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Validation of the EmojiGrid as an intuitive, immersive and efficient tool to assess emotions evoked by immersive VR experiences

Human decision making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases.To enhance our understanding of cognitive heuristics and biases we propose a neural network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. In order to substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena.

Results of studies performed to validate the EmojiGrid as a graphical self-report tool to assess experienced end perceived valence and arousal.

Background: In this study we measured the affective appraisal of sounds and video clips using a newly developed graphical self-report tool: the EmojiGrid. The EmojiGrid is a square grid, labeled with emoji that express different degrees of valence and arousal. Users rate the valence and arousal of a given stimulus by simply clicking on the grid. Methods: In Experiment I, observers (N=150, 74 males, mean age=25.2±3.5) used the EmojiGrid to rate their affective appraisal of 77 validated sound clips from nine different semantic categories, covering a large area of the affective space. In Experiment II, observers (N=60, 32 males, mean age=24.5±3.3) used the EmojiGrid to rate their affective appraisal of 50 validated film fragments varying in positive and negative affect (20 positive, 20 negative, 10 neutral). Results: The results of this study show that for both sound and video, the agreement between the mean ratings obtained with the EmojiGrid and those obtained with an alternative and validated affective rating tool in previous studies in the literature, is excellent for valence and good for arousal. Our results also show the typical universal U-shaped relation between mean valence and arousal that is commonly observed for affective sensory stimuli, both for sound and video. Conclusions: We conclude that the EmojiGrid can be used as an affective self-report tool for the assessment of sound and video-evoked emotions.

This study investigates the relation between several factors that are known to influence the affective appraisal of odors through probabilistic network analysis.

This site provides the images needed to implement the EmojiGrid affective self-report tool.

Questionnaire to measure the quality of mediated social communication

The Covid-19 pandemic has brought countries in the world and Indonesia is no exception entering a new phase of life. The purpose of this study is to find various policy packages by the Government and individual countries to protect their citizens in relation to various laws and regulations to overcome the Covid-19 pandemic. This type of research uses normative legal research, and the research methodology used is Foucault's theory of power relations, that is, understanding will be built to find a synthesis in this study. The results of research investigations are related to various policy packages issued by various countries and also by the Indonesian Government to protect their citizens to overcome the Covid-19 pandemic including the various effects of its dominance through large-scale social restrictions and new normal life Covid-19. However, it turns out that this provision is not effective in Indonesia because people tend to ignore it. The solution offered is that the community as a civil society must be able to manage issues in the face of inadequate public compliance with various laws and regulations that govern the handling of the Covid-19 pandemic. Therefore, Civil society in Indonesia must be encouraged to change the basis of thought so that it has the same spatial frequency as the Government

先行研究では, 形式的に無標である語彙的使役が意味的にも無標で, 形式的に有標である生産的使役が意味的にも有標であることが｢語用論的分業｣として捉えられることがある。更に, 発話過程においては解釈過程も参照され, 解釈過程においては発話過程も参照される双方向的な形式的モデルも提唱されている。主語の指示対象が何らかの属性を有することを含意する｢属性叙述受動文｣と, 主語の指示対象と何らかの関連を有する｢潜在的受影者｣が文の表す事象から影響を受けることを含意する｢潜在的受影者受動文｣も, 対応する能動文に比して有標な形式であるだけでなく, 対応する能動文には必ずしも伴わない含意を持つことにより意味的にも有標であると言える。本稿では, これらの現象がどこまで類似するかを吟味しながら, 語用論的分業の概念を部分的に属性叙述受動文と潜在的受影者受動文に適用し, 二受動文の双方向的な最適性理論的モデルを提示する。, In the literature on causatives, including but not limited to those in Japanese, it has been argued that there is a'division of pragmatic labor'between productive and lexical causatives, such that the former are marked relative to the latter not only in form but also in meaning. This has additionally been captured in a bidirectional model, such that the production process makes reference to the interpretation process and vice versa. Japanese'subject-characterizing passives', which imply that the subject referent has a certain property, and'implied affectee passives', which imply that an umnentioned entity connected to the subject referent is affected by the eventuality expressed by the sentence, can likewise be seen as marked relative to the corresponding actives, not only formally but also semantically, due to the fact that they carry implicit meaning that is absent from these actives. In this paper Ie xamine the degree to which these two phenomena are similar, partly applying the concept of a division of pragmatic labor to subject-characterizing passives and implied affectee passives. In particular, I argue that the interpretation process of subject-characterizing passives and implied affectee passives is equivalent to that of productive causatives, both of them featuring implicature derivation through bidirectionality. I also discuss an Optimality-Theoretic model of the production and bidirectional interpretation processes of subject-characterizing passives, implied affectee passives, and other passives in Japanese.

The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at the national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long-term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short-term, point observations to estimate large-scale annual totals, and quantify the uncertainty associated with this upscaling. Here, we review some approaches to this problem and synthesize the work in the recent UK Greenhouse Gas Emissions and Feedbacks Program, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near-continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterize the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates.

Nitrous oxide (N2O) is a potent greenhouse gas (GHG) with a global warming potential 298 times that of carbon dioxide (CO2). Measurements of soil N2O emissions typically use manual chambers, with samples taken at low temporal resolution over long durations (months), or at higher temporal resolution (multiple samples per day) over short durations. Automated GHG flux systems have allowed the measurement of high frequency (sub-daily) N2O fluxes over longer periods (weeks to months), revealing that emissions can vary diurnally by up to 400% in agricultural soils.Contributing approximately 70% of global anthropogenic N2O emissions, agriculture represents the largest area of uncertainty for GHG reporting and the most challenging sector for emissions reduction: global N2O emissions are increasing at double the rate estimated by the Intergovernmental Panel on Climate Change (IPCC). Improvements to agricultural GHG emission estimates have increased the accuracy of GHG reporting, but N2O emissions from agricultural soils still contribute 25% of the uncertainty of total GHG emissions across all sectors. Our project, diurnal variation in soil nitrous oxide emissions (DIVINE), combines field and laboratory experiments that exploit high-resolution, robotic and continuous N2O measurement technology, to investigate the drivers and mechanisms underpinning diurnal variation in N2O.We will present work from a field study investigating the effect of soil properties and nitrogen (N) fertiliser management on diurnal variation in N2O emissions from a wheat crop. We assess how N fertiliser application (rate and frequency) and soil gas diffusivity (determined by bulk density and rain events), affect the depth of N2O production and N2O transport in the soil, and resultant impacts on the peak timing and amplitude of diurnal N2O emissions across the crop life cycle and seasons.N2O emissions will be compared in paired transects with contrasting bulk density but similar soil texture and history, with three ammonium nitrate fertiliser scenarios. N2O is being measured continuously using SkyLine2D automated flux chamber technology. To resolve depth/gas transfer coefficients after N fertiliser and rain events, we will measure soil N2O concentration profiles across the rooting zone in discrete campaigns during the crop life cycle.Further, we will discuss how our data will be used to improve the accuracy of N2O emission factors by accounting for environmental and diurnal variation. Bayesian statistical modelling will be used to represent the spatial and temporal distribution of emissions following fertilisation, and the effects of known environmental factors (e.g. temperature, soil moisture, light intensity), as well as the residual effect explicable by the diurnal cycle.

Forests are often considered to be able to play a significant role in tackling global warming. To fully understand their potential in mitigating climate change and to develop more accurate ecosystem GHG flux budgets and process-based models of forests, we require more knowledge of methane (CH4) and nitrous oxide (N2O) exchange in forests, their underlying processes, environmental controls and responses to forest management. In recent years, it is becoming evident that not only soils but also the trees themselves may significantly contribute to CH4 and N2O fluxes in forest ecosystems.Our research mainly focussed on greenhouse gas (GHG) exchange in temperate UK forests on both mineral and organic soils. We will primarily concentrate on CH4 fluxes as N2O fluxes were often relatively low in these forests and, by including CO2 fluxes, we will put them into the context of the overall ecosystem GHG exchange. A range of flux methods at different scales were used in our field studies to be able to capture the often high temporal and spatial variability of the GHG exchange between the atmosphere and either soils, tree stems or entire trees aboveground, and to identify potential drivers of the fluxes. The impact of management practices including clear fell, drainage and the resulting micro-topography, and forest-to-bog restoration on CH4 fluxes from organic soils following the first forest rotation will also be described. We regularly used novel automated and chamber approaches and technologies, and the advantages and limitations of the different flux approaches and their use to upscale fluxes to the landscape scale will be evaluated.

In the last few years, employee performance has decreased because the wages received are considered insufficient to meet the needs of a decent life. The purpose of this study was to examine and analyze those influencing wage policies and employee performance in companies in the Bekasi Regency industrial area. The study population was employees of companies in the industrial area of ​​Bekasi Regency. In accordance with structural equition modeling (SEM), a sample of 285 respondents was selected through a purposive sampling technique. Data analysis was applied by applying government regulation theory, trade union theory, labor productivity theory, wage policy theory and employee performance theory. Hypothesis testing was carried out using SEM with the Lisrel Version 8.80 application. The results showed that the direct effect of government regulations, labor unions, work productivity and living costs has a significant effect on wage policies and government regulations, labor unions, wage policies have a significant effect on employee performance and indirect effects. government regulations, labor unions, work productivity, cost of living, have a significant effect on employee performance through wage policies.

The ways that extraneous visual and auditory stimuli impair human performance are reviewed with aim of distinguishing those sensory, perceptual and cognitive effects relevant to the design of human-machine systems. Although commonly regarded as disruptive, distractions reflect the adaptability of the organism to changing circumstances. Depending on the context, our knowledge of the ways in which distraction works can be exploited in the form of alarms or other attention-getting devices, or resisted by changing the physical and psychological properties of the stimuli. The research described here draws from contemporary research on distraction. The review underscores the vulnerability of performance even from stimuli of modest magnitude while acknowledging that distraction is a necessary consequence of our adaptive brain that leads to effects that are (and sometimes, but not always) beneficial to safety, efficiency and wellbeing. Low intensity distractors are particularly sensitive to the context in which they occur. The mechanisms outlined can be exploited either to grab attention (and even temporarily disable the individual, but more usefully to warn or redirect the individual) or to modify it in subtle ways across the gamut of human activity.

Immersive communication systems provide increasingly realistic virtual environments, which may afford immersive social interactions that approach the quality of face-to-face (F2F) meetings by eliciting a sense of social presence; the feeling of being physically together with another person and having an affective and intellectual connection. To optimize a system’s ability to convey social presence, there is a need for tools that efficiently and reliably measure the degree to which users experience social presence. Currently, the most widely used tools to measure (social) presence are questionnaires. As their ecological validity is questionable, there is a need for objective and non-intrusive measures to measure social presence during naturalistic social interactions. In our study, we aimed to identify a set of determinants of social presence that enable the assessment of a system’s ability to convey social presence, preferably using easy to use, off-the-shelf tools. Considering eye gaze behavior is modulated by social presence and can be measured with relative ease for both F2F and mediated communication, we propose to use three eye gaze measures as an accessible means to assess the level of social presence a system can elicit.

Advances in robotics and multisensory displays allow extending telepresence ambitions beyond only the feeling of being present at a remote location In this paper, we discuss what may lie beyond telepresence and how we can transport both the functional and social self of a user. We introduce the embodiment illusion and its potential contribution to task performance and list important cues to evoke this illusion, including synchronicity in multisensory information, a first-person visual perspective, and a human-like visual appearance and anatomy of the telepresence robot. We also introduce the concept of social presence and the important bidirectional social cues it needs, including eye contact, facial expression, posture, gestures, and social touch. For all these multisensory and social cues, we explain how they can be implemented in a telepresence system and describe our solution consisting of a closed control pod and a humanoid telepresence robot.