The Oxford Handbook of Information Structure (edited by Caroline Féry and Shinichiro Ishihara) will come out in the US on September 28. It has been available in the UK and online since last Spring. This book is an editorial masterpiece that brings clarity into an area that can be very confusing at times. The editors performed a miracle in getting the authors of individual articles to converge on a unified theoretical perspective while still documenting all major current approaches to information structure.
Another invaluable resource on information structure has just come out in the US: Daniel Büring’s survey of Intonation and Meaning. The book gives a state-of-the art introduction to the discourse-related notions of focus and givenness and their impact on prosodic structure. It also has a chapter on the meaning of tones.
Here are some reflections on the latest academic dishonesty scandal, as reported in the New York Times, 29 May 2015:
“The graduate student at the center of a scandal over a newly retracted study that has shaken trust in the conduct of social science apologized for lying about aspects of the study, including who paid for it and its methodology, but he said Friday in his first interview since the scandal broke that he stands by its finding that gay canvassers can influence voters’ attitudes on same-sex marriage.” Mr. LaCour objects to one of the main charges against him – that he improperly destroyed his raw data. But he admits that he lied about the agencies that funded his research. “Mr. LaCour said he thought the funding sources he claimed would shore up the plausibility of the work.”
Mr. LaCour very clearly crossed a line by misrepresenting funding sources for his study. But where is that line exactly? In academia, people can get away with lying in a more indirect way by exploiting implicatures in CVs, personnel evaluations, or grant applications. For example, X may list grants when reporting their achievements without specifying their exact role in those grants. This triggers the implicature that X was a Principal Investigator or Co-Principal Investigator for the grants. Or X may submit a list of PhD students, leaving out information about the exact role they played in those students’ training. In the US, this generates the implicature that X was chair or co-chair of the respective PhD committees. What if X shores up their CV in this way, calculating that false implicatures will be derived by supervisors and funding agencies? Is this a case of academic dishonesty? Has the line been crossed?
What can we do to foster more sensitivity and respect for scholarly integrity in academia? Colin S. Diver, the former President of Reed College, makes a plea for abandoning the “higher education’s arms race” (from the Boston Globe, 5 September 2012): “Finally, no institution can convincingly preach ethical behavior to its students unless its own behavior is governed by the highest ethical standards. When higher education gets caught up in a frenzy of exaggerated marketing claims, misreporting of data, sale of admission slots, or varsity-sport abuses, it destroys its moral authority. As Reed’s president, I was proud to lead a college that refused to cooperate with the notorious US News & World Report rankings, which symbolize the distortion of academic virtues in pursuit of higher education’s arms race.”
Nature, 26 May 2015. Animal behavior: Inside the cunning, caring, and greedy minds of fish. This is an article describing the remarkable discoveries about fish intelligence made by behavioral ecologist Redouan Bshary.
“Primate chauvinism may now be poised to decline, thanks in large part to Bshary’s fish work,” says primatologist and ethologist Frans de Waal of Emory University in Atlanta, Georgia. “They now really do have to take on board that most species are going to have a type of smart intelligence.”
“Redouan has thrown down the gauntlet to us primatologists,” says Carel van Schaik, an expert in orang-utan culture at the University of Zurich in Switzerland. “He has made us realize that some of the explanations of primate intelligence that we have cherished don’t hold water anymore.”
The word “cooperation” covers a wide range of rather different behaviors, though. Here is a video on what human toddlers and chimps can do in the way of cooperation. I yet have to see a fish recognize what I am trying to do and come to my help.
“The National Science Foundation has announced that the University of Maryland’s Language Science Center (LSC) will (again!) receive a $3M grant for innovative research and graduate training, this time as part of the first cohort of awards made through its new NSF Research Traineeship (NRT) program. This 5-year award will support a model of interdisciplinary graduate training that prepares students to be adaptable scientists in multiple settings and career paths. The project will connect research on humans and machines, via a focus on how to succeed when Big Data is not available. The project is led by faculty and students from 10 departments across the entire university.”
“I’m excited by the research theme, which takes a “Beyond Big Data” approach,” says Colin Phillips, program PI and LSC Director. “We’re interested in how humans and machines can learn more efficiently from ‘multi-scale data’. Everybody’s talking these days about Big Data, but the current frontier in language science involves how to do more with less, you could call it ‘medium data’ or ‘small data’. It’s important for building better language technology, and it’s important for improving language learning outcomes in children and adults. Current language technologies like Google Translate and Apple’s Siri rely on a Big Data approach that stores billions of utterances. But that approach won’t generalize to the vast majority of the world’s 7000 languages. And human children easily outperform the best current technology, though they learn language from far less data. Child brains somehow learn the language around them more efficiently. But we all know that learning a new language as an adult is much harder. And we’re learning more and more about how children who experience ‘language poverty’, growing up with smaller amounts of quality language interaction, face negative consequences that last a lifetime. We want to understand why some learners fare better than others.” A key venue for exploring the program’s research goals will be yearly “Summer Camps”, intensive research-only workshops that will bring together students and faculty from UMD and beyond.” Colin Phillips video.
“A study by Massachusetts General Hospital (MGH) investigators has discovered two groups of neurons that play key roles in social interactions between primates – one that is activated when deciding whether to cooperate with another individual and another group involved in predicting what the other will do. Their findings appear in the March 12 issue of Cell.”
In this study, pairs of Rhesus monkeys repeatedly played a version of the Prisoner’s Dilemma game. The most remarkable result of the study is that the ‘predictor neurons’ of monkey A predicted the choices of monkey B as accurately as a ‘rational’ algorithm that tried to predict the choices of monkey B based on his/her prior choices. Here is a beautiful article on the Prisoner’s Dilemma and its role in Evolutionary Biology from Quanta Magazine.
A video summary of the paper: Nguyen, Anh, Jason Yosinski, and Jeff Clune. “Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images.” arXiv preprint arXiv:1412.1897 (2014). The paper is available here.
From MIT Technology Review: “A technique called deep learning has enabled Google and other companies to make breakthroughs in getting computers to understand the content of photos. Now researchers at Cornell University and the University of Wyoming have shown how to make images that fool such software into seeing things that aren’t there. The researchers can create images that appear to a human as scrambled nonsense or simple geometric patterns, but are identified by the software as an everyday object such as a school bus. The trick images offer new insight into the differences between how real brains and the simple simulated neurons used in deep learning process images” (December 24, 2014).
“Put rats in an IMAX-like surround virtual world limited to vision only, and the neurons in their hippocampi seem to fire completely randomly — and more than half of those neurons shut down — as if the neurons had no idea where the rat was, UCLA neurophysicists found in a recent experiment. Put another group of rats in a real room (with sounds and odors) designed to look like the virtual room, and they were just fine.” Kurzweil Accelerating Intelligence, November 25, 2014.
This raises many interesting questions: What happens when humans hear or read spatial descriptions or look at maps? Are their hippocampi building maps? Partial maps? No maps at all? How does this relate to the results reported in Benjamin Bergen’s book? How does the brain distinguish reality and fiction?
From Nature, volume 516, issue 7531, December 17 2014. “When Radhika Nagpal was a high-school student in India, she hated biology: it was the subject that girls were supposed to study so that they could become doctors. Never being one to follow tradition, Nagpal was determined to become an engineer. Now she is — leading an engineering research team at Harvard University in Cambridge, Massachusetts. But she also has a new appreciation for the subject she once disliked. This year, her group garnered great acclaim for passing a milestone in biology-inspired robotics. Taking their cue from the way in which ants, bees and termites build complex nests and other structures with no central direction, Nagpal’s group devised a swarm of 1,024 very simple ‘Kilobots’. Each Kilobot was just a few centimetres wide and tall, moved by shuffling about on three spindly legs and communicated with its immediate neighbours using infrared light. But the team showed that when the Kilobots worked together, they could organize themselves into stars and other two-dimensional shapes.” Earlier entry: Inferring simple rules from complex structures.
“What if the mind is not a storehouse of knowledge, but an engine of prediction? What if we are not Homo sapiens, but Homo Prospectus?” Martin E. P. Seligman
The University of Pennsylvania Positive Psychology Center has established a new branch of Cognitive Psychology: Prospective Psychology. Prospective Psychology investigates the mental representation and evaluation of possible futures. Through the Templeton Science of Prospection Awards, 22 two-year projects will explore the field of prospection.
“The crisis results mainly from ambiguities concerning the place of neuroscience in the HBP. From the beginning, neuroscientists pointed out that large-scale simulations make little sense unless constrained by data, and used to test precise hypotheses. In fact, we lack, among other resources, a detailed ‘connectome’, a map of connections between neurons within and across brain areas that could guide simulations. There is no unified format for building functional databases or for annotating data sets that encompass data collected under varying conditions. Most importantly, there are no formulated biological hypotheses for these simulations to test.”
Update on September 16: Independent EC evaluators have now recommended “the effective integration of the cognitive neuroscience community” into the Human Brain Project and the project has appointed a mediator to help them implement this and other recommendations, including those concerning its governance structure.