The Feeling of Knowing

To err is human. To admit to erring… well, that’s difficult. Like most people, I don’t like the feeling of realizing that I’m wrong. I always think I’m right (I can’t help it!), but I don’t think I’m always right. Past experience confirms that I do err; it’s consistent but I can’t predict when it will happen. And I feel I’m right… right up to the moment that I’m proven wrong.

 

The subject of Wrong-ology is taken up by Kathryn Schulz in her book, Being Wrong. Our minds are funny things and the way we learn and remember things is much more complex and mysterious than we imagine. We think, as Plato suggested, that memory works like a wax tablet: “Everything you experience, from your own thoughts and sensory impressions to interactions with others, creates an imprint in that wax… an unchanging mental replica of the events of the past, captured at the moment they occurred.” This may contribute to that feeling of knowing, even when in gross error.

 

As a professor, I’m well practiced at professing. Students think you’re more knowledgeable and know what you’re talking about when you present the material confidently. That’s not hard to do because I feel that I know the material. Even when I don’t know it as well as I should, I still present it confidently. Fake it till you make it. Did I do so when I first started teaching? Was I more diffident back then? Honestly, I don’t remember. I’ve learned not to trust my memory even if I feel I can visualize it in my mind’s eye. My spouse provides me a very useful signal when I might be professing with confidence about something I know little about; she says: “You say that so confidently”. That gets me to chuckle, stop, check, and think.

 

Schulz discusses medical cases of brain issues where patients confidently describe or explain something with no correspondence to actual reality. And they seemingly believe it. This is known as confabulation. Here’s how Schulz describes it: “Imagine, by way of analogy, that each of us possesses an inner writer and an inner fact-checker. As soon as the writer begins devising a story, the fact-checker gets busy comparing it with the input from our senses, checking it against our memory, examining it for internal consistencies, thinking through our database of facts about the world, and, once we utter it, gauging other people’s reactions to assess its credibility… When the fact-checker falls asleep on the job, however, our theories about the world can become wholly unmoored from reality. All of us have experienced this, because the one time our fact-checkers reliably fall asleep is when we do, too. Think about dreams again for a moment, and about how weird even just the averagely weird ones can be… Now, two bizarre things are going on here. The first is that your brain is generating representations of the world that are only lightly tethered to the real, or even to the possible. The second is that you are completely untroubled by this fact.”

 

Being surprised is a good wake-up call to discovering error. You’d think that by now I would have gotten used to being surprised every time I err. But I am surprised every single time. Consistently, yet unpredictable in when it will happen. I’m heartened when Schulz writes that saying “I don’t know” is a good sign of brain function because in some forms of dementia, the fact-checker falls asleep and confabulation ensues. It also turns out that being confabulatory is part of how the human brain works. It’s an engine, possibly the engine, of creativity and imagination. I can think about and imagine things that are not real. I can make mental models of things that are abstract or invisible (which I must do frequently in thinking about chemistry). Our minds have adapted to come up with quick instinctive solutions, not always thought through, that do serve us well on many an occasion. The feeling of knowing allows us to act quickly when needed.

 

When grading exams, I still get surprised by the occasional confabulatory explanations of students. A student who has no idea what’s going on is yet able to come up with a fantastical story involving throwing together chemistry concepts completely untethered to reality. It doesn’t happen often, but it’s interesting for me to read these “answers” and try to imagine how a student came up with them. I wonder if that student had the confident feeling of knowing. But actually didn’t. Not knowing what you don’t know isn’t a great situation to be in.

 

Reading Being Wrong has made me a little quicker to say “I was wrong” in my classes when I make a mistake on the board and it is pointed out by a student. I apologize to the class, then thank the student for paying attention and being brave enough to tell me so that I don’t mislead the class any further. While it doesn’t happen often, I feel that as I age it has ticked up in frequency. I’m not as sharp as I used to be, perhaps. Or maybe I am less well prepared because I’m overconfident in the feeling of knowing, having taught the subject matter multiple times over a couple of decades. But if I actually learn something from my errors, that’s a good thing!

The Optimality of Forgetting

In the education business, we’re often emphasize the business of remembering. Remembering what you learned is good. Forgetting what you learned is bad. Students may wish they had better memories to remember all the stuff I’m telling them. Heck, I often wish for better memory as I age and forgetfulness increases in frequency. So why do we forget when improved remembering seems like what we want? If remembering was so adaptively so much better than forgetting, evolution should have selected for the best memorizers!

 

What has our memory evolved for? And why might forgetting be just important as remembering? One possibility is that in a noisy and ever-changing environment, having specific detailed memories that persist make it difficult to learn new things and adapt appropriately to analogous yet different situations. I didn’t come up with this myself. I just spent the last hour reading a perspective article: “The Persistence and Transience of Memory” by Richards and Frankland (Neuron 2017, 94, 1071-1084). Parts of the article were slow-going because I lack the background related to the experimental work being reviewed, but I think I got the gist of it. And that’s the point! Getting the gist may be what matters adaptively.

 

The authors argue that the interplay between persistence (remembering) and transience (forgetting or erasing memories) is key. In particular, transience “enhances flexibility, by reducing the influence of outdated information on memory-guided decision making, and prevents overfitting to specific past events, thereby promoting generalization.” There are supporting experiments in rats and fruit flies for this hypothesis. Neural network models also suggest a congruence with the experiments: Injecting “noise” into the network, reducing weighting factors, encoding sparsely rather than densely, seem to improve the network’s ability to handle generalized situations.

 

When teaching physical chemistry (and to a lesser extent in general chemistry), I try to emphasize the models underlying the equations we used. The simpler the model, the simpler the equation and the more generalizable it is: the ideal gas law equation (PV = nRT) is an example of a very powerful equation that works for any gas, as long as it behaves close to ideally. The model of an ideal gas imagines a large number of particles moving randomly in a box with plenty of empty space with all collisions being elastic. That’s a good approximation for N₂, O₂, CO₂ and Ar which constitute over 99% in dry air. We can elaborate the model further for “real” gases through the two-parameter van der Waals equation or a multi-parameter virial equation. A mathematical model is powerful because its quantitative aspect allows it to make predictions of future situations to be encountered.

 

But putting in too many parameters can result in over-fitting, which can then result in incorrect predictions. So if we go through life encoding every moment in dense detail, it might actually hamper our ability to see the forest from the trees and adapt to new situations. Everything is a detail and the big picture is lost. The article’s introduction mentions the oft-quoted story of a patient with seemingly photographic memory of his entire life, but had plenty of problems navigating life because of this. I’m also reminded of how we learn when encountering something new. If you’re a novice, you try to absorb as much as you can but you have no idea which “details” are important and which are not. But if you already have some background, you’re able to ignore the artifacts and focus on abstracting the most crucial features. How exactly that happens, I don’t know. But I see it every day in my teaching. I constantly have to remind myself that I have the curse of knowledge in that I can’t quite remember or fathom how hard it was for me to build my chemistry scaffold oh so many years ago.

 

We humans haven’t had enough time to evolve towards learning academic subjects. Or even the seemingly simple acts of reading, writing and arithmetic. I don’t remember how I learned to read. I improved my writing through sheer practice and repetition. I have a vague “memory” that algebra was completely obtuse when I first encountered it; but I had an aha(!) moment at some point in life and somehow grasped it in a gestalt experience. Now algebra is obvious to me, at sometimes I’m at a loss helping students work a chemistry problem and realize they don’t get algebra. (This is a very small number, but I’ve noticed a few more post-pandemic.) Learning is still mysterious to me.

 

What can I do to help students learn chemistry? In class and through homework and practice, I try to emphasize the things students need to remember. I repeat the salient points a lot such that I sound like a broken record, but I think it’s crucial to keep the students attending to the main thing. The first time I say something the strongest students may grasp the salience but the majority of the class hasn’t yet. So I need to keep repeating and emphasizing the most general principles. But I have to do this in the context of multiple examples that look different from each other. Same principle, different example. This is the key to “transfer”, the ability to effectively apply something you’ve learned in a different situation; and this includes knowing the limits of applicability!

 

I also add a lot of tidbits (history, broader applications, interdisciplinary connections) to my lectures. I hope that the students find them interesting, possibly strengthening a neural connection; but even if students forget these, that’s okay. For the things I need them to remember and use, there’s no substitute for repetition to strengthen the memory (both conceptual and procedural). If the students don’t practice retrieving these memories and using them, they will forget. It’s not a bad thing. Transience and persistence go together and I wouldn’t want my students to be maladaptive to new situations. So I’m not looking for them to have better memories (even though they might wish for it), but I’m trying to strengthen the neural connections they do have and maybe even replace some incorrect misconceptions they might have. Forgetting has its place in learning!

The Predictive Brain

What is our brain for? Making predictions. Why? Because that’s one way for a living organism to survive and possibly thrive in an environment that’s constantly changing. In the words of Andy Clark, author of Surfing Uncertainty, the brain is “an action-oriented engagement machine, adept at finding efficient embodied solutions that make the most of body and world.” I’m glad Clark provided that pithy summary at the end of his book. Because I’m not a neuroscientist, it took me a while to work my way through his argument. But I’m glad I did because it made me think a lot about how humans learn and about my origin-of-life research; both are key topics I think about a lot in my professional life.

 

I haven’t fully digested his argument which is essentially using a model he calls Predictive Processing (PP) to explain what the brain does and why. Many open questions remain, and Clark early on acknowledges that the specific details of his model may turn out to be wrong, but that the overarching idea of top-down predictive processes coupled with bottom-up error-signaling processes work together in concert to home in on a best guess of any encountered situation. But this isn’t an isolated brain in a jar. Embodied action is a critical part of honing the process. I will quote parts that really struck me and muse about them briefly in a meandering way. Like a surfer perhaps. This may be fitting given the title of his book.

 

More than a decade ago, when I first encountered the notion of System 1 and System 2 thinking (made famous by Daniel Kahneman’s Thinking Fast and Slow), I was enamored by the idea. But over time I’ve found the separation a little too clean. Clark argues they are one multi-faceted system. We might “use some quick-and-dirty heuristic strategy to identify a context in which to use a richer one, or use intensive model-exploring strategies to identify a context in which a simpler one will do. The most efficient strategy is simply the (active) inference that minimizes overall complexity costs… system 1 and system 2… are now just convenient labels for different admixtures of resource and influence, each of which is recruited as circumstances dictate.” I have a feeling Clark is correct and that his emphasis on multi-timescale processes is a key part of how organisms do what they do. I don’t quite understand how the longer timescale ‘higher-level’ brain processes couple to shorter timescale sensory signals, but I suspect the dynamic coupling of such processes is the beating heart of life.

 

Thermodynamic terms show up in Clark’s treatise. There’s free energy minimization when the brain tries to be efficient and make a prediction at the lowest cost. It’s why we continue to make mistakes (and learn from them) as we encounter new situations or variations of what we thought were things we knew. Entropy is defined in terms of surprisal; when prediction goes awry and we have an oops moment, this allows us to recalibrate. As a chemist, I define these terms differently, but I see a kinship between how I think about thermodynamics and what Clark is trying to do with these terms. However, having seen thermodynamic principles invoked in multiple areas, in my opinion I see more and more muddied thinking that may introduce more confusion than clarity.

 

I very much appreciated Clark’s emphasis on perception and action being inseparable. He writes that they are “two sides of a single computational coin. Rooted in multilevel prediction-error minimizing routines, perception and action are locked in a complex circular causal flow… Percepts and action-recipes here co-emerge, combining motor prescriptions with continuous effort at understanding our world.” While I mostly thought of sensory signals as exteroception, I appreciated Clark’s reminder that proprioception and interoception are just as important, and our brain needs to make sense of all three incoming channels. This made me ponder how to include all three in origin-of-life modeling, and also how to structure the seeming digital-analog divide. Information is efficiently stored digitally, but the action of life is analog. I’m sure that different timescales are important here, but I haven’t figured out how these could or should be modeled.

 

In Chapter 6, “Beyond Fantasy”, Clark delves into the idea that “perception is controlled hallucination”. He thinks we should be circumspect about the notion that our brains and thoughts are akin to virtual reality. Action on our part is important to continuously update the “probabilistic prediction-driven learning… able to see past superficial noise and ambiguity in the sensory signal, revealing the shape of the distal realm itself.” But our brain has also evolved to be an efficient computing machine, and this means pruning out or ignoring a lot of the sensory stimuli to focus on what is salient. I’m reminded about the mystery of learning, especially when it comes to the nonintuitive subject of chemistry. When the aha moment occurs, it’s a gestalt experience. After that I can’t unsee what I now know. It also blinds me as a teacher through the curse of knowledge. It reminds me that I constantly have to work hard at teaching because things obvious to me are not obvious to students encountering it for the first time. I can provide helpful scaffolding but how one actually learns is still mysterious. And my learning needs to be continuously updated. I’m sure I have erroneous notions I’m still passing along to students, but they’re in my blind spot – and I won’t know until I’m surprised by them.

 

Uncertainty surfaces when you least expect it. Perhaps that’s the moral of the story.

Attention Matters!

You’re reading something, then all of a sudden, BOOM! Out of nowhere a stray thought flies in and disturbs my concentration. I’ve noticed this happens more as I’ve aged. I’ve heard anecdotally that peak concentration is age 20. That’s the age of many of my students, and they have trouble concentrating. I used to worry that I could no longer focus. But now I think that having stray thoughts is natural. Often the first stray thought is related to what I’m reading (and sometimes relevant and useful), but if left unchecked it daisy chains into other stray thoughts further afield. At some point, I’m distracted and have lost all attention on my original task.

 

Today’s post is about Chapters 4 of Michelle Miller’s book Remembering and Forgetting in an Age of Technology. It builds on my previous post on how memory works from Chapters 2 and 3. Attention is the key thing I’m trying to marshal in my students when we’re in class together. Marshal is an appropriate analogy; I’m trying to direct students’ attention so they focus on the right things needed to learn the material. The more students can pay attention and be less distracted, the more they are likely to learn, especially if that attention is reflective rather than being like mindless sheep. Thus, I’m a marshal and not a sheepherder. (My apologies to sheep; I’m just using them as a metaphor here.)

 

What are we trying to do? Help students learn new things. To do this they have to make memories – in particular, semantic memories (see previous post for definition). But there’s a battle for attention. Our ancient brains have evolved to constantly be aware of peripheral changes. You don’t want to be food for the predator hiding in the bushes. Thus, you naturally perk up at distractions. No lions and tigers on my campus, but cellphones abound. Miller writes: “Distraction is kryptonite for memory, and unfortunately, distraction is what personal technology does best. Left unchecked, the alerts generated by the myriad programs that most of us use in the course of a day will inevitably erode memory. This is not because the constant interruptions permanently alter us at a fundamental level, but because they interfere with the process of making new memories when they’re happening. This is a significant threat.”

 

Let’s get to the nuts and bolts. How does attention work? Turns out if you’re trying to pay attention your brain must do two things simultaneously: shine a spotlight (by directing cognitive resources) at what you’re attending to, while suppressing what is irrelevant. This means your brain will “constantly scan the environment for stimuli what might be important”. Your brain is always multi-tasking. It’s a tricky balance “between letting too much in and keeping too much out”. Miller prefers the metaphor of a bouncer to a spotlight or a gate. The bouncer needs to actively scan for the desired guests (to let in) while keeping the riffraff out and watching for signs of trouble.

 

Most of the learning we do in school (being biologically secondary) is effortful. My chemistry students aren’t going to be learning by osmosis. (A chemistry metaphor!) So if their attention is elsewhere during class, they aren’t going to be learning in class which is where some of their best learning can happen – when you’re there as a partner to converse in the language of chemistry. Is it true that students can only be attentive for 10-15 minutes at a time? Turns out this isn’t true. Miller debunks this myth along with another that claims there is an attention time-span – there is a “capacity” or “bandwidth” of attention, but it’s not related to time. That doesn’t mean you shouldn’t switch back and forth between activities. Well-designed switches and pivots can help to focus attention. And sleep-induced death by droning through Power Point slides is real.

 

Miller also debunks the idea that “technology is to blame for shrinking attentional capacity” or that it “burns us out mentally and neurologically”. Distractions can be a problem (see quote in the third paragraph) but not because technology use is eroding your brain. I’m not going into the details Miller provides, but I highly recommend Chapter 4 of her book if you’re interested in the evidence she provides. I will however provide one salient quote: “Our ability to stay attentive even when we’re bored or disengaged may not have decreased, but perhaps our willingness has… we may have experienced a global decrease in our tolerance for the discomfort of empty time or activities that aren’t enticing.” Here’s another teaser for her book: Miller also discusses the connection between burnout and “continuous partial attention”, an activity many of us professors engage in when we have our e-mail, Slack, social media, and phone notifications turned on.

 

Our brains multitask all the time. The integration of different subsystems working in parallel is remarkably seamless; we hardly notice it… until we do. When there are multiple tasks that require significant cognitive resources and conscious attention, that’s when we run into problems. You may have heard about the costs of “task switching” and that’s what the studies show thus far. I’m going to do it right now by mentioning what seems like a tangent. Miller has an interesting discussion on the doorway effect. It’s what happens when you walk from one room into another, get distracted, and don’t remember why you entered the room in the first place. As you walk out past the doorway, you suddenly remember again. It has to do with prospective memory (which is different from episodic, semantic, and procedural memory).

 

Now back to our main discussion. Miller and colleagues have worked on a project titled Attention Matters! – the title of today’s blog post. It’s a module with three short units. The students watch some short interesting videos about the limits of attention (“The Amazing Color Changing Card Trick” and “The Impossible Texting and Driving Test” are mentioned – I watched both). These videos prompt a discussion about attention and memory and how to get the most out of their learning. But the key at the end is getting students to make behavioral changes. Miller has used the hypocrisy effect to great effect! The idea is “persuading people to change behavior by asking them to take a hypothetical stance on an issue or to articulate a desired point of view. Once they give a hypothetical opinion on a subject, people tend to stick to that opinion, despite the fact that it was only something they said because they were asked to do so.” Basically, Miller asked students to “write down their plan for how they would manage distractions going forward” in concrete terms. I really liked this idea and I think I will try it with my first-year academic advisees in the Fall semester.

 

My original plan for this post was to also discuss Chapter 5, but you, dear reader, might be reaching your attention capacity so I’m going to stop momentarily. My quick synopsis: Miller provides a nuanced discussion of whether, when and how technology (laptops, tablets, phones) should be used in the classroom. It can both help and distract. There are some very interesting experiments (go read her book!) and she does a particularly nice job summarizing the studies that have looked at the effects of note-taking old-school (pencil and paper), using a stylus on a tablet, and typing on a laptop. I won’t tell you the conclusion to keep your interest piqued. There was also an interesting aside about the effect of taking pictures when you’re on vacation and how that affects your memory of what you photographed.

 

In looking back at my previous posts, I realized I’ve discussed attention multiple times in my blog from other books I have read, but because my blog is written to offload my thoughts and memories, I couldn’t recall chunks of what I had written. But I knew where to find it! Miller also discusses this phenomenon. But here’s a selected list of my previous posts by title, more for my own use.

·      Paying Attention to Attention

·      Attention Arms Race: Ad Version

·      Filtering Attention

·      The Distracted Mind

How Memory Works

Worried that Google has rotted your memory and that of your students? Then, this book is for you: Remembering and Forgetting in the Age of Technology: Teaching, Learning and the Science of Memory in a Wired World. The author is Michelle Miller, a cognitive psychologist from Northern Arizona University. Her previous book, Minds Online: Teaching Effectively with Technology, is superb and I blogged about it eight years ago. It’s a book I regularly recommend to others interested in the topic, and I’m pleased to say that her latest book joins the list of books I would recommend to my colleagues. Today’s blog will touch on Chapters 2 and 3.

 

Miller discusses some neuromyths, and provides some historical background to the evolving models cognitive scientists have used to uncover how human memory works. Memory is not like taking a videorecording. This misconception misleads us teachers to “assume that just because some new piece of information was introduced during class, students would naturally notice and remember it.” There’s also the classic three-box model of memory that uses the metaphor of a factory conveyor belt to move information from short-term to long-term memory via the technique of rehearsal. (I recommend reading Chapter 2 in Miller’s book for why this model doesn’t work so well.) Neither are students’ brains like containers to be filled with knowledge through transmission into a computer-esque memory bank.

 

Our brains aren’t a blank slate to etch or “code” memories into. Instead, when we learn, we re-code. In fact, every time we try to rehearse a piece of information, we are recoding. But rehearsal isn’t necessarily the best way to recode or to really learn the material. We’ll get to more effective methods a little later, but first I want to highlight the groundwork Miller is laying down. She outlines the evidence for the different subsystems of working memory. One of these is a “visuospatial sketchpad… [which] kicks in when we are doing mental tasks where visualization is key” – this is very relevant in chemistry problem-solving. Several subystems relate to language analysis, and it’s amazing how we parse language and meaning in our native language so fluidly! Miller also goes into detail on the phonological subsystem, it’s the best-studied one. Where it’s important in a subject such as chemistry with significant new scientific terminology: it’s “job is to replay and refresh the pieces of the word’s sound, buying time while other mechanisms create a permanent representation of that new word.”

 

What about long-term memory? It’s that significant limitless “bank” which someone with expertise draws from, myriad connections and all. Miller distinguishes the three types: episodic, semantic and procedural. Episodic memory is tied to a specific experience that you have and you can remember the place and how you were feeling. It’s why certain memorable experiences even from long ago can be triggered. In chemistry class, students will remember a flash-bang demo with surprising sounds, colors, and smells, that they may not have expected! Semantic memory, the primary target in my chemistry courses, is the buildup of conceptual knowledge organized in some form or schema. The richness of semantic memory is what distinguishes the expert chemist from the novice. Both episodic and semantic memory can be triggered by the appropriate cues. Procedural memory is what allows you to carry out a practiced skill without taxing your working memory; knitting or driving a car are examples. In chemistry lab, you can clearly tell the students who’ve had more practice pipetting than others.

 

We remember and we forget. Why does memory work the way it does? Miller’s answer in a word: Survival. “Instead of being a place to store things, memory is an ability that our minds and brains have evolved in order to keep us alive… communication, avoiding danger, prospecting for good thing out there in the world, replicating strategies that have served us in the past, distinguishing friend from foe, solving problems and acquiring skills… makes it more likely that we’ll have only the most relevant, most useful material on hand, and that we will be able to really pick out the thing we need when the chips are down. It also explains the exasperating, now-you-see-it, now-you-don’t quirks of long-term memory.”

 

Miller does a particularly good job articulating why committing certain things to memory is important in learning. She effectively argues against tropes such as “drill and kill” and she highlights problems with the hierarchical representation of Bloom’s Taxonomy. I wholeheartedly agree with her point that memory is an important pillar of learning, and she provides evidence of how strengthening or enhancing memory is crucial for complex reasoning, that “memory and thinking skills enjoy a complementary, not competitive relationship within learning.” I like how Miller cuts to the chase: A key problem in learning is “transfer”, the ability to apply conceptual principles to other cases beyond the exact examples you’ve seen before. Effective memorization of information helps you with transfer. In Miller’s words, transfer “is what makes learning useful.” But it can be hard to do with information that’s not a matter of life-and-death; don’t forget that our brain evolved for survival!

 

Fortunately, we now know more about how humans learn (and we’re still learning) and we can design activities to enhance useful or effective learning. Miller provides examples of how memory tasks can help students develop metacognition in their learning. She provides practical examples of why you might need (as a practitioner) knowledge at your fingertips. As a teacher, if I kept having to look up every piece of information, I’d be a goner. Instead, I try to model in class which pieces of information I need to have immediately available, and which ones students shouldn’t need to memorize. (I actually look them up in class.) But committing information to semantic memory isn’t easy especially since much of what we do in school is biologically secondary. But it’s not easy. Miller reminded me that “it typically takes students a lot of practice and a lot of insight to achieve transfer, and is an area where teachers typically overestimate how quickly and well students really are achieving it” (emphasis mine)!

 

Given the importance of building and enhancing memory, how can we best go about it with our students? Here are my quick summaries of the principles Miller provides:

 

·      Meaning and structure. The key here is meaningful interpretation, and helping students build an organizational scheme. We do this via scaffolding. We do this by telling stories, i.e., providing a narrative structure.

·      Visualization. Humans have evolved to prioritize vision. It’s why a good figure is indeed worth a thousand words or more. That’s why I favor textbooks that not just have good narrative structure, but have excellent figures that are well-designed for learning and not just a pretty picture.

·      Emotional charge. There’s no doubt that emotions heighten memory. For the classroom, there’s evidence that providing a supportive and nonthreatening environment promotes learning. The emotion of surprise can be very effective (we remember plot twists in a story). I’ve been trying to build in more of these aha moments into class.

·      Attention. Duh. Maintaining attention is surprisingly tricky. Much of classroom organization is about this.

·      Connection to goals. I thought this would be more important, but Miller downplays it. Without the other factors, being motivated (having a goal) isn’t quite enough but it can help with attention.

 

Miller goes through several pedagogical strategies to achieve the task of enhancing memory. I was familiar with all of these and they fall under the large rubric of “active learning” approaches. Which strategy you use will depend on who you are, your subject matter of the day, your goals, and who your students are. Local context matters. This is why I’m skeptical of those who promote their particular (and often narrow) pedagogical approach as a silver bullet. I don’t promote my own particular approach either. It works for me. I’m willing to share what I do with others if they’re interested, but I’m not interested in pushing pedagogical strategies.

 

That being said, Miller is preaching to the choir when she highlights one technique that works very well for students: retrieval practice. She writes: “Rarely have we seen a set of findings with such clear and compelling implications for learning… Essentially, what the studies demonstrate is that when students answer questions about material, they remember it better and for longer… It works best when students get immediate feedback, and when the questions are open-ended or short answer in style.” It’s why I give many low-stakes short quizzes at the beginning of class. It’s why I give closed-book exams and require students to generate, and not just recognize, answers. It’s why I try to phrase my study guide in the form of questions and give students questions to test themselves. It’s why I’m always asking them to explain, explain, explain. Some students find it frustrating that I always ask “why” in response to an answer they’ve provided after an initial question. In my experience, this is an effective technique (although not the only one) to learning an abstract, conceptually challenging, jargon-laden, subject such as chemistry. We need to help our students move away from sub-optimal techniques (which Miller also discusses).

 

Miller provides effective rebuttals to those who “are heavily invested in the philosophical stance that tests are a creativity-killing blight on education” and who think that testing only leads to superficial learning. She also addresses the notion that “testing kills authentic enthusiasm for learning and heightens anxiety” – the claim is not only highly subjective, it can “become a self-fulfilling prophecy”. Approaches to testing can be done well and they can be done badly. Low-stakes quizzes help smooth the path, or you could even pipe in a fun way of doing this via Kahoot and other applications that Miller describes. And technology can help our students with retrieval practice. We shouldn’t shun it where it can be very useful.

 

Towards the end of Chapter 3, Miller discusses the symbiosis we have with our electronic information-providing and information-storing devices. Part of why I blog is to offload things I learn from books I’ve read so that I know where to find that information (with “search”!) but I’ve also noticed that by doing so, I do not retain as much of this information for immediate use (the so-called “Google effect”). And that’s okay. But if I didn’t write about it, I’d probably remember less. But I do agree with Miller that “when it comes to the bedrock knowledge of a discipline or professional skill, students shouldn’t fall to looking things up.” This of course begs the question of what that bedrock knowledge is in one’s discipline. Ah, a subject for a different post!

 

P.S. This is the fifth book I’m reading in the “Teaching and Learning in Higher Education” series. For links to the other four, see this previous post.

Two Heads

Two Heads is a delightful “exploration of how our brains work with other brains” composed in a beautifully illustrated format. The main protagonists are cognitive science emeritus professors Uta and Chris Frith, who are also married to each other. Their son, Alex, is an established non-fiction author of books aimed at children. Not to worry if you don’t know anything about brains or neuroscience. They teach you as you read along. And each chapter is masterfully connected to the next so you just want to keep going!

 

While I was familiar with a number of the classic experiments they describe, there were more than enough new things for me to mull over. One that really caught my attention was over-imitation. Many animals learn through imitation, as do humans. Children are imitating what they see and hear all the time! Both children and adults also learn through being taught something explicitly – the basis for setting up an education system! But the interesting part is that when we first learn something, we do it through over-imitation. Instead of just copying, we try to copy exactly. I see this all the time when teaching chemistry, especially because the subject matter is often counter-intuitive.

 

Why do humans over-imitate? (Apes don’t, apparently.) The Friths argue that it’s for social reasons: “We do it because this is the way our group does things, and we want to fit in with out group.” And sometimes we do the opposite: “Deliberately not over-imitating can be a way to mark ourselves part of one group rather than another.”( Interestingly, some autistic kids tend not to over-imitate.) But there’s more. We don’t often notice that we have a tendency to imitate someone who seem more like us (the in-group) rather than someone who seems more different (the out-group). I wonder how that impacts the teaching-learning nexus. As someone who grew up in a different country but who now teaches (mostly) Americans between the ages of 18-22, and who has a noticeable accent when speaking English, I wonder if and how that affects the subconscious parts of student learning in the classroom.

 

There’s an interesting chapter about how the brain recognizes self from other. Apparently, you can tickle yourself if you use a double-robot arm contraption where the second robot arm has a time delay response. Weird. And sometimes the feedback self-recognition loop can break down, and we see this manifested in certain types of delusions, hearing voices, and schizophrenia. Apparently, schizophrenics can often tickle themselves. This discussion leads to the famous experiments by Benjamin Libet – before taking an action, the brain activity can be observed before one consciously recognizes the decision to take the action –  which brings up questions of free-will. The Friths think that interpreting “that your body moves, then your brain retroactively decides that the movement was deliberate” is incorrect. Instead, “your brain predicts it is going to move, then compares the final movement with the prediction. Only after the prediction has been tested does the movement get logged by your brain as a deliberate movement. It’s a quirk of biophysics that this operation takes an amount of time that can be observed and measured.”

 

I’d been thinking along those lines after reading Robert Rosen’s Anticipatory Systems. I think an interesting way to characterize life is that it’s an anticipatory system whose function cannot be cleanly separated from its genesis. Evolution of the brain is to improve anticipatory ability, particularly when it comes to social interactions – at least that’s what I gather from reading Terrence Deacon. This fits well with the overall discussion in Two Heads, and much of the book focuses on the social aspects of cognition of neuroscience. Early in the first chapter, they explicitly say that “your brain is a Bayesian prediction engine”.

 

Halfway through the book there is an interlude chapter discussing how challenging some of these psychology experiments can be with their many limitations and pitfalls. It can be so tempting to over-interpret the data towards pre-conceived notions or something that will be media-buzzworthy. There’s also an interesting section describing a collaboration with an anthropologist who studied what happens at a research institute and compares it to “a Georgian house, where this is a clear hierarchy of people, and set rooms for set tasks. The overall task of the house is to turn nature into science…”

 

Another potential take-home message from the book is that collaboration leads to better outcomes but there’s a caveat: The collaborators need to have similar levels of competency and also similar levels of confidence. There’s also some evidence that diversity improves the outcome. The experiments described are limited so I don’t know how well those conclusions extend to broader settings, but I’m certainly seeing the business world use these ideas to create buzz. I also liked how the Friths’ conclusion, as psychologists, that most people are instinctively nice (because of reputation and group dynamics) in contrast to a purely homo economicus view.  But they also acknowledge that in-group and out-group factors can complicate things.

 

Overall, if learning about the brain, cognition, and social psychology, is something you’re interested in, I recommend Two Heads. It’s an engaging book that threads the needle between giving you the details (without being overwhelming) and the big picture (with examples that you might care about). Overall two thumbs up.

Symbolic Consciousness

Today’s post is Part 3 of Terrence Deacon’s The Symbolic Species (links to Part 1 and Part 2). I finished the book. The last chapter is interesting and thought-provoking. If you don’t want to plow through 464 pages, the last 32 pages will give you the gist of his argument. My quick version: Deacon’s thesis is that what makes humans unique among all other creatures is the co-evolution of the human brain and early hominid ‘societal’ behavior that leads to referential symbolic consciousness and language. Symbolic consciousness, which is necessary from abstract thought, emerges from indexical consciousness which in turn is supported by iconic consciousness. (Other creatures may exhibit indexical and iconic consciousness, and it’s possible they may attain the symbolic but it will be difficult!) Deacon builds his case methodically, although there are still many unknowns and gaps, which he acknowledges.

 

In this day and age, we imagine the mind to be like a computer. Thus, the question arises as to whether our computers’ artificial intelligence can be conscious. Last month Google fired Blake Lemoine, an engineer who claimed that the A.I. chatbot has a soul. Does it? I don’t know. Depends on how one defines soul or consciousness, I suppose. Deacon argues that human symbolic consciousness is virtual in a way that it transcends the physical flesh, blood, and guts. But he’d also say there are no disembodied souls. Cartesian Mind/Body dualism, he thinks, is an ineffective way of tackling the problem of consciousness. Deacon’s argument is more nuanced (you’ll have to read his book for the full version), and while I think his theory still has many unanswered questions, I find his co-evolutionary approach helpful in sketching out the boundary issues. And he seriously takes into account mutual feedback between individual organisms and their environment (that may include fellow organisms). Physical science and social science can’t be separated so cleanly.

 

Deacon considers the pitfalls of equating mind to computing and he carefully intrigues the Searle Chinese Room argument and it’s criticisms. I’ll quote Deacon: “Part of the danger in current computer metaphors comes from our tendency to call typographical characters ‘symbols’, as though their referential power was intrinsic, and to call the deterministic switching of signals in an electronic device a ‘computation’, because it simulates operations we might perform to derive an output string of numbers from an input string according to the laws of mathematics. We fall into the trap of imagining that the sets of electronic tokens (data) that are automatically substituted for one another in a computer according to patterns specified by other set of tokens (programs or algorithms) are self-sufficient symbols, because of their parallelism to the surface features of corresponding human activities. This brackets out of the description the critical fact that the ‘computation’ is only a computation to the extent that someone is able to interpret the inputs and outputs as related in this way… All the representational properties are vested in the interpreter.”

 

At the beginning of chapter 13, Deacon provides the following quote from the journalist Sydney Harris: “The real danger is not that computers will begin to think like men, but that men will begin to think like computers.” Deacon’s book was published 25 years ago. The quote is even more apt today. As we charge into online mass education and using A.I.’s for so-called ‘adaptive learning’, this is precisely what we are doing – embracing what I think is a myopic vision of using computers to teach us to think like them. How could we not? The machine is efficient and tireless, but only at its narrow task. Taylorism rears its ugly head again, and machine-like productivity is king. Before we know it, we’ll no longer know what a joke is, and become artificially unintelligent. Deacon writes: “Our cherished belief in the specialness of consciousness has not prevented us from thoughtlessly treating people as throw-away tools… The question before us is whether we will begin to treat people like unconscious computers, or come to treat conscious computers like people.”

Locating Language

Among many myths of the human brain that have securely latched themselves as parasitic brain-worms is that artistic and creative types are right-brain dominant and that scientist-analyst types are left-brain dominant. Phrenology just won’t die. When it comes to language learning, there are similar pronouncements – usually backed up by some experimental evidence. Broca’s area and Wernicke’s area (which I first learned about from a board game over a decade ago) have been implicated as distinct locations for language because of their associated aphasias (language disorders). But then things get complicated. These two areas aren’t located in the same brain areas in different patients. Neither can one distinguish them at the microanatomical level from other brain areas. That’s not to say they are unimportant; there’s just much more to the messy story.

 

I’ve made it through Part 2 of Terrence Deacon’s The Symbolic Species. (Here’s my take on Part 1.) It’s all about brain structure and evolution. I can get geeky about the brain but some of the detail Deacon provides made my brain mushy and my eyes heavy. But there are nuggets, and admittedly I skimmed my way through some parts to feast on those tasty morsels. Here’s one: “Classic high-level models of language functions were conceived in order to explain the large-scale features of language breakdown, what might be called macrocognition. We must now face up to the daunting task of analyzing the microcognition of language: analyzing phenomena at a level of scale where the functional categories often no longer correspond to any of the familiar behavioral and experiential phenomena we ultimately hope to explain.”

 

So where are the language modules located? And are there even modules to begin with? How did the brain evolve to allow for or accommodate the use of language? My take on Deacon’s story (and admittedly I might not grasp what he’s really getting at) is that the facility of language is distributed over many parts of the brain although one does see localized concentration in some parts that might hint at modular subfunctions. The takeaway I found most interesting was his argument on the evolution of neuronal connections in the developing brain. In particular differential signal input can lead to “cell and axonal displacement effects” and subdivision into discrete areas with different functions.

 

As to why humans are unique among primates and other mammals, Deacon uses the morbid analogy of an alien brain transplant experiment. Imagine transplanting the embryonic brain of a giant extinct eight-foot ape (Gigantopithecus) into the embryonic body of a modern-day chimpanzee. Then let the Franken-ape grow. What happens as the organism develops? Essentially, because the body isn’t going to be so large, fewer parts of the developing brain need to be recruited to take care of motor and other physical functions. But evolution does what it does, and neural nets shift their connections (with differential signal input) and get recruited for other things. Like Daredevil’s amazing sense of hearing because he is blind. I’m not doing Deacon’s more nuanced argument justice so I recommend reading his book (dry as it is in parts) and going through his detailed examples if you find any of this even mildly interesting or wildly unbelievable. And if not, you can at least read his take on Hoover, the talking seal, a very interesting story. Hoover’s brain (at autopsy) showed some damage possibly related to early encephalitis. Could that be why he talked? Short-circuit? We don’t know.

 

I close this post by quoting a paragraph of Deacon’s that speaks to the limits of a reductionist approach: “The central problem faced by researchers studying the brain and language is that even the minutest divisions of cognitive function we hope to explain at the psychological level are ultimately products of the functioning of a whole brain – even if a damaged one – whereas the functions we must explain at a neurological level are the operations (or computations) of structures. If there was ever a structure for which it makes sense to argue that the function of the whole is not the sum of the functions of its parts, the brain is that structure. The difficulty of penetrating very deeply into the logic of brain organization almost certainly reflects the fact that the brain has been designed according to a very different logic than is evident in its most elaborated behavioral and cognitive performances. This is precisely where the comparative and evolutionary approaches can provide their most crucial contribution.”

 

When Deacon tries to tackle the problem of consciousness in a later book, he discusses parallels to the problem of the origin-of-life, possibly an easier problem with a similar architecture. Since the chemical origins of life are my research area, my takeaway is that I need to combine both the logic of chemical evolution and how to functionally think about biochemistry in vivo. Well, I just checked out another library book for that but I should first finish reading Deacon’s book. One more part with 150 pages to go!