A multimodal investigation of moral decision making in harmful contexts

Since the two landmark publications in moral psychology (Greene, Sommerville, Nystrom, Darley, & Cohen, 2001; Haidt, 2001), the field has experienced an affective revolution that has put emotions at the center of the stage. Although work on exploring role of emotions in assessing morality of various types of moral acts (impure, unfair, etc.; Haidt, 2007) abounds, studying its role in harmful behaviors presents a unique challenge. The aversion to harming others is an integral part of the foundations of human moral sense and it presents itself in the form of deeply ingrained moral intuitions (Haidt, 2007). Since creating laboratory situations to investigate harm aversion raises ethical issues, research has primarily relied on studying hypothetical cases. In the current thesis, we utilize hypothetical vignettes to explore role of emotions in both moral judgment and behavior in harmful contexts, both when harm is carried out intentionally or produced accidentally. Study 1 investigates the role of emotion in motivating utilitarian behavior in moral dilemmas when presented in contextually salient virtual reality format as compared to judgment about the same cases for their textual versions. Study 2 investigates divergent contributions of two different sources of affect, one stemming from self-focused distress and the other focused on other-oriented concern, on utilitarian moral judgments in autistics. Study 3 investigates the role of empathic arousal in condemning agents involved in unintentional harms and why harmful outcomes have a greater bearing on blame as compared to acceptability judgments

Virtual Keyboard Interaction Using Eye Gaze and Eye Blink

A Human-Computer Interaction (HCI) framework that is de-marked for people with serious inabilities to recreate control of a conventional machine mouse is presented. The cam based framework, screens a client's eyes and permits the client to simulate clicking the mouse utilizing deliberate blinks and winks. For clients who can control head developments and can wink with one eye while keeping their other eye obviously open, the framework permits complete utilization of a regular mouse, including moving the pointer, left and right clicking, two fold clicking, and click-and-dragging. For clients who can't wink yet can blink voluntarily the framework permits the client to perform left clicks, the most well-known and helpful mouse activity. The framework does not oblige any preparation information to recognize open eyes versus shut eyes. Eye classification is expert web amid ongoing co-operations. The framework effectively permits the clients to reproduce a tradition machine mouse. It allows users to open a document and perform typing of letters with the help of blinking of their eye. Along with framework allows users to open files and folders present on a desktop. DOI: 10.17762/ijritcc2321-8169.150710

see: an R package for visualizing statistical models

The see package is embedded in the easystats ecosystem, a collection of R packages that operate in synergy to provide a consistent and intuitive syntax when working with statistical models in the R programming language (R Core Team, 2021). Most easystats packages return comprehensive numeric summaries of model parameters and performance. The see package complements these numeric summaries with a host of functions and tools to produce a range of publication-ready visualizations for model parameters, predictions, and performance diagnostics. As a core pillar of easystats, the see package helps users to utilize visualization for more informative, communicable, and well-rounded scientific reporting

datawizard: an R package for easy data preparation and statistical transformations

The {datawizard} package for the R programming language (R Core Team, 2021) provides a lightweight toolbox to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. Therefore, it can be a valuable tool for R users and developers looking for a lightweight option for data preparation

Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India

False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India

statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details

The `statsExpressions` package is designed to facilitate producing dataframes with rich statistical details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages to display these details

Trait psychopathy and utilitarian moral judgement: The mediating role of action aversion

Although past research has established that the utilitarian bias (increased willingness to agree to personally kill someone for the greater good) in psychopathy on moral dilemmas stems from weaker negative affect at the prospect of harming others due to reduced harm aversion, it remains to be seen if this is owing to reduced aversion to witnessing harmful outcomes (outcome aversion) or performing harmful actions (action aversion). In this study, we show that trait psychopathy is associated with both reduced outcome and action aversion and that only action aversion negatively mediates the influence of trait psychopathy on utilitarian moral judgement. Thus, the increased tendency in psychopathy to make utilitarian moral judgements is in part due to reduced aversion to carrying out harmful actions

Reasoning supports forgiving accidental harms

International audienceAbstract People experience a strong conflict while evaluating actors who unintentionally harmed someone—her innocent intention exonerating her, while the harmful outcome incriminating her. Different people solve this conflict differently, suggesting the presence of dispositional moderators of the way the conflict is processed. In the present research, we explore how reasoning ability and cognitive style relate to how people choose to resolve this conflict and judge accidental harms. We conducted three studies in which we utilized varied reasoning measures and populations. The results showed that individual differences in reasoning ability and cognitive style predicted severity of judgments in fictitious accidental harms scenarios, with better reasoners being less harsh in their judgments. Internal meta-analysis confirmed that this effect was robust only for accidental harms. We discuss the importance of individual differences in reasoning ability in the assessment of accidental harms