Well, what do you think?... Read more »
Batts, Shelley A., Anthis, Nicholas J., & Smith, Tara C. (2008) Advancing Science through Conversations: Bridging the Gap between Blogs and the Academy. . PLoS Biology, 6(9), 240-245. DOI: 10.1371/journal.pbio.0060240
According to Dick Lewontin (evolutionary biologist, geneticist and social commentator) there is no way to know the evolution of cognition. He argued that we should ‘give up the childish notion that everything that is interesting about nature can be understood. [..] It might be interesting to know how cognition (whatever that is) arose and spread and changed, but we cannot know. Tough luck.’ (Lewontin, 1998:130)... Read more »
Honing, H., & Ploeger, A. (2012) Cognition and the Evolution of Music: Pitfalls and Prospects. Topics in Cognitive Science. DOI: 10.1111/j.1756-8765.2012.01210.x
Are you hiding something negative about yourself from your partner? If so, it may be ruining your relationship. ... Read more »
Uysal A, Lin HL, Knee CR, & Bush AL. (2012) The association between self-concealment from one's partner and relationship well-being. Personality , 38(1), 39-51. PMID: 22109250
Recently, psychologists Joseph Simmons, Leif Nelson and Uri Simonsohn made waves when they published a provocative article called False-Positive PsychologyThe paper's subtitle was "Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant". It explained how there are so many possible ways to gather and analyze the results of a (very simple) psychology experiment that even if there's nothing interesting really happening, it'll be possible to find some "significant" positive results purely by chance. Then you could publish those 'findings' and not mention all the other things you tried.It's not a new argument, and the problem has been recognized for a long time as the "file drawer problem", "p-value fishing", "outcome reporting bias", and by many other names. But not much has been done to prevent it.The problem's not just seen in psychology however, and I'm concerned that it's especially dangerous in modern neuroimaging research.*Let's assume a very simple fMRI experiment. The task is a facial emotion visual response. Volunteers are shown 30 second blocks of Neutral, Fearful and Happy faces during a standard functional EPI scanning. We also collect a standard structural MRI as required to analyze that data.This is a minimalist study. Most imaging projects include have more than one task, commonly two or three and maybe up to half a dozen, as part of one scan. If one task failed to show positive results, it need never be reported at all, so additional tasks would Our study is comparing two groups: people with depression, and healthy controls.How many different ways could you analyze that data? How much flexibility is there?*First some ground rules. We'll stick to validated, optimal approaches. There are plenty of commonly used less favoured approaches, like using uncorrected thresholds (and then, which ones?) or voodoo stats, but let's assume we want to stick to 'best practice'.As far as I can see, here's all the different things you could try. Please suggest more in the comments if you think I've missed any:First off, general points: What's the sample size? Unless it's fixed in advance, data peeking - checking whether you've got a significant result after each scan, and stopping the study when you get one - gives you multiple bites at the cherry.Do you use parametric, or nonparametric analysis?Now, what do you do with the data?PreprocessingHow much smoothing?Do you reject subjects for "too much head movement"? If so, what's too much?Straightforward whole-brain general linear model (GLM) analysis followed by a group comparison.What's the contrast of interest? You could make a case for Fear vs Neutral, Happy vs Neutral, Happy vs Fear, "Emotional" vs Neutral. Fixed effects or random effects group comparison?Do you reject outliers? If so, what's an 'outlier'?Do you consider all of the Fear, Happy and Neutral blocks to be equivalent, or do you model the first of each kind of block seperately? etc. "Region of Interest" (ROI) GLM analysis. Same options as above, plus:Which ROI(s)?How do you define a given ROI?Functional connectivity analysisWhole-brain analysis, or seed region analysis?If seed region, which region(s)? Functional connectivity in response to which stimuli?Dynamic Causal Modelling?Lots and lots of options here. MVPA?Lots and lots of options here.But remember we also got structural MRIs, and while they may have been intended to help analyze the functional data, you could also examine structural differences between groups. What method?Manual measurement of volume of certain regions.Which region(s)?VBM. Cortical morphometry.What measure? Thickness? Curvature...?That's just the imaging data. You've almost certainly got some other data on these people as well, if only age and gender but maybe depression questionnaire scores, genetics, cognitive test performance...You could try and correlate every variable with every imaging measure discussed above. Plus:Do you only look for correlations in areas where there's a significant group difference (which would increase your chances of finding a correlation in those areas, as there'd be fewer multiple comparisons)? You could define subgroups based on these variables.*So even a very straightforward experiment could give rise to hundreds or thousands of possible analyses. 1 in 20 of these would give a statistically significant result at p=0.05 by chance alone, and even if you throw out half those for being "in the wrong direction" (and that's subjective in most cases) you've got plenty of false positives.This problem is growing. As computation power continues to expand, running multiple analyses is cheaper and faster than ever, and new methods continue to be invented (DCM and MVPA were very rarely used even 5 years ago.) I want to emphasize that I am not saying that all fMRI studies of this kind are in fact junk. My worry is that it's hard to be confident that any given published study is sound, given that papers are written only after all the data has been collected and analyzed.I'll also point out that some imaging research, especially what might be called "pure" neuroscience investigating brain function per se rather than "clinical" studies looking at differences between groups, has many fewer variables to play with, but still quite a lot.As to how to solve this problem, the one solution I believe would work in practice is to require pre-approval of study protocols.Simmons JP, Nelson LD, and Simonsohn U (2011). False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological science, 22 (11), 1359-66 PMID: 22006061... Read more »
Simmons JP, Nelson LD, & Simonsohn U. (2011) False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological science, 22(11), 1359-66. PMID: 22006061
I'm still reading (and very much enjoying) last Friday's Science issue on the flu pandemic. In , Anthony Fauci and Francis Collins summarize very well why the flu presents a potential threat: "Influenza viruses have animal reservoirs, especially in birds and pigs. They can undergo extensive genetic changes and even jump species, sometimes resulting in a virus to which humans may be highly vulnerable."Over the last hundred years, this happened four times: in 1918 (the Spanish flu), in 1957 (the Asian flu), in 1968 (Hong Kong flu), and, the last time, in 2009 with the H1N1 pandemic (swine flu). H5N1 has not initiated a pandemic because it is rarely passed between humans and it infects only through direct contact with infected birds. So far there have been about 600 total cases, of which nearly 60% have resulted in deaths (though this last number is likely to be inflated as often not all non-fatal cases are reported to hospitals). Given these statistics, if the virus were to spread more easily (for example through sneezing or coughing), this would entirely change the threat level, hence the concern when two labs independently announced that they had found an H5N1 mutant able to spread through aerosol in ferrets. "One of the goals of pandemic influenza research is to recognize and anticipate how viruses are evolving in the wild toward a phenotype that is dangerous to humans, thereby staying one step ahead of potential pandemics."That's why studies like the ones conducted on ferrets are so important. Yes, the virus was genetically engineered in a lab, but once you have it, you can address the following questions: how likely are those mutations to appear spontaneously? Can we use the virus to produce a vaccine? Are the current antiviral drugs successful in containing the infection or is there a need to develop new drugs? "However, whenever one deliberately manipulates a virus or a microbe, it is always possible, at least theoretically, that the research results could be used by bioterrorists to intentionally cause harm, or that an accidental release of a pathogen from a laboratory could inadvertently cause harm."Any research that presents such dual potential of benefit and risk/threat is referred as DURC, which stands for dual-use research of concern. The controversy around the two H5N1 papers clearly proves that we need better ways to assess and regulate DURC research, as well as mitigating the risks while highlighting the benefits. "There is still no consensus on how to practically define DURC; whether it is feasible to identify and regulate DURC experiments; how to address risks associated with DURC; and how to balance this risk with the necessity of fostering life sciences research for public health and biodefense ."It is a necessity for scientists to be open about their research. Research is rarely conducted in isolation. Open discussion is what fuels great ideas. A DURC policy that requires new protocols and manuscript redactions threatens this openness. Furthermore, it isn't feasible to retroactively restrict scientific information since most scientists are required to write reports describing their current research, as well as give talks and presentations at official meetings. When the NSABB made its recommendation not to publish the two H5N1 studies last year, it was probably already too late. Many people in the scientific community had already heard sufficient details, even prior to their publication. What are your thoughts on the matter?  Anthony S. Fauci,, & Francis S. Collins (2012). Benefits and Risks of Influenza Research: Lessons Learned Science, 336 (6088), 1522-1523 Carrie D. Wolinetz (2012). Implementing the New U.S. Dual-Use Policy Science, 336 (6088), 1525-1527... Read more »
Anthony S. Fauci,, & Francis S. Collins. (2012) Benefits and Risks of Influenza Research: Lessons Learned . Science, 336(6088), 1522-1523. info:/
In the world of abused performance metrics, the impact factor is the undisputed heavyweight champion of the (publishing) world.
And it’s been an eventful year in the ring scientific publishing too. A new journal called PeerJ launched with a radical new publish ’til you perish business model . There’s another new journal on the way too in the shape of eLifeSciences - with it’s own significant differences from current publishing models. Then there was the Finch report on Open Access. And if that wasn’t enough fun, there’s been the Altmetrics movement gathering pace , alongside a hint that the impact factor may be losing its grip on the supposed “title” .
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“Oral Histories of Comics Scholarship” hopes to crowdsource digital surrogates of analogue audio recordings or digital recordings that the comics scholarship community might have in their personal collections.... Read more »
Tim Causer, Justin Torra, & Valerie Wallace. (2012) Transcription maximized; expense minimized? Crowdsourcing and editing The Collected Works of Jeremy Bentham. Literary and Linguistic Computing, 27(2). DOI: 10.1093/llc/fqs004
Je zit in je auto en draait wat aan de knop van de radio. Je hoort al snel of bepaalde muziek je bevalt of niet. Je herkent een stem, een liedje of zelfs de uitvoering ervan. Iedereen doet het, iedereen kan het. En vaak ook nog eens razendsnel: sneller dan een noot gemiddeld klinkt.Als u gevraagd zou worden om naar een reeks muziekfragmenten van 0,2 seconde te luisteren, zal blijken dat u met gemak aan kan geven welk fragment klassiek, jazz, R&B of pop is (zie luistertest). Een snippertje geluid geeft ons toegang tot de herinnering aan eerder gehoorde muziek, ook al hebben we deze serie noten nog nooit eerder gehoord. Die herinnering kan heel specifiek zijn: aan een liedje van Björk, bijvoorbeeld. Maar ze kan ook heel algemeen zijn: we herkennen een bepaald genre: klassiek, country, jazz. De nuances in klankkleur, karakteristiek voor een liedje of een heel genre, zitten kennelijk op een abstracte manier in ons geheugen opgeslagen. Daarom is de draaiknop (of tiptoets) van de autoradio zo’n succesvolle interface geworden…Vandaag verschenen er verschillende items in de media n.a.v. van een stukje in Volkskrant over de oorwurm en de hype rond Song Pop, een app die gebruik maakt van het hierboven beschreven muzikale talent dat we allemaal delen: het razendsnel herkennen van muziek.Over oorwurm: Volkskrant, NOS op 3 Over Song Pop App: Editie NL Gjerdingen, Robert O., & Perrott, D. (2008). Scanning the Dial: The Rapid Recognition of Music Genres Journal of New Music Research, 37 (2), 93-100 DOI: 10.1080/09298210802479268... Read more »
Gjerdingen, Robert O., & Perrott, D. (2008) Scanning the Dial: The Rapid Recognition of Music Genres. Journal of New Music Research, 37(2), 93-100. DOI: 10.1080/09298210802479268
Karlheinz Stockhausen is listening."Neue Musik ist anstrengend", wrote Die Zeit some time ago: "Der seit Pythagoras’ Zeiten unternommene Versuch, angenehme musikalische Klänge auf ganzzahlige Frequenzverhältnisse der Töne zurückzuführen, ist schon mathematisch zum Scheitern verurteilt. Außereuropäische Kulturen beweisen schließlich, dass unsere westliche Tonskala genauso wenig naturgegeben ist wie eine auf Dur und Moll beruhende Harmonik: Die indonesische Gamelan-Musik und Indiens Raga-Skalen klingen für europäische Ohren schräg."The definition of music as “sound” wrongly suggests that music, like all natural phenomena, adheres to the laws of nature. In this case, the laws would be the acoustical patterns of sound such as the (harmonic) relationships in the structure of the dominant tones, which determine the timbre. This is an idea that has preoccupied primarily the mathematically oriented music scientists, from Pythagoras to Hermann von Helmholtz. The first, and oldest, of these scientists, Pythagoras, observed, for example, that “beautiful” consonant intervals consist of simple frequency relationships (such as 2:3 or 3:4). Several centuries later, Galileo Galilei wrote that complex frequency relationships only “tormented” the eardrum. But, for all their wisdom, Pythagoras, Galilei, and like-minded thinkers got it wrong. In music, the “beautiful,” so-called “whole-number” frequency relationships rarely occur—in fact, only when a composer dictates them. The composer often even has to have special instruments built to achieve them, as American composer Harry Partch did in the twentieth century. Contemporary pianos are tuned in such a way that the sounds produced only approximate all those beautiful “natural” relationships. The tones of the instrument do not have simple whole number ratios, as in 2:3 or 3:4. Instead, they are tuned so that every octave is divided into twelve equal parts (a compromise to facilitate changes of key). The tones exist, therefore, not as whole number ratios of each other, but as multiples of 12√2 (1:1.05946).According to Galilei, each and every one of these frequency relationships are “a torment” to the ear. But modern listeners experience them very differently. They don’t particularly care how an instrument is tuned, otherwise many a concertgoer would walk out of a piano recital because the piano sounded out of tune. It seems that our ears adapt quickly to “dissonant” frequencies. One might even conclude that whether a piano is “in tune” or “out of tune” is entirely irrelevant to our appreciation of music. [fragment from Honing, 2011.]Julia Kursell (2011). Kräftespiel. Zur Dissymmetrie von Schall und Wahrnehmung. Zeitschrift für Medienwissenschaft, 2 (1), 24-40 DOI: 10.4472_zfmw.2010.0003Honing, H. (2012). Een vertelling. In S. van der Maas, C. Hulshof, & P. Oldenhave (Eds.), Liber Plurum Vocum voor Rokus de Groot (pp. 150-154). Amsterdam: Universiteit van Amsterdam (ISBN 978-90-818488-0-0).Whalley, Ian. (2006). William A. Sethares: Tuning, Timbre, Spectrum, Scale (Second Edition). Computer Music Journal, 30 (2) DOI: 10.1162/comj.2006.30.2.92... Read more »
Whalley, Ian. (2006) William A. Sethares: Tuning, Timbre, Spectrum, Scale (Second Edition). Computer Music Journal, 30(2). DOI: 10.1162/comj.2006.30.2.92
Despite its many faults (see part I), the Journal Impact Factor (JIF) is considered an influential index to a journal’s quality, and publishing in high-impact journals is essential to a researcher’s academic career. Reminder: to calculate, for example, the 2010 JIF for a journal - JIF= (2010 citations to 2009+2008 articles)/(no. of “citable” articles published in [...]
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Amin, M, & Mabe, M. (2007) Impact factors: use and abuse. Perspectives in Publishing. info:/
Archambault, E., & Lariviere, V. (2009) History of the journal impact factor: Contingencies and consequences . Scientometrics, 635-649. DOI: 10.1007/s11192-007-2036-x
Seglen, P. O. (1997) Why the impact factor of journals should not be used for evaluating research. BMJ. DOI: 10.1136/bmj.314.7079.497
Kostoff, R. N. (2007) The difference between highly and poorly cited medical articles in the journal Lancet. Scientometrics, 513-520. DOI: 10.1007/s11192-007-1573-7
Campanario, J. M. (2011) Empirical study of journal impact factors obtained using the classical two-year citation window versus a five-year citation window. Scientometrics. DOI: 10.1007/s11192-010-0334-1
Vanclay, J.K. (2012) Impact factor: outdated artefact or stepping-stone to journal certification?. Scientometrics. DOI: 10.1007/s11192-011-0561-0
I know I am late to the party here, as the "Science is a girl thing" video (embedded below) came out Friday and has already been ripped to shreds by many a blog. But I just couldn't stop thinking about it, so here's my opinion on that video and pinkifying science in general.Hello Kitty Microscope I am reminded of a quote from Pres. Obama's initial campaign. He said (something like) "We need to shatter the blasphemy that says a black child with a book is acting white." It is equally true that "we need to shatter the blasphemy that says a woman in science is acting masculine." Sometimes women in science dress down and wear less make up on purpose because they are worried that they won't be taken seriously if they look 'girlie' or even 'attractive'. Or specifically, "If I look like I took a long time doing my hair, nails, and make up, people might think I am not spending time doing science." It is just as sexist to think that a woman wearing heels in the lab is less capable or less dedicated to science as it is to think that women shouldn't be in a lab in the first place. What a woman wears to the lab (within lab safety guidelines anyway) has NOTHING to do with how good she is at science or with how seriously she takes her work. NOTHING. The woman who wears heels and makeup, the woman who wears the same sweatpants 3 days in a row, and the woman who wears a t-shirt with jeans should all be taken equally seriously as scientists and judged on their work and not their appearance. Being 'masculine' does not help you be a better scientist and for crissake women, do not brag about having a 'masculine brain'. You are not helping inspire young girls to success when you attribute your intelligence and ability to being like a man. I fully support feminine scientists and pink science equipment. There is no reason that a microscope shouldn't be pink and there is no reason that a girl shouldn't be able to have her own Computer Engineer Barbie if she wants one. Computational Neuroscientist BarbieThe message that you can both care about shoes and be a successful working scientist is important. It helps shatter the stereotype that scientific women are or should be masculine. Recently a study by Betz and Sekaquaptewa (2012) investigated the influence of overtly feminine cues (such as pink clothes, makeup, etc) on middle-school girls' interest in math as a future career. They had the students read an magazine-style interview with a feminine woman (pictured as wearing make up and pink and described as liking fashion magazines), and a neutral woman (pictured with dark clothes and glasses and described as liking reading). The finding that has been the focus of this paper's blog coverage is that for the girls that did not label math or science as their favorite subject, the feminine cues significantly reduced their self-reported interest in math as a career. They attribute this to the girls thinking that the feminine women are 'too good' and thus being discouraged because they don't see themselves as ever reaching those heights. While this is an interesting hypothesis, a closer look at the study is merited. First of all, there is not un-gendered control study. Might a similar effect might show up for middle school boys when shown either a muscular, athletic man who likes football or a neutral man who isn't particularly muscular, wears glasses and likes reading? This could be a general 'attractivity' thing where kids are intimidated by role models who 'have it all' or 'are too good'. Maybe it's not really a gender issue. Secondly, what hasn't been discussed much is that 54% of the girls initially listed math or science as their favorite subject! And for those girls, the femininity of the woman in the article had no statistically significant effects on their interest in science and math. 54% ! more than half of the girls in this study reported science, math, or both as their FAVORITE subject! and while there were no significant effects, there was a trend (p=0.19) toward the femininity of the role model increasing these girls' report of how 'attainable' both her femininity and scientific success might be. Betz and Sekaquaptewa, 2012 Figure 3This graph shows the "self-reported likelihood of attaining role model femininity and STEM (science, tech, engineering, and math) success" If anything the feminine role model slightly increased the attainability of these characteristics for girls who already like science (not significant, p=0.19). Regardless, I think the conclusion that feminine-looking science is bad for girls is not on sound footing. And again, I defend pink microscopes. If you have already seen the It's a girl thing video, you might be now expecting me to support it. But don't worry, I think it is as idiotic and every other scientist on the planet does. Far from showing feminine scientists, it is showing a scientist (the man) and separate from that a bunch of giggly girls dropping things. NOT HELPFUL. The only part I liked was the girl looking like she was concentrating and writing equations on the clear board. She looked feminine and pretty and she was 'doing' science. If these girls had been actually doing science through the entire video (sitting at microscopes like the guy did, for example), I might actually have liked it.The thing that is wrong with this video is not that these girls are wearing heels (though short skirts do violate lab safety codes), or that they look feminine or pretty. That's all fine. The problem is that they are not doing science, they are giggling and blowing kisses. If your goal is to combine femininity and prettiness with science, you have to COMBINE them, not present them as two completely separate things.In conclusion, I'll leave you with one of the best comments so far: Cartomancer on Pharyngula suggests some equally stereotypical and offensive videos to promote diversity in science: "67. cartomancer says: 22 June 2012 at 10:41 amI want to see what they’d come up with to get more LGBT people and ethnic minorities into science. “Science: it’s a gay thing!” featuring Abercrombie and Fitch models, bare-chested apart from an open lab coat, playing with unfeasibly suggestive phallic test tubes. Cut to discotheque-esque laser equipment and the word “science” with the C as a big rainbow.Or how about “Science: it’s a black thing!” – grinding hip-hop beats acco... Read more »
Diana E. Betz, & Denise Sekaquaptewa. (2012) My Fair Physicist? Feminine Math and Science Role Models Demotivate Young Girls. Social Psychological and Personality Science. DOI: 10.1177/1948550612440735
Few agricultural debates come close to generating the same passionate and heated responses that organic farming seems to elicit. The discussion surpasses the interests of producers with conflicting ideologies to be hotly debated by assertive consumers as well; people who highlight the paradox created by their interest in the safe and responsible production of their food, while avoiding all involvement in its creation. The originally proposed Organic Foods Production Act of 1990 received nearly 300,000 comments on the proposed requirements, more than any other piece of legislation in history (Vos, 2000). Clearly this indicated that the role organic farming played in food production was extremely important to U.S. citizens then, and continues to be a relevant topic as organic operations have grown by 40-50% every five years since 1992 (USDA, 2010)...... Read more »
Vonne Lund, & Bo Algers. (2003) Research on animal health and welfare in organic farming—a literature review. Livestock Production Science, 80(1-2), 55-68. info:/10.1016/S0301-6226(02)00321-4
The definition of music as “sound” wrongly suggests that music, like all natural phenomena, adheres to the laws of nature. In this case, the laws would be the acoustical patterns of sound such as the (harmonic) relationships in the structure of the dominant tones, which determine the timbre. This is an idea that has preoccupied primarily the mathematically oriented music scientists, from Pythagoras to Hermann von Helmholtz. But, for all their wisdom, Pythagoras, Galilei, and like-minded thinkers got it wrong. ... Read more »
by Stephen Yang in ExerGame Lab
I often wonder if previous experience in playing an #exergame impacts the overall experience and success of game play. Levac et al. also wanted to know whether motivation to succeed at the game...
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Levac D, Pierrynowski MR, Canestraro M, Gurr L, Leonard L, & Neeley C. (2010) Exploring children's movement characteristics during virtual reality video game play. [Exergame]. Human movement science. PMID: 20724014
Learning the names and locations of the different parts of the brain is a task that has brought grief to generations of students.I myself didn't know my caudate from my cingulate cortex all through my undergraduate studies and the first year of my doctorate. I only cracked it after spending a couple of days in the library, surrounded by a stack of anatomy textbooks, copying diagrams and coloring them in over and over until I could do it from memory.Now a group of Australian physiologists say there's a better way - Showercap Mindmap: a spatial activity for learning physiology terminology and locationBasically, undergraduate students were split into groups of 3 or 4 and each team was given a pack:The Showercap Mindmap packs included a clear, unmarked plastic shower cap, a whiteboard marker, and 15 sticky, color-coded labels. Lobe labels were blue (occipital, temporal, parietal, and frontal). Specialist areas were green (the corpus callosum, Broca’s area, and Wernicke’s area). Labels relating to information processing were yellow (hearing; heat; pain and temperature; and interpretation and integration of information). Cortex labels were orange (motor, association, somatosensory, auditory, and visual).One student on each team wore the cap and the others had 10 minutes to attach the labels to the correct parts of their head, corresponding to the different brain areas, with the help of a neuroanatomy textbook.This strikes me as a fantastic idea, and something that could actually make learning neuroanatomy fun, or at least a bit more involving than it usually is. The authors of the paper say that students using the method learned more effectively than those using a more conventional approach. Even the cap-wearers benefited. They couldn't see the labels being placed, but they could feel them.Vanags T, Budimlic M, Herbert E, Montgomery MM, and Vickers T (2012). Showercap Mindmap: a spatial activity for learning physiology terminology and location. Advances in physiology education, 36 (2), 125-30 PMID: 22665427... Read more »
Vanags T, Budimlic M, Herbert E, Montgomery MM, & Vickers T. (2012) Showercap Mindmap: a spatial activity for learning physiology terminology and location. Advances in physiology education, 36(2), 125-30. PMID: 22665427
Socioloog Christian von Scheve (Freie Universität Berlin) en muziek-psycholoog Glenn Schellenberg (University of Toronto) analyseerden zo’n duizend liedjes die tussen 1965 en 2009 in de Amerikaanse hitlijsten stonden. Daarbij vergeleken ze onder meer toonsoorten en tempo’s.De onderzoekers concludeerden dat er nu meer liedjes in de hitlijsten verschijnen die in mineur worden geschreven dan in de jaren zestig. Van mineurnummers is bekend dat ze een gevoel van verdriet opwekken. Derhalve stellen de onderzoekers dat nummers in de hitlijsten steeds treuriger worden (bron: Nu.nl).Maar hoe ‘mineur’ klikken popsongs in mineur eigenlijk? Denk bijvoorbeeld aan Everybody van de Backstreet Boys of Love Game van Lady Gaga… [Item op Radio 2] Schellenberg, E., & von Scheve, C. (2012). Emotional Cues in American Popular Music: Five Decades of the Top 40. Psychology of Aesthetics, Creativity, and the Arts DOI: 10.1037/a0028024... Read more »
Schellenberg, E., & von Scheve, C. (2012) Emotional Cues in American Popular Music: Five Decades of the Top 40. Psychology of Aesthetics, Creativity, and the Arts. DOI: 10.1037/a0028024
London 1940 was a grey place. In June, smog and grey skies made way for sunshine. Not that there was any summer cheer. Homes were in a perpetual gloom because of blacked-out windows. Food was scarce and kitchen broth was the family staple meal. And then the Germans were approaching. Against this backdrop, the new … Continue reading »... Read more »
Carl Herder, Courtney Howard, Chad Nye, & Martine Vanryckeghem. (2006) Effectiveness of Behavioral Stuttering Treatment: A Systematic Review and Meta-Analysis. CONTEMPORARY ISSUES IN COMMUNICATION SCIENCE AND DISORDERS, 33(`), 61-73. info:/
Prins, D., & Ingham, R. (2008) Evidence-Based Treatment and Stuttering--Historical Perspective. Journal of Speech, Language, and Hearing Research, 52(1), 254-263. DOI: 10.1044/1092-4388(2008/07-0111)
Mindfulness: Learn how being open to new experiences and alternative perspectives can improve your relationship. ... Read more »
The scientific method begins with a hypothesis about our reality that can be tested via experimental observation. Hypothesis formation is iterative, building off prior scientific knowledge. Before one can form a hypothesis, one must have a thorough understanding of previous research to ensure that the path of inquiry is founded upon a stable base of established facts. But how can a researcher perform a thorough, unbiased literature review when over one million scientific articles are published annually? The rate of scientific discovery has outpaced our ability to integrate knowledge in an unbiased, principled fashion. One solution may be via automated information aggregation. In this manuscript we show that, by calculating associations between concepts in the peer-reviewed literature, we can algorithmically synthesize scientific information and use that knowledge to help formulate plausible low-level hypotheses.Oh man I've been waiting to write this post for over a year now. I'm so. Flippin'. Excited.I'm really proud to announce that our paper, "Automated Cognome Construction and Semi-automated Hypothesis Generation" has been accepted for publication in the Journal of Neuroscience Methods.Here's the pre-print PDF.I've been writing about this project on this blog for quite a while now, mostly in talking about brainSCANr and the many, many rejections we received while trying to publish it along the way.Seventeen journals to be exact. Which is fun to note in the Rejections & Failures section of my CV. It makes a game out of failing!I'll start by telling the story of how this project got started, then get into some of the more sciencey details.Back in May 2010 I was invited to speak at the (now) annual Cognitive Science Student Association (CSSA) Conference run by the undergraduate CogSci student association at Berkeley. They're an incredibly talented group and I've had a lot of fun working with them over the years.At that conference I sat on a Q&A panel with a hell of a group of scientists, including George Lakoff and the Chair of Stanford's Psychology department, James McClelland (who helped pioneer Parallel Distributed Processing).Berkeley CSSA ConferenceOn that panel I A'd many Qs, one of which was a fairly high-level question about the challenge of integrating the wealth of neuroscientific literature. It was a variant on the classic line that neuroscience is "data rich but theory poor". This is a problem I'd been struggling with for a long time and I'd had a few ideas.In my response I said that one of our problems as a field was that we had so many different people with different backgrounds speaking different jargons who aren't effectively communicating. I followed with an off-hand comment that "The Literature" was actually pretty smart when taken as a system, but that we individual puny brains just weren't bright enough to integrate all that information. I went on to claim that, if there was some way to automatically integrate information from the peer-review literature, we could probably glean a lot of new insights.Well James McClelland really seemed to disagree with me, but the idea kept kicking around my brain for a while.One night, several months later (while watching Battlestar Galactica with my wife), I turned to her and explained my idea. She asked me how I was planning on coding it up and, after I explained it, she challenged me by saying that she could definitely code that faster than I could.Fast-forward a couple of hours to around 2am and she had her results. Bah.The idea boils down to a very simple (and probably simplistic) assumption that the more frequently two neuroscientific terms appear in the title or abstracts of papers together, the more likely those terms are to be associated. For example, if "learning" and all of its synonyms appears in 100 papers with "memory" and all of its synonyms while both of those terms appear in a total of 1000 papers without one another, then the probability of those two terms being associated is 100/1000, or 0.1.We calculated such probabilities for every pair of terms using a dictionary that we manually curated. It contained 124 brain regions, 291 cognitive functions, and 47 diseases. Brain region names and associated synonyms were selected from the NeuroNames database, cognitive functions were obtained from Russ Poldrack's Cognitive Atlas, and disease names are from the NIH. The initial population of the dictionary was meant to represent the broadest, most plausibly common search terms that were also relatively unique (and thus likely not to lead to spurious connections).We counted the number of published papers containing pairs of terms using the National Library of Medicine's ESearch utility and the count return type. Here's the example for "prefrontal cortex" and "striatum":Conjunction:http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&field=word&term=("prefrontal+cortex"+OR+"prefrontal+cortices")+AND+("striatum"+OR+"neostriatum"+OR+"corpus+striatum")&rettype=countDisjunctions:http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&field=word&term=("prefrontal+cortex"+OR+"prefrontal+cortices")+NOT+("striatum"+OR+"neostriatum"+OR+"corpus+striatum")&rettype=counthttp://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&field=word&term=("striatum"+OR+"neostriatum"+OR+"corpus+striatum")+NOT+("prefrontal+cortex"+OR+"prefrontal+cortices")&rettype=countHere's what the method looks like:Voytek & Voytek - Figure 1We note in our manuscript that this method is rife with caveats, but this wasn't meant to be an end-point, but rather a proof-of-concept beginning.In the end we get a full matrix of 175528 term pairs. Once we got this database we hacking together the brainSCANr website to allow people to play around with terms and their relationships. We wanted to create a tool for researchers and the public alike to use to help simplify the complexities of neuroscience. You enter a search term, it shows the relationships and gives you links to the relevant peer-reviewed papers.As an example, here's Alzheimer's:brainSCANr Alzheimer's diseaseMy wife and co-author(!) Jessica Voytek and I threw the first version together (with help from my Uber ... Read more »
Voytek, J., & Voytek, B. (2012) Automated cognome construction and semi-automated hypothesis generation. Journal of Neuroscience Methods. DOI: 10.1016/j.jneumeth.2012.04.019
Schmidt M, & Lipson H. (2009) Distilling free-form natural laws from experimental data. Science (New York, N.Y.), 324(5923), 81-5. PMID: 19342586
Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, & Wager TD. (2011) Large-scale automated synthesis of human functional neuroimaging data. Nature methods, 8(8), 665-70. PMID: 21706013
Lein, E., Hawrylycz, M., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A., Boguski, M., Brockway, K., Byrnes, E.... (2006) Genome-wide atlas of gene expression in the adult mouse brain. Nature, 445(7124), 168-176. DOI: 10.1038/nature05453
Science blog royalty SciCurious recently had a post up about whether it was okay for science bloggers to blog about their own work. Travis brought it up on his Science of Blogging site as well, and I started thinking about it. One of the big issues we struggle with as researchers is getting our research out [...]... Read more »
Franck, G. (1999) ESSAYS ON SCIENCE AND SOCIETY:Scientific Communication--A Vanity Fair?. Science, 286(5437), 53-55. DOI: 10.1126/science.286.5437.53
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