Browse through our publications and watch some talks by Marc and other lab members. For incoming Masters and PhD students, the talks "How to become a good scientist" and "Changing Science" may be worth watching.
You can find all our publications listed on PubMed.
Talks & Media
"Wo die Musik spielt: Wie wir wissen, woher ein Geräusch kommT"
Blog post on Spektrum.de by Ole Bialas on acoustic cues for sound localisation (in German). 2021.
- "Ist es zu laut bei uns?"
Video of Marc and a few other researchers and governmental people talk about the effects of noise on our health (in German).
MDR Wissen. 2020.
How the Shape of Your Ears Affects What You Hear
Our research on sound localization.
New York Times. 2018.
"Große, schiefe Ohren sind die besten"
You know it's good science when the teaser image is from StarTrek. There is also a radio interview on this topic: MDR Aktuell (in German, 2:54min).
MDR Wissen. 2018.
"Kann das Gehirn sich selbst verstehen?"
Video of Marc at the Science Slam of the German Neurological Society (in German).
Radio interview on whether you can enhance your sport performance by listening to music: here (in German).
Edit by Marc: I sound somewhat sleepy - this was a morning interview for DeutschlandRadio in Cologne, i.e. in the middle of the night for me in Montreal.
"Vom Klang der Seele"
A somewhat dreamy report about the opening of the BRAMS institute, founded by a group of neuroscientists and music researchers in Montreal as a joint-venture of the universities of Montreal and McGill. UofM committed one new professorship to the institute and this became my first faculty position.
Süddeutsche Zeitung. 2008. (in German)
Opening talk at the Erasmus Mundus symposium in Auditory Cognitive Neuroscience, Leipzig, Germany by Marc Schönwiesner. 25 April 2014.
Notes for "Changing Science"
00:30— These are all papers indexed by Medline. I had written a Python script to get the number of records per year in the Medline database, but it turns out that you can get this information with a single PubMed search: 1600:2100[dp]. You can then download a csv file of paper numbers per year in the sidebar.
01:00— Google Ngram
Changes in the way we publish
02:01— The cost of knowledge. I served as faculty representative at a library committee at one of my previous universities, and frankly I had underestimated how much drama, big money, racketeering, boycotts, and even connections with the weapons industry (not kidding) are part of this seemingly benign world of academic publishing.
02:15— That's MGM. The average margin of companies in the US is around 7 to 10%. A back-of-the-napkin calculation suggests a average revenue per paper in 2011 of 2000$ (10 billion revenue divided by 5 million papers).
02:50— This problem is relatively recent. Elsevier and others started to buy journals from non-profit academic societies in the 70s, thinking quite rightly that they could raise the prize substantially without loosing costumers. See also: www.nature.com/news/open-access-the-true-cost-of-science-publishing-1.12676
03:40— The impact factor is the average number of citations that papers receive in a given journal over 2 years. It is generated by a private company (Thompson-Reuters) from a private database, using unreproducible methods. These authors bought the database for 3 journals from Thompson-Reuters, but the numbers did not add up: "When queried about the discrepancy, Thomson Scientific explained that they have two separate databases—one for their “Research Group” and one used for the published impact factor … When we requested the database used to calculate the published impact factors, Thomson Scientific sent us a second database. But these data still did not match the published impact factor data. This database appeared to have been assembled in an ad hoc manner to create a facsimile of the published data that might appease us." The distribution of citation counts to individual articles in a journal is highly skewed and approximates a power law. The mean is not representative and vastly overestimates the typical number of citations. For instance, Nature mentioned that 90% of their 2004 IF was generated by only 25% of the papers and that the great majority of their papers received fewer than 20 citations. Think about it: do you have a paper that was cited 20 times during its 2. and 3. year after publication? A report by the international mathematical union calculated the probability that a randomly selected paper in a certain mathematical journal had at least as many citations as a randomly selected paper in another journal with twice the impact factor. The answer was 62%. 62% of time you'd be wrong when assuming that a paper in the better journal (twice the impact factor) was better (cited more often)! Of course, it is easy to look at the actual numbers of citations for individual papers now and disregard the journal impact factor altogether. Newer journals are trying interesting metrics to measure the importance of individual papers. See also: "The Assessment of Science: The Relative Merits of Post-Publication Review, the Impact Factor, and the Number of Citations"
04:18— The open access is 3000$ and colour is 600$ per figure, plus 60$ per page, and 75$ just for submitting. Libraries pay another 3000$ for a subscription this year. (A personal subscription costs 1000$). As it turns out, we will have to pay for open access, because our granting organization requires it.
05:00— Bret Victor
05:40— Cerebral Cortex online submission guidelines (finally updated...)
06:30— At the University of Montreal, the journal subscriptions cost 1700$ per professor and year. If we'd switch entirely to PLOS and pay 1300 per paper, we'd probably exceed those savings. (8.5 million CA$ library budget divided by 5000 professors.
08:05— Here is an interview with another PLOS founder and Nobel laureate Harold Varmus.
08:12— Here are the first few sentences of the opening of a paper called Tragic loss or good riddance from around this time:
Traditional printed journals are a familiar and comfortable aspect of scholarly work. They have been the primary means of communicating research results, and as such have performed an invaluable service. However, they are an awkward artifact, although a highly developed one, of the print technology that was the only means available over the last few centuries for large-scale communication.
08:50— See also: "Reproducibility of peer review in clinical neuroscience - Is agreement between reviewers any greater than would be expected by chance alone?" An example of successful post-publication review: "Science self-corrects – instantly"
Changes in the way we do science
10:10— Read the paper for free at PLOS Medicine.
10:32— The base rate fallacy, like all fallacies, comes from faulty brain wiring. Humans have no intuitions for Bayes theorem. Here is a famous example: In 1978 Casscells, Schoenberger, and Grayboys asked students and staff at Harvard Medical School (I'm paraphrasing): Let's assume there is a very accurate test for a certain lethal disease: if you have the disease, the test will always show it. The test also has a high correct rejection rate of 95%, if you don't have the disease, the test will show that with 95% chance. You are a doctor and in front of you sits a person who just got a positive result. What do you say to that person? How likely is it that he/she actually has this lethal disease? You also know that the disease has a prevalence of 1 in 1000 people who participate in routine screening. The most common answer was 95%. Only 15% of doctors gave the right answer (Casscells, Schoenberger, and Grayboys 1978; Eddy 1982; Gigerenzer and Hoffrage 1995). The correct reasoning can be made intuitive by imagining an actual number of persons, for instance 1000. Only one of them has the disease (and a positive test result). Fifty persons will have a false positive test result (5%). The chance that a person with a positive result has the disease is only about 2% (1 in 51). Let's rephrase the example: Now you are not a doctor, but a scientist, and in front of you sits, not a patient, but a hypothesis that just got the results of a statistical test (in the form of a p-value). This is part of Ioannides' main argument. Some think that there are principle problems in using p-values, ubiquitous as they are (Fischer would have over 3 million citations if people cared to cite him). For an intuition of one of the problems with p-values, check out the dance of the p-values demo by Geoff Cumming and read his paper.
10:40— Nature's announcement "Reducing our Irreproducibility"
12:30— Uri Simonsohn's paper.
13:20— The Chrysalis effect. This was in management research; there is no data for our field, but it would be interesting to know we do any better.
15:25— Evaluation of Replication Results. Of course, you can always check whether authors build on their own findings in subsequent studies, which would indicate that at least they believe in the results, and give a sort of lower bound on replicability.
Changes in the way we teach science
16:13— Here is a quick example concerned with Newton's third law (this is mentioned in Mazur's talk, see below):
A heavy truck and a light car collide head-on. During the collision, the force exerted by the heavy truck on the light car is: a) larger than that exerted by the light car on the heavy truck, b) they are equal, c) the light care exerts a larger force on the heavy truck than the other way around, d) they are not exerting any force on each other, they are just in each others way.
16:13— The plot can be found in this paper
17:38— Hestenes wrote: "Since the students have evidently not learned the most basic Newtonian concepts, they must have failed to comprehend most of the material in the course. They have been forced to cope with the subject by rote memorization of isolated fragments and by carrying out meaningless tasks. No wonder so many are repelled! The few who are successful have become so by their own devices." Carl Wieman: People do not develop true understanding of a complex subject like science by listening passively to explanations. True understanding only comes through the student actively constructing their own understanding through a process of mentally building on their prior thinking and knowledge through effortful study.
18:30— A similar approach, called flipped classroom, was developed by two high-school chemistry teachers, Bergmann and Sams.
19:00—Another very real obstacle is that especially early-stage professors seriously risk their career by spending time on improving their teaching methods. Teaching success plays almost no role in tenure evaluations (and no-one actually measures teaching success). Aren't educated young people the main product of a university?