs across cortical laminae, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing.

Limit Values Acquisition in Field Control Problems Using Information-Based Time-Reversal Technique

Despite numerous theories proposed to describe the near-field focusing problem, a non-negligible error still exists between the calculation result and the target, and the process involved is quite complicated. While the time reversal algorithm has greatly reduced the problem’s complexity and improved accuracy under complex scenarios, it has limitations in problems involving large angles and complex area focusing. Both traditional and time reversal algorithms experience errors due to the loss of different types of information. In this paper, we propose an accurate focusing algorithm that combines time reversal with the vector fitting method. This algorithm effectively solves the limitations of traditional focusing algorithms in problems involving large angles, complex area focusing, and other scenarios. The proposed algorithm has the advantages of being fast, enabling global optimization, and not accumulating errors. To verify the accuracy of the algorithm, we design a tunable phase-modulated metasurface. By controlling different vias metallization, we obtain a 360° transmission phase shift with a transmission loss of less than 1 dB.

Temporal and spatial reference frames in visual working memory are defined by ordinal and relational properties.

Natural environments provide a rich spatiotemporal context that allows for visual objects to be differentiated based on different types of information: their absolute or relative spatial or temporal coordinates, or their ordinal positions in a spatial or temporal sequence. Here, we investigated which spatial and temporal properties are incidentally encoded along with to-be-remembered features to provide reference frames in visual working memory (VWM). We tested the different possibilities in a spatiotemporal color change-detection task by transforming spatial and/or temporal structures of item presentation at retrieval relative to encoding. More precisely, spatial and/or temporal coordinates were (a) switched, changing the order of items in a spatial or temporal sequence (ordinal transformation); (b) multiplied by different factors, changing interitem distances (relational transformation); or (c) multiplied by a constant factor, expanding or shrinking the entire configuration (global transformation). Such transformations of the external reference frame at retrieval should only interfere with VWM if the internal reference frame relies on the spatial or temporal properties affected by the respective transformation. We found that ordinal and relational transformations of either the spatial or temporal structure impaired performance, whereas global transformations did not. Thus, reference frames appear to be primarily defined by interitem relations-including relative distances between items as well as their order-rather than absolute positions in space or time. These results corroborate and extend previous findings for the spatial domain, and highlight functional similarities of the spatial and temporal dimensions in VWM by revealing the same metrical properties for temporal reference frames. (PsycInfo Database Record (c) 2022 APA, all rights reserved).