Daniel A. Shore on why literary studies abandoned linguistics

I've been reading some interesting remarks by Daniel A. Shore on why literary studies (at least in much of the Anglophone world) abandoned linguistics, though clinging to remnants of Saussure, Jakobson and the like. His remarks are an interesting complement to, counterpoint to, or, as the case may be, antidote my own observations in Transition! The 1970s in Literary Criticism.

Consider his post, Making books, Making Language (August 3, 2018), which opens:

This post is about an asymmetry in the current disciplinary configuration of literary studies: scholars of my generation often possess detailed professional knowledge of how the books of their period of study, as physical objects, were made. Yet very few of them have an account of how the utterances in those books were made, apart from the patently inadequate notion of word choice (“words in their sites,” as Ian Hacking once put it) and, perhaps, familiarity with classical rhetorical tropes and figures. In the 60’s and 70s Linguistics provided the primary technical resource the discipline literary criticism; now that role is filled by descriptive bibliography.

And so:

How come current Shakespeareans study in great detail how a leather binding is stitched, embossed, and stamped, without having more than a rudimentary account of how the sentences written by Shakespeare were produced?

I think the most crucial part of the answer has to do with the current disciplinary organization of knowledge. Because there are whole departments of Linguistics devoted to studying language, with a faculty, major requirements, grad programs, and course offerings, and so on, the burden of studying and understanding language has been effectively offloaded. Book history, by contrast, has nowhere else to live, no disciplinary home of its own, outside of departments of Literature, History, and scattered programs in Media Studies, communications, etc. (That it also lives in libraries, archives, and rare book shops is another thing altogether.) Grad students who want to understand recent theories of how language works – how we produce and understand utterances we’ve never witnessed before, how the meaning of those utterances is composed of the meaning their parts, etc. – can take courses in Linguistics… in their spare time, with spare credits, which is to say rarely if at all. What gets offloaded gets forgotten or left out.

This disciplinary division tracked onto one of the biggest intellectual divides of the second half of the 20th century. By the latter 1960s, language took on entirely opposed functions for the opposing camps. For Foucault and the humanities work he inspired, language was the “mankiller,” the premier “positivity external to Man” that constituted human being historically, producing it as a contingent “figure,” and the goal of studying language was to detranscendentalize the claims of the human sciences. Around the same time, by contrast, the study of language in the Chomskyan paradigm became the most prestigious domain for the production of truths about universal human nature. It purported to establish the biological transcendentals and species-specific endowment of human beings abstracted away from cultural and historical difference. Once it was clear, circa 1980, that literary studies would embrace the detranscendentalizing project, claims about language beyond the cultural specificity and contingency of the lexicon became suspect.

Language itself became a divided terrain: to the linguists went grammar, generality, and universality; to humanists went words, arbitrariness, and cultural specificity. Book history lent itself rather easily to the historicizing project, or at least travelled alongside it without conflict, since the material supports for communication, publication, and distribution vary quite dramatically across periods and cultures; better still, book history compensated for the tendency to idealism of various linguistic, social, and cultural constructivisms precisely because of its hard-nosed and workmanlike empiricism (see “the new boredom”). In this sense, book history didn’t come “after theory” at all but was theory’s easy companion and (depending on what one supposes is the current status of “theory”) survivor.

On the rhythmic nature of speech

Is speech rhythmic? Definitely not perfectly isochronous, but still sound quite temporally regular to me. Check out this recording (and also other clips of this drummer). https://t.co/J9m1jyNmqY

— Andrew Chang (@candrew123) June 8, 2019

The anti-tech coalition

Big (internet) tech is under attack. Alexis Madrigal lists the players in The Atlantic, The Coalition Out to Kill Tech as We Know It. He observes:

At a broad ideological level, two things have happened. First, the idea of cyberspace, a transnational, individualistic, largely unregulated, and free place that was not exactly located in any governmental domain, has completely collapsed. Second, the mythology of tech as the carrier of progress has imploded, just as it did for the robber barons of the late 19th century, ushering in the trust-busting era. While Big Tech companies try to establish a new reason for their privileged treatment and existence (hint: screaming “CHINA!”), they are vulnerable to attacks on their business practices that suddenly make sense.

But these changes did not occur in the ether among particles of discourse. Over the past three years, an ecosystem of tech opponents has emerged and gained strength. Here’s a catalog of the coalition that has pulled tech from the South Lawn into the trenches.

He organizes the coalition under these headings:

• Angry Conservatives
• Disillusioned Liberal Tech Luminaries
• Antitrust Theoreticians
• Democratic Presidential Candidates
• Rank-and-File Tech Workers
• Traditional Democratic Corporate Reformers
• Privacy Advocates
• European Regulators
• The Media Industry
• The Telecom Industry
• Scholarly Tech Critics
• Apple
• Oracle and Other Business-Software Companies
• Yelp and Other Consumer-Protection Organizations
• The Chinese Internet Industry

Another classic text from 1957, Frank Rosenblatt's tech report introducing the idea of perceptrons [#DH]

Here's a link to a downloadable PDF of the report: The Perceptron — A Perceiving and Recognizing Automaton. That introduced the idea of artificial neural networks, which has revolutionized AI and CL in the last quarter century or so. That's from this page, which has several other and more recent links. 

In my chronology of cog. sci. and literary theory (PDF here) I list Chomsky's Syntactic Structures, Roland Barthes' Mythologies, and Fry's Anatomy of Criticism from 1957 as well. I note as well that Norbert Weiner's Cybernetics was published almost a decade earlier, in 1948. Warren Weaver's memo, "Translation", was published in 1949; it introduced the idea of machine translation.

Consider the intellectual history crudely indicated here:

Once more, kin selection and group selection

Jonathan Birch, Are kin and group selection rivals or friends? Current Biology, Volume 29, ISSUE 11, PR433-R438, June 03, 2019, https://doi.org/10.1016/j.cub.2019.01.065.

Abstract:

Kin selection and group selection were once seen as competing explanatory hypotheses but now tend to be seen as equivalent ways of describing the same basic idea. Yet this ‘equivalence thesis’ seems not to have brought proponents of kin selection and group selection any closer together. This may be because the equivalence thesis merely shows the equivalence of two statistical formalisms without saying anything about causality. W.D. Hamilton was the first to derive an equivalence result of this type. Yet Hamilton was aware of its limitations, and saw that, while illuminating, it papered over some biologically important distinctions. Attending to these distinctions leads to the concept of ‘K-G space’, which helps us see where the biological disagreements between proponents of kin selection and group selection really lie.

Definitions:

Concepts from network theory, such as the ‘clustering coefficient’ and the ‘relative density’ [20], can help us quantify, at any particular moment, the ‘groupiness’ of a social network — the extent to which it contains real, non-arbitrary social groups at that time. If we choose an appropriate measure and take a time-average for the whole population over one generation, we have a rough measure of the extent to which groups are ‘clearly in evidence’. We can define a quantity G that takes the value 1 when social groups are fully discrete and isolated from each other for long periods (as in the Haystacks model) and the value 0 when there is no population structure at all, with more realistic cases in between.

Meanwhile, the extent to which genetic correlation is explained by kinship can be quantified by comparing the locus-specific correlation with respect to the gene of interest to the average correlation across the entire genome, since only kinship can generate correlation at every locus. We can define a quantity K that takes the value 1 when all the correlation is whole-genome correlation and 0 when all the correlation is specific to the locus in question.

K-G space:

These variables lead naturally to the representational device of ‘K-G space’. A population’s place in K-G space depends on the extent to which real groups are clearly in evidence and the extent to which genetic correlation is explained by kinship. As Hamilton himself said in other words, selection in high K, low G populations seems aptly described as ‘kin selection’, whereas selection in high G, low K populations seems aptly described as ‘group selection’. In the high K, high G region we have hybrid cases that are aptly described as ‘kin-group selection’, because assortment is kin-based and groups are clearly in evidence. In these cases, there really is no meaningful debate to be had about which process is at work.

A bit further on:

What is the use of K-G space? It is an unorthodox way of thinking about the relation between kin and group selection, so there had better be some payoff for adopting this unorthodox way of thinking. Otherwise it will just lead to confusion.

The payoff, in my view, is that this representational tool helps us see what is really at stake when proponents of kin selection and group selection debate particular cases, such as the origins of eusociality or the evolution of human cooperation. These are not just non-empirical debates about which mathematical formalism we should use to describe the process. But nor are they black-and-white clashes between vastly different alternatives. They tend to be debates about where the population of interest should be located in K-G space.

Red and yellow with shadows

Computation as a paradigm in the humanities (not!) #DH

Computation isn't a real theoretical paradigm, but it's probably a convenient shibboleth for one that usually goes un- or poorly articulated.

— Scott B. Weingart 🤹 (@scott_bot) June 7, 2019

How about a pair of irises?

Predicting history

Joseph Risi, Amit Sharma, Rohan Shah, Matthew Connelly & Duncan J. Watts, Predicting history, Nature Human Behaviour (03 June2019)

Abstract

Can events be accurately described as historic at the time they are happening? Claims of this sort are in effect predictions about the evaluations of future historians; that is, that they will regard the events in question as significant. Here we provide empirical evidence in support of earlier philosophical arguments that such claims are likely to be spurious and that, conversely, many events that will one day be viewed as historic attract little attention at the time. We introduce a conceptual and methodological framework for applying machine learning prediction models to large corpora of digitized historical archives. We find that although such models can correctly identify some historically important documents, they tend to overpredict historical significance while also failing to identify many documents that will later be deemed important, where both types of error increase monotonically with the number of documents under consideration. On balance, we conclude that historical significance is extremely difficult to predict, consistent with other recent work on intrinsic limits to predictability in complex social systems. However, the results also indicate the feasibility of developing ‘artificial archivists’ to identify potentially historic documents in very large digital corpora.

From the discussion:

Therefore, on balance, our results suggest that Danto was substantively correct. As the number of events being evaluated grows, successful predictions will be increasingly outnumbered by events that seem insignificant at the time, but which come to be viewed as important by future historians in part because of events that have not yet taken place. More generally, our results provide further evidence for the observation that the combination of nonlinearity, stochasticity and competition for scarce attention that is inherent to human systems poses serious difficulties for ex ante predictions—a pattern that has previously been noted in outcomes such as political events, success in cultural markets, the scientific impact of publications and the diffusion of information in social networks. Given that historical significance is typically evaluated on longer time scales than these other examples, it is especially vulnerable to unintended consequences, sensitivity to small fluctuations and reinterpretation of previous information in light of new discoveries or societal concerns. A further complication is that historical significance, even when it can be meaningfully assigned, is specific to observers whose evaluation may depend on their own idiosyncratic interests and priorities. Although we speak of history as a single entity, in reality there may be many histories, within each of which the same set of events may be recalled and evaluated differently.

Addendum (8 June 2019): Compare the above with a passage from T. S. Eliot, "Tradition and the Individual Talent", 1920:

No poet, no artist of any art, has his complete meaning alone. His significance, his appreciation is the appreciation of his relation to the dead poets and artists. You cannot value him alone; you must set him, for contrast and comparison, among the dead. I mean this as a principle of æsthetic, not merely historical, criticism. The necessity that he shall conform, that he shall cohere, is not one-sided; what happens when a new work of art is created is something that happens simultaneously to all the works of art which preceded it. The existing monuments form an ideal order among themselves, which is modified by the introduction of the new (the really new) work of art among them. The existing order is complete before the new work arrives; for order to persist after the supervention of novelty, the whole existing order must be, if ever so slightly, altered; and so the relations, proportions, values of each work of art toward the whole are readjusted; and this is conformity between the old and the new. Whoever has approved this idea of order, of the form of European, of English literature, will not find it preposterous that the past should be altered by the present as much as the present is directed by the past. And the poet who is aware of this will be aware of great difficulties and responsibilities.

The human cerebellum, universal transform or multiple functionality?

At what level does a uniform circuitry really imply a uniform function? Maybe it's time to rethink the "the cerebellum is an internal model" doctrine. Our review in @NeuroCellPress is out today. @Brains_CAN. https://t.co/g2QD7B780a

— DiedrichsenLab (@diedrichsenlab) June 6, 2019

The concluding discussion:

We have reviewed convergent evidence that highlights the functional diversity of the human cerebellum. This diversity makes the formulation of a domain-general theory of cerebellar function, at best, very challenging. It is also important to keep in mind that an algorithmic account of cerebellar function may entail multiple computational concepts and that these may differ across domains, an idea we termed multiple functionality.

The relative merits of the universal transform and multiple functionality hypotheses will, in the end, be an empirical question. For now, we think there is considerable value in carefully developing hypotheses of cerebellar function for specific cognitive domains without being limited, a priori, by the assumption that the function is somehow analogous to those established for motor control. For example, most of our hypotheses and experiments in the sensorimotor domain focus on the role of the cerebellar circuit in the adult organism. However, in the cognitive domain, the cerebellum may play a more important role in development than in mature function (Badura et al., 2018). Furthermore, although damage to the cerebellum in adulthood frequently results in rather subtle symptoms on cognitive and affective measures (Alexander et al., 2012), the same damage in the developing brain may have much more profound consequences. Thus, in cognitive and social domains, the cerebellum may help set up cortical circuitry during certain sensitive phases of development. When established, the cortical circuits may no longer require substantial cerebellum-based modulation. This hypothesis may be important for understanding why cerebellar dysfunction has been attributed to neuropsychiatric developmental disorders such as autism (Wang et al., 2014) and schizophrenia (Moberget et al., 2018), even though damage to the cerebellum in adulthood will not result in the symptoms associated with these disorders.

When exploring cerebellar function in each task domain, there are two critical issues that must be addressed. First, cerebellar activity should be studied in the context of the activity patterns in the cerebral cortex. In isolation, the study of cerebellar activity may lead to interesting punctuated insights, such as “the cerebellum represents reward” (Wagner et al., 2017). However, to gain a deeper understanding of cerebellar function, we need to compare cerebellar and cortical representations (Wagner et al., 2019). Do the pontine nuclei simply transmit information from the neocortex to the cerebellum in a non-selective manner, or are specific aspects of cortical representations emphasized and other aspects omitted? The circuitry in the pontine nuclei suggests that these subcortical nuclei can perform non-linear integration and gating of cortical input (Schwarz and Thier, 1999). Thus, the information reaching the cerebellum may differ in informative ways from the way it is represented in the neocortex.

Identifying these differences is likely to yield important insight into the role of the cerebellum. For example, if a cerebellar area is especially important in a specific phase of skill activation, then we would expect different activity time courses for the relevant cerebellar and cortical regions: disproportionately higher activity in early phases of learning when the cerebellum is involved in initial acquisition and disproportionately higher activity in later phases when it is important for the performance of automatized behaviors. To perform such experiments and analyses, a full model of cortical-cerebellar connectivity is required, allowing the researcher to identify the relevant pairs of cortical and cerebellar regions.

Second, it will be important to understand what information is carried by the climbing fiber system. According to the Marr-Albus-Ito model, the climbing fiber input specifies the “learning goal” for the cerebellar circuit and, therefore, plays a pivotal role in shaping the output of the cerebellum. Although the climbing fiber input has traditionally been assumed to represent an error signal, new evidence suggests that it may be better conceptualized as a general teaching signal that may sometimes also relate to reward rather than error (Heffley et al., 2018). At present, we have virtually no insight concerning the information content of the climbing fiber system in the “cognitive” regions of the human cerebellum. Thus, we do not know what these cerebellar circuits are being instructed to learn. Understanding the learning goal (or cost function) will likely provide an important key to understanding cerebellar function in the domain of cognition.

In summary, careful investigation of cerebellar function within well-specified task domains will provide a clearer picture of the functional diversity of this major subcortical structure. Looking across domains, we may ultimately discover a universal cerebellar transform. It is likely, however, that this computation will not be easily captured in the functional terms we can intuitively describe: ideas such as timing, automatization, prediction, error correction, or internal models. Rather, a common principle may only emerge in terms of a more abstract language describing the population dynamics of neuronal networks.

Connectionism, symbolic AI, and machine learning in AI

Where do the predictive techniques of machine learning come from? How do they represent and organise the social? Join us for a public talk with’s Dominique Cardon (@Karmacoma), Wed 24th April 5pm: https://t.co/oBplRQvRgZ

— Digital Humanities (@kingsdh) April 19, 2019

If you follow the link you'll see this abstract:

Neurons spike back. The invention of inductive machine and the Artificial intelligence controversy - Dominique Cardon (Sciences Po Médialab)

Since 2010, machine learning based predictive techniques, and more specifically deep learning neural networks, have achieved spectacular performances in the fields of image recognition or automatic translation, under the umbrella term of “Artificial Intelligence”. But their relation to this field of research is not straightforward. In the tumultuous history of AI, learning techniques using so-called "connectionist" neural networks have long been mocked and ostracized by the "symbolic" movement. This talk retraces the history of artificial intelligence through the lens of the tension between symbolic and connectionist approaches. From a social history of science and technology perspective, it seeks to highlight how researchers, relying on the availability of massive data and the multiplication of computing power have undertaken to reformulate the symbolic AI project by reviving the spirit of adaptive and inductive machines dating back from the era of cybernetics.

The hypothesis behind this communication is that the new computational techniques used in machine learning provide a new way of representing society, no longer based on categories but on individual traces of behaviour. The new algorithms of machine learning replace the regularity of constant causes with the "probability of causes". It is therefore another way of representing society and the uncertainties of action that is emerging. To defend this argument, this communication will propose two parallel investigations. The first, from a science and technology history perspective, traces the emergence of the connexionist paradigm within artificial intelligence techniques. The second, based on the sociology of statistical categorization, focuses on how the calculation techniques used by major web services produce predictive recommendations.

This talk will be partly based on the article (in French): Cardon (Dominique), Cointet (Jean-Philippe), Mazières (Antoine), «La revanche des neurones. L’invention des machines inductives et la controverse de l’intelligence artificielle», Réseaux, n°211, 2018, pp. 173-220.

Wall Street's looking for peole who know about culture

Wall Street Is Desperate for Wonks Who Can Explain the Rise of Populism https://t.co/Qfp13tmLW4

— Charlie Tapp (@charleswtapp) June 5, 2019

Untouchability and open defecation in rural India

Dean Spears and Amit Thorat, "The Puzzle of Open Defecation in Rural India: Evidence from a Novel Measure of Caste Attitudes in a Nationally Representative Survey," Economic Development and Cultural Change 0, no. 0 (-Not available-): 000. https://doi.org/10.1086/698852

Abstract

Uniquely widespread and persistent open defecation in rural India has emerged as an important policy challenge and puzzle about behavioral choice in economic development. One candidate explanation is the culture of purity and pollution that reinforces and has its origins in the caste system. Although such a cultural account is inherently difficult to quantitatively test, we provide support for this explanation by comparing open defecation rates across places in India where untouchability is more and less intensely practiced. In particular, we exploit a novel question in the 2012 India Human Development Survey that asked households whether they practice untouchability, meaning whether they enforce norms of purity and pollution in their interactions with lower castes. We find an association between local practice of untouchability and open defecation that is robust; is not explained by economic, educational, or other observable differences; and is specific to open defecation rather than other health behavior or human capital investments more generally. We verify that practicing untouchability is not associated with general disadvantage in health knowledge or access to medical professionals. We interpret this as evidence that the culture of purity, pollution, untouchability, and caste contributes to the exceptional prevalence of open defecation in rural India.

H/t Tyler Cowen.

A tale of two cities, among other things

Why do people die in novels?

Olivier Morin, Alberto Acerbi, Oleg Sobchuk, Why people die in novels: testing the ordeal simulation hypothesis, Palgrave Communications 5, Article number: 2 (2019)

Abstract

What is fiction about, and what is it good for? An influential family of theories sees fiction as rooted in adaptive simulation mechanisms. In this view, our propensity to create and enjoy narrative fictions was selected and maintained due to the training that we get from mentally simulating situations relevant to our survival and reproduction. We put forward and test a precise version of this claim, the “ordeal simulation hypothesis”. It states that fictional narrative primarily simulates “ordeals”: situations where a person’s reaction might dramatically improve or decrease her fitness, such as deadly aggressions, or decisions on long-term matrimonial commitments. Experience does not prepare us well for these rare, high-stakes occasions, in contrast with situations that are just as fitness-relevant but more frequent (e.g., exposure to pathogens). We study mortality in fictional and non-fictional texts as a partial test for this view. Based on an analysis of 744 extensive summaries of twentieth century American novels of various genres, we show that the odds of dying (in a given year) are vastly exaggerated in fiction compared to reality, but specifically more exaggerated for homicides as compared to suicides, accidents, war-related, or natural deaths. This evidence supports the ordeal simulation hypothesis but is also compatible with other accounts. For a more specific test, we look for indications that this focus on death, and in particular on death caused by an agent, is specific to narrative fiction as distinct from other verbal productions. In a comparison of 10,810 private letters and personal diary entries written by American women, with a set of 811 novels (also written by American women), we measure the occurrence of words related to natural death or agentive death. Private letters and diaries are as likely, or more likely, to use words relating to natural or agentive death. Novels written for an adult audience contain more words relating to natural deaths than do letters (though not diary entries), but this is not true for agentive death. Violent death, in spite of its clear appeal for fiction, does not necessarily provide a clear demarcation point between fictional and non-fictional content.

Comment: FWIW, or various reasons, color me skeptical. For one thing, the adaptive hypothesis implies that death shows up in fiction because it is something we all must confront, but that we can't prepare for by rehearsal. But is homicide more prevalent in 20th C. American novels because Americans want to prepare themselves for the threat of murder? Seems unlikely to me. I rather suspect that murder shows up because of the moral and psychological issues it raises about the murderer. Fans of The Sopranos, for example, weren't preparing for the possibility of being murdered by a mob boss. They're interested in how a mob boss thinks and feels about the murders he orders and the ones he commits.

Addendum: 9.20.19: Moreover I don't see how simply reading about this or that violent death at the hands of someone else can provide any simulation that would be of value in an actual physical confrontation. Fighters don't prepare for a match by reading about matches, or even watching film – though they may do some of that. Their primary preparation takes the form of sparing, actual physical practice. Reading about fights does nothing to train your physical actions and reactions.