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Daniel Casasanto - One of the best experts on this subject based on the ideXlab platform.
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The QWERTY Effect: How stereo-typing shapes the mental lexicon. - eScholarship
2020Co-Authors: Kyle Jasmin, Daniel CasasantoAbstract:The QWERTY Effect: How stereo-typing shapes the mental lexicon. Kyle Jasmin 1,2,3 (kyle.jasmin@mpi.nl) Daniel Casasanto 1,4,5 (casasanto@alum.mit.edu) Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, NL Institute of Cognitive Neuroscience, University College London, UK Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA Donders Center for Brain, Cognition, and Behaviour, Nijmegen, NL Department of Psychology, The New School for Social Research, New York, USA Abstract The QWERTY keyboard mediates communication for millions of Language users. Here we investigated whether differences in the way words are typed correspond to differences in their meanings. Some words are spelled with more letters on the right side of the keyboard and others with more letters on the left. We tested whether asymmetries in the way people interact with keys on the right and left of the keyboard influence their evaluations of the emotional valence of the words. In Experiment 1, we found a relationship between emotional valence and QWERTY key position, across three Languages (English, Spanish, and Dutch). Words with more right-hand letters were rated as more positive in meaning on average than words with more left-hand letters. In Experiment 2 we replicated this pattern in nonce words. Although these data are correlational, the fact that a similar pattern was found across Languages suggests that the QWERTY keyboard is shaping the meanings of words, as people Filter Language through their fingers. Widespread typing introduces a new mechanism by which semantic changes in Language can arise. Keywords: body-specificity hypothesis; meaning; orthography; typing; valence. motor action; Introduction For many people, Language may be typed and read almost as much as it is spoken and heard. Today the phrase “talk to you later” (abbreviated “ttyl”) often means that conversational partners will continue “talking” with their fingers. When they do, they are likely to use the QWERTY keyboard. The QWERTY layout was invented in 1878, as a remedy for mechanical problems with the original Remington typewriters, the keys of which were arranged alphabetically. During fast typing, neighboring keys would jam when used in succession. QWERTY was designed to separate frequently-used letter pairs to opposite sides of the keyboard, avoiding mechanical clashes. The final arrangement was constrained by the inner workings of the Remington machine, and by the need to place the letters in “t-y-p-e-w-r-i-t-e-r” conveniently on the top row of keys, to help salesmen tap out what was, at the time, a brand name (David, 1985). The QWERTY keyboard, which originated as a tool for journalists, is now everywhere in our culture. Increasingly, coffee shop chatter is being replaced by the sound of clicking keystrokes. Conversations and even courtships can take place entirely through text. Smart phones and laptops let people type messages from virtually anywhere. Routinely, Language is produced without speech. When linguists and psychologists talk about the articulators used in Language production, they are ordinarily referring to parts of the mouth. But increasingly, the articulators that mediate our day-to-day Language production are the fingers. The way words are articulated with the mouth is related to their meaning. Although many sound-meaning mappings are arbitrary (de Saussure, 1966), there are aspects of meaning that appear non-arbitrarily linked to the configuration of the vocal-tract articulators used to produce them (Ohala, 1984). Here we propose a link between the meanings of words and the action of the manual articulators used for typing them. Because patterns of articulation are not independent of meaning, typing introduces a new mechanism by which semantic changes in Language can arise. We propose that typing words on the keyboard may influence their emotional valence (i.e., the positivity or negativity of their meanings). Typing is a special kind of motor action. Performing motor actions fluently generally leads to positive feelings and evaluations (Oppenheimer, 2008; Ping, Dhillon, & Beilock, 2009). Therefore, motor fluency could mediate relationships between the location of letters on the QWERTY keyboard and the valence of the words they compose, either directly or indirectly. A direct link between motor fluency and valence could result from the distribution of letters on the right and left sides of the keyboard. In standard QWERTY typing, the left hand is responsible for typing more letters than the right hand (15 letters vs. 11). For skilled and unskilled typists alike, fingers on the left hand are responsible for more keys than fingers on the right. This should make planning and executing keystrokes more difficult with the left hand than with the right, because the amount of cognitive control required to strike one key among its neighbors should increase with the number of keys, due to increased response
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CogSci - The QWERTY effect: How stereo-typing shapes the mental lexicon
2017Co-Authors: Kyle Jasmin, Daniel CasasantoAbstract:The QWERTY keyboard mediates communication for millions of Language users. Here we investigated whether differences in the way words are typed correspond to differences in their meanings. Some words are spelled with more letters on the right side of the keyboard and others with more letters on the left. We tested whether asymmetries in the way people interact with keys on the right and left of the keyboard influence their evaluations of the emotional valence of the words. In Experiment 1, we found a relationship between emotional valence and QWERTY key position, across three Languages (English, Spanish, and Dutch). Words with more right-hand letters were rated as more positive in meaning on average than words with more left-hand letters. In Experiment 2 we replicated this pattern in nonce words. Although these data are correlational, the fact that a similar pattern was found across Languages suggests that the QWERTY keyboard is shaping the meanings of words, as people Filter Language through their fingers. Widespread typing introduces a new mechanism by which semantic changes in Language can arise.
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The QWERTY Effect: How typing shapes the meanings of words.
Psychonomic Bulletin & Review, 2012Co-Authors: Kyle Jasmin, Daniel CasasantoAbstract:The QWERTY keyboard mediates communication for millions of Language users. Here, we investigated whether differences in the way words are typed correspond to differences in their meanings. Some words are spelled with more letters on the right side of the keyboard and others with more letters on the left. In three experiments, we tested whether asymmetries in the way people interact with keys on the right and left of the keyboard influence their evaluations of the emotional valence of the words. We found the predicted relationship between emotional valence and QWERTY key position across three Languages (English, Spanish, and Dutch). Words with more right-side letters were rated as more positive in valence, on average, than words with more left-side letters: the QWERTY effect . This effect was strongest in new words coined after QWERTY was invented and was also found in pseudowords. Although these data are correlational, the discovery of a similar pattern across Languages, which was strongest in neologisms, suggests that the QWERTY keyboard is shaping the meanings of words as people Filter Language through their fingers. Widespread typing introduces a new mechanism by which semantic changes in Language can arise.
Calton Pu - One of the best experts on this subject based on the ideXlab platform.
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BigData Congress - Nimbus: Tuning Filters Service on Tweet Streams
2015 IEEE International Congress on Big Data, 2015Co-Authors: Jim Donahue, Aibek Musaev, Calton PuAbstract:With hundreds of millions of tweets being generated by Twitter users every day, tweet analysis has drawn considerable attention for event detection and trending sentiment indication. The problem is finding the few important tweets in this huge volume of traffic. A number of systems provide applications the ability to Filter a complete or partial Twitter stream based on keywords and/or text properties to try to separate the relevant tweets from all of the noise. Designing a Filter to produce useful results can be extremely difficult. For instance, consider the problem of finding tweets related to the Target Corporation or Guess USA. Just scanning the text of tweets for "target" or "guess" is likely to generate lots of hits, but few really relevant tweets. Nimbus is a service that can be used to tune Filters on tweet streams. The Nimbus service builds a database of tweets from a Twitter stream (it does not have to be a full Twitter fire hose) and provides an API for testing Filters (based on the Power Track Language and Spark as evaluation engine) against the database. The important feature of Nimbus is that it allows repeatable testing of Filter expressions against real Twitter data using the same Filter Language that can be used against live Twitter streams. This makes it possible for users of the service to tune their Filters before putting them into production use.
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Nimbus: Tuning Filters Service on Tweet Streams
2015 IEEE International Congress on Big Data, 2015Co-Authors: Jim Donahue, Aibek Musaev, Calton PuAbstract:With hundreds of millions of tweets being generated by Twitter users every day, tweet analysis has drawn considerable attention for event detection and trending sentiment indication. The problem is finding the few important tweets in this huge volume of traffic. A number of systems provide applications the ability to Filter a complete or partial Twitter stream based on keywords and/or text properties to try to separate the relevant tweets from all of the noise. Designing a Filter to produce useful results can be extremely difficult. For instance, consider the problem of finding tweets related to the Target Corporation or Guess USA. Just scanning the text of tweets for "target" or "guess" is likely to generate lots of hits, but few really relevant tweets. Nimbus is a service that can be used to tune Filters on tweet streams. The Nimbus service builds a database of tweets from a Twitter stream (it does not have to be a full Twitter fire hose) and provides an API for testing Filters (based on the Power Track Language and Spark as evaluation engine) against the database. The important feature of Nimbus is that it allows repeatable testing of Filter expressions against real Twitter data using the same Filter Language that can be used against live Twitter streams. This makes it possible for users of the service to tune their Filters before putting them into production use.
Kyle Jasmin - One of the best experts on this subject based on the ideXlab platform.
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The QWERTY Effect: How stereo-typing shapes the mental lexicon. - eScholarship
2020Co-Authors: Kyle Jasmin, Daniel CasasantoAbstract:The QWERTY Effect: How stereo-typing shapes the mental lexicon. Kyle Jasmin 1,2,3 (kyle.jasmin@mpi.nl) Daniel Casasanto 1,4,5 (casasanto@alum.mit.edu) Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, NL Institute of Cognitive Neuroscience, University College London, UK Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA Donders Center for Brain, Cognition, and Behaviour, Nijmegen, NL Department of Psychology, The New School for Social Research, New York, USA Abstract The QWERTY keyboard mediates communication for millions of Language users. Here we investigated whether differences in the way words are typed correspond to differences in their meanings. Some words are spelled with more letters on the right side of the keyboard and others with more letters on the left. We tested whether asymmetries in the way people interact with keys on the right and left of the keyboard influence their evaluations of the emotional valence of the words. In Experiment 1, we found a relationship between emotional valence and QWERTY key position, across three Languages (English, Spanish, and Dutch). Words with more right-hand letters were rated as more positive in meaning on average than words with more left-hand letters. In Experiment 2 we replicated this pattern in nonce words. Although these data are correlational, the fact that a similar pattern was found across Languages suggests that the QWERTY keyboard is shaping the meanings of words, as people Filter Language through their fingers. Widespread typing introduces a new mechanism by which semantic changes in Language can arise. Keywords: body-specificity hypothesis; meaning; orthography; typing; valence. motor action; Introduction For many people, Language may be typed and read almost as much as it is spoken and heard. Today the phrase “talk to you later” (abbreviated “ttyl”) often means that conversational partners will continue “talking” with their fingers. When they do, they are likely to use the QWERTY keyboard. The QWERTY layout was invented in 1878, as a remedy for mechanical problems with the original Remington typewriters, the keys of which were arranged alphabetically. During fast typing, neighboring keys would jam when used in succession. QWERTY was designed to separate frequently-used letter pairs to opposite sides of the keyboard, avoiding mechanical clashes. The final arrangement was constrained by the inner workings of the Remington machine, and by the need to place the letters in “t-y-p-e-w-r-i-t-e-r” conveniently on the top row of keys, to help salesmen tap out what was, at the time, a brand name (David, 1985). The QWERTY keyboard, which originated as a tool for journalists, is now everywhere in our culture. Increasingly, coffee shop chatter is being replaced by the sound of clicking keystrokes. Conversations and even courtships can take place entirely through text. Smart phones and laptops let people type messages from virtually anywhere. Routinely, Language is produced without speech. When linguists and psychologists talk about the articulators used in Language production, they are ordinarily referring to parts of the mouth. But increasingly, the articulators that mediate our day-to-day Language production are the fingers. The way words are articulated with the mouth is related to their meaning. Although many sound-meaning mappings are arbitrary (de Saussure, 1966), there are aspects of meaning that appear non-arbitrarily linked to the configuration of the vocal-tract articulators used to produce them (Ohala, 1984). Here we propose a link between the meanings of words and the action of the manual articulators used for typing them. Because patterns of articulation are not independent of meaning, typing introduces a new mechanism by which semantic changes in Language can arise. We propose that typing words on the keyboard may influence their emotional valence (i.e., the positivity or negativity of their meanings). Typing is a special kind of motor action. Performing motor actions fluently generally leads to positive feelings and evaluations (Oppenheimer, 2008; Ping, Dhillon, & Beilock, 2009). Therefore, motor fluency could mediate relationships between the location of letters on the QWERTY keyboard and the valence of the words they compose, either directly or indirectly. A direct link between motor fluency and valence could result from the distribution of letters on the right and left sides of the keyboard. In standard QWERTY typing, the left hand is responsible for typing more letters than the right hand (15 letters vs. 11). For skilled and unskilled typists alike, fingers on the left hand are responsible for more keys than fingers on the right. This should make planning and executing keystrokes more difficult with the left hand than with the right, because the amount of cognitive control required to strike one key among its neighbors should increase with the number of keys, due to increased response
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CogSci - The QWERTY effect: How stereo-typing shapes the mental lexicon
2017Co-Authors: Kyle Jasmin, Daniel CasasantoAbstract:The QWERTY keyboard mediates communication for millions of Language users. Here we investigated whether differences in the way words are typed correspond to differences in their meanings. Some words are spelled with more letters on the right side of the keyboard and others with more letters on the left. We tested whether asymmetries in the way people interact with keys on the right and left of the keyboard influence their evaluations of the emotional valence of the words. In Experiment 1, we found a relationship between emotional valence and QWERTY key position, across three Languages (English, Spanish, and Dutch). Words with more right-hand letters were rated as more positive in meaning on average than words with more left-hand letters. In Experiment 2 we replicated this pattern in nonce words. Although these data are correlational, the fact that a similar pattern was found across Languages suggests that the QWERTY keyboard is shaping the meanings of words, as people Filter Language through their fingers. Widespread typing introduces a new mechanism by which semantic changes in Language can arise.
-
The QWERTY Effect: How typing shapes the meanings of words.
Psychonomic Bulletin & Review, 2012Co-Authors: Kyle Jasmin, Daniel CasasantoAbstract:The QWERTY keyboard mediates communication for millions of Language users. Here, we investigated whether differences in the way words are typed correspond to differences in their meanings. Some words are spelled with more letters on the right side of the keyboard and others with more letters on the left. In three experiments, we tested whether asymmetries in the way people interact with keys on the right and left of the keyboard influence their evaluations of the emotional valence of the words. We found the predicted relationship between emotional valence and QWERTY key position across three Languages (English, Spanish, and Dutch). Words with more right-side letters were rated as more positive in valence, on average, than words with more left-side letters: the QWERTY effect . This effect was strongest in new words coined after QWERTY was invented and was also found in pseudowords. Although these data are correlational, the discovery of a similar pattern across Languages, which was strongest in neologisms, suggests that the QWERTY keyboard is shaping the meanings of words as people Filter Language through their fingers. Widespread typing introduces a new mechanism by which semantic changes in Language can arise.
Frans M Kaashoek - One of the best experts on this subject based on the ideXlab platform.
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dpf fast flexible message demultiplexing using dynamic code generation
ACM Special Interest Group on Data Communication, 1996Co-Authors: Dawson Engler, Frans M KaashoekAbstract:Fast and flexible message demultiplexing are well-established goals in the networking community [1, 18, 22]. Currently, however, network architects have had to sacrifice one for the other. We present a new packet-Filter system, DPF (Dynamic Packet Filters), that provides both the traditional flexibility of packet Filters [18] and the speed of hand-crafted demultiplexing routines [3]. DPF Filters run 10-50 times faster than the fastest packet Filters reported in the literature [1, 17, 18, 27]. DPF's performance is either equivalent to or, when it can exploit runtime information, superior to hand-coded demultiplexors. DPF achieves high performance by using a carefully-designed declarative packet-Filter Language that is aggressively optimized using dynamic code generation. The contributions of this work are: (1) a detailed description of the DPF design, (2) discussion of the use of dynamic code generation and quantitative results on its performance impact, (3) quantitative results on how DPF is used in the Aegis kernel to export network devices safely and securely to user space so that UDP and TCP can be implemented efficiently as user-level libraries, and (4) the unrestricted release of DPF into the public domain.
Miles Osborne - One of the best experts on this subject based on the ideXlab platform.
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EMNLP-CoNLL - Smoothed Bloom Filter Language Models: Tera-Scale LMs on the Cheap
2007Co-Authors: David Talbot, Miles OsborneAbstract:A Bloom Filter (BF) is a randomised data structure for set membership queries. Its space requirements fall significantly below lossless information-theoretic lower bounds but it produces false positives with some quantifiable probability. Here we present a general framework for deriving smoothed Language model probabilities from BFs. We investigate how a BF containing n-gram statistics can be used as a direct replacement for a conventional n-gram model. Recent work has demonstrated that corpus statistics can be stored efficiently within a BF, here we consider how smoothed Language model probabilities can be derived efficiently from this randomised representation. Our proposal takes advantage of the one-sided error guarantees of the BF and simple inequalities that hold between related n-gram statistics in order to further reduce the BF storage requirements and the error rate of the derived probabilities. We use these models as replacements for a conventional Language model in machine translation experiments.
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Smoothed Bloom Filter Language models: Tera-scale LMs on the cheap
EMNLP-2007, 2007Co-Authors: David Talbot, Miles OsborneAbstract:A Bloom Filter (BF) is a randomised data structure for set membership queries. Its space requirements fall significantly below lossless information-theoretic lower bounds but it produces false positives with some quantifiable probability. Here we present a general framework for deriving smoothed Language model probabilities from BFs. We investigate how a BF containing n-gram statistics can be used as a direct replacement for a conventional n-gram model. Recent work has demonstrated that corpus statistics can be stored efficiently within a BF, here we consider how smoothed Language model probabilities can be derived efficiently from this randomised representation. Our proposal takes advantage of the one-sided error guarantees of the BF and simple inequalities that hold between related n-gram statistics in order to further reduce the BF storage requirements and the error rate of the derived probabilities. We use these models as replacements for a conventional Language model in machine translation experiments.