Kept in Mind

Juan Uriagereka

Language in Our Brain: The Origins of a Uniquely Human Capacity
by Angela Friederici
The M.I.T. Press, 304pp., USD$45.00.

https://inference-review.com/article/kept-in-mind

Juan Uriagereka is a linguist at the University of Maryland.

[[All emphasis below is mine. D.G.]]

ANGELA FRIEDERICI is the director of the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig. An internationally renowned neuropsychologist, she is known as well for her expertise in linguistics. Language in Our Brain thus offers an insider’s account of the play between language and the neurosciences. In his endorsement, David Poeppel describes her book as a “masterful summary of decades of work on the neurobiological foundations of language.” He goes on to remark that it “develops a comprehensive account of how this most complex of human computational functions is organized, providing a detailed and lucid perspective on the neuroscience of language.”¹

In Language in Our Brain, we are dealing with an obviously important book.

Drums and Symbols

AFTER STUDYING Alexis St. Martin’s stomach through a gastric fistula in the 1820s, US Army surgeon William Beaumont determined that digestion is more chemical than mechanical. Muscular contractions of the stomach mashed his patient’s food, but it was the stomach’s gastric acid that dissolved it. Beaumont is today known as the father of gastric physiology, if only because two centuries ago, physicians had no idea how digestion worked.

At the beginning of the twentieth century, Santiago Ramón y Cajal advanced the daring hypothesis that neurons form a discrete lattice. If neurons are discrete, synaptic transmission follows inevitability. One neuron can do little, after all, and if many neurons can do more, they must be in touch with one another. This is precisely the conclusion that Cajal drew.

In 1906, Cajal shared the Nobel Prize with Camillo Golgi, who argued that the brain comprises a single continuous but reticulated network. Cajal was correct and Golgi wrong. Or so it seems today.

In the mid-1950s, Noam Chomsky solidified theoretical syntax. Thinking in general, and language in particular, he argued, boils down to symbolic manipulation—a perspective known as the computational theory of mind. It is a point of view deeply indebted to the work undertaken by the great logicians of the 1930s: Kurt Gödel, Alonzo Church, Alan Turing, and Emil Post. For all the sophistication of their ideas, surprisingly simple questions remain. Which part of our brain carries information forward in time? No one knows. For that matter, no one knows what a symbol is, or where symbolic interactions take place. The formal structures of linguistics and neurophysiology are disjoint, a point emphasized by Poeppel and David Embick in a widely cited study.² There is an incommensurability between theories of the brain, T_B, and theories of the mind, T_M. This is the sort of granularity issue that concerned Poeppel and Embick. T_M deals with formal devices and how they interact, while T_B deals with waves of different frequencies and amplitudes, and how they overlap in time sequences across brain regions. In the absence of a common vocabulary and conceptual space, T_M and T_B are, at best, conceptual strangers. Are they elementarily equivalent, or is one an extension of the other? Do they share a common model? Is there a mapping between them such that one is interpretable in the other? Language in Our Brain does not attempt explicitly to ask such questions. It is worth considering whether they have answers, and if not, whether they are correctly posed.

Friederici takes linguistics seriously, and this is all to the good. Few neuropsychologists have studied how sentences break down into phrases, or how words carry meanings, or why speech is more than just sound. No one has distinguished one thought from another by dissecting brains. Neuroimaging tells us only when some areas of the brain light up selectively. Brain wave frequencies may suggest that different kinds of thinking are occurring, but a suggestion is not an inference—even if there is a connection between certain areas of the brain and seeing, hearing, or processing words. Connections of this sort are not nothing, of course, but neither are they very much. Is this because techniques have not yet been developed to target individual neurons? Or is it because thinking is more subtle than previously imagined?

We may not figure this out within our lifetimes.

There have historically been many theories that rival the computational theory of mind. In his famous review of B. F. Skinner’s Verbal Behavior, Chomsky demonstrated that no such theory could explain the fact that human language is compositional, representational, and recursive.³ It is within the space marked by the computational theory of mind that these properties receive an explanation. Progress is slow. Language in Our Braintalks of information or representations, but the corresponding entries are not in the glossary or index. The book says little about them. When Friederici writes about the “fast computation of the phonological representation,” an obvious inferential lapse is involved.⁴ Some considerable distance remains between the observation that the brain is doing something and the claim that it is manipulating various linguistic representations. Friederici notes the lapse. “How information content is encoded and decoded,” she remarks, “in the sending and receiving brain areas is still an open issue—not only with respect to language, but also with respect to the neurophysiology of information processing in general.”⁵

At the Limits of Neuroimaging

ANY DISCUSSION about language and the brain must be focused on human language, and throughout her book Friederici assumes that something like the minimalist program is its underlying theory. Minimalism is a streamlined version of generative grammar, and it is precisely because of this theoretical streamlining that finding syntax within the brain is even possible. Neuroimaging techniques depict the brain as it digests information. This is material that Friederici handles expertly. The techniques that she describes yield a variety of markers typically signaling time in milliseconds, and the electrophysiological polarity in the signal. There has been some progress in determining precisely where what is taking place takes place. Overall oscillation packages in brain waves can also be studied through some of the same techniques. Even more recent techniques allow the analysis of neurotransmitters or even single neurons. The results suggest the existence of neural pathways connecting brain regions, or representational networks.

“[E]ven during task-dependent functional magnetic resonance imaging,” Friederici acknowledges, “only about 20 percent of the activation is explained by the specific task whereas about 80 percent of the low frequency fluctuation is unrelated.”⁶ A lot is going on at any given time within a given brain, and experimenters have to ingeniously subtract what is irrelevant from whatever task is observed. This is familiar enough from daily life. We do many things at once. With present technology, there is no way to determine what each neuron is doing at any given moment, or whether neuronal teams are firing together to perform a given task. Below a millisecond, sensing techniques yield noise.

Cognitive scientists cannot say how the mass or energy of the brain is related to the information it carries. Everyone expects that more activity in a given area means more information processing. No one has a clue whether it is more information or more articulated information, or more interconnected information, or whether, for that matter, the increased neuro-connectivity signifies something else entirely. Friederici remarks:

The picture that can be drawn from the studies reviewed here is neuroanatomically quite concise with respect to sensory processes and those cognitive processes that follow fixed rules, namely, the syntactic processes. Semantic processes that involve associative aspects are less narrowly localized.⁷

And then there are event-related potential effects, or stereotyped electrophysiological responses to a stimulus:

Acoustic processes and processes of speech sound categorization take place around 100 ms (N100). Initial phrase structure building takes place between 120 and 250 ms (ELAN), while the processing of semantic, thematic, and syntactic relations is performed between 300 and 500 ms (LAN, N400). Integration of different information types takes place around 600 ms (P600). These processes mainly involve the left hemisphere.⁸

Markers like N100, N400, or P600 signal whether the electrophysiological reading is positive (P) or negative (N). No one knows what such polarities entail. It is the

functions of brain areas and timeframes, Friederici assumes, that determine whether something is early or late, anterior or posterior, lateral or bilateral. If the perception of a signal presupposes some sensory modality, the modality must swing into action before computation begins. Language in Our Brain is written in the expectation, or the hope, that a division of labor into phonetics, morphology, syntax, semantics, and pragmatics more or less corresponds to the tasks the brain executes in aggregating representations from more elementary bits.

Minimalism

MERGE IS THE essential operation of Chomsky’s minimalism, because it is the simplest way of putting linguistic items together. “Merge,” Friederici assumes, “has a well-defined localization in the human brain.”⁹

Localized? Localized where?

“[I]n the most ventral anterior portion of the BA 44.”¹⁰

The data, Friederici writes, citing Poeppel’s work, “suggest that neural activation reflects the mental construction of hierarchical linguistic structures.”¹¹ But hierarchical linguistic structures are one thing, and Merge is quite another. It is by being merged that the and ship yield the ship. To go beyond that to sail the ship involves the merger of sail and the ship. In what order do these two mergers take place? The merger of the ship must take place before that particular ship sets sail because the ship is a phrase, and there is no word to which sail could have merged within this sentence. Sail the is not a phrase of English.

There is a difference between the temporal order of events in neurophysiology and the logical order of events in syntax. It is obvious that, in the phrase sail the ship, we first pronounce sail and next the, with ship coming last. What could it mean to say that I first merged the and ship, and then merged them with sail? When I have heard or read sail the ship I have encountered sail the… first, and at that point I cannot know whether what is next is going to be ship, boat, or even skies.

Consider:

1.  
1. The man sailed the ship.
2. [[_NP The man ]_NP [_VP sailed [_NP the ship ]_NP ]]

Sentence 1a has a subject, the noun phrase the man, and a predicate, the verb phrase sailed the ship. There is a logical order in which a sentence like this is assembled, in terms of what grammarians call thematic relations. Friederici is sensitive to the apparatus of modern syntax. Thematic relations, she writes, express “the relation between the function denoted by a noun and the meaning of the action expressed by the verb.”¹² It follows that the relationship between sailed and what (the) ship denotes is logically prior to that between what (the) man denotes and the rest of the sentence. In 1b the man sailed the ship. In 1b the ship is merged first, but what is first said is the man. The speech sequence (as perceived) and the syntactic sequence (as generated) are at odds.

Generative grammar addresses this sort of orthogonality by separating competence from performance. Competence reveals that in 1a Merge works from the bottom up, following the brackets in 1b. That what is first encountered in speech is the man is a fact of performance, a matter of parsing. This poses a serious puzzle. Hearing or reading a sentence is an affair from before to after. It is not bottom up. Parsing even something as simple as 1a is a gambit. After the phrase the man has been parsed, it is held on a memory buffer in order to allow the mental parser to concentrate on what comes next, so as to establish thematic integration. In 1a, that happens to be sailed the ship, but consider:

2. The man…
1. sailed a balloon.
2. sailed a kite.
3. sailed a space-probe.

These are all sailings, but rather different actions are asserted for the subject, which is assigned dissimilar thematic roles depending on information that is only accessed upon parsing the direct object of each sentence. Neuroimaging cannot possibly determine whether theta relations are at work in such an elaborate parsing, or whether considerations of memory and attention are paramount instead. How would one decide that whatever is going on at BA 44 is Merge, as opposed to, for instance, the processed phrase being assigned to an active memory buffer? Merge involves systematic and phrasally complex combinatorial information, which is why language recognizers routinely invoke such notions as a memory stack. As far as I can see, present-day observational technology does not seem capable of teasing apart these different components of syntax at work, so it seems to me premature to claim that the observables localize Merge.

The Functional Language Network

THERE IS evidence, Friederici suggests, that different neuronal networks support early and late syntactic processes. These networks are bound together by fiber tracts. There is also a language network at the molecular level. “Information flows,” Friederici writes, “from the inferior frontal gyrus back to the posterior temporal cortex via the dorsal pathway.”¹³ This is, of course, inferential: no one has seen information flowing, if only because no one has ever seen information. But brain events cohere at different levels into a pattern, which is consistent with what can be surmised from brain deficits and injuries. A functional language network, if more abstract than the digestive system, is no less real.

The question is how the thing works; indeed, the question of what the functional language network might be doing should, in my view, be subordinated to the distinction between competence and performance. What the mind must know and what the brain must process are very generally orthogonal. Consider the feat involved in recognizing a word’s syntactic category, distinguishing transform from transformation and either of those from transformational. The grammatical morphemes -tionand -al come at the tail of the word. We process words from their onset, trans first, then form, and finally the suffixes. So what does the mind actually do as it encounters each of these, in that sequential order?

Faced with such considerations, Morris Halle and Kenneth Stevens pioneered the concept of analysis by synthesis in 1962.¹⁴ Thomas Bever and Poeppel remind us how this “heuristic model emphasizes a balance of bottom-up and knowledge-driven, top-down, predictive steps in speech perception and language comprehension.”¹⁵ In their view, a model integrating the orthogonality of narrow competence-driven computations and broad performative strategies is computationally tractable and biopsychologically plausible. In processing transformationalize, say, the model may make a first-pass prediction upon parsing transform that needs to be adjusted upon processing -tion, then -al, then -ize.

A functional language network is, no doubt, playing some kind of role in such processes, but whether the activity that imaging techniques reveal when our brain entertains these symbolic dependencies involves the grammarian’s Merge, or something else entirely, no one really knows. Language in Our Brain begins by quoting Paul Flechsig: “[I]t is rather unlikely that psychology, on its own, will arrive at the real, lawful characterization of the structure of the mind, as long as it neglects the anatomy of the organ of the mind.”¹⁶ I am left wondering whether neurobiology shouldn’t have to take in all seriousness the central results of cognitive psychology—including the competence/performance divide—if seeking a lawful understanding of the human mind.

1. MIT Press, “Language in Our Brain: The Origins of a Uniquely Human Capacity.” ↩
2. David Poeppel and David Embick, “Defining the Relation between Linguistics and Neuroscience,” in Twenty-first Century Psycholinguistics: Four Cornerstones, ed. Anne Cutler (Mahwah: Lawrence Erlbaum, 2005), 103–20. ↩
3. Noam Chomsky, “A Review of B. F. Skinner’s Verbal Behavior,” Language 35 no. 1 (1959): 26–58. ↩
4. Angela Friederici, Language in Our Brain: The Origins of a Uniquely Human Capacity (Cambridge, MA: MIT Press, 2017), 20. ↩
5. Ibid., 121. ↩
6. Ibid., 127. ↩
7. Ibid., 82. ↩
8. Ibid. ↩
9. Ibid., 4. ↩
10. Ibid., 42. ↩
11. Ibid., 53. ↩
12. Ibid., 237. ↩
13. Ibid., 129. ↩
14. Morris Halle and Kenneth Stevens, “Speech Recognition: A Model and a Program for Research,” IRE Transactions on Information Theory 8 (1962): 155–59. ↩
15. Thomas Bever and David Poeppel, “Analysis by Synthesis: A (Re-)Emerging Program of Research for Language and Vision,” Biolinguistics 4, no. 2–3 (2010): 174. ↩
16. Angela Friederici, Language in Our Brain: The Origins of a Uniquely Human Capacity (Cambridge, MA: MIT Press, 2017), v. ↩

Published on March 1, 2019 in Volume 4, Issue 3.

Monday, March 11, 2019

Monday, March 11, 2019 Apocalypse not On its opinion page the other day, The Wall Street Journal reprinted without comment an ample excerpt from an Associated Press dispatch dated June 29, 1989. The story was headlined “U.N. Predicts Disaster if Global Warming Not Checked,” and while most 30-year-old news accounts are unmistakably archaic, this one wouldn’t seem out of place if it ran in the paper tomorrow with nothing changed but a date or two: UNITED NATIONS — A senior U.N. environmental official says entire nations could be wiped off the face of the Earth by rising sea levels if the global warming trend is not reversed by the year 2000. Coastal flooding and crop failures would create an exodus of “eco-refugees,” threatening political chaos, said Noel Brown, director of the New York office of the U.N. Environment Program, or UNEP. He said governments have a 10-year window of opportunity to solve the greenhouse effect before it goes beyond human control. As the warming melts polar icecaps, ocean levels will rise by up to three feet, enough to cover the Maldives and other flat island nations, Brown told the Associated Press in an interview on Wednesday. Coastal regions will be inundated; one-sixth of Bangladesh could be flooded, displacing a fourth of its 90 million people. A fifth of Egypt’s arable land in the Nile Delta would be flooded, cutting off its food supply, according to a joint UNEP and U.S. Environmental Protection Agency study. . . . Sound familiar? Of course it does. It sounds like any of a thousand-and-one predictions of climate change, mass death, global starvation, and other varieties of impending doom that have been forecast by environmental alarmists for the past half-century or more.

From Paul Ehrlich declaring that the 1970s would bring famines in which “hundreds of millions of people are going to starve to death in spite of any crash programs embarked upon now,” to Al Gore warning in 2008 that the entire Arctic polar ice cap “may well be completely gone in five years,” to Alexandria Ocasio-Cortez asserting in January that “the world is going to end in 12 years if we don’t address climate change,” fearmongers keep telling us that the end of human existence as we know it is only a few years away. These apocalyptic forecasts are invariably terrifying, earnest, and adamant. They are always said to be grounded in scientific clarity. They are unfailingly held out as a mandate for immediate and sweeping change at which no reasonable person would balk. And none of them has ever come to pass. As a college student in the 1970s, I had to read lengthy excerpts from the 1972 jeremiad, The Limits to Growth , which warned that life on earth and industrial development were on an inevitable collision course, and that it was only a matter of time until the planet ran out of mineral resources, food, and breathable air. To be fair, the authors did hedge their scary scenarios with acknowledgements of “mankind’s ingenuity and social flexibility,” and they suggested that the most alarming outcomes were still a century away. Plainly, though, they would have regarded it as inconceivable that 50 years later, despite an increase in global population and industrialization, human beings worldwide would be enjoying greater wealth, better nutrition, improved health, and longer lives. Yet that is just what has happened. Even in the 1970s there was much alarmist talk of climate change — but the change most often forecast was global cooling. “There are specialists who say that a new ice age is on the way,” reported The New York Times on May 21, 1975. That Times article, as George F. Will showed in a citation-studded 2009 column , was no outlier: Although some disputed that the “cooling trend” could result in “a return to another ice age” (the Times, Sept. 14, 1975), others anticipated “a full-blown 10,000-year ice age” involving “extensive Northern Hemisphere glaciation” (Science News, March 1, 1975, and Science magazine, Dec. 10, 1976, respectively). The “continued rapid cooling of the Earth” (Global Ecology, 1971) meant that “a new ice age must now stand alongside nuclear war as a likely source of wholesale death and misery” (International Wildlife, July 1975). “The world's climatologists are agreed” that we must “prepare for the next ice age” (Science Digest, February 1973). Because of “ominous signs” that “the Earth's climate seems to be cooling down,” meteorologists were “almost unanimous” that “the trend will reduce agricultural productivity for the rest of the century,” perhaps triggering catastrophic famines (Newsweek cover story, “The Cooling World ,” April 28, 1975). Armadillos were fleeing south from Nebraska, heat-seeking snails were retreating from Central European forests, the North Atlantic was “cooling down about as fast as an ocean can cool,” glaciers had “begun to advance” and “growing seasons in England and Scandinavia are getting shorter” (Christian Science Monitor, Aug. 27, 1974). Today, of course, the panic-mongering over climate change swings in the other direction. Where once we were told that the “rapid advance of some glaciers” would make much of Alaska, Iceland, and Canada uninhabitable, the worry now is that two-thirds of the Himalayan glaciers will melt, with devastating effects on life in India, China, and Pakistan. “Snowfalls are now just a thing of the past,”reported The Independent in 2000. The story quoted a climate scientist’s lament that within a few years, “children just aren’t going to know what snow is.”   In his bestselling 1968 book "The Population Bomb," Paul Ehrlich asserted that within a few years, “hundreds of millions of people are going to starve to death in spite of any crash programs embarked upon now." It never happened. What has been true of climate doomsaying has been true of every other kind as well. Ecologists were convinced that the growth in numbers of people would trigger a “population bomb” of mass starvation and a worldwide “die-off.” Would-be Cassandras warned fervently that resource depletion, especially “peak oil,” would force a drastic rollback of modern industry and transportation. Let’s accept for the sake of argument that all these dreadful prophecies are made with utter sincerity. Let’s disregard the fact that they always seem to be accompanied by calls for vastly expanded government power and reduced individual freedom. Let’s assume it is only by coincidence that most of this “apocalypse now” rhetoric comes from the political left, with its affinity for top-down, command-and-control solutions. Even so, shouldn’t there come a point at which the alarmists acknowledge their nearly unbroken record of faulty predictions? Shouldn’t an Ehrlich or a Gore feel obliged to concede that the deadline by which they said mankind would fall off the cliff has come and gone, and make a good-faith effort to explain why human society continues, stronger, richer, healthier, and safer than ever? Shouldn’t the mainstream media and the educational establishment reconsider the wisdom of repeating every ominous and cataclysmic environmental prognosis as if its truth cannot possibly be doubted? If a financial adviser or TV meteorologist or sports analyst proved as consistently wrong as climate alarmists have proved, everyone would take their latest prediction with more than a grain or two of salt. Doesn’t it make sense to do the same when confronted with yet another terrifying environmental forecast? Yes, it is always possible that all those false alarms are being followed, this time, by a real one. But maybe, just maybe, a touch of skepticism would be prudent .

Inflation: Status Update

Posted by Sabine Hossenfelder at 12:03 PM 

 [[My enmpahsis D.G.]]

http://backreaction.blogspot.com/2019/03/inflation-status-update.html?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+blogspot%2Fermku+%28Backreaction%29

Model of Inflation. img src: umich.edu

The universe hasn’t always been this way. That the cosmos as a whole evolves, rather than being eternally unchanging, is without doubt one of the most remarkable scientific insights of the past century. It follows from Einstein’s theory of general relativity: Einstein’s theory tells us that the universe must expand. As a consequence, in the early universe matter must have been compressed to high density.

But if you follow the equations back in time, general relativity eventually stops working. Therefore, no one presently knows how the universe began. Indeed, we may never know.

Since the days of Einstein, physicists have made much progress detailing the history of the universe. But the deeper they try to peer into our past, the more difficult their task becomes.

This difficulty arises partly because new data are harder and harder to come by. The dense matter in the early universe blocked light, so we cannot use light to look back to any time earlier than the formation of the cosmic microwave background. For even earlier times, we can make indirect inferences, or hope for new messengers, like gravitational waves or neutrinos. This is technologically and mathematically challenging, but these are challenges that can be overcome, at least in principle. (Says the theorist.)

The more serious difficulty is conceptual. When studying the universe as whole, physicists face the limits of the scientific method: The further back in time they look, the simpler their explanations become. At some point, then, there will be nothing left to simplify, and so there will be no way to improve their explanations. The question isn’t whether this will happen, the question is when it will happen.

The miserable status of today’s theories for the early universe makes me wonder whether it has already happened. Cosmologists have hundreds of theories, and many of those theories come in several variants. It’s not quite as bad as in particle physics, [[!!!]] but the situation is similar in that cosmologists, too, produce loads of ill-motivated models for no reason other than that they can get them published. (And they insist this is good scientific practice. Don’t get me started.)

The currently most popular theory for the early universe is called “inflation”. According to inflation, the universe once underwent a phase in which volumes of space increased exponentially in time. This rapid expansion then stopped in an event called “reheating,” at which the particles of the standard model were produced. After this, particle physics continues the familiar way.

Inflation was originally invented to solve several finetuning problems. (I wrote about this previously, and don’t want to repeat it all over again, so if you are not familiar with the story, please check out this earlier post.) Yall know that I think finetuning arguments are a waste of time, so naturally I think these motivations for inflations are no good. However, just because the original reason for the idea of inflation doesn’t make sense doesn’t mean the theory is wrong.

Ever since the results of the Planck in 2013 it hasn’t looked good for inflation. After the results appeared, Anna Ijjas, Paul Steinhardt, and Avi Loeb argued in a series of papers that the models of inflation which are compatible with the data themselves require finetuning, and therefore bring back the problem they were meant to solve. They popularized their argument in a 2017 article in Scientific American, provocatively titled “Pop Goes the Universe.”

The current models of inflation work not simply by assuming that the universe did undergo a phase of exponential inflation, but they moreover introduce a new field – the “inflaton” – that supposedly caused this rapid expansion. For this to work, it is not sufficient to just postulate the existence of this field, the field also must have a suitable potential. This potential is basically a function (of the field) and typically requires several parameters to be specified.

Most of the papers published on inflation are then exercises in relating this inflaton potential to today’s cosmological observables, such as the properties of the cosmic microwave background.

Now, in the past week two long papers about all those inflationary models appeared on the arXiv:

Cosmic Inflation: Trick or Treat?
By Jerome Martin
arXiv:1902.05286 [astro-ph.CO]

and 

Inflation after Planck: Judgement Day
By Debika Chowdhury, Jerome Martin, Christophe Ringeval, Vincent Vennin
arXiv:1902.03951 [astro-ph.CO]

The first paper, by Jerome Martin alone, is a general overview of the idea of inflation. It is well-written and a good introduction, but if you are familiar with the topic, nothing new to see here.

The second paper is more technical. It is a thorough re-analysis of the issue of finetuning in inflationary models and a response to the earlier papers by Ijjas, Steinhardt, and Loeb. The main claim of the new paper is that the argument by Ijjas et al, that inflation is “in trouble,” is wrong because it confuses two different types of models, the “plateau models” and the “hilltop models” (referring to different types of the inflaton potential).

According to the new analysis, the models most favored by the data are the plateau models, which do not suffer from finetuning problems, whereas the hilltop models do (in general) suffer from finetuning but are not favored by the data anyway. Hence, they conclude, inflation is doing just fine.

The rest of the paper analyses different aspects of finetuning in inflation (such as quantum contributions to the potential), and discusses further problems with inflation, such as the trans-planckian problem and the measurement problem (as pertaining to cosmological perturbations). It is a very balanced assessment of the situation.

The paper uses standard methods of analysis (Bayesian statistics), but I find this type of model-evaluation generally inconclusive. The problem with such analyses is that they do not take into account the prior probability for the models themselves but only for the initial values and the parameters of the model. Therefore, the results tend to favor models which shove unlikeliness from the initial condition into the model (eg the type of function for the potential).

This is most obvious when it comes to the so-called “curvature problem,” or the question why the universe today is spatially almost flat. You can get this outcome without inflation, but it requires you to start with an exponentially small value of the curvature already (curvature density, to be precise). If you only look at the initial conditions, then that strongly favors inflation.

But of course inflation works by postulating an exponential suppression that comes from the dynamical law. And not only this, it furthermore introduces a field which is strictly speaking unnecessary to get the exponential expansion. I therefore do not buy into the conclusion that inflation is the better explanation. On the very contrary, it adds unnecessary structure.

This is not to say that I think inflation is a bad idea. It’s just that I think cosmologists are focusing on the wrong aspects of the model. Finetuning arguments will forever remain ambiguous because they eventually depend on unjustifiable assumptions. What’s the probability for getting any particular inflaton potential to begin with? Well, if you use the most common measure on the space of all possible function, then all so-far considered potentials have probability zero. This type of reasoning just does not lead anywhere. So why waste time talking about finetuning?

Instead, let us talk about those predictions whose explanatory value does not depend on finetuning arguments, of which I suspect (but do not know) that ET-correlations in the CMB power spectrum are an example. Since finetuning debates will remain unsolvable, it would be more fruitful to focus on those benefits of inflation that can be quantified unambiguously.

In any case, I am sure the new paper will make many cosmologists happy, and encourage them to invent many more models for inflation. Sigh.

Merchants of Hype

Sabine Hossenfelder
Research Fellow at the Frankfurt Institute for Advanced Studies

http://backreaction.blogspot.com/2019/03/merchants-of-hype.html?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+blogspot%2Fermku+%28Backreaction%29

Once upon a time, the task of scientists was to understand nature. “Merchants of Light,” Francis Bacon called them. They were a community of knowledge-seekers who subjected hypotheses to experimental test, using what we now simply call “the scientific method.” Understanding nature, so the idea, would both satisfy human curiosity and better our lives.

Today, the task of scientists is no longer to understand nature. Instead, their task is to uphold an illusion of progress by wrapping incremental advances in false promise. Merchants they still are, all right. But now their job is not to bring enlightenment; it is to bring excitement.

Nowhere is this more obvious than with big science initiatives. Quantum computing, personalized medicine, artificial intelligence, simulated brains, mega-scale particle colliders, and everything nano and neuro: While all those fields have a hard scientific core that justifies some investment, the big bulk is empty headlines. Most of the money goes into producing papers whose only purpose is to create an appearance of relevance.

Sooner or later, those research-bubbles become unsustainable and burst. But with the current organization of research, more people brings more money brings more people. And so, the moment one bubble bursts, the next one is on the rise already.

The hype-cycle is self-sustaining: Scientists oversell the promise of their research and get funding. Higher education institutions take their share and deliver press releases to the media. The media, since there’s money to make, produce headlines about breakthrough insights. Politicians are pleased about the impact, talk about international competitiveness, and keep the money flowing.

Trouble is, the supposed breakthroughs rarely lead to tangible progress. Where are our quantum computers? Where are our custom cancer cures? Where are the nano-bots? And why do we still not know what dark matter is made of?

Most scientists are well aware their research floats on empty promise, but keep their mouths shut. I know this not just from my personal experience. I know this because it has been vividly, yet painfully, documented in a series of anonymous interviews with British and Australian scientists about their experience writing grant proposals. These interviews, conducted by Jennifer Chubb and Richard Watermeyer (published in Studies in igher Education), made me weep: 

“I will write my proposals which will have in the middle of them all this work, yeah but on the fringes will tell some untruths about what it might do because that’s the only way it’s going to get funded and you know I’ve got a job to do, and that’s the way I’ve got to do it. It’s a shame isn’t it?”
(UK, Professor)

“If you can find me a single academic who hasn’t had to xxxxx or bluff or lie or embellish in order to get grants, then I will find you an academic who is in trouble with his Head of Department. If you don’t play the game, you don’t do well by your university. So anyone that’s so ethical that they won’t bend the rules in order to play the game is going to be in trouble, which is deplorable.”
(Australia, Professor)

“We’ll just find some way of disguising it, no we’ll come out of it alright, we always bloody do, it’s not that, it’s the moral tension it places people under.”
(UK, Professor)

“They’re just playing games – I mean, I think it’s a whole load of nonsense, you’re looking for short term impact and reward so you’re playing a game... it’s over inflated stuff.”
(Australia, Professor)

“Then I’ve got this bit that’s tacked on... That might be xxxx enough to get funded but I don’t believe in my heart that there’s any correlation whatsoever... There’s a risk that you end up tacking bits on for fear of the agenda and expectations when it’s not really where your heart is and so the project probably won’t be as strong.”
(Australia, Professor)

In other interviews, the researchers referred to their proposals as “virtually meaningless,” “made up stories” or “charades.” They felt sorry for their own situation. And then justified their behavior by the need to get funding.

Worse, the above quotes only document the tip of the iceberg. That’s because the people who survive in the current system are the ones most likely to be okay with the situation. This may be because they genuinely believe their field is as promising as they make it sound, or because they manage to excuse their behavior to themselves. Either way, the present selection criteria in science favor skilled salesmanship over objectivity. Need I say that this is not a good way to understand nature?

The tragedy is not that this situation xxxx, though, of course, it does. The tragedy is that it’s an obvious problem and yet no one does anything about it. If scientists can increase their chances to get funding by exaggeration, they will exaggerate. If they can increase their chances to get funding by being nice to their peers, they will be nice to their peers. If they can increase their chances to get funding by publishing on popular topics, they will publish on popular topics. You don’t have to be a genius to figure that out.

Tenure was supposed to remedy scientists’ conflict of interest between truth-seeking and economic survival. But tenure is now a rarity. Even the lucky ones who have it must continue to play nice, both to please their institution and keep the funding flowing. And honesty has become self-destructive. If you draw attention to shortcomings, if you debunk hype, if you question the promise of your own research area, you will be expelled from the community. A recent commenter on this blog summed it up like this: 

“at least when I was in [high energy physics], it was taken for granted that anyone in academic [high energy physics] who was not a booster for more spending, especially bigger colliders, was a traitor to the field.”

If you doubt this, think about the following. I have laid out clearly why I do not think a bigger particle collider is currently a good investment. No one who understands the scientific and technological situation seriously disagrees with my argument; they merely disagree with the conclusions. This is fine with me. This is not the problem. I don’t expect everyone to agree with me.

But I also don’t expect everyone to disagree with me, and neither should you. So here is the puzzle: Why can you not find any expert, besides me, willing to publicly voice criticism on particle physics? Hint: It’s not because there is nothing to criticize.

And if you figured this one out, maybe you will understand why I say I cannot trust scientists any more. It’s a problem. It’s a problem in dire need of a solution. “

This rant, was, for once, not brought on by a particle physicist, but by someone who works in quantum computing. Someone who complained to me that scientists are overselling the potential of their research, especially when it comes to large investments. Someone distraught, frustrated, disillusioned, and most of all, unsure what to do.

I understand that many of you cannot break the ranks without putting your jobs at risk. I do not – and will not – expect you to sacrifice a career you worked hard for; no one would be helped by this. But I want to remind you that you didn’t become a scientist just to shut up and advocate.

Posted by Sabine Hossenfelder at 10:08 AM 

Smarter Parts Make Collective Systems Too Stubborn

By Jordana Cepelewicz

https://www.quantamagazine.org/smarter-parts-make-collective-systems-too-stubborn-20190226/?utm_source=Quanta+Magazine&utm_campaign=c6b69a1ba3-RSS_Daily_Biology&utm_medium=email&utm_term=0_f0cb61321c-c6b69a1ba3-389846569&mc_cid=c6b69a1ba3&mc_eid=61275b7d81

[[Waking up from naive "scientific" fantasies.....The bold font below is my addition. D.G.]] 

The poet John Donne immortalized the idea that “no man is an island,” but neither, it turns out, are most other species. Many natural and artificial systems are characterized by the collective, from neurons firing in sync and immune cells banding together, to schools of fish and flocks of birds moving in harmony, to new business models and robot designs operating in the absence of a single leader. “Collective systems are more the rule than the exception,” said Albert Kao, a researcher at the Santa Fe Institute who models decision-making in such systems.

Whether biological, technological, economic or social, collective systems are often considered to be “decentralized,” meaning that they lack a main control hub for coordinating their individual components. Instead, control is distributed among the components, which make their own decisions based on local information; complex behaviors arise through their interactions. That kind of setup can be advantageous, in part because it is resilient: If one part isn’t working properly, the system can continue to function — in stark contrast to when a central brain or leader stops doing its job.

Decentralization has ridden a wave of hype, particularly among those hoping to revolutionize marketplaces with blockchain technology and societies with more dispersed governments. “Some of this stems from political ideology having to do with a preference for bottom-up governing styles and systems with natural checks on the emergence of inequality,” Jessica Flack, an evolutionary biologist and complexity scientist at the Santa Fe Institute, wrote in an email. “And some of it stems from engineering biases … that are based on the assumption these types of structures are more robust, less exploitable.”

But “most of this discussion,” she added, “is naive.” The line between centralization and decentralization is often blurry, and deep questions about the flow and aggregation of information in these networks persist. Even the most basic and intuitive assumptions about them need more scrutiny, because emerging evidence suggests that making networks bigger and making their parts more sophisticated doesn’t always translate to better overall performance.

In a paper published earlier this month in Science Advances, for instance, a team led by Neil Johnson, now a physicist at George Washington University, demonstrated that a decentralized model performed best under Goldilocks conditions, when its parts were neither too simple nor too capable. That finding echoes other results from complexity research about the optimal use of information and the tradeoff between independence and correlation. The new insights could help to point out the strengths and limitations of decentralized designs for robots, self-driving vehicles, medical treatments and corporate structures — and might even help to explain aspects of natural evolution.

From the Marketplace to the Lab

Johnson’s investigation began as an attempt to understand the feedback loops in financial systems: Traders, each trying to maximize their profits while obeying certain rules, make decisions that contribute to a general outcome — say, some change in stock price — which in turn influences the traders’ subsequent decisions.

Then one day, while he was still on the faculty at the University of Miami, Johnson’s attention was caught by the work of a colleague toiling over something seemingly unrelated to Wall Street: the movements of fly larvae. A larva crawls automatically to positions that are neither too hot nor too cold, but it doesn’t rely on its brain to guide this journey. Instead, each of its body segments responds to signals from temperature-sensing neurons by compressing on one side or the other. The collective movement of all the segments causes the larva to turn. The resulting trajectories toward heat sources reminded Johnson of the financial models he worked with — so he decided to use them to search for principles common to all decentralized systems.

He and his team built a model that mimicked how the larvae behaved. Like the larva’s segments, the collection of agents in the model shared a common goal but had no way to communicate and coordinate their activities. Each agent chose repeatedly to move either left or right, based on whether past decisions had led the entire system to head closer to or farther away from a specified target. To guide its decision, each agent used a strategy drawn from a subset of possibilities assigned to it. If a given strategy performed well, the agent continued to use it; otherwise, it turned to a different one in its arsenal.

The researchers observed that when the agents could remember only one or two outcomes, fewer strategies were possible, so more agents responded in the same way. But because the agents’ actions were then too correlated, the collective movement in the model took it along a zigzagging route that involved many more steps than necessary to reach the target. Conversely, when the agents remembered seven or more past outcomes, they became too uncorrelated: They tended to stick with the same strategy for more rounds, treating a short string of recent negative outcomes as an exception rather than a trend. The model became less agile and more “stubborn,” according to Johnson.

In an experiment, the movements of decentralized systems hinged on the independent decisions of their components, which had memories of some past outcomes. After the memory size reached a certain threshold, the systems performed worse.

The trajectories were most efficient when the length of the agents’ memory was somewhere in the middle: for about five past events. This number grew slightly as the number of agents increased, but no matter how many agents the model used, there was always a sweet spot — an upper limit on how good their memory could get before the system started to perform poorly.

“It’s counterintuitive,” said Pedro Manrique, a postdoctoral associate at the University of Miami and a co-author of the Science Advances paper. “You would think that improving the sophistication level of the parts, in this case the memory, would improve and improve and improve the performance of the organism as a whole.”

A Second Wave

Kao sees a striking connection between Johnson’s and Manrique’s findings and his own work on the behavior of crowds. Over the past few years, he and others have found that medium-size groups of animals or humans are optimal for decision-making. That conclusion runs contrary to the standard beliefs about the “wisdom of crowds,” Kao said, “where the larger the group, the better the collective performance.” Success lies in achieving the right balance between coordination and independence among the system’s components.

“It’s like a second wave of this kind of research,” Kao said. “The first wave was naive enthusiasm for these collective systems. Now, it feels like … we’re questioning a lot of the assumptions we made initially and finding more complex behaviors.”

Further research is needed on how the sophistication of components, their interconnectivity and other parameters affect the overall robustness and limitations of a network. Johnson and others plan to study how the availability of information affects phenomena as diverse as the formation of opinions among voters, the behavior of better robots, and potential mechanisms of recovery from neurological diseases. They also hope the work could help explain why natural evolution has made organisms a mix of centralized and decentralized systems (these kinds of results, Johnson said, could help to justify “why we aren’t just fantastic larvae”).

Ultimately, complexity in nature isn’t simply a story of its emergence from entirely uncorrelated groups of dumb parts — and probing that story could one day yield more universal principles about cooperation, coordination and collective information processing.

Neuroscientists Say They've Found an Entirely New Form of Neural Communication

[[Just when some people think that everything is understood - sigh!]]

Scientists think they've identified a previously unknown form of neural communication that self-propagates across brain tissue, and can leap wirelessly from neurons in one section of brain tissue to another – even if they've been surgically severed.

The discovery offers some radical new insights about the way neurons might be talking to one another, via a mysterious process unrelated to conventionally understood mechanisms, such as synaptic transmission, axonal transport, and gap junction connections.

"We don't know yet the 'So what?' part of this discovery entirely," says neural and biomedical engineer Dominique Durand from Case Western Reserve University.

"But we do know that this seems to be an entirely new form of communication in the brain, so we are very excited about this."

Before this, scientists already knew there was more to neural communication than the above-mentioned connections that have been studied in detail, such as synaptic transmission.

For example, researchers have been aware for decades that the brain exhibits slow waves of neural oscillations whose purpose we don't understand, but which appear in the cortex and hippocampus when we sleep, and so are hypothesised to play a part in memory consolidation.

"The functional relevance of this input‐ and output‐decoupled slow network rhythm remains a mystery," explains neuroscientist Clayton Dickinson from the University of Alberta, who wasn't involved in the new research but has discussed it in a perspective article.

"But [it's] one that will probably be solved by an elucidation of both the cellular and the inter‐cellular mechanisms giving rise to it in the first place."

To that end, Durand and his team investigated slow periodic activity in vitro, studying the brain waves in hippocampal slices extracted from decapitated mice.

What they found was that slow periodic activity can generate electric fields which in turn activate neighbouring cells, constituting a form of neural communication without chemical synaptic transmission or gap junctions.

"We've known about these waves for a long time, but no one knows their exact function and no one believed they could spontaneously propagate," Durand says.

"I've been studying the hippocampus, itself just one small part of the brain, for 40 years and it keeps surprising me." [[My emphasis.]]

This neural activity can actually be modulated - strengthened or blocked - by applying weak electrical fields and could be an analogue form of another cell communication method, called ephaptic coupling.

The team's most radical finding was that these electrical fields can activate neurons through a complete gap in severed brain tissue, when the two pieces remain in close physical proximity.

"To ensure that the slice was completely cut, the two pieces of tissue were separated and then rejoined while a clear gap was observed under the surgical microscope," the authors explain in their paper.

"The slow hippocampal periodic activity could indeed generate an event on the other side of a complete cut through the whole slice."

If you think that sounds freaky, you're not the only one. The review committee at The Journal of Physiology – in which the research has been published – insisted the experiments be completed again before agreeing to print the study.

Durand et al. dutifully complied, but sound pretty understanding of the cautiousness, all things considered, given the unprecedented weirdness of the observation they're reporting.

"It was a jaw-dropping moment," Durand says, "for us and for every scientist we told about this so far."

"But every experiment we've done since to test it has confirmed it so far."

It'll take a lot more research to figure out if this bizarre form of neural communication is taking place in human brains – let alone decoding what exact function it performs – but for now, we've got new science that's shocking in all kinds of ways, as Dickson adroitly observes.

"While it remains to be seen if the [findings] are relevant to spontaneous slow rhythms that occur in both cortical and hippocampal tissue in situ during sleep and sleep‐like states," Dickson writes, "they should probably (and quite literally) electrify the field."

The findings are reported in The Journal of Physiology.

Against Karl Popper's method of falsification

Anyone who still takes Popper seriously must read this:
https://www.academia.edu/38299069/Why_POPPERs_logical_negativism_fails_2019_Slides_?email_work_card=thumbnail-desktop

Why POPPER's logical negativism fails (2019) (Slides)

Susan Haack