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Abstract: Implicatures like ‘Some politicians are smart’ (interpreted as ‘Some but not all politicians are smart’) are defined scalar implicatures. A heated linguistic debate has focused on how we derive those implicatures: some authors consider the computational process as linguistic in nature (Levinson, 2000), others as pragmatic in nature (Sperber & Wilson, 1995). A growing body of research, prompted by pioneering work by Bott and Noveck (2004), focused on the computational cost related with the computation of scalar implicatures. The present study addresses such topic through the use of different experimental techniques. With Experiment 1 (N = 57) we replicated the third experiment of Bott and Noveck (2004), the first study that identified a cost related to a pragmatic response. With Experiment 2 (N = 58), using a pseudo-word paradigm, we excluded the possibility that the computational cost is due to an experimental artifact, such as an increased difficulty in moving up in the conceptual hierarchy (e.g., ‘Some elephants are mammals’) than in moving down (e.g. ‘Some mammals are elephants’). In Experiment 3 (N = 54), with a Sentence Evaluation Task, we collected reading times, reaction times and eye gaze data. Results showed that the cost of the computation disappears when there is contextual support. Overall, our results seem to support the idea that scalar implicatures are not automatically computed with context playing an important role.
Keywords: scalar implicatures, eye-tracking, experimental pragmatics, reaction times
Published in RUNG: 22.09.2021; Views: 2709; Downloads: 9
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Abstract: COVID-19 tracking tools or contact-tracing apps are getting developed at a rapid pace by different governments in their respective countries. This study explores one such tool called Aarogya Setu, developed by the Government of India. It is a mobile application developed under the Health Ministry, as a part of the E-Governance initiative, to track and sensitize the citizens of India in a joint battle against COVID-19 spread. The study aims to understand various useful features of this tool and to present different concepts of data science applied within the application along with its importance in managing the ongoing pandemic. The App uses Bluetooth and GPS technologies to alert a user when they are nearby a COVID-19 infected person. The application uses various Data Science concepts such as Classification, Association Rule Mining, and Clustering to analyze COVID-19 spread in India. The study also shows potential upgradations in the application, which includes usage of Artificial Intelligence and Computer Vision to detect COVID-19 patients. The study would be useful for mobile technology professionals, data science professionals, medical practitioners, health-related frontline workers, public administrators, and government officials.
Keywords: COVID-19, India, contact-tracing, tracking tool, Bluetooth, GPS, COVID19 reporting tool, Aarogya Setu
Published in RUNG: 01.04.2021; Views: 2787; Downloads: 61
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