Muse Headband: Measuring Tool or a Collaborative Gadget?

Przegalińska, Aleksandra; Ciechanowski, Leon; Magnuski, Mikołaj; Gloor, Peter A.

doi:10.1007/978-3-319-74295-3_8

Cited by 31 publications

(15 citation statements)

References 10 publications

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“…As robots reach near-human like forms, the uncanny valley effect becomes salient. For instance, a study showed that participants experienced more negative feelings with a complex animated avatar chatbot than with a simpler text-based chatbot [37]. The original UVM did not predict the eerie feeling triggered by a human agent (perceived as human) exhibiting robot characteristics (e.g., robotic appearances or demeanor).…”

Section: Interaction Between Identity and Inquiries On Donationmentioning

confidence: 99%

Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies

Shi

Wang

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Intelligent conversational agents, or chatbots, can take on various identities and are increasingly engaging in more humancentered conversations with persuasive goals. However, little is known about how identities and inquiry strategies influence the conversation's effectiveness. We conducted an online study involving 790 participants to be persuaded by a chatbot for charity donation. We designed a two by four factorial experiment (two chatbot identities and four inquiry strategies) where participants were randomly assigned to different conditions. Findings showed that the perceived identity of the chatbot had significant effects on the persuasion outcome (i.e., donation) and interpersonal perceptions (i.e., competence, confidence, warmth, and sincerity). Further, we identified interaction effects among perceived identities and inquiry strategies. We discuss the findings for theoretical and practical implications for developing ethical and effective persuasive chatbots. Our published data, codes, and analyses serve as the first step towards building competent ethical persuasive chatbots.

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Section: Interaction Between Identity and Inquiries On Donationmentioning

confidence: 99%

Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies

Shi

Wang

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…Although, we developed the algorithm to address the large data file random access problem, it can be also be used for small data files as well particularly for expensive, complex calculations like ACF , M SD etc. One of the immediate application area of this tool would be instant query embedded devices like fitbit [50], [51], MUSE [52], [53], wearable blood pressure monitor like Glabella [54] where intelligence is gathered from raw data samples collected through sensors within the device itselft. One easy way to use this tool is to generate the index file on-the-fly so that, subsequent access won't necessitate reading the file from start again.…”

Section: Discussion Strength and Limitationsmentioning

confidence: 99%

GSFRS: Giant Signal File Random Sampler

Pal¹,

Brewer²

2021

Preprint

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<div> <div> <div> <p> In the modern world, it is hard to imagine a day without some form of interaction with digital data. Real world data originating from signal generating transducers or communication channels are often recorded as streams of data samples separated by time stamps, sample counters or simply data record delimiter e.g. newline (\n), comma (,) etc. Sampling is the basis of statistical estimation from any data source containing signal records. The process of random sampling has been in practice since time immemorial. However, rapid scale of data generation processes working in tandem with of computing infrastructures , the volume of data is getting quite unmanageably large in nearly every discipline of science. On the other hand, mere volume of data is of no consequence if we can’t extract effective intelligence out of it on an “on demand” basis. Of particular interest is the case where data is stored in a file as a record separated by newline(or any other delimiter) character. When the number of records in the file is greater than a threshold, random sampling is a formidable task. It is nearly impossible to pragmatically load the entire file in the computer memory or even if theoretically possible, the time it takes to load the data in its entirety from natural data sources can be overwhelmingly long or often unnecessary! We can strategically bypass these problems by carefully designing a data interface tool such that any part of a given file can be instantly accessed for random sampling or other kinds of processing tasks by loading only the necessary parts of the data. With this goal, we created a novel, portable and highly efficient rapid data access tool named GSFRS: Giant Signal File Random Sampler, written in modern C++ language to enable near real-time access to any part of an arbitrarily large sized data file that is almost independent of the file size for all practical scenarios. Also, big-data processing would become relatively commonplace and cost effective even in commodity hardwares once the indices are made available through its indexing protocol. This capability would potentially revolutionize the way we gather intelligence from files containing large samples. Adaptation of GSFRS at the source level of various data generators, processing times and energy footprints of various computations can be dramatically reduced. </p> </div> </div> </div>

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“…The Muse headband was originally designed to facilitate meditation, but many researchers used Muse in their studies as a measurement tool. In two previous experiments, researchers used Muse to measure event-related potential [5] and peoples' concentration level during a lecture [6]. In this study, Muse is used to track how participants' gamma waves varied while they completed a test as a function of what type of expectation they were given regarding their performance.…”

Section: Brain Waves Gamma Waves and Muse Headbandsmentioning

confidence: 99%

High-expectation comments increase students’ mental activity and confidence in the test

Wang

2020

E3S Web Conf.

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Past research has consistently found that teachers’ expectations for students’ performance shape their long-term academic success. This study extends this work by examining possible short-term pathways through which instructors’ test-related expectations may influence their academic success, specifically students’ confidence, mental activity and immediate test score performance. It was hypothesized that after receiving high- expectation encouragement, students would have experience greater confidence, superior immediate test performance, and stronger gamma waves (i.e., brain waves that indicate more intense mental activity). To test these hypotheses, an experiment was conducted with nine students from Guangxi, China. Results showed that students who received high-expectation comments tended to generate stronger EEG gamma waves and experience higher levels of confidence. However, no relationship was found between the expressed expectation and students’ test scores. Future experiments with a larger participant pool can build upon the preliminary outcomes of my study.

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Muse Headband: Measuring Tool or a Collaborative Gadget?

Cited by 31 publications

References 10 publications

Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies

Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies

GSFRS: Giant Signal File Random Sampler

High-expectation comments increase students’ mental activity and confidence in the test

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