Roberts has produced a compelling sketch of Conway both as a highly creative mathematician and as an individual, and somehow also managed to make the fascination of (more or less pure!) mathematics palpable for the layperson. Through their (Roberts and Conway’s) often humorous interactions, we gain a small window into Conway’s background, work ethic, habits, mindset, and struggles (alongside refreshing hints of how at least some of his image was consciously constructed), all of which the academic in me would love to draw some lessons from.

Bonus neuroscience content: Towards the end of the book, they pay a visit to Sandra Witelson, the neuroscientist who has made it her mission to study the brains of individuals thought to have remarkable minds. I am only very loosely acquainted with her work, so I may just be missing the complete picture, but I found myself nodding along to the more skeptical stance of neuroscience-layman Conway in response to some of her thoughts and methods, such as when she said, “I have people asking me whether Einstein’s brain got to be the way it is because he did so much physics. And of course I think it is the other way around. I think he did so much physics because his brain had a certain anatomy.”
I doubt her narrative, but that is far from the point of the book, so I’ll leave it at that. Suffice it to say that I don’t think that her fMRI studies of Conway will produce any meaningful insights into his creative ingenuity.

I took a lot of additional pleasure in the vivid scenes of his life in Cambridge (between the 50s and the 80s), both in the sense of its historical insights, as well as that reminiscent delight of tracing a historical narrative in a physical place that one is (at least slightly) familiar with.

I really enjoyed this trip, and look forward to further encounters with mathematical ideas and concepts (and personalities). (Interestingly, the “beauty and truth” of mathematics, as propagated here, conveniently show the seduction with which certain pockets of theoretical physics may have gotten lost in math.)

Genuis at play: The curious mind of John Horton Conway (Siobhan Roberts)

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This is less of a review and more of a reflection of how the issues raised in this book relate to my area of science. As a psychologist, it is refreshing to learn that psychology’s struggles with theories aren’t unique to psychology (and other social sciences), particularly when precise measurement is a challenge (which it almost always is when your subjects are human beings). As soon as you have difficulty measuring the phenomena that you’re interested in, good old human cognitive biases come into play, regardless of whether you’re a psychologist or physicist, regardless of your mathematical proficiency, regardless of the sophistication of your analytical models.

If particle physicists can be biased by subjective perceptions of mathematical beauty, imagine how much of a struggle it must be to deal with theorists (and outsiders) who feel that they can weigh in on your discussions simply because they have intuitive feelings about the constructs in question, which they may or may not justify with a couple of anecdotes. (Which is not to say that that can never be useful or valid in the process of refining one’s ideas, just that it leads to an inflation of self-assured interjections.)

Of particular interest (but only a brief point in the book) is the observation that this struggle apparently applies not only to theory-building, but also to statistical analyses of empirical observations. Hossenfelder provides examples of multiple published results (in particle physics) that were, in hindsight, probably the result of the type of researcher degrees of freedom that have been identified as a cause for many of the unreliable results (particularly false positive findings) in psychology and co. The fact that it seems to be a relatively new development for collaborations in physics, according to Hossenfelder, “… to settle on a method of analysis before even looking at the data (before the data is ‘unblinded’)” suggests that the current wave of open science measures that seeks to boost reproducibility and replicability in the social sciences isn’t too far behind fields that have typically been perceived as being more methodologically rigorous, and needs to be given time to grow. (Although it is important to stress that most of the core issues of these questionable research practices were known and written about by psychologists decades ago, so none of this is an attempt to absolve the field of its belated response to the blame that it is finally attempting to deal with on a more collective basis.)

There’s a fitting quote often attributed (in different forms) either to Richard Feynman or Murray Gell-Mann: “Imagine how much harder physics would be if electrons had emotions.” I currently don’t have access to any sources to verify whether either (or both, or neither) of them said or wrote that thought, but it sure rings true. As Sanjay Srivastava says, if we were to rethink the common continuum of sciences ranging from “hard” to “soft”, and instead differentiate between “hard” and “easy”, all of the extraneous variables that add complexity to the questions that psychology seeks to answer would probably make it one of the hardest sciences. Alongside theoretical particle physics.

Lost in math: How beauty leads physics astray (Sabine Hossenfelder)

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“Upend the rain stick and what happens next
Is a music that you never would have known
To listen for. In a cactus stalk …”

Seamus Heaney inadvertently provides an introduction to the premise of Haskell’s book, which expands on the idea of actually paying close acoustic attention to life forms that aren’t usually thought of as producers of sound. He does so by visiting and revisiting a selection of a dozen trees around the world, observing and listening to them and their surroundings through a variety of methods, and using his vast knowledge of nature to put everything into context. And the way that he unpacks this context is the main point of the book.

I was absolutely enchanted by Haskell’s first effort, ‘The Forest Unseen‘. I found the first couple of chapters of ‘The Songs of Trees’ a bit more challenging to get into, but I was more than ready to rhapsodise about it by the end. Haskell once again managed to capture facts of nature in exceedingly beautiful prose. Case in point, this excursion on sea foam: “The foam is made from the pulverized remains of algae and other microscopic life. When these cells break apart in the tumult of the ocean, they release proteins and fats into the water. These chemicals act like soap in a bath, changing the surface tension of water. When the wind agitates the water surface, like a hand whipping a bubble bath, the result is a froth. Sea-foam is a memory of the biology of the ocean, blown to land.”

(Although I did feel that it was a tad bit overdone at certain points — e.g., in describing the information on whether or not to swim in a creek after the rain had added sewage to it, “A click on my social media account tells me whether it would be wise to dunk in this effluvium”).

Regardless, like Haskell and his trees, I definitely plan to revisit both of his books in the future, and I’m already looking forward to it.

“… Who cares if all the music that transpires
Is the fall of grit or dry seeds through a cactus?
You are like a rich man entering heaven
Through the ear of a raindrop. Listen now again.”
(Seamus Heaney, ‘The Rain Stick’)

The songs of trees: Stories from nature’s great connectors (David Haskell)

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This book is an easy read, it contains some good ideas and potential information, and it provides some food for thought with regard to its main topic, so I’m not about to advise anyone against checking it out just based on the quibbles that I may have had (some of which may be due to decisions by the publisher/editor to oversimplify some of the content and clearly aim for a pop science flavour to it).

Still, I am afraid that quite a number of points bothered me about this book. I found myself not quite buying some of the interpretations of experimental manipulations. I understand that the descriptions are simplified, so it may very well be that additional manipulation checks just weren’t mentioned, but the issue extended beyond experimental assumptions to interpretations of behaviour in other studies.

If my issues with the book were limited to interpretations, it wouldn’t be quite so bad, but I was also left absolutely unsure about whether the magnitude of the effects were really reported in a fair manner. (For individuals not familiar with statistical methods: the vast majority of psychological differences still result in huge overlap between groups, so simply observing a “difference” doesn’t mean that this difference also has practical relevance.)
I am less concerned about the author’s own studies, but his interpretations of other studies are unfortunately questionable. For example, he introduces the infamous Facebook study, in which emotional content within news feeds was manipulated. The author describes the findings this way: “The results were unambiguous; people’s moods moved towards the contents of their manipulated news feeds. Those who saw more sad posts subsequently posted more sad updates themselves. Those who saw more happy posts showed a similar mirroring.”
Having looked at the actual effects reported in the journal article, these statements are extremely misleading. Yes, the study found “significant” statistical differences. However, these differences were only statistically significant because they had several hundred thousand “participants” to magnify these differences. To draw a direct example from the article: “When positive posts were reduced in the News Feed, … the percentage of words that were negative increased by B = 0.04% (t = 2.71, p = 0.007, d = 0.001)”.
In other words 0.04% = 1 word for every 2,500 words typed across many, many people! That is such a small effect that it is surely hardly worth mentioning, much less presenting as “unambiguous”.

The non-critical description of the Facebook study wasn’t the only point in the book where I found myself questioning either the effect size or the statistical power of the study design of a cited study, which suggests that the author may not have paid attention to these details in at least some of the findings reported in the book (particularly when they weren’t based on studies from his own lab). On top of that, some of the conclusions drawn from fMRI studies seem vastly overstated (with some of them sounding like reverse inferencing).

Casual readers may not place much importance on the next point, but as someone with an interest in the research, I was disappointed in the lack of in-text citations. (I also found it strange that the text cites a particular study by Wilhelm Hofmann several times throughout the book — but at one point introduces Kathleen Vohs, lists her credentials, then describes the study conducted by Hofmann in Germany with words such as “after she studies people’s behavior in their natural environs”, even though it is quite probable that she only played a role in writing the manuscript.)

Overall, I think that the book has many important ideas, but I’m unsure about the strategy of writing in this supposed accessible manner. And for a book meant to summarise other studies, it didn’t do a good job of critically presenting these studies, thus leaving me with too much uncertainty regarding the ideas.

Emotional success: The motivational power of gratitude, compassion and pride (David DeSteno)

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For outsiders, a lot of neuroscience research can feel like it’s only interesting as foundational/basic research, while the interpretation of social neuroscience, which is presented as being more relevant, can be really frustrating (such as when meaning is assigned to the “activation” of a certain brain area based on what these areas have been linked to in the past — which isn’t appropriate!). The research presented in this book provides a good accessible example of the complementary value of neuroscience research in providing support for a theory that is relevant to our understanding of how we think and feel in our everyday lives.

Having said that, the writing was a tad bit repetitive to me — even beyond the fact that it was written to be accessible for readers with not much knowledge of neuroscience. While mentioning nitpicky points, some of the illustrative examples didn’t quite work for me (although I was able to picture more plausible examples for myself, so these aren’t terrible faults). I’m also unsure about how critically the author looked at studies that she cites to support points that she made outside the context of her own research expertise. For example, she mentions a study on hungry judges several times, although a closer look at the study should prompt skepticism (see this post by Daniel Lakens for the implausibility of the effect size + links to plausible mechanisms/other criticisms). As such, I would be more careful about accepting some of her supporting arguments and recommendations as readily as the ones directly derived from research that she is actually active in. As with the illustrative examples, however, this shouldn’t be seen as a major flaw.

The main point of the book is of course the description of emotions that the author would like to propagate, and the proposed framework definitely provides a lot of food for thought. I must also laud the amount of work that she invested, not just into the extensive references (which should be a given for any serious science writer), but especially into the additional explanations (e.g., of basic neuroanatomy) AND the supporting website that explains some of the points that she makes in even further detail. See https://how-emotions-are-made.com/notes/All_endnotes

How emotions are made: The secret life of the brain (Lisa Feldman Barrett)

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A lot has already been said about this classic. I would just like to recommend two videos by the Numberphile channel on YouTube that greatly enhanced my enjoyment of the material covered in the book:

On the Feigenbaum constant https://www.youtube.com/watch?v=ETrYE4MdoLQ

On the chaos game (in the chapter “Images of Chaos”) https://www.youtube.com/watch?v=kbKtFN71Lfs&t

Chaos: Making a new science (James Gleick)

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Paul Ellis provides a very accessible introduction to several basic topics that are vital to good research practice. In order to encourage readers who enjoyed this book to dive deeper into the literature, I feel the need to point out that the content is slightly out of date on several counts; two points that immediately come to mind are (1) the non-critical presentation (or even the outright recommendation?) of the flawed “fail-safe N” method (see Becker, 2005; Ioannidis, 2008; Sutton, 2009; Ferguson & Heene, 2012 for criticisms, or this Cochrane handbook link for a brief summary) and (2) the lack of mention of viable methods to provide support for null-hypotheses (e.g., equivalence testing or Bayesian methods).

That should, however, not take away from the fact that the text offers an easily comprehensible treatment of the subject matter, and I would recommend it to anyone who happens to be searching for a non-technical introduction to the topics that it covers.

Becker, B. J. (2005). Failsafe N or file-drawer number. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysis. Sussex: Wiley.
Ferguson, C. J., and Heene, M. (2012). A vast graveyard of undead theories: Publication bias and psychological science’s aversion to the null. Perspectives on Psychological Science, 7, 555–561. doi: 10.1177/1745691612459059
Ioannidis, J. P. A. (2008). Interpretation of tests of heterogeneity and bias in meta-analysis. Journal of Evaluation in Clinical Practice, 14, 951–957. doi: 10.1111/j.1365-2753.2008.00986.x
Sutton, A. J. (2009). Publication bias. In H. Cooper, L. Hedges, and J. Valentine (Eds.), The handbook of research synthesis and meta-analysis (pp. 435–452). New York: Russell Sage Foundation.

The essential guide to effect sizes (Paul Ellis)

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I had never heard of Alexander von Humboldt prior to my arrival in Berlin (in 2010), but several years of calling this city my home (as well as employment at the university named after him and his brother) have ensured an accumulation of facts associated with his name, facts that were but a superficial nod of acknowledgement now that I have had the pleasure of learning about his tremendous dedication to his craft, his progressive beliefs and insights, and, possibly most eye-opening to me, his enormous influence on both contemporary and following generations of scientists, thinkers, and poets. (One of my favourite chapters covers this aspect from the perspective of Charles Darwin.) There were moments when I found myself questioning whether the author may have been overstating her case, but she ultimately presented more than enough evidence to satisfy my personal hesitation. I don’t know whether there is a comparable biography of Alexander von Humboldt out there, but I’m truly glad that Andrea Wulf decided to dedicate herself to the exposure of this story at this point of time.

The invention of nature: Alexander von Humboldt’s new world (Andrea Wulf)

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Dienes provides an introduction to some foundational topics that are sorely missing from the typical Psychology degree programme — the philosophy of science (from several different perspectives) and a conceptual comparison of three main methods of statistical inference: the frequentist, Bayesian, and likelihood approaches. The book could have benefited from a more meticulous editor, but Dienes’s writing is, for the most part, clear and insightful, and he provides very useful suggestions for further reading.

I would highly recommend this book (alongside the free Coursera course on “Improving your statistical inferences” by Daniel Lakens) to anyone interested in the logic behind empirical science. (And I do hope that most students of related fields would feel spoken to here.)

Understanding psychology as a science: An introduction to scientific and statistical inference (Zoltan Dienes)

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