Tag: science education

Determinism versus free will, a false dichotomy 

from wikipedia – Brownian motion

You might be constrained and contingent on past events, but you are not determined! (that said you are not exactly free either).

AI Generated Summary: Arguments for and against determinism and free will in relation to biological systems often overlook the fact that neither is entirely consistent with our understanding of how these systems function. The presence of stochastic, or seemingly random, events is widespread in biological systems and can have significant functional effects. These stochastic processes lead to a range of unpredictable but often useful behaviors. When combined with self-consciousness, such as in humans, these behaviors are not entirely determined but are still influenced by the molecular and cellular nature of living systems. They may feel like free actions, but they are constrained by the inherent biological processes.

Recently two new books have appeared arguing for (1) and against (2) determinism in the context of biological systems. There have also been many posts on the subject (here is the latest one by John Horgan. These works join an almost constant stream of largely unfounded, and bordering on anti-scientific speculation, including suggestions that consciousness has non-biological roots and exists outside of animals. Speaking as a simple molecular/cellular biologist with a more than passing interest in how to teach scientific thinking effectively, it seems necessary to anchor any meaningful discussion of determinism vs free will in clearly defined terms. Just to start, what does it mean to talk about a system as “determined” if we can not accurately predict its behavior?  This brings us to a discussion of what are known as stochastic processes.  

The term random is often use to describe noise, unpredictable variations in measurements or the behavior of a system. Common understanding of the term random implies that noise is without a discernible cause. But the underlying assumption of the sciences, I have been led to believe, is that the Universe is governed exclusively by natural processes; magical or supernatural processes are not necessary and are excluded from scientific explanations. The implication of this naturalistic assumption is that all events have a cause, although the cause(s) may be theoretically or practically unknowable. For example, there are the implications of Heisenberg’s uncertainty principle, which limits our ability to measure all aspects of a system. On the practical side, measuring the position and kinetic energy of each molecule (and the parts of larger molecules) in a biological system is likely to kill the cell. The apparent conclusion is that the measurement accuracy needed to consider a system, particularly a biological system as “determined” is impossible to achieve. In a strict sense, determinism is an illusion. 

The question that remains is how to conceptualize the “random”and noisy aspects of systems.  I would argue that the observable reality of stochasticity, particularly in biological systems at all levels of organization, from single cells to nervous systems, largely resolves the scientific paradox of randomness. Simply put, stochastic processes display a strange and counter-intuitive behavior: they are unpredictable at the level of individual events, but the behaviors of populations become increasingly predictable as population size increases. Perhaps the most widely known examples of stochastic processes are radioactive decay and Brownian motion. Given a large enough population of atoms, it is possible to accurately predict the time it takes for half of the atoms to decay. But knowing the half-life of an isotope does not enable us to predict when any particular atom will decay. In Schrödinger’s famous scenario a living cat is placed in an opaque box containing a radioactive atom; when the atom decays, it activates a process that leads to the death of the cat.  At any particular time after the box is closed, it is impossible to predict with certainty whether the cat is alive or dead because radioactive decay is a stochastic process. Only by opening the box can we know for sure the state of the cat.  We can, if we know the half-life of the isotope, estimate the probability that the cat is alive but rest assured, as a biologist who has a cat, at no time is the cat both alive and dead. We cannot know the “state of the cat” for sure until we open the box.

Something similar is going on with Brownian motion, the jiggling of pollen grains in water first described by Robert Brown in 1827. Einstein reasoned that “if tiny but visible particles were suspended in a liquid, the invisible atoms in the liquid would bombard the suspended particles and cause them to jiggle”. His conclusion was that Brownian motion provided evidence for the atomic and molecular nature of matter. Collisions with neighboring molecules provides the energy that drives diffusion; it drives the movement of molecules so that regulatory interactions can occur and provides the energy needed to disrupt such molecular interactions. The stronger the binding interaction between atoms or molecules the longer, ON AVERAGE, they will remain associated with one another. We can measure interaction affinities based on the half-life of interactions in a large enough population, but as with radioactive decay when exactly any particular complex dissociates cannot be predicted.

Molecular processes clearly “obey” rules. Energy is moved around through collisions, but we cannot predict when any particular event will occur. Gene expression is controlled by the assembly and disassembly of multicomponent complexes. The result is that we cannot know for sure how long a particular gene will be active or repressed. The result of such unpredictable assembly/disassembly events leads to what is known as transcriptional bursting; bursts of messenger RNA synthesis from a gene followed by periods of “silence” (3).  A similar behavior is associated with the synthesis of polypeptides (4). Both processes can influence cellular and organismic behaviors. Many aspects of biological systems,  including embryonic development, immune system regulation, and the organization and activity of neurons and supporting cells involved in behavioral responses to external and internal signals (5), display such noisy behaviors.

Why are biological systems so influenced by stochastic processes? Two simple reasons – they are composed of small, sometimes very small numbers of specific molecules. The obvious and universal extreme is that a cell typically contains one to two copies of each gene. Remember, a single change in a single gene can produce a lethal effect on the cell or organism that carries it.  Whether a gene is “expressed” or not can alter, sometimes dramatically, cellular and system behaviors. The number of regulatory, structural, and catalytic molecules (typically proteins) present in a cell is often small leading to a situation in which the effects of large numbers do not apply. Consider a “simple” yeast cell. Using a range of techniques Ho et al (6) estimated that such cells contain about 42 million protein molecules. A yeast cell has around 5300 genes that encode protein components, with an average of 8400 copies of each protein. In the case of proteins present at low levels, the effects of noise can be functionally significant. While human cells are larger and contain more genes (~25,000) each gene remains at one to two copies per cell. In particular, the number of gene regulatory proteins tends to be on the low side. If you are curious the B10NUMB3R5 site hosted by Harvard University provides empirically derived estimates of the average number of various molecules in various organisms and cell types. 

The result is that noisy behaviors in living systems are ubiquitous and their effects unavoidable. Uncontrolled they could lead to the death of the cell and organism. Given that each biological system appears to have an uninterrupted billion year long history going back to the “last universal common ancestor”, it is clear that highly effective feedback systems monitor and adjust the living state, enabling it to respond to molecular and cellular level noise as well as various internal and external inputs. This “internal model” of the living state is continuously updated to (mostly) constrain stochastic effects (7).  

Organisms exploit stochastic noise in various ways. It can be used to produce multiple, and unpredictable behaviors  from a single genome, and are one reason that identical twins are not perfectly identical (8). Unicellular organisms take advantage of stochastic processes to probe (ask questions of) their environment, and respond to opportunities and challenges. In a population of bacteria it is common to find that certain cells withdraw from active cell division, a stochastic decision that renders them resistant to antibiotics that kill rapidly dividing cells. These “persisters” are no different genetically from their antibiotic-sensitive relatives (9). Their presence enables the population to anticipate and survive environmental challenges.  Another unicellular stochastically-regulated system is the bacteria E. coli‘s lac operon, a classic system that appears to have traumatized many a biology student.  It enables the cell to ask “is there lactose in my environment?”  How?  A repressor molecule, LacI, is present in about 10 copies per cell. When more easily digestible sugars are absent the cell enters a stress state. In this state, when the LacI protein is knocked off the gene’s regulatory region there is a burst of gene expression. If lactose is present the proteins encoded by the operon are synthesized and enable lactose to enter and be broken down. One of the breakdown products inhibits the repressor protein, so that the operon remains active. No lactose present? The repressor rebinds and the gene goes silent (10).  Such noisy regulatory processes enables cells to periodically check their environment so that genes stay on only when they are useful.   

As noted by Honegger & de Bivort (11)(see also post on noise) decades of inbreeding with rodents in shared environments eliminated only 20–30% of the observed variance in a number of phenotypes. Such unpredictability can be beneficial. If an organism always “jumps” in the same direction on the approach of a predator it won’t take long before predators anticipate their behavior. Recent molecular techniques, particularly the ability to analyze the expression of genes at the single cell level, have revealed the noisiness of gene expression within cells of the same “type”.  Surprisingly, in about 10% of human genes, only the maternal or the paternal version of a gene is expressed in a particular cell, leading to regions of the body with effectively different genomes.  This process of “monoallelic expression” is distinct from the dosage compensation associated with the random “inactivation” of one or the other X-chromosomes in females. Monoallelic expression has been linked to  “adaptive signaling processes, and genes linked to age-related diseases such as neurodegeneration and cancer” (12). The end result of noisy gene expression, mutation, and various “downstream” effects is that we are all mosaics, composed of clones of cells that behave differently due to noisy molecular differences.  

Consider your brain. On top of the recently described identification of  over 3000 neural cell types in the human brain (13), there is noisy as well as experience-dependent variation in gene expression, neuronal morphology and connectedness, and in the rates and patterns of neuronal firing due to differences in synaptic structure, position, strength, and other factors. Together these can be expected to influence how you (your brain) perceives and processes the external world, your own internal state, and the effects associated with the interaction between these two “models”.  Of course the current state of your brain has been influenced, constrained by and contingent upon by past inputs and experiences, and the noisy events associated with its development. At the cellular level, the sum of these molecular and cellular interactions can be considered the consciousness of the cell, but this is a consciousness not necessarily aware of itself. In my admittedly naive view, as neural systems, brains, grow in complexity, they become aware of their own workings. As Godfrey-Smith (14) puts it, “brain processes are not causes of thoughts and experiences; they are thoughts and experiences”.  Thoughts become inputs into the brain’s model of itself.

What seems plausible is that as nervous systems increase in complexity, processing increasing amounts of information including information arising from its internal workings, it may come to produce a meta-model that for reasons “known” only to itself needs to make sense of those experiences, feelings, and thoughts. In contrast to the simpler questions asked by bacteria, such as “is there an antibiotic or lactose in my world?”, more complex (neural) systems may ask “who is to blame for the pain and suffering in the world?”  I absent-mindedly respond with a smile to a person at a coffeehouse, and then my model reconsiders (updates) itself depending, in part, upon their response, previous commitments or chores, and whether other thoughts distract or attract “me”. Out of this ferment of updating models emerges self-conscious biological activities – I turn to chat or bury my head back in my book. How I (my model) responds is a complex function of how my model works and how it interprets what is going on, a process influenced by noise, genetics, and past experiences; my history of rewards, traumas, and various emotional and “meaningful” events.

Am I (my model) free to behave independently from these effects? no! But am I (my model) determined by them, again no! The effects of biological noise in its various forms, together with past and present events will be reinforced or suppressed by my internal network and my history of familial, personal, and social experiences. I feel “free” in that there are available choices, because I am both these models and the process of testing and updating them. Tentative models of what is going on (thinking fast) are then updated based on new information or self-reflection (thinking slower). I attempt to discern what is “real” and what seems like an appropriate response. When the system (me) is working non-pathologically, it avoids counter-productive, self-destructive ideations and actions; it can produce sublime metaphysical abstractions and self-sacrificing (altruistic) behaviors.  Mostly it acts to maintain itself and adapt, often resorting to and relying upon the stories it tells itself.  I am neither determined nor free, just an organism coping, or attempting to cope, with the noisy nature of existence, its own internal systems, and an excessively complex neural network.

Added notes: Today (5 Dec. 23) was surprised to discover this article (Might There Be No Quantum Gravity After All?) with the following quote “not all theories need be reversible, they can also be stochastic. In a stochastic theory, the initial state of a physical system evolves according to an equation, but one can only know probabilistically which states might occur in the future—there is no unique state that one can predict.” Makes you think! Also realized that I should have cited Zechner et al (added to REF 11) and now I have to read “Free will without consciousness? by Mudrik et al.,  2022. Trends in Cog. Sciences 26: 555-566.

Literature cited

  1. Sapolsky, R.M. 2023. Determined: A Science of Life Without Free Will. Penguin LLC US
  2. Mitchell, K.J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton. 
  3. Fukaya, T. (2023). Enhancer dynamics: Unraveling the mechanism of transcriptional bursting. Science Advances, 9(31), eadj3366.
  4. Livingston, N. M., Kwon, J., Valera, O., Saba, J.A., Sinha, N.K., Reddy, P., Nelson, B. Wolfe, C., Ha, T.,Green, R., Liu, J., & Bin Wu (2023). Bursting translation on single mRNAs in live cells. Molecular Cell. 
  5. Harrison, L. M., David, O., & Friston, K. J. (2005). Stochastic models of neuronal dynamics. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1457), 1075-1091. 
  6. Ho, B., Baryshnikova, A., & Brown, G. W. (2018). Unification of protein abundance datasets yields a quantitative Saccharomyces cerevisiae proteome. Cell systems, 6, 192-205. 
  7. McNamee & Wolpert (2019). Internal models in biological control. Annual review of control, robotics, and autonomous systems, 2, 339-364.
  8. Czyz, W., Morahan, J. M., Ebers, G. C., & Ramagopalan, S. V. (2012). Genetic, environmental and stochastic factors in monozygotic twin discordance with a focus on epigenetic differences. BMC medicine, 10, 1-12.
  9. Manuse, S., Shan, Y., Canas-Duarte, S.J., Bakshi, S., Sun, W.S., Mori, H., Paulsson, J. and Lewis, K., 2021. Bacterial persisters are a stochastically formed subpopulation of low-energy cells. PLoS biology, 19, p.e3001194.
  10. Vilar, J. M., Guet, C. C. and Leibler, S. (2003). Modeling network dynamics: the lac operon, a case study. J Cell Biol 161, 471-476.
  11. Honegger & de Bivort. 2017. Stochasticity, individuality and behavior & Zechner, C., Nerli, E., & Norden, C. 2020. Stochasticity and determinism in cell fate decisions. Development, 147, dev181495.
  12. Cepelewicz 2022. Nature Versus Nurture? Add ‘Noise’ to the Debate. 
  13. Johansen, N., Somasundaram, S.,Travaglini, K.J., Yanny, A.M., Shumyatcher, M., Casper, T., Cobbs, C., Dee, N., Ellenbogen, R., Ferreira, M., Goldy, J., Guzman, J., Gwinn, R., Hirschstein, D., Jorstad, N.L.,Keene, C.D., Ko, A., Levi, B.P.,  Ojemann, J.G., Nadiy, T.P., Shapovalova, N., Silbergeld, D., Sulc, J., Torkelson, A., Tung, H., Smith, K.,Lein, E.S., Bakken, T.E., Hodge, R.D., & Miller, J.A (2023). Interindividual variation in human cortical cell type abundance and expression. Science, 382, eadf2359.
  14. Godfrey-Smith, P. (2020). Metazoa: Animal life and the birth of the mind. Farrar, Straus and Giroux.

Sounds like science, but it ain’t …

we are increasingly assailed with science-related “news” – stories that too often involve hype and attempts to garner attention (and no, half-baked ideas are not theories, they are often non-scientific speculation or unconstrained fantasies).

The other day, as is my addiction, I turned to the “Real Clear Science” website to look for novel science-based stories (distractions from the more horrifying news of the day). I discovered two links that seduced me into clicking: “Atheism is not as rare or as rational as you think” by Will Gervais and Peter Sjöstedt-H’s “Consciousness and higher spatial dimensions“.  A few days later I encountered “Consciousness Is the Collapse of the Wave Function” by Stuart Hameroff. On reading them (more below), I faced the realization that science itself, and its distorted popularization by both institutional PR departments and increasingly by scientists and science writers, may be partially responsible for the absurdification of public discourse on scientific topics [1].  In part the problem arises from the assumption that science is capable of “explaining” much more than is actually the case. This insight is neither new nor novel. Timothy Caulfield’s essay Pseudoscience and COVID-19 — we’ve had enough already focuses on the fact that various, presumably objective data-based, medical institutions have encouraged the public’s thirst for easy cures for serious, and often incurable diseases.  As an example, “If a respected institution, such as the Cleveland Clinic in Ohio, offers reiki — a science-free practice that involves using your hands, without even touching the patient, to balance the “vital life force energy that flows through all living things” — is it any surprise that some people will think that the technique could boost their immune systems and make them less susceptible to the virus?” That public figures and trusted institutions provide platforms for such silliness [see Did Columbia University cut ties with Dr. Oz?] means that there is little to distinguish data-based treatments from faith- and magical-thinking based placebos. The ideal of disinterested science, while tempered by common human frailties, is further eroded by the lure of profit and/or hope of enhanced public / professional status and notoriety.  As noted by Pennock‘ “Science never guarantees absolute truth, but it aims to seek better ways to assess empirical claims and to attain higher degrees of certainty and trust in scientific conclusions“. Most importantly, “Science is a set of rules that keep the scientists from lying to each other. [2]

It should surprise no one that the failure to explicitly recognize the limits, and evolving nature of scientific knowledge, opens the door to self-interested hucksterism at both individual and institutional levels. Just consider the number of complementary/alternative non-scientific “medical” programs run by prestigious institutions. The proliferation of pundits, speaking outside of their areas of established expertise, and often beyond what is scientifically knowable (e.g. historical events such as the origin of life or the challenges of living in the multiverse which are, by their very nature, unobservable) speaks to the increasingly unconstrained growth of pathological, bogus, and corrupted science  which, while certainly not new [3], has been facilitated by the proliferation of public, no-barrier, no-critical feedback platforms [1,4].  Ignoring the real limits of scientific knowledge and rejecting, or ignoring, the expertise of established authorities, rejects the ideals that have led to science that “works”.  

Of course, we cannot blame the distortion of science for every wacky idea; crazy, conspiratorial and magical thinking may well be linked to the cognitive “features” (or are they bugs) of the human brain. Norman Cohn describes the depressing, and repeated pattern behind the construction of dehumanizing libels used to justify murderous behaviors towards certain groups [5].  Recent studies indicate that brains, whether complex or simple neural networks, appear to construct emergent models of the world, models they use to coordinate internal perceptions with external realities [6].  My own (out of my area of expertise) guess is that the complexity of the human brain is associated with, and leads to the emergence of internal “working models” that attempt to make sense of what is happening to us, in part to answer questions such as why the good die young and the wicked go unpunished. It seems likely that our social nature (and our increasing social isolation) influences these models, models that are “checked” or “validated” against our experiences. 

It was in this context that Gervais’s essay on atheism caught my attention. He approaches two questions: “how Homo sapiens — and Homo sapiens alone — came to be a religious species” and “how disbelief in gods can exist within an otherwise religious species?”  But is Homo sapiens really a religious species and what exactly is a religion? Is it a tool that binds social groups of organisms together, a way of coping with, and giving meaning to, the (apparent) capriciousness of existence and experience, both, or something else again?  And how are we to know what is going on inside other brains, including the brains of chimps, whales, or cephalopods? In this light I was struck by an essay by Sofia Deleniv “The ‘me’ illusion: How your brain conjures up your sense of self” that considers the number of species that appear to be able to recognize themselves in a mirror. Turns out, this is not nearly as short a list as was previously thought, and it seems likely that self-consciousness, the ability to recognize yourself as you, may be a feature of many such systems.  Do other organisms possess emergent “belief systems” that help process incoming and internal signals, including their own neural noise? When the author says, “We then subtly gauge participants’ intuitions” by using “a clever experiment to see how people mentally represent atheists” one is left to wonder whether there are direct and objective measures of “intuitions” or “mental representations”?   Then the shocker, after publishing a paper claiming that “Analytic Thinking Promotes Religious Disbelief“, the authors state that “the experiments in our initial Science paper were fatally flawed, the results no more than false positives.’ One is left to wonder did the questions asked make sense in the first place. While it initially seemed scientific (after all it was accepted and published in a premiere scientific journal), was it ever really science? 

Both “Consciousness and Higher Spatial Dimensions” and “Consciousness Is the Collapse of the Wave Function”, sound very scientific. Some physicists (the most sciencey of scientists, right?) have been speculating via “string theory” and “multiverses”, a series of unverified (and likely unverifiable) speculations, that they universe we inhabit has many many more than the three spatial dimensions we experience.  But how consciousness, an emergent property of biological (cellular) networks, is related to speculative physics is not clear, no matter what Nobel laureates in physics may say.  Should we, the people, take these remarks seriously?  After all these are the same folks who question the reality of time (for no good reason, as far as I can tell, as I watch my new grandchild and myself grow older rather than younger). 

Part of the issue involves what has been called “the hard problem of consciousness”, but as far as I can tell, consciousness is not a hard problem, but a process that emerges from systems of neural cells, interacting with one another and their environment in complex ways, not unlike the underlying processes of embryonic development, in which a new macroscopic organism composed of thousands to billions of cells emerges from a single cell.  And if the brain and body are generating signals (thoughts) then in makes sense these in turn feed back into the system, and as consciousness becomes increasingly complex, these thoughts need to be “understood” by the system that produced them.  The system may be forced to make sense of itself (perhaps that is how religions and other explanatory beliefs come into being, settling the brain so that it can cope with the material world, whether a nematode worm, an internet pundit, a QAnon wack-o, a religious fanatic, or a simple citizen, trying to make sense of things.

Thanks to Melanie Cooper for editorial advice and Steve Pollock for checking my understanding of physics; all remaining errors are mine alone!

  1. Scheufele, D. A. and Krause, N. M. (2019). Science audiences, misinformation, and fake news. Proceedings of the National Academy of Sciences 116, 7662-7669
  2. Kenneth S. Norris, cited in False Prophet by Alexander Kohn (and cited by John Grant in Corrupted Science. 
  3.  See Langmuir, I. (1953, recovered and published in 1989). “Pathological science.” Research-Technology Management 32: 11-17; “Corrupted Science: Fraud, Ideology, and Politics in Science” and “Bogus Science: or, Some people really believe these things” by John Grant (2007 and 2009)
  4.  And while I personally think Sabine Hossenfelder makes great explanatory videos, even she is occasionally tempted to go beyond the scientifically demonstrable: e.g. You don’t have free will, but don’t worry and An update on the status of superdeterminism with some personal notes  
  5.  Norman Cohn’s (1975) “Europe’s Inner Demons” will reveal.
  6. Kaplan, H. S. and Zimmer, M. (2020). Brain-wide representations of ongoing behavior: a universal principle? Current opinion in neurobiology 64, 60-69.

Misinformation in and about science.

originally published as https://facultyopinions.com/article/739916951 – July 2021

There have been many calls for improved “scientific literacy”. Scientific literacy has been defined in a number of, often ambiguous, ways (see National Academies of Sciences and Medicine, 2016 {1}). According to Krajcik & Sutherland (2010) {2} it is “the understanding of science content and scientific practices and the ability to use that knowledge”, which implies “the ability to critique the quality of evidence or validity of conclusions about science in various media, including newspapers, magazines, television, and the Internet”. But what types of critiques are we talking about, and how often is this ability to critique, and the scientific knowledge it rests on, explicitly emphasized in the courses non-science (or science) students take? As an example, highlighted by Sabine Hossenfelder (2020) {3}, are students introduced to the higher order reasoning and understanding of the scientific enterprise needed to dismiss a belief in a flat (or a ~6000 year old) Earth?

While the sources of scientific illiteracy are often ascribed to social media, religious beliefs, or economically or politically motivated distortions, West and Bergstrom point out how scientists and the scientific establishment (public relations departments and the occasional science writer) also play a role. They identify the problems arising from the fact that the scientific enterprise (and the people who work within it) act within “an attention economy” and “compete for eyeballs just as journalists do.” The authors provide a review of all of the factors that contribute to misinformation within the scientific literature and its media ramifications, including the contribution of “predatory publishers” and call for “better ways of detecting untrustworthy publishers.” At the same time, there are ingrained features of the scientific enterprise that serve to distort the relevance of published studies, these include not explicitly identifying the organism in which the studies are carried out, and so obscuring the possibility that they might not be relevant to humans (see Kolata, 2013 {4}). There are also systemic biases within the research community. Consider the observation, characterized by Pandey et al. (2014) {5} that studies of “important” genes, expressed in the nervous system, are skewed: the “top 5% of genes absorb 70% of the relevant literature” while “approximately 20% of genes have essentially no neuroscience literature”. What appears to be the “major distinguishing characteristic between these sets of genes is date of discovery, early discovery being associated with greater research momentum—a genomic bandwagon effect”, a version of the “Matthew effect” described by Merton (1968) {6}. In the context of the scientific community, various forms of visibility (including pedigree and publicity) are in play in funding decisions and career advancement. Not pointed out explicitly by West and Bergstrom is the impact of disciplinary experts who pontificate outside of their areas of expertise and speculate beyond what can be observed or rejected experimentally, including speculations on the existence of non-observable multiverses, the ubiquity of consciousness (Tononi & Koch, 2015 {7}), and the rejection of experimental tests as a necessary criterion of scientific speculation (see Loeb, 2018 {8}) spring to mind.

Many educational institutions demand that non-science students take introductory courses in one or more sciences in the name of cultivating “scientific literacy”. This is a policy that seems to me to be tragically misguided, and perhaps based more on institutional economics than student learning outcomes. Instead, a course on “how science works and how it can be distorted” would be more likely to move students close to the ability to “critique the quality of evidence or validity of conclusions about science”. Such a course could well be based on an extended consideration of the West and Bergstrom article, together with their recently published trade book “Calling bullshit: the art of skepticism in a data-driven world” (Bergstrom and West, 2021 {9}), which outlines many of the ways that information can be distorted. Courses that take this approach to developing a skeptical (and realistic) approach to understanding how the sciences work are mentioned, although what measures of learning outcomes have been used to assess their efficacy are not described.

literature cited

  1. Science literacy: concepts, contexts, and consequencesCommittee on Science Literacy and Public Perception of Science, Board on Science Education, Division of Behavioral and Social Sciences and Education, National Academies of Sciences, Engineering, and Medicine.2016 10 14; PMID: 27854404
  2. Supporting students in developing literacy in science. Krajcik JS, Sutherland LM.Science. 2010 Apr 23; 328(5977):456-459PMID: 20413490
  3. Flat Earth “Science”: Wrong, but not Stupid. Hossenfelder S. BackRe(Action) blog, 2020, Aug 22 (accessed Jul 29, 2021)
  4. Mice fall short as test subjects for humans’ deadly ills. Kolata G. New York Times, 2013, Feb 11 (accessed Jul 29, 2021)
  5. Functionally enigmatic genes: a case study of the brain ignorome. Pandey AK, Lu L, Wang X, Homayouni R, Williams RW.PLoS ONE. 2014; 9(2):e88889PMID: 24523945
  6. The Matthew Effect in Science: The reward and communication systems of science are considered.Merton RK.Science. 1968 Jan 5; 159:56-63 PMID: 17737466
  7. Consciousness: here, there and everywhere? Tononi G, Koch C.Philos Trans R Soc Lond B Biol Sci. 2015 May 19; 370(1668)PMID: 25823865
  8. Theoretical Physics Is Pointless without Experimental Tests. Loeb A. Scientific American blog, 2018, Aug 10 [ Blog piece] (accessed Jul 29, 2021)
  9. Calling bullshit: the art of skepticism in a data-driven world.Bergstrom CT, West JD. Random House Trade Paperbacks, 2021ISBN: ‎ 978-0141987057

Is it possible to teach evolutionary biology “sensitively”?

Michael Reiss, a professor of science education at University College London and an Anglican Priest, suggests that “we need to rethink the way we teach evolution” largely because conventional approaches can be unduly confrontational and “force religious children to choose between their faith and evolution” or to result in students who”refuse to engage with a lesson.” He suggests that a better strategy would be akin to those use to teach a range of “sensitive” subjects “such as sex, pornography, ethnicity, religion, death studies, terrorism, and others” and could “help some students to consider evolution as a possibility who would otherwise not do so.” [link to his original essay and a previous post on teaching evolution: Go ahead and teach the controversy].

There is no doubt that an effective teacher attempts to present materials sensitively; it is the rare person who will listen to someone who “teaches” ideas in a hostile, alienating, or condescending manner. That said, it can be difficult to avoid the disturbing implications of scientific ideas, implications that can be a barrier to their acceptance. The scientific conclusion that males and females are different but basically the same can upset people on various sides of the theo-political spectrum. 

In point of fact an effective teacher, a teacher who encourages students to question their long held, or perhaps better put, familial or community beliefs, can cause serious social push-back  – Trouble with a capital T.  It is difficult to imagine a more effective teacher than Socrates (~470-399 BCE). Socrates “was found guilty of ‘impiety’ and ‘corrupting the young’, sentenced to death” in part because he was an effective teacher (see Socrates was guilty as charged).  In a religious and political context, challenging accepted Truths (again with a capital T) can be a crime.  In Socrates’ case”Athenians probably genuinely felt that undesirables in their midst had offended Zeus and his fellow deities,” and that, “Socrates, an unconventional thinker who questioned the legitimacy and authority of many of the accepted gods, fitted that bill.”  

So we need to ask of scientists and science instructors, does the presentation of a scientific, that is, a naturalistic and non-supernatural, perspective in and of itself represent an insensitivity to those with a super-natural belief system. Here it is worth noting a point made by the philosopher John Gray, that such systems extend beyond those based on a belief in god(s); they include those who believe, with apocalyptic certainty, in any of a number of Truths, ranging from the triumph of a master race, the forced sterilization of the unfit, the dictatorship of the proletariat, to history’s end in a glorious capitalist and technological utopia. Is a science or science instruction that is “sensitive” to, that is, uncritical of or upsetting to those who hold such beliefs, possible? 

My original impression is that one’s answer to this question is likely to be determined by whether one considers science a path to Truth, with a purposeful capital T, or rather that the goal of scientists is to build a working understanding of the world around and within us.  Working scientists, and particularly biologists who must daily confront the implications of apparently un-intelligent designed organisms (due to ways evolution works) are well aware that absolute certainty is counterproductive. Nevertheless, the proven explanatory and technological power of the scientific enterprise cannot help but reinforce the strong impression that there is some deep link between scientific ideas and the way the world really works.  And while some scientists have advocated unscientific speculations (think multiverses and cosmic consciousness), the truth, with a small t, of scientific thinking is all around us.  

Photograph of the Milky Way by Tim Carl photography, used by permission 

 A science-based appreciation of the unimaginable size and age of the universe, taken together with compelling evidence for the relatively recent appearance of humans (Homo sapiens from their metazoan, vertebrate, tetrapod, mammalian, and primate ancestors) cannot help but impact our thinking as to our significance in the grand scheme of things (assuming that there is such a, possibly ineffable, plan)(1). The demonstrably random processes of mutation and the generally ruthless logic by which organisms survive, reproduce, and evolve, can lead even the most optimistic to question whether existence has any real meaning.  

Consider, as an example, the potential implications of the progress being made in terms of computer-based artificial intelligence, together with advances in our understanding of the molecular and cellular connection networks that underlie human consciousness and self-consciousness. It is a small step to conclude, implicitly or explicitly, that humans (and all other organisms with a nervous system) are “just” wet machines that can (and perhaps should) be controlled and manipulated. The premise, the “self-evident truth”, that humans should be valued in and of themselves, and that their rights should be respected (2) is eroded by the ability of machines to perform what were previously thought to be exclusively human behaviors. 

Humans and their societies have, after all, been around for only a few tens of thousands of years.  During this time, human social organizations have passed from small wandering bands influenced by evolutionary kin and group selection processes to produce various social systems, ranging from more or less functional democracies, pseudo-democracies (including our own growing plutocracy), dictatorships, some religion-based, and totalitarian police states.  Whether humans have a long term future (compared to the millions of years that dinosaurs dominated life on Earth) remains to be seen – although we can be reasonably sure that the Earth, and many of its non-human inhabitants, will continue to exist and evolve for millions to billions of years, at least until the Sun explodes. 

So how do we teach scientific conclusions and their empirical foundations, which combine to argue that science represents how the world really works, without upsetting the most religiously and politically fanatical among us?  Those who most vehemently reject scientific thinking because they are the most threatened by its apparently unavoidable implications. The answer is open to debate, but to my mind it involves teaching students (and encouraging the public) to distinguish empirically-based, and so inherently limited observations and the logical, coherent, and testable scientific models they give rise to from unquestionable TRUTH- and revelation-based belief systems. Perhaps we need to focus explicitly on the value of science rather than its “Truth”. To reinforce what science is ultimately for; what justifies society’s support for it, namely to help reduce human suffering and (where it makes sense) to enhance the human experience, goals anchored in the perhaps logically unjustifiable, but nevertheless essential acceptance of the inherent value of each person.   

  1. Apologies to “Good Omens”
  2. For example, “We hold these truths to be self-evident, that all men are created equal, that they are endowed by their creator with certain unalienable rights, that among these are life, liberty and the pursuit of happiness.”