Do people really prefer verbal probabilities?

When people communicate uncertainty, do they prefer to use words (e.g., “a chance”, “possible”) or numbers (e.g., “20%”, “a 1 in 2 chance”)? To answer this question, past research drew from a range of methodologies, yet failed to provide a clear-cut answer. Building on a review of existing methodologies, theoretical accounts and empirical findings, we tested the hypothesis that the preference for a particular format is driven by the variant of uncertainty that people experience. We expected that epistemic uncertainty would be more often communicated in words, whereas distributional uncertainty would be more often communicated in numbers; for the dispositional uncertainty, we expected that an individual’s disposition would be more often communicated in words, whereas dispositions from the world would be more often communicated numerically. In three experiments (one oral, two written), participants communicated their uncertainty regarding two outcomes per variants of uncertainty: epistemic, dispositional and distributional. Overall, participants communicated their uncertainty more often in words, but this preference depended on the variants of uncertainty. Participants conveyed their epistemic and dispositional uncertainties more often in words and their distributional uncertainty in numbers (Experiments 1 and 2) but this effect was greatly reduced when the precision of uncertainty was held constant (Experiment 3), pointing out the key role of uncertainty vagueness. We have reviewed the implications of our findings for the existing accounts of format preferences

Discriminant Analysis of Deaf Persons Communication Systems

Deaf persons, in their communications, use verbal and non-verbal communication systems, as well as bilingual communication. The aim of this article is to determine which communication system the deaf people prefer, and to determine whether there is a statistically significant difference between the sub-samples of the respondents in the preference of the communication systems using discriminant analysis. Study findings have shown that deaf people prefer a non-verbal communication system and a bilingual manner of communicating, and do not reject the verbal communication system because it is essential to communicating with hearers but, they do not prefer it. Discriminant analysis revealed that there was no statistically significant difference between the sub-groups of the respondents at a statistical significance level of 0.01

Discriminant Analysis of Deaf Persons Communication Systems

Deaf persons, in their communications, use verbal and non-verbal communication systems, as well as bilingual communication. The aim of this article is to determine which communication system the deaf people prefer, and to determine whether there is a statistically significant difference between the sub-samples of the respondents in the preference of the communication systems using discriminant analysis. Study findings have shown that deaf people prefer a non-verbal communication system and a bilingual manner of communicating, and do not reject the verbal communication system because it is essential to communicating with hearers but, they do not prefer it. Discriminant analysis revealed that there was no statistically significant difference between the sub-groups of the respondents at a statistical significance level of 0.01

e-SNLI: Natural Language Inference with Natural Language Explanations

In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Our dataset thus opens up a range of research directions for using natural language explanations, both for improving models and for asserting their trust.Comment: NeurIPS 201

Voice Driven Email Client

With the advancements of technology, today the needs and expectations of humans keep growing seeking for more convenience. People prefer to communicate with technology using natural interactive approaches rather than with a key-board or mouse. Voice recognition is one such mostly preferred natural interactive approach. Though voice recognition was originally introduced in applications for differently abled individuals, today we see with the complexity in work environments, a majority of people prefer to use more natural interactive approaches such as voice commands due to its’ convenience. The main objective of this project is to develop an Email client which allows users to perform tasks within the application by eliminating the use of the key-board or mouse. The system allows users to perform all the tasks within the application using voice commands. At present widely used web email service providers such as Gmail, yahoo etc do not support voice commands, the Voice Driven Email Client makes it possible to connect to a web based email client and carry out tasks using special voice commands. It will allow the users to dictate as well as navigate purely on voice commands. This voice enabled email client introduces a better approach to accessing web-based email clients instead of remaining at the traditional text-based, typing and clicking approach. Further it will reach a wider target audience including differently abled individuals such as people without hands, people with difficulties in hand movements, dyslexic people who find it difficult to write and spell etc. This document outlines the motivation, background, problem description, scope, research and analysis, design, implementation and testing carried out to develop the Voice Driven Email Client

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v. 2 (1931

A Behavioral Confirmation and Reduction of the Natural versus Synthetic Drug Bias

Research reveals a biased preference for natural versus synthetic drugs; however, this research is based upon self-report and has not examined ways to reduce the bias. We examined these issues in five studies involving 1,125 participants. In a Pilot Study (N = 110), participants rated the term natural to be more positive than the term synthetic, which reveals a default natural-is-better belief. In Studies 1 (N = 109) and 2 (N = 100), after a supposed personality study, participants were offered a thank you “gift” of a natural or synthetic pain reliever. Approximately 86% (Study 1) and 93% (Study 2) of participants chose the natural versus synthetic pain reliever, which provide a behavioral choice confirmation of the natural drug bias. In Studies 3 (N = 350) and 4 (N = 356), participants were randomly assigned to a control or experimental condition and were asked to consider a scenario in which they had a medical issue requiring a natural versus synthetic drug. The experimental condition included a stronger (Study 3) or weaker (Study 4) rational appeal about the natural drug bias and a statement suggesting that natural and synthetic drugs can be good or bad depending upon the context. In both studies, the natural bias was reduced in the experimental condition, and perceived safety and effectiveness mediated this effect. Overall, these data indicate a bias for natural over synthetic drugs in preferences and behavioral choices, which might be reduced with a rational appeal