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This article contains several data tables that show the different modes of communication and how often we use each one. Certain graphs organize data by gender, age, impact on school, and much more. Something that interested me was that our time spent on text-based technology has negatively impacted our ability to write. This article analyzes each set of data points and puts our usage of electronics into perspective, as technology has dominated the way we converse.

Two University of Washington undergraduates have won a $10,000 Lemelson-MIT Student Prize for gloves that can translate sign language into text or speech. The Lemelson-MIT Student Prize is a nationwide search for the most inventive undergraduate and graduate students. This year, UW sophomores Navid Azodi and Thomas Pryor - who are studying business administration and aeronautics and astronautics engineering, respectively - won the "Use It" undergraduate category that recognizes technology-based inventions to improve consumer devices. Their invention, "SignAloud," is a pair of gloves that can recognize hand gestures that correspond to words and phrases in American Sign Language. Each glove contains sensors that record hand position and movement and send data wirelessly via Bluetooth to a central computer. The computer looks at the gesture data through various sequential statistical regressions, similar to a neural network. If the data match a gesture, then the associated word or phrase is spoken through a speaker.

A 2022 University of Cambridge study conducted by Sahakian, Langley, Chen, et al., and published in the journal _Neurology_, shows that that social isolation is linked to changes in brain structure and cognition - the mental process of acquiring knowledge - it even carries an increased risk of dementia in older adults. Previous research established that brain regions consistently involved in diverse social interactions are strongly linked to networks that support cognition, including the default mode network (which is active when we are not focusing on the outside world), the salience network (which helps us select what we pay attention to), the subcortical network (involved in memory, emotion and motivation) and the central executive network (which enables us to regulate our emotions). This particular study examined how social isolation affects grey matter - brain regions in the outer layer of the brain, consisting of neurons. It investigated data from nearly 500,000 people from the UK Biobank, with a mean age of 57. People were classified as socially isolated if they were living alone, had social contact less than monthly and participated in social activities less than weekly. The study also included neuroimaging (MRI) data from approximately 32,000 people. That data revealed that socially isolated people had poorer cognition, including in memory and reaction time, and lower volume of grey matter in many parts of the brain. These areas included the temporal region (which processes sounds and helps encode memory), the frontal lobe (which is involved in attention, planning and complex cognitive tasks) and the hippocampus - a key area involved in learning and memory, which is typically disrupted early in Alzheimer's disease. We also found a link between the lower grey matter volumes and specific genetic processes that are involved in Alzheimer's disease. Follow-ups with participants 12 years later showed that those who were socially isolated, but not

This article combines data from two lists. This first one was compiled by National Language Resources Monitoring and Research Center at Beijing Language and Culture University: "Big data analysis" of over a billion online posts and forum discussions from the Chinese internet in 2021 was reportedly used to decide on the final list, but it's clear from the selection that the artificial intelligence tool used has a good understanding of socialism with Chinese characteristics. The second list comes from Yaowen Jiaozi, a magazine founded in 1995 that publishes stories about the the misuse and abuse of language in Chinese society. This combined list has items that made BOTH lists. Separately, the magazine Yǎowén Jiáozì (咬文嚼字) published its year-end list of "popular buzzwords" (2021年十大流行语). Yaowen Jiaozi is a magazine founded in 1995 that publishes stories about the the misuse and abuse of language in Chinese society. Its name is variously translated as "Correct Wording," "Verbalism," and "Chewing Words." The Yaowen Jiaozi list does not claim to be created by big data, but rather from reader suggestions, online polling, and selection by specialists.

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There are so many different languages in the world, that it's impossible for us to be able to communicate with everyone in the world...right? Well, in this article, it discusses the possibility of everyone in the world being able to communicate with each other because of computer translations. These computer translations are now exponentially growing in data, and with more data, the easier it will be for people to translate words, phrases, and text more accurately.

I've noticed this trend, too, in a lot of the articles I've posted recently. Something to be aware of: "...we worry that shorter, single-study articles can be poor models of science. ... we are troubled by the link between small study size and publication bias. Theoretically, if several small studies on a topic, each with its own small data set, are sent to publishers, the overall published results should be equivalent to the results of a single large study on that topic using a complete data set. But according to several "meta-studies" that have been conducted, this is often not the case: rather than the small studies' converging on the same result as a large study when published, the small studies give a very different result. ... Small studies are inherently unreliable - larger studies or, better still, multiple studies on the same topic, are more likely to give definitive, accurate results."

One significant concern that pilots have about cockpit auditory warnings is that the signals presently used lack a sense of priority. The relationship between auditory warning sound parameters and perceived urgency is, therefore, an important topic of enquiry in aviation psychology. The present investigation examined the relationship among subjective assessments of urgency, reaction time, and brainwave activity with three auditory warning signals. Subjects performed a tracking task involving automated and manual conditions, and were presented with auditory warnings having various levels of perceived and situational urgency. Subjective assessments revealed that subjects were able to rank warnings on an urgency scale, but rankings were altered after warnings were mapped to a situational urgency scale. Reaction times differed between automated and manual tracking task conditions, and physiological data showed attentional differences in response to perceived and situational warning urgency levels. This study shows that the use of physiological measures sensitive to attention and arousal, in conjunction with behavioural and subjective measures, may lead to the design of auditory warnings that produce a sense of urgency in an operator that matches the urgency of the situation.

"An array of electrodes receiving input from a non-tactile information source (a camera, for instance) applies small, controlled, painless currents...to the skin at precise locations according to an encoded pattern. The encoding of the electrical pattern essentially attempts to mimic the input that would normally be received by the non-functioning sense. ... When the encoded pulses are applied to the skin, the skin is actually receiving image data." "After training in laboratory tests, blind subjects were able to perceive visual traits like looming, depth, perspective, size and shape."

"EF Education First, an English-teaching company, compiled the biggest ever internationally comparable sample of English learners: some 2m people took identical tests online in 44 countries." Interesting data, especially about how different factors correlate with English ability. Direct link to report: http://www.ef.com/sitecore/__/~/media/efcom/epi/pdf/EF-EPI-2011.pdf

Ben Blatt, _Slate_ journalist, shares and reports on some maps of the United States that incorporate data from the Census Bureau's American Community Survey regarding the languages spoken in American homes. One map shows what language, after English, is most commonly spoken in each of the 50 states (Spanish, for the most part), and another, the second most-spoken language. I personally question the veracity of the data for Hawai'i, which lists Tagalog as the second-most spoken language behind English. Surely it's Hawai'i Creole English (HCE), but perhaps it's because survey respondents don't know HCE= its own language. Also, Ilocano seems to be more commonly spoken than Tagalog in the 808, but maybe because Tagalog= the language of school instruction in the Philippines, it's universally spoken by everyone who speaks some Filipino variant. Some caveats on the construction of these maps. A language like Chinese is not counted as a single language, but is split into different dialects: Cantonese, Mandarin, Shanghaiese and treated as different languages. If those languages had been grouped together, the marking of many states would change. In addition, Hawaiian is listed as a Pacific Island language, so following ACS classifications, it was not included in the Native American languages map.

A new study conducted by the University of Pennsylvania has shown that children as young as two understand basic grammar rules when they first learn to speak and are not simply imitating adults. The study also applied the same statistical analysis on data from one of the most famous animal language-acquisition experiments -- Project Nim -- and showed that Nim Chimpsky, a chimpanzee who was taught sign language over the course of many years, never grasped rules like those in a two-year-old's grammar. "When you compare what children should say if they follow grammar against what children do say, you find it to almost indistinguishable," Professor of Linguistics Charles Yang said. "If you simulate the expected diversity when a child is only repeating what adults say, it produces a diversity much lower than what children actually say." As a comparison, Yang applied the same predictive models to the set of Nim Chimpsky's signed phrases, the only data set of spontaneous animal language usage publicly available. He found further evidence for what many scientists, including Nim's own trainers, have contended about Nim: that the sequences of signs Nim put together did not follow from rules like those in human language. Nim's signs show significantly lower diversity than what is expected under a systematic grammar and were similar to the level expected with memorization. This suggests that true language learning is -- so far -- a uniquely human trait, and that it is present very early in development.

Novelist Kurt Vonnegut once opined, "There is no reason why the simple shapes of stories can't be fed into computers. They are beautiful shapes." Thanks to new text-mining techniques, this has now been done. Researchers at the University of Vermont's Computational Story Lab have analysed over 1,700 English novels to reveal six basic story types - you could call them archetypes - that form the building blocks for more complex stories. They are: 1. Rags to riches - a steady rise from bad to good fortune 2. Riches to rags - a fall from good to bad, a tragedy 3. Icarus - a rise then a fall in fortune 4. Oedipus - a fall, a rise then a fall again 5. Cinderella - rise, fall, rise 6. Man in a hole - fall, rise The researchers used sentiment analysis to get the data - a statistical technique often used by marketeers to analyse social media posts in which each word is allocated a particular 'sentiment score', based on crowdsourced data. Depending on the lexicon chosen, a word can be categorised as positive (happy) or negative (sad), or it can be associated with one or more of eight more subtle emotions, including fear, joy, surprise and anticipation. For example, the word 'happy' is positive, and associated with joy, trust and anticipation. The word 'abolish' is negative and associated with anger. Do sentiment analysis on all the words in a novel, poem or play and plot the results against time, and it's possible to see how the mood changes over the course of the text, revealing a kind of emotional narrative. While not a perfect tool - it looks at words in isolation, ignoring context - it can be surprisingly insightful when applied to larger chunks of text

A traveling journalist in Beijing utilizes both Baidu (China's version of Google) and Google voice-translation apps with mixed results. You speak into the apps, they listen and then translate into the language you choose. They do it in writing, by displaying text on the screen as you talk; and out loud, by using your phone's speaker to narrate what you've said once you're done talking. Typically exchanges are brief: 3-4 turns on average for Google, 7-8 for Baidu's translate app. Both Google and Baidu use machine learning to power their translation technology. While a human linguist could dictate all the rules for going from one language to another, that would be tedious, and yield poor results because a lot of languages aren't structured in parallel form. So instead, both companies have moved to pattern recognition through "neural machine translation." They take a mountain of data - really good translations - and load it into their computers. Algorithms then mine through the data to look for patterns. The end product is translation that's not just phrase-by-phrase, but entire thoughts and sentences at a time. Not surprisingly, sometimes translations are successes, and other times, epic fails. Why? As Macduff Hughes, a Google executive, notes, "there's a lot more to translation than mapping one word to another. The cultural understanding is something that's hard to fully capture just in translation."

This article talks about how one's genetics and one's language could possibly be connected. This study took place in Cape Verde, where people speak Kriolu, a mixture of European and African languages that formed with the trans Atlantic slave trade. Researchers recorded multiple individual's speech and compared the recordings to the individual's DNA. They found that there was a significant correlation between one's ancestry and the words they use - for example, those with more African genetic ancestry used more African derived words. While this doesn't necessarily conclude that linguistic traits are passed on like genetic traits are, it is interesting that in a language that is a mix of other languages, individuals still use more words that are derived from their ethnic backgrounds.

The LENA System measures the early language environment of children birth to 48 months. It consists of a compact digital recorder with clothing so a child can wear it comfortably; software that turns the recording into data; and a cloud-based system for managing the data. Feedback from LENA helps parents and caregivers increase the quantity and quality of interactive talk. While words are important, "conversational turns" are even more so - times when an adult says something and the child responds, or vice versa. Turns measure interactions, and according to research, they're a very powerful predictor of brain growth. LENA devices were mentioned in "In the Beginning Was the Word" article from _The Economist_.

"The women reported giving 37% more apologies than the men did... The diary data suggest that women offer more apologies than men do because women have a lower threshold for what constitutes offensive behavior... [T]here was no gender difference in how men and women apologized." A comprehensive study, unfortunately not available online, although this post is pretty detailed.

TechRadar spoke to Dr Matthew Voice, an Assistant Professor in Applied Linguistics at the UK's University of Warwick, to find out the science behind the struggle to deduce Wordle Puzzle #256. "[In your live blog] you've already talked about _ATCH as a kind of trap. This is an example of an n-gram, i.e. a group of letters of a length (n) that commonly cluster together. So this is an n-gram with a length of four letters: a quadrigram," Professor Voice tells us. "Using [this] Project Gutenberg data, it's interesting to note that _ATCH isn't listed as one of the most common quadrigrams in English overall, but the [same] data considers words of all lengths, rather than just the five letters Wordle is limited to. I don't know of any corpus exclusively composed of common 5 letter words, but it might be the case that _ATCH happens to be particularly productive for that length." "The other thing to mention," Professor Voice adds, "would be that the quadrigram _ATCH is made up of smaller n-grams, like the bigram AT, which is extremely common in English. So we're seeing a lot of common building blocks in one word, which means that sorting individual letters might not be narrowing down people's guesses as much as it would with other words."