End of Summer Roundup

Summer has come and gone. Classes start next week. What did I accomplish and enjoy these last three months? (My one-month-in report can be found here.)

Research Goals.

I wrote up the paper for the project I’ve been working on most of the summer. Just submitted it this morning! Now we’ll wait and see what the reviewers have to say. That’s probably my biggest accomplishment for the summer workwise. I spent most of July running calculations and getting lots of good results, and then wrote up a full draft of the paper in the first half of August. Then I let the paper sit for a week and a half before doing my edits and preparing the supporting information. On the other hand, I did not start working on the other paper where the results are mostly in because we’ll see if my very talented student (who did almost all the work) wants to take a stab at the first full draft.

My conference presentations in July and August went well. I was adequately prepared for both, and had some good conversations that may lead to new collaborations. In-person conversations have resulted in follow-up e-mails from fellow scientists who I met at the conference. That, of course, might mean more work added to my schedule – but I’m looking forward to involving students in part of the process.

Teaching Goals.

No progress on the Potions textbook yet. When there’s no deadline or urgency, sometimes things just don’t happen.

I did complete the syllabi for my classes this fall semester. I’m excited about my General Chemistry class where I’ve moved around the material to fit my new theme: “Hiding in Plain Sight: Uncovering the Secret Structure of Matter”. This morning I worked on selecting the online homework problems for the first several weeks of class. Basically, I look at what I assigned last year and then make small modifications so it’s not much work on my part. I have approximately 3-4 weeks worth of notes transcribed into electronic form. (My previous first semester G-Chem notes were hard-copy; I converted my second semester notes into electronic form five years ago. Not sure why I did the second before the first.) I’m also looking forward to Research Methods after talking to my colleagues about what they’ve been doing in the new class format.

Hobby Goals.

I read a bunch more books in July and August, and tried cooking three new dishes. Three in two months pales compared to June where I tried three new things in the same month. I should continue working on this in September. Also finally got around to playing a bunch of games of Bios Genesis. I like the new design! I also got to play several games of War of the Ring, although I did not get round to re-reading The Silmarillion – and that’s unlikely to happen until, perhaps winter break.

The one drawback of the new semester is that there will be lots of meetings, particularly at the beginning of the semester. That’s why it’s good to get a bunch of class prep and research done before the semester begins. I have a number of returning research students, and no new ones starting this semester so that frees up some time. (The learning curve for a new student at the beginning is challenging. We normally set aside two full days for training just before classes start.) I look forward to meeting new first year students in G-Chem! I’ve exchanged e-mails with a few of them already as I e-mailed out the syllabus to them last Friday, and I’m also their academic adviser. It’s a new (academic) year!

Life and ET Sample Collection

I finally watched Life, the 2017 movie about an international team of astronauts who collect and study a Martian life-form. It’s an intense movie. I’m glad I did not watch in the movie theater but from the comfort of my own couch on a much smaller computer screen. Thank you, local library, for getting the DVD, thus enabling me to watch the movie (and many others) for free. (Image below from Wikipedia.)

Life takes place aboard an international space station. At the beginning of the movie the team receives an incoming unmanned spacecraft that has collected specimens that may indicate the first extra-terrestrial (ET) life-form outside of planet Earth. The station houses a lab with a myriad of safety protocols, which proceed to fail one after another as the malevolent life-form that eventually grows to have an Alien-like visage turns out to be highly intelligent, strong and dangerous. Why are all these alien life-forms malevolent? Well, if they weren’t there might not be much of a movie.

Having recently attended the ISSOL meeting, I’ve been thinking about the next steps in the search for the origins of life. While many scientists are making inroads into elucidating the mechanistic aspects of the origin of life here on Earth, others have been looking outside our planet. These include space agencies with large, albeit limited, budgets trying to maximize what they can learn from a mission to Mars or Enceladus. There’s only so much on-board analytical instrumentation you can pack on an unmanned mission. Thus, not surprisingly, there have been a number of proposals for sample collection and return.

The journal Astrobiology recently had a special issue on Enceladus, one of Saturn’s moons. It came to prominence in 2005 when the Cassini spacecraft discovered geyser plumes including water vapor and other molecules shooting out near its south pole. More recent evidence suggests a significant subsurface ocean below the ice that could harbor life. Sample collection would be much easier because there’s no need to try and land a spacecraft or rover. A flyby through the plume would be sufficient. What might be an indicator for an onboard instrument that a sample might contain signs of life? Steve Benner has an interesting article in the issue that focuses on looking for polyelectrolytes, reminiscent of DNA – but not identical. He argues quite convincingly that the polyelectrolyte backbone is crucial for Darwinian evolution of genetic material. On the other hand, he also argues that homochirality and C_2n biosignatures are not as crucial.

I don’t know how the funding process works for a sample collection and return mission to be approved. But if the public has anything to say about it after watching Life, I think they would insist on the most stringent protocols to protect us from the malevolent ETs out there. We shouldn’t underestimate the power of fictional narrative in influencing how we think about certain scenarios regardless of the statistical possibilities or impossibilities. In any case, the scientists aboard the station in Life, before the mayhem starts, do discuss how important studying alien life could be in thinking about life origins. Interestingly, the way they reanimate the alien supercell “fossil” is by retuning its environment to match Earth’s early Proterozoic era in terms of the mixtures of gases and the liquid environment.

Overall, the movie is just okay. I’m still waiting for Alien Covenant to show up at the local library. The reviews and ratings weren’t sufficient for me to plunk down money to see it in the movie theater. I predict a closely related storyline.

Information and Chemical Diversity

Another Nautilus article has gotten me thinking about chemical complexity and the origin of life. The article itself is not related to either topic. How Information Got Re-Invented is “the story behind the birth of the information age”. It is a selected biography of Claude Shannon and the influences that led him to him famous paper, “A Mathematical Theory in Communication”. If you’re a physical chemist like me, you’ve heard of Shannon entropy and its similarities to Boltzmann entropy.

The article does a great job tracing the work of Harry Nyquist and Ralph Hartley, both engineers who had contributed significant insights that led to Shannon’s breakthrough work. There are also anecdotal stories from friends of Shannon during his time at Bell Labs. (I highly recommend The Idea Factory by Jon Gertner about the golden age of innovation thanks to the remarkable setup of Bell Labs.) The article also clearly lays out the basics of information transition theory and introduces the bit as a measure of information content.

More importantly, the authors of the article Jimmy Soni and Rob Goodman, do a great job uncovering the counter-intuitive definition of quantified information. Here’s an excerpt:

“What does information really measure? It measures the uncertainty we overcome. It measures our chances of learning something we haven’t yet learned. Or, more specifically: when one thing carries information about another – just as a meter reading tells us about a physical quantity, or a book tells us about a life – the amount of information it carries reflects the reduction in uncertainty about the object. The messages that resolve the greatest amount of uncertainty – that are picked from the widest range of symbols with the fairest odds – are the richest in information.  But where there is perfect certainty, there is no information: There is nothing to be said.”

The article goes on to discuss languages and code-breaking. The English language, for example, has many redundancies – including the letters used in the alphabet such as vowels. The authors quote an example from Shannon: “MST PPL HV LTTL DFFCLTY N RDNG THS SNTNC.” Code-breakers exploit these redundancies to their advantage. In fact, “every human language is highly redundant. “From the dispassionate perspective of the information theorist, the majority of what we say – whether out of convention, or grammar, or habit – could just as well go unsaid.” Having attempted to learn languages that have more or less redundancy in my adult life gives me more appreciation and patience for people speaking in their non-native tongue and making “grammatical errors”. Many of these aren’t errors per se, at least in terms of communication. You can understand what they are saying – it just doesn’t sound “right” to your native ears. But rightness in this case is simply the current convention a native speaker uses. Languages do evolve over time

Why the redundancy? It turns out that “every signal is subject to noise. Every message is liable to corruption, distortion, scrambling.” The speed at which a message can be propagated is dependent on how the message is encoded or packaged – Shannon proved there is a “point of maximum compactness”. So it turns out that redundancy is important for communication or propagation. In the case of our genetic material DNA, copying isn’t perfect – there are errors; but they are mitigated by redundancy and evolved error-correcting helper molecules. Darwinian evolution takes advantage of such errors. It allows for variation – I think of it as creativity in exploring biological space.

Chemistry operates in the same way. The riddle of origin-of-life chemistry has less to do with making a large variety of complex molecules – it’s about why life only picks out a select few and uses them over and over. I study the oligomerization of small molecules. Starting with a single substance such as formaldehyde (CH₂O), a whole plethora of molecules can be formed including polyethers, oxanes, and a whole range of sugars. Now add a second substance into the mix and the diversity of molecules explodes exponentially.

Now if indeed we humans have evolved to be information guzzlers, as suggested by Gazzaley and Rosen, and there is a thermodynamic law that favors the increase of information akin to entropy, there should be a way to quantify this in terms of the information carried in molecules. But what is this information? Number of elements? Number of atoms? Number of bonds? Number of adjacent reaction types? Number of downstream cascades? There are also likely to be constraints that increase certainty and decrease information – I’m thinking of thermodynamic sinks here. I’m reminded of Jeffrey Wicken’s book, Evolution, Thermodynamics and Information. It's on my bookshelf. I read it six years ago when I got interested in origin-of-life research and didn’t understand a lot of it – let’s just say it was very information-dense. I should revisit the book, but my summer is almost over! Maybe it will be my project next summer.

The Nautilus article reminded me that in thinking about quantifying information, we should concentrate on the symbols that weren’t used, the words that weren’t said that could have been. A couple of speakers at the recent ISSOL conference made essentially the same point. We as researchers shouldn’t just focus on how we got to the current molecules of life, we should be exploring adjacent chemistries – molecular systems closely related that will give us a clue into why extant life uses what it uses chemically. Many groups are already doing this and our chemical community is the richer for it.

The Distracted Mind

I’ve been more conscious about maintaining focus this week. It’s probably because I’ve been reading The Distracted Mind: Ancient Brains in a High-Tech World by Adam Gazzaley and Larry Rosen. The book is divided into three parts. In Part I, they discuss the issue of cognitive control with many examples coming from Gazzaley’s neuroscience lab. Part II covers behavior in our high-tech milieu, an area that the psychologist Rosen has been studying for decades. Finally Part III is titled “Taking Control” and discusses how we can improve cognitive control and possibly even modify our behaviors.

You’ve got a goal to get something done. But then the distractors seem to attack in full force. Your mind wanders. You check your text messages. A sound in the background catches your attention. Or you get interrupted when the phone rings or your computer beeps saying you’ve got mail. In Chapter 1, the authors ask “Why are we so susceptible to interference?” It turns out our cognitive control is rather limited. (Cognitive control is defined in the book as “our ability to carry out [high-level] goals [that are] dependent on an assemblage of related cognitive abilities.”) Our ancient brains “have a restricted ability to distribute, divide, and sustain attention; actively hold detailed information in mind; and concurrently manage or even rapidly switch between competing goals.”

The authors make an evolutionary argument that we are wired to take interest in novelty in a broad sense, but especially information in a narrower sense. Thus, the theoretical framework presented layers an “optimal information foraging theory” on top of Charnov’s evolutionary marginal-value-theorem (Figure below from the Wikipedia link). Like a squirrel foraging for acorns that has to figure out when at some point to switch trees because of diminishing returns, so are our brains wired to flit from one information source to another. Except that high-tech advances and particularly the world of information at the touch of your cellphone exacerbates the situation immensely.

Being professors, the authors discuss the effects of the Distracted Mind on students and education. The studies showing how often we are “distracted” even in a 15-20 minute time block are astounding. Having read the book, I’m now much more conscious of my mind wandering when I’m doing any task that requires concentration – reading an information-laden book such as The Distracted Mind for instance. Worse, the experiments show that cognitive control peaks in one’s early 20s (the age of college students!) and then declines. Children can be easily distracted, but holding one’s attention is actually challenging for older adults. For those of us “over the hill”, it turns out that the main challenge is being able to switch back to the main task after interruption. And these studies were for healthy older adults independent of dementia-related illnesses.

None of us really multi-task because of our limitations in three key areas: attention, working memory, and goal management. We don’t parallel process; instead we perform task-switching. And task-switching gets harder as you age. I’ve been taking these lessons to heart this month. The past couple of weeks I’ve essentially concentrated on research and writing at work, thus putting off class prep. I’m very pleased that I finished a full draft of a manuscript yesterday. Just in time for me to go to a conference in DC, although my presentation is only tangentially related to the work in the manuscript. I did devote all of Monday to preparing the slides for my presentation; I finished a full draft and then haven’t looked at them since. I’ll have my second go at it when I’m in DC or in an airport/airplane.

The second change I’ve made is to reduce my internet surfing and social media outside-of-work. I don’t do much social media to begin with (just FB), but I’ve cut down to checking once a week. I’ve also reduced my news reading from every day to every other day, and not reading all the difference sources from my foraging habits in a single sitting. While I do “surf” the net at work, it tends to be work-related, i.e., I’m reading about science or education. I’ve also started to consider the period before going to bed. I turn off my cellphone and laptop earlier in the evening, and I’m trying to progressively reduce the amount of light so that my body starts to build melatonin. Sleep is really important for the brain, and being plugged-in and stimulated by blue-spectrum light is not helpful.

How will I pass on some lessons to my students? I’m thinking of using the “five minutes before class” with a slide that discusses something related to focus and distraction. Maybe I’ll put up a pithy phrase or claim, and discuss it with the students! That might be a way to help them build up more metacognitive practices, something that Gazzaley and Rosen suggest in their book!

Naked Statistics

Every college student should read Naked Statistics. Actually, everyone who does not regularly use statistics in their work should read it. The author, Charles Wheelan, does a marvelous job conveying the key elements of quantitative reasoning that everyone should be able to use when confronted with a news headline claiming some connection between two or more things. Even better, the book is breezy reading and highly entertaining; it’s subtitle is “stripping the dread from the data” hence Naked Statistics. Interesting vignettes and helpful examples are aplenty, and Wheelan’s sense of humor is delightful – there are running gags throughout the book.

After going through the bare basics, Wheelan does a masterful job with the Central Limit Theorem in chapter 8 of 13. His emphasis on the importance of drawing good representative samples litters the book highlighting the power of statistics when done well and the dangers when done poorly. Each chapter builds on the ones before, and I can see Wheelan employing the strategies of an effective teacher as he guides the reader through the material. The meat comes after the Central Limit Theorem and the book subsequently covers inference, polling, regression analysis, and program evaluation. The examples are always interesting and enlightening, and a strength of the book is how Wheelan connects theory to application.

While there are examples in sports, economics, medicine, governance, and more, there are also several related to education. The chapter on program evaluation is starts by asking the question “How would going to Harvard affect your life?” But evaluating it is tricky. “Well, to answer that question, we have to know what happens to you after you go to Harvard – and what happens to you after you don’t go to Harvard. Obviously we can’t have data on both.” Unless you can replay the tape or you’re able to access the multiverse where every choice creates an additional alternate reality bubble.

Instead of jumping straight into the question of whether going to an “elite” school impacts your life in some significant way, Wheelan pivots to another question to illustrate key concepts. “Does putting more police offers on the street deter crime?” You’ll have to read the book to see the connections! In any case, Wheelan systematically goes through counterfactual strategies one can use when randomized controlled experiments cannot be carried out. Harvard wouldn’t agree to participate on having random students, and students (and their parents) would likely not want to be randomly assigned not to attend Harvard. This is one of the best parts of the book – I was impressed by the ingenuity of researchers trying to get at a difficult question as carefully as possible amidst complicated and confounding factors.

In the conclusion, Wheelan poses five big questions (no, it’s not an exhaustive list) that statistics can and should help answer. One is education related: “How can we identify and reward good teachers and schools?” This turns out not to be an easy question to answer despite the punditry that abounds on this topic. I read a lot of education news so I’m exposed to it on a very regular basis. Wheelan brings up the very interesting study by Carrell and West at the Air Force Academy that attempts to answer the question of which professors are the most effective. It makes use of the “natural experiment” where students are randomly assigned to introductory calculus sections every year. Syllabi and exams are similar across sections. The results are interesting and oft-quoted. (I won’t tell you here because I’m encouraging you to read the book!)

I wonder if the magical education world of Harry Potter has similar issues. Does a Hogwarts education make a difference? Is there an advantage to completing your education? (After all the Weasley twins seem to have done well without “graduating”.) Is there a good reason to send your budding wizard-of-a-kid to Hogwarts rather than Durmstrang? Does Durmstrang really churn out more “bad” wizards? Statistics could likely answer such questions. Come to think of it, maybe I could use some of these as examples in my classes, now that “quantitative reasoning” is a new core requirement at my institution. It would be fun to make up some of the data!

P.S. I’ve also read Teaching Naked. I wonder if there’s a new trend. Maybe zombies are finally starting to go out of fashion?

Evolution and Learning

Why do we need school? Are there things that can’t just be caught, but have to be taught? At an early age children seem to effortlessly learn how to understand and speak a language, recognize faces, and pick up stuff from older folks around them (some of which we’d rather they didn’t learn). But there are some things that are not learned readily without teaching: reading, writing, and mathematics. Schooling is required for these. By schooling, I’m not referring to a particular type of institution setup, but rather explicit teaching needs to take place from “teacher” to pupil. The teacher may be a parent, sibling, formal schoolteacher, friend, or possibly even Sesame Street on TV.

In Educating the Evolved Mind: Conceptual Foundations for an Evolutionary Educational Psychology, David Geary sets up an important categorization. (The chapter/article is 100 pages long including references and was well worth my reading time.) There are two domains of knowledge: Biologically Primary and Biologically Secondary. The human brain is evolutionarily primed to learn the first – and this is the seemingly effortless learning that is “caught”. On the other hand, culture-specific skills (including reading, writing and arithmetic), have to be “taught”. In the first paragraph of his article, Geary sets up the stakes. (See original article for the references.)

“It is widely accepted that all children in modern societies will receive formal and extended instruction in a variety of core domains, such as mathematics, and at the very least they will acquire the basic skills, as in being able to read and write, necessary for employment and day-to-day living in these societies. Unfortunately, the instructional approaches used to achieve these goals and in fact the details of the goals themselves are points of continued and often divisive debate… At one extreme is a child-centered approach, whereby adults should come to understand how children learn and then construct educational goals and instructional methods around children’s learning biases. At the other extreme is the assumption that adults should decide the content to be taught in schools… and the methods by which this content is taught should be based on experimental studies of learning, often without much consideration of children’s preferences. In addition to this lack of consensus about how to approach children’s learning, educational goals can be further complicated by attempts to use schools to socialize children in one ideological perspective or another.”

Geary’s theoretical framework helped me on the one hand to puzzle out why the Michel Thomas method exists, and to understand a bit better Frank Smith’s diatribe in the Book of Learning and Forgetting. On the other hand, it fits well with my struggles teaching chemistry as I have been thinking about the cognitive load imposed on students, and how I can potentially restructure the subject matter to avoid persistent confusion.

Thinking about brain development evolutionarily is key. According to Geary: “Biologically primary domains encompass evolutionary-significant content areas and are composed of folk knowledge (e.g., inferential biases) and primary abilities (e.g. language, spatial). Folk knowledge results from the organization of the brain systems that have evolved to process and integrate specific forms of information.” Geary goes into great detail explaining this in terms of how our brain, perception and attention can be connected to survival-reproductive evolutionary behavior. There are three categories of folk knowledge: Psychology, Biology and Physics. The first relates to processing social information, while the latter two relate to processing ecological information about how nature behaves – from both the living and non-living entities. As a chemist whose interests also span biology and physics, these latter categories are of particular interest.

What distinguishes folk biology and folk physics from the science one might learn in school is that evolution shapes heuristics to be used in a given situation. Geary writes: “These biases may often provide good enough explanations for day-to-day living and self-serving explanations for social and other phenomena… but many of these explanations and attributional biases are scientifically inaccurate and may actually interfere with the learning of scientific concepts…”

The concepts of modern science are in the biologically secondary domain of knowledge. However, they initially emerge and are built from biologically primary domain folk knowledge. Geary argues that advances in science and technology that allow “better control of ecologies… are likely to be retained across generations as cultural artifacts (e.g. books) and traditions (e.g. apprenticeships).” So why do we need schooling? Geary argues that schools arose to bridge the growing gap “between folk knowledge and the competencies needed for living in [modern] society.”

Okay, so perhaps we need some sort of schooling to survive and perhaps thrive in modern society. Why is there still divisive debate about how best to set up schooling? Learning and teaching in this secondary domain is challenging. Geary has a list of principles in Table 1 of the article. Here are the last two. “Children’s inherent motivational bias to engage in activities that will adapt folk knowledge to local conditions will often conflict with the need to engage in activities that will result in secondary learning. The need for explicit instruction will be a direct function of the degree to which the secondary competency differs from the supporting primary systems.”

And amazingly modern technology, thanks to what we’ve learned from cognitive psychology, is attuned to distracting us from learning and schooling. I will be learning more about this next week when I start reading The Distracted Mind: Ancient Brains in a High-Tech World. I just got my copy of the book last week and it was autographed by one of the authors who was present at the time! I expect to blog about it later this month so stay tuned.