I know (1) that you think (2) it’s funny, and you know (3) that I know (4) that, too.

A large part of human humour depends on understanding that the intention of the person telling the joke might be different to what they are actually saying. The person needs to tell the joke so that you understand that they’re telling a joke, so they need to to know that you know that they do not intend to convey the meaning they are about to utter… Things get even more complicated when we are telling each other jokes that involve other people having thoughts and beliefs about other people. We call this knowledge nested intentions, or recursive mental attributions. We can already see, based on my complicated description, that this is a serious matter and requires scientific investigation. Fortunately, a recent paper by Dunbar, Launaway and Curry (2015) investigated whether the structure of jokes is restricted by the amount of nested intentions required to understand the joke and they make a couple of interesting predictions on the mental processing that is involved in processing humour, and how these should be reflected in the structure and funniness of jokes. In today’s blogpost I want to discuss the paper’s methodology and some of its claims.

Continue reading “I know (1) that you think (2) it’s funny, and you know (3) that I know (4) that, too.”

What’s in a Name? – “Digital Humanities” [#DH] and “Computational Linguistics”

In thinking about the recent LARB critique of digital humanities and of responses to it I couldn’t help but think, once again, about the term itself: “digital humanities.” One criticism is simply that Allington, Brouillette, and Golumbia (ABG) had a circumscribed conception of DH that left too much out of account. But then the term has such a diverse range of reference that discussing DH in a way that is both coherent and compact is all but impossible. Moreover, that diffuseness has led some people in the field to distance themselves from the term.

And so I found my way to some articles that Matthew Kirschenbaum has written more or less about the term itself. But I also found myself thinking about another term, one considerably older: “computational linguistics.” While it has not been problematic in the way DH is proving to be, it was coined under the pressure of practical circumstances and the discipline it names has changed out from under it. Both terms, of course, must grapple with the complex intrusion of computing machines into our life ways.

Digital Humanities

Let’s begin with Kirschenbaum’s “Digital Humanities as/Is a Tactical Term” from Debates in the Digital Humanities (2011):

To assert that digital humanities is a “tactical” coinage is not simply to indulge in neopragmatic relativism. Rather, it is to insist on the reality of circumstances in which it is unabashedly deployed to get things done—“things” that might include getting a faculty line or funding a staff position, establishing a curriculum, revamping a lab, or launching a center. At a moment when the academy in general and the humanities in particular are the objects of massive and wrenching changes, digital humanities emerges as a rare vector for jujitsu, simultaneously serving to position the humanities at the very forefront of certain value-laden agendas—entrepreneurship, openness and public engagement, future-oriented thinking, collaboration, interdisciplinarity, big data, industry tie-ins, and distance or distributed education—while at the same time allowing for various forms of intrainstitutional mobility as new courses are approved, new colleagues are hired, new resources are allotted, and old resources are reallocated.

Just so, the way of the world.

Kirschenbaum then goes into the weeds of discussions that took place at the University of Virginia while a bunch of scholars where trying to form a discipline. So:

A tactically aware reading of the foregoing would note that tension had clearly centered on the gerund “computing” and its service connotations (and we might note that a verb functioning as a noun occupies a service posture even as a part of speech). “Media,” as a proper noun, enters the deliberations of the group already backed by the disciplinary machinery of “media studies” (also the name of the then new program at Virginia in which the curriculum would eventually be housed) and thus seems to offer a safer landing place. In addition, there is the implicit shift in emphasis from computing as numeric calculation to media and the representational spaces they inhabit—a move also compatible with the introduction of “knowledge representation” into the terms under discussion.

How we then get from “digital media” to “digital humanities” is an open question. There is no discussion of the lexical shift in the materials available online for the 2001–2 seminar, which is simply titled, ex cathedra, “Digital Humanities Curriculum Seminar.” The key substitution—“humanities” for “media”—seems straightforward enough, on the one hand serving to topically define the scope of the endeavor while also producing a novel construction to rescue it from the flats of the generic phrase “digital media.” And it preserves, by chiasmus, one half of the former appellation, though “humanities” is now simply a noun modified by an adjective.

And there we have it. Continue reading “What’s in a Name? – “Digital Humanities” [#DH] and “Computational Linguistics””

Chomsky, Hockett, Behaviorism and Statistics in Linguistics Theory

Here’s an interesting (and recent) article that speaks to statistical thought in linguistics: The Unmaking of a Modern Synthesis: Noam Chomsky, Charles Hockett, and the Politics of Behaviorism, 1955–1965 (Isis, vol. 17, #1, pp. 49-73: 2016), by Gregory Radick (abstract below). Commenting on it at Dan Everett’s FB page, Yorick Wilks observed: “It is a nice irony that statistical grammars, in the spirit of Hockett at least, have turned out to be the only ones that do effective parsing of sentences by computer.”

Abstract: A familiar story about mid-twentieth-century American psychology tells of the abandonment of behaviorism for cognitive science. Between these two, however, lay a scientific borderland, muddy and much traveled. This essay relocates the origins of the Chomskyan program in linguistics there. Following his introduction of transformational generative grammar, Noam Chomsky (b. 1928) mounted a highly publicized attack on behaviorist psychology. Yet when he first developed that approach to grammar, he was a defender of behaviorism. His antibehaviorism emerged only in the course of what became a systematic repudiation of the work of the Cornell linguist C. F. Hockett (1916–2000). In the name of the positivist Unity of Science movement, Hockett had synthesized an approach to grammar based on statistical communication theory; a behaviorist view of language acquisition in children as a process of association and analogy; and an interest in uncovering the Darwinian origins of language. In criticizing Hockett on grammar, Chomsky came to engage gradually and critically with the whole Hockettian synthesis. Situating Chomsky thus within his own disciplinary matrix suggests lessons for students of disciplinary politics generally and—famously with Chomsky—the place of political discipline within a scientific life.

Future tense and saving money: no correlation when controlling for cultural evolution

This week our paper on future tense and saving money is published (Roberts, Winters & Chen, 2015).  In this paper we test a previous claim by Keith Chen about whether the language people speak influences their economic decisions (see Chen’s TED talk here or paper).  We find that at least part of the previous study’s claims are not robust to controlling for historical relationships between cultures. We suggest that large-scale cross-cultural patterns should always take cultural history into account.

Does language influence the way we think?

There is a longstanding debate about whether the constraints of the languages we speak influence the way we behave. In 2012, Keith Chen discovered a correlation between the way a language allows people to talk about future events and their economic decisions: speakers of languages which make an obligatory grammatical distinction between the present and the future are less likely to save money.

Continue reading “Future tense and saving money: no correlation when controlling for cultural evolution”

A Note on Dennett’s Curious Comparison of Words and Apps

I continue to think about Dan Dennett’s inadequate account of words-as-memes in his paper, The Cultural Evolution of Words and Other Thinking Tools (PDF), Cold Spring Harbor Symposia on Quantitative Biology, Volume LXXIV, pp. 1-7, 2009. You find the same account in, for example, this video of a talk he gave in 2011: “A Human Mind as an Upside Down Brain”. I feel it warrants (yet another) long-form post. But I just don’t want to wrangle my way through that now. So I’m just going to offer a remark that goes a bit beyond what I’ve already said in my working paper, Cultural Evolution, Memes, and the Trouble with Dan Dennett, particularly in the post, Watch Out, Dan Dennett, Your Mind’s Changing Up on You!.

In that article Dennett asserts that “Words are not just like software viruses; they are software viruses, a fact that emerges quite uncontroversially once we adjust our understanding of computation and software.” He then uses Java applets to illustrate this comparison. I believe the overstates the similarity between words and apps or viruses to the point where the comparison has little value. The adjustment of understanding that Dennett calls for is too extreme.

In particular, and here is my new point, it simply vitiates the use of computation as an idea in understanding the modeling mental processes. Dennett has spent much of his career arguing that the mind is fundamentally a computational process. Words are thus computational objects and our use of them is a computational process.

Real computational processes are precise in their nature and the requirements of their physical implementation – and there is always a physical implementation for real computation. Java is based on a certain kind of computational objects and processes, a certain style of computing. But not all computing is like that. What if natural language computing isn’t? What happens to the analogy then? Continue reading “A Note on Dennett’s Curious Comparison of Words and Apps”

Languages adapt to their contextual niche (Winters, Kirby & Smith, 2014)

ResearchBlogging.orgLast week saw the publication of my latest paper, with co-authors Simon Kirby and Kenny Smith, looking at how languages adapt to their contextual niche (link to the OA version and here’s the original). Here’s the abstract:

It is well established that context plays a fundamental role in how we learn and use language. Here we explore how context links short-term language use with the long-term emergence of different types of language systems. Using an iterated learning model of cultural transmission, the current study experimentally investigates the role of the communicative situation in which an utterance is produced (situational context) and how it influences the emergence of three types of linguistic systems: underspecified languages (where only some dimensions of meaning are encoded linguistically), holistic systems (lacking systematic structure) and systematic languages (consisting of compound signals encoding both category-level and individuating dimensions of meaning). To do this, we set up a discrimination task in a communication game and manipulated whether the feature dimension shape was relevant or not in discriminating between two referents. The experimental languages gradually evolved to encode information relevant to the task of achieving communicative success, given the situational context in which they are learned and used, resulting in the emergence of different linguistic systems. These results suggest language systems adapt to their contextual niche over iterated learning.


Context clearly plays an important role in how we learn and use language. Without this contextual scaffolding, and our inferential capacities, the use of language in everyday interactions would appear highly ambiguous. And even though ambiguous language can and does cause problems (as hilariously highlighted by the ‘What’s a chicken?’ case), it is also considered to be communicatively functional (see Piantadosi et al., 2012).  In short: context helps in reducing uncertainty about the intended meaning.

If context is used as a resource in reducing uncertainty, then it might also alter our conception of how an optimal communication system should be structured (e.g., Zipf, 1949). With this in mind, we wanted to investigate the following questions: (i) To what extent does the context influence the encoding of features in the linguistic system? (ii) How does the effect of context work its way into the structure of language?  To get at these questions we narrowed our focus to look at the situational context: the immediate communicative environment in which an utterance is situated and how it influences the distinctions a speaker needs to convey.

Of particular relevance here is Silvey, Kirby & Smith (2014): they show that the incorporation of a situational context can change the extent to which an evolving language encodes certain features of referents. Using a pseudo-communicative task, where participants needed to discriminate between a target and a distractor meaning, the authors were able to manipulate which meaning dimensions (shape, colour, and motion) were relevant and irrelevant in conveying the intended meaning. Over successive generations of participants, the languages converged on underspecified systems that encoded the feature dimension which was relevant for discriminating between meanings.

The current work extends upon these findings in two ways: (a) we added a communication element to the setup, and (b) we further explored the types of situational context we could manipulate.  Our general hypothesis, then, is that these artificial languages should adapt to the situational context in predictable ways based on whether or not a distinction is relevant in communication.

Continue reading “Languages adapt to their contextual niche (Winters, Kirby & Smith, 2014)”

John Lawler on Generative Grammar

From a Facebook conversation with Dan Everett (about slide rules, aka slipsticks, no less) and others:

The constant revision and consequent redefining and renaming of concepts – some imaginary and some very obvious – has led to a multi-dimensional spectrum of heresy in generative grammar, so complex that one practically needs chromatography to distinguish variants. Babel comes to mind, and also Windows™ versions. Most of the literature is incomprehensible in consequence – or simply repetitive, except it’s too much trouble to tell which.

–John Lawler

Why I Abandoned Chomskian Linguistics, with Links to 2 FB Discussions with Dan Everett

It wasn’t a matter of deep and well-thought princple. It was simpler than that. Chomsky’s approach to linguistics didn’t have the tools I was looking for. Let me explain.

* * * * *

Dan Everett’s kicked off two discussions on Facebook about Chomksy. This one takes Christina Behme’s recent review article, A ‘Galilean’ science of language, as its starting point. And this one’s about nativism, sparked by Vyv Evans’ The Language Myth.

* * * * *

I learned about Chomsky during my second year as an undergraduate at Johns Hopkins. I took a course in psycholinguistics taught by James Deese, known for his empirical work on word associations. We read and wrote summaries of classic articles, including Lee’s review of Syntactic Structures and Chomsky’s review of Skinner’s Verbal Behavior. My summary of one of them, I forget which, prompted Deese to remark that my summary was an “unnecessarily original” recasting of my argument.

That’s how I worked. I tried to get inside the author’s argument and then to restate it in my own words.

In any event I was hooked. But Hopkins didn’t have any courses in linguistics let alone a linguistics department. So I had to pursue Chomsky and related thinkers on my own. Which I did over the next few years. I read Aspects, Syntatic Structures, Sound Patterns of English (well, more like I read at that one), Lenneberg’s superb book on biological foundations (with an appendix by Chomsky), found my way to generative semantics, and other stuff. By the time I headed off to graduate school in English at the State University of New York at Buffalo I was mostly interested in that other stuff.

I became interested in Chomsky because I was interested in language. While I was interested in language as such, I was a bit more interested in literature and much of my interest in linguistics followed from that. Literature is made of language, hence some knowledge of linguistics should be useful. Trouble is, it was semantics that I needed. Chomsky had no semantics and generative semantics looked more like syntax.

So that other stuff looked more promising. Somehow I’d found my way to Syd Lamb’s stratificational linguistics. I liked that for the diagrams, as I think diagrammatically, and for the elegance. Lamb used the same vocabulary of structural elements to deal with phonology, morphology, and syntax. That made sense to me. And the s work within his system actually looked like semantics, rather than souped up syntax, though there wasn’t enough of it. Continue reading “Why I Abandoned Chomskian Linguistics, with Links to 2 FB Discussions with Dan Everett”

Vyv Evans: The Human Meaning-Making Engine

If you read my last post here at Replicated Typo to the very end, you may remember that I promised to recommend a book and to return to one of the topics of this previous post. I won’t do this today, but I promise I will catch up on it in due time.

What I just did – promising something – is a nice example for one of the two functions of language which Vyvyan Evans from Bangor University distinguished in his talk on “The Human Meaning-Making Engine” yesterday at the UK Cognitive Linguistics Conference. More specifically, the act of promising is an example for the interactive function of language, which is of course closely intertwined with its symbolic function. Evans proposed two different sources for this two functions. The interactive function, he argued, arises from the human instinct for cooperation, whereas meaning arises from the interaction between the linguistic and the conceptual system. While language provides the “How” of meaning-making, the conceptual system provides the “What”. Evans used some vivid examples (e.g. this cartoon exemplifying nonverbal communication) to make clear that communication is not contingent on language. However, “language massively amplifies our communicative potential.” The linguistic system, he argued, has evolved as an executive control system for the conceptual system. While the latter is broadly comparable with that of other animals, especially great apes, the linguistic system is uniquely human. What makes it unique, however, is not the ability to refer to things in the world, which can arguably be found in other animals, as well. What is uniquely human, he argued, is the ability to symbolically refer in a sign-to-sign (word-to-word) direction rather than “just” in a sign-to-world (word-to-world) direction.  Evans illustrated this “word-to-word” direction with Hans-Jörg Schmid’s (e.g.  2000; see also here)  work on “shell nouns”, i.e. nouns “used in texts to refer to other passages of the text and to reify them and characterize them in certain ways.” For instance, the stuff I was talking about in the last paragraph would be an example of a shell noun.

According to Evans, the “word-to-word” direction is crucial for the emergence of e.g. lexical categories and syntax, i.e. the “closed-class” system of language. Grammaticalization studies indicate that the “open-class” system of human languages is evolutionarily older than the “closed-class” system, which is comprised of grammatical constructions (in the broadest sense). However, Evans also emphasized that there is a lot of meaning even in closed-class constructions, as e.g. Adele Goldberg’s work on argument structure constructions shows: We can make sense of a sentence like “Someone somethinged something to someone” although the open-class items are left unspecified.

Constructions, he argued, index or cue simulations, i.e. re-activations of body-based states stored in cortical and subcortical brain regions. He discussed this with the example of the cognitive model for Wales: We know that Wales is a geographical entity. Furthermore, we know that “there are lots of sheep, that the Welsh play Rugby, and that they dress in a funny way.” (Sorry, James. Sorry, Sean.) Oh, and “when you’re in Wales, you shouldn’t say, It’s really nice to be in England, because you will be lynched.”

On a more serious note, the cognitive models connected to closed-class constructions, e.g. simple past -ed or progressive -ing, are of course much more abstract but can also be assumed to arise from embodied simulations (cf. e.g. Bergen 2012). But in addition to the cognitive dimension, language of course also has a social and interactive dimension drawing on the apparently instinctive drive towards cooperative behaviour. Culture (or what Tomasello calls “collective intentionality”)  is contigent on this deep instinct which Levinson (2006) calls the “human interaction engine”. Evans’ “meaning-making engine” is the logical continuation of this idea.

Just like Evans’ theory of meaning (LCCM theory), his idea of the “meaning-making engine” is basically an attempt at integrating a broad variety of approaches into a coherent model. This might seem a bit eclectic at first, but it’s definitely not the worst thing to do, given that there is significant conceptual overlap between different theories which, however, tends to be blurred by terminological incongruities. Apart from Deacon’s (1997) “Symbolic Species” and Tomasello’s work on shared and joint intentionality, which he explicitly discussed, he draws on various ideas that play a key role in Cognitive Linguistics. For example, the distinction between open- and closed-class systems features prominently in Talmy’s (2000) Cognitive Semantics, as does the notion of the human conceptual system. The idea of meaning as conceptualization and embodied simulation of course goes back to the groundbreaking work of, among others, Lakoff (1987) and Langacker (1987, 1991), although empirical support for this hypothesis has been gathered only recently in the framework of experimental semantics (cf. Matlock & Winter forthc. – if you have an account at academia.edu, you can read this paper here). All in all, then, Evans’ approach might prove an important further step towards integrating Cognitive Linguistics and language evolution research, as has been proposed by Michael and James in a variety of talks and papers (see e.g. here).

Needless to say, it’s impossible to judge from a necessarily fairly sketchy conference presentation if this model qualifies as an appropriate and comprehensive account of the emergence of meaning. But it definitely looks promising and I’m looking forward to Evans’ book-length treatment of the topics he touched upon in his talk. For now, we have to content ourselves with his abstract from the conference booklet:

In his landmark work, The Symbolic Species (1997), cognitive neurobiologist Terrence Deacon argues that human intelligence was achieved by our forebears crossing what he terms the “symbolic threshold”. Language, he argues, goes beyond the communicative systems of other species by moving from indexical reference – relations between vocalisations and objects/events in the world — to symbolic reference — the ability to develop relationships between words — paving the way for syntax. But something is still missing from this picture. In this talk, I argue that symbolic reference (in Deacon’s terms), was made possible by parametric knowledge: lexical units have a type of meaning, quite schematic in nature, that is independent of the objects/entities in the world that words refer to. I sketch this notion of parametric knowledge, with detailed examples. I also consider the interactional intelligence that must have arisen in ancestral humans, paving the way for parametric knowledge to arise. And, I also consider changes to the primate brain-plan that must have co-evolved with this new type of knowledge, enabling modern Homo sapiens to become so smart.



Bergen, Benjamin K. (2012): Louder than Words. The New Science of How the Mind Makes Meaning. New York: Basic Books.

Deacon, Terrence W. (1997): The Symbolic Species. The Co-Evolution of Language and the Brain. New York, London: Norton.

Lakoff, George (1987): Women, Fire, and Dangerous Things. What Categories Reveal about the Mind. Chicago: The University of Chicago Press.

Langacker, Ronald W. (1987): Foundations of Cognitive Grammar. Vol. 1. Theoretical Prerequisites. Stanford: Stanford University Press.

Langacker, Ronald W. (1991): Foundations of Cognitive Grammar. Vol. 2. Descriptive Application. Stanford: Stanford University Press.

Levinson, Stephen C. (2006): On the Human “Interaction Engine”. In: Enfield, Nick J.; Levinson, Stephen C. (eds.): Roots of Human Sociality. Culture, Cognition and Interaction. Oxford: Berg, 39–69.

Matlock, Teenie & Winter, Bodo (forthc): Experimental Semantics. In: Heine, Bernd; Narrog, Heiko (eds.): The Oxford Handbook of Linguistic Analysis. 2nd ed. Oxford: Oxford University Press.

Schmid, Hans-Jörg (2000): English Abstract Nouns as Conceptual Shells. From Corpus to Cognition. Berlin, New York: De Gruyter (Topics in English Linguistics, 34).

Talmy, Leonard (2000): Toward a Cognitive Semantics. 2 vol. Cambridge, Mass: MIT Press.


QWERTY: The Next Generation


[This is a guest post by

Oh wait, I’m not a guest anymore. Thanks to James for inviting me to become a regular contributor to Replicated Typo. I hope I will have to say some interesting things about the evoution of language, cognition, and culture, and I promise that I’ll try to keep my next posts a bit shorter than the guest post two weeks ago.

Today I’d like to pick up on an ongoing discussion over at Language Log. In a series of blog posts in early 2012, Mark Liberman has taken issue with the so-called “QWERTY effect”. The QWERTY effect seems like an ideal topic for my first regular post as it is tightly connected to some key topics of Replicated Typo: Cultural evolution, the cognitive basis of language, and, potentially, spurious correlations. In addition, Liberman’s coverage of the QWERTY effect has spawned an interesting discussion about research blogging (cf. Littauer et al. 2014).

But what is the QWERTY effect, actually? According to Kyle Jasmin and Daniel Casasanto (Jasmin & Casasanto 2012), the written form of words can influence their meaning, more particularly, their emotional valence. The idea, in a nutshell, is this: Words that contain more characters from the right-hand side of the QWERTY keyboard tend to “acquire more positive valences” (Jasmin & Casasanto 2012). Casasanto and his colleagues tested this hypothesis with a variety of corpus analyses and valence rating tasks.

Whenever I tell fellow linguists who haven’t heard of the QWERTY effect yet about these studies, their reactions are quite predictable, ranging from “WHAT?!?” to “asdf“. But unlike other commentors, I don’t want to reject the idea that a QWERTY effect exists out of hand. Indeed, there is abundant evidence that “right” is commonly associated with “good”. In his earlier papers, Casasanto provides quite convincing experimental evidence for the bodily basis of the cross-linguistically well-attested metaphors RIGHT IS GOOD and LEFT IS BAD (e.g. Casasanto 2009). In addition, it is fairly obvious that at the end of the 20th century, computer keyboards started to play an increasingly important role in our lives. Also, it seems legitimate to assume that in a highly literate society, written representations of words form an important part of our linguistic knowledge. Given these factors, the QWERTY effect is not such an outrageous idea. However, measuring it by determining the “Right-Side Advantage” of words in corpora is highly problematic since a variety of potential confounding factors are not taken into account.

Finding the Right Name(s)

Frequencies of some (almost) randomly selected names in the USA.

In a new CogSci paper, Casasanto, Jasmin, Geoffrey Brookshire, and Tom Gijssels present five new experiments to support the QWERTY hypothesis. Since I am based at a department with a strong focus on onomastics, I found their investigation of baby names particularly interesting. Drawing on data from the US Social Security Administration website, they analyze all names that have been given to more than 100 babys in every year from 1960 to 2012. To determine the effect of keyboard position, they use a measure they call “Right Side Adventage” (RSA): [(#right-side letters)-(#left-side letters)]. They find that

“that the mean RSA has increased since the popularization of the QWERTY keyboard, as indicated by a correlation between the year and average RSA in that year (1960–2012, r = .78, df = 51, p =8.6 × 10-12

In addition,

“Names invented after 1990 (n = 38,746) use more letters from the right side of the keyboard than names in use before 1990 (n = 43,429; 1960–1990 mean RSA = -0.79; 1991–2012 mean RSA = -0.27, t(81277.66) = 33.3, p < 2.2 × 10-16 […]). This difference remained significant when length was controlled by dividing each name’s RSA by the number of letters in the name (t(81648.1) = 32.0, p < 2.2 × 10-16)”

Mark Liberman has already pointed to some problematic aspects of this analysis (but see also Casasanto et al.’s reply). They do not justify why they choose the timeframe of 1960-2012 (although data are available from 1880 onwards), nor do they explain why they only include names given to at least 100 children in each year. Liberman shows that the results look quite different if all available data are taken into account – although, admittedly, an increase in right-side characters from 1990 onwards can still be detected. In their response, Casasanto et al. try to clarify some of these issues. They present an analysis of all names back to 1880 (well, not all names, but all names attested in every year since 1880), and they explain:

“In our longitudinal analysis we only considered names that had been given to more than 100 children in *every year* between 1960 and 2012. By looking at longitudinal changes in the same group of names, this analysis shows changes in names’ popularity over time. If instead you only look at names that were present in a given year, you are performing a haphazard collection of cross-sectional analyses, since many names come and go. The longitudinal analysis we report compares the popularity of the same names over time.

I am not sure what to think of this. On the one hand, this is certainly a methodologically valid approach. On the other hand, I don’t agree that it is necessarily wrong to take all names into account. Given that 3,625 of all name types are attested in every year from 1960 to 2013 and that only 927 of all name types are attested in every year from 1880 to 2013 (the total number of types being 90,979), the vast majority of names is simply not taken into account in Casasanto et al.’s approach. This is all the more problematic given that parents have become increasingly individualistic in naming their children: The mean number of people sharing one and the same name has decreased in absolute terms since the 1960s. If we normalize these data by dividing them by the total number of name tokens in each year, we find that the mean relative frequency of names has continuously decreased over the timespan covered by the SSA data.

Mean frequency of a name (i.e. mean number of people sharing one name) in absolute and relative terms, respectively.

Thus, Casasanto et al. use a sample that might be not very representative of how people name their babies. If the QWERTY effect is a general phenomenon, it should also be found when all available data are taken into account.

As Mark Liberman has already shown, this is indeed the case – although some quite significant ups and downs in the frequency of right-side characters can be detected well before the QWERTY era. But is this rise in frequency from 1990 onwards necessarily due to the spread of QWERTY keyboards – or is there an alternative explanation? Liberman has already pointed to “the popularity of a few names, name-morphemes, or name fragments” as potential factors determining the rise and fall of mean RSA values. In this post, I’d like to take a closer look at one of these potential confounding factors.

Sonorous Sounds and “Soft” Characters
When I saw Casasanto et al.’s data, I was immediately wondering if the change in character distribution could not be explained in terms of phoneme distribution. My PhD advisor, Damaris Nübling, has done some work (see e.g. here [in German]) showing an increasing tendency towards names with a higher proportion of sonorous sounds in Germany. More specifically, she demonstrates that German baby names become more “androgynous” in that male names tend to assume features that used to be characteristic of (German) female names (e.g. hiatus; final full vowel; increase in the overall number of sonorous phonemes). Couldn’t a similar trend be detectable in American baby names?

Names showing particularly strong frequency changes among those names that appear among the Top 20 most frequent names at least once between 1960 and 2013.

If we take a cursory glance at those names that can be found among the Top 20 most frequent names of at least one year since 1960 and if we single out those names that experienced a particularly strong increase or decrease in frequency, we find that, indeed, sonorous names seem to become more popular. Those names that gain in popularity are characterized by lots of vowels, diphthongs (Aiden, Jayden, Abigail), hiatus (Liam, Zoey), as well as nasals and liquids (Lily, Liam).
To be sure, these cursory observations are not significant in and of themselves. To test the hypothesis if phonological changes can (partly) account for the QWERTY effect in a bit more detail, I basically split the sonority scale in half. I categorized characters typically representing vowels and sonorants as “soft sound characters” and those typically representing obstruents as “hard sound characters”. This is of course a ridiculously crude distinction entailing some problematic classifications. A more thorough analysis would have to take into account the fact that in many cases, one letter can stand for a variety of different phonemes. But as this is just an exploratory analysis for a blog post, I’ll go with this crude binary distinction. In addition, we can justify this binary categorization with an argument presented above: We can assume that the written representations of words are an important part of the linguistic knowledge of present-day language users. Thus, parents will probably not only be concerned with the question how a name sounds – they will also consider how it looks like in written form. Hence, there might be a preference for characters that prototypically represent “soft sounds”, irrespective of the sounds they actually stand for in a concrete case. But this is highly speculative and would have to be investigated in an entirely different experimental setup (e.g. with a psycholinguistic study using nonce names).

Distribution of “hard sound” vs. “soft sound” characters on the QWERTY keyboard.

Note that the characters representing “soft sounds” and “hard sounds”, respectively, are distributed unequally over the QWERTY keyboard. Given that most “soft sound characters” are also right-side characters, it is hardly surprising that we cannot only detect an increase in the “Right-Side Advantage” (as well as the “Right-Side Ratio”, see below) of baby names, but also an increase in the mean “Soft Sound Ratio” (SSR – # of soft sound characters / total # of characters). This increase is significant for the time from 1960 to 2013 irrespective of the sample we use: a) all names attested since 1960, b) names attested in every year since 1960, c) names attested in every year since 1960 more than 100 times.

“Soft Sound Ratio” in three different samples: a) All names attested in the SSA data; b) all names attested in every year since 1960; c) all names attested in every year since 1960 at least 100 times.

Note that both the “Right-Side Advantage” and the “Soft Sound Ratio” are particularly high in names only attested after 1990. (For the sake of (rough) comparability, I use the relative frequency of right-side characters here, i.e. Right Side Ratio = # of right-side letters / total number of letters.)

“Soft Sound Ratio” and “Right-Side Ratio” for names only attested after 1990.

Due to the considerable overlap between right-side and “soft” characters, both the QWERTY Effect and the “Soft Sound” Hypothesis might account for the changes that can be observed in the data. If the QWERTY hypothesis is correct, we should expect an increase for all right-side characters, even those that stand for “hard” sounds. Conversely, we should expect a decrease in the relative frequency of left-side characters, even if they typically represent “soft” sounds. Indeed, the frequency of “Right-Side Hard Characters” does increase – in the time from 1960 to the mid-1980s. In the QWERTY era, by contrast, <h>, <p>, <k>, and <j> suffer a significant decrease in frequency. The frequency of “Left-Side Soft Characters”, by contrast, increases slightly from the late 1960s onwards.

Frequency of left-side “soft” characters and right-side “hard” characters in all baby names attested from 1960 to 2013.

Further potential challenges to the QWERTY Effect and possible alternative experimental setups
The commentors over at Language Log have also been quite creative in coming up with possible alternative explanations and challenging the QWERTY hypothesis by showing that random collections of letters show similarly strong patterns of increase or decrease. Thus, the increase in the frequency of right-side letters in baby names is perhaps equally well, if not better explained by factors independent of character positions on the QWERTY keyboard. Of course, this does not prove that there is no such thing as a QWERTY effect. But as countless cases discussed on Replicated Typo have shown, taking multiple factors into account and considering alternative hypotheses is crucial in the study of cultural evolution. Although the phonological form of words is an obvious candidate as a potential confounding factor, it is not discussed at all in Casasanto et al.’s CogSci paper. However, it is briefly mentioned in Jasmin & Casasanto (2012: 502):

“In any single language, it could happen by chance that words with higher RSAs are more positive, due to sound–valence associations. But despite some commonalities, English, Dutch, and Spanish have different phonological systems and different letter-to-sound mappings.”

While this is certainly true, the sound systems and letter-to-sound mappings of these languages (as well as German and Portugese, which are investigated in the new CogSci paper) are still quite similar in many respects. To rule out the possibility of sound-valence associations, it would be necessary to investigate the phonological makeup of positively vs. negatively connotated words in much more detail.

Right-Side Advantage (RSA) for male vs. female names in two different samples (all names attested in the SSA data and all names attested in every year since 1960).

The SSA name lists provide another means to critically examine the QWERTY hypothesis since they differentiate between male and female names. If the QWERTY effect does play a significant role in parents’ name choices, we would expect it to be equally strong for boys names and girls names – or at least approximately so.

Right-Side Ratio for three different samples (all names attested in the SSA lists, all names attested in every year since 1960, all years attested in every year since 1960 at least 100 times).

On the hypothesis that other factors such as trend names play a much more important role, by contrast, differences between the developments of male vs. female names are to be expected. Indeed, the data reveal some differences between the RSA / RSR development of boys vs. girls names. At the same time, however, these differences show that the “Soft Sound Hypothesis” can only partly account for the QWERTY Effect since the “Soft Sound Ratios” of male vs. female names develop roughly in parallel.

“Soft Sound Ratio” of male vs. female names .

Given the complexity of cultural phenomena such as naming preferences, we would of course hardly expect one factor alone to determine people’s choices. The QWERTY Effect, like the “Soft Sound” Preference, might well be one factor governing parents’ naming decisions. However, the experimental setups used so far to investigate the QWERTY hypothesis are much too prone to spurious correlations to provide convincing evidence for the idea that words with a higher RSA assume more positive valences because of their number of right-side letters.

Granted, the amount of experimental evidence assembled by Casasanto et al. for the QWERTY effect is impressive. Nevertheless, the correlations they find may well be spurious ones. Don’t get me wrong – I’m absolutely in favor of bold hypotheses (e.g. about Neanderthal language). But as a corpus linguist, I doubt that such a subtle preference can be meaningfully investigated using corpus-linguistic methods. As a corpus linguist, you’re always dealing with a lot of variables you can’t control for. This is not too big a problem if your research question is framed appropriately and if potential confounding factors are explicitly taken into account. But when it comes to a possible connection between single letters and emotional valence, the number of potential confounding factors just seems to outweigh the significance of an effect as subtle as the correlation between time and average RSA of baby names. In addition, some of the presumptions of the QWERTY studies would have to be examined independently: Does the average QWERTY user really use their left hand for typing left-side characters and their right hand for typing right-side characters – or are there significant differences between individual typing styles? How fluent is the average QWERTY user in typing? (The question of typing fluency is discussed in passing in the 2012 paper.)

The study of naming preferences entails even more potentially confounding variables. For example, if we assume that people want their children’s names to be as beautiful as possible not only in phonological, but also in graphemic terms, we could speculate that the form of letters (round vs. edgy or pointed) and the position of letters within the graphemic representation of a name play a more or less important role. In addition, you can’t control for, say, all names of persons that were famous in a given year and thus might have influenced parents’ naming choices.

If corpus analyses are, in my view, an inappropriate method to investigate the QWERTY effect, then what about behavioral experiments? In their 2012 paper, Jasmin & Casasanto have reported an experiment in which they elicited valence judgments for pseudowords to rule out possible frequency effects:

“In principle, if words with higher RSAs also had higher frequencies, this could result in a spurious correlation between RSA and valence. Information about lexical frequency was not available for all of the words from Experiments 1 and 2, complicating an analysis to rule out possible frequency effects. In the present experiment, however, all items were novel and, therefore, had frequencies of zero.”

Note, however, that they used phonologically well-formed stimuli such as pleek or ploke. These can be expected to yield associations to existing words such as, say, peak connotated) and poke, or speak and spoke, etc. It would be interesting to repeat this experiment with phonologically ill-formed pseudowords. (After all, participants were told they were reading words in an alien language – why shouldn’t this language only consist of consonants?) Furthermore, Casasanto & Chrysikou (2011) have shown that space-valence mappings can change fairly quickly following a short-term handicap (e.g. being unable to use your right hand as a right-hander). Considering this, it would be interesting to perform experiments using a different kind of keyboard, e.g. an ABCDE keyboard, a KALQ keyboard, or – perhaps the best solution – a keyboard in which the right and the left side of the QWERTY keyboard are simply inverted. In a training phase, participants would have to become acquainted with the unfamiliar keyboard design. In the test phase, then, pseudowords that don’t resemble words in the participants’ native language should be used to figure out whether an ABCDE-, KALQ-, or reverse QWERTY effect can be detected.



Casasanto, D. (2009). Embodiment of Abstract Concepts: Good and Bad in Right- and Left-Handers. Journal of Experimental Psychology: General 138, 351–367.

Casasanto, D., & Chrysikou, E. G. (2011). When Left Is “Right”. Motor Fluency Shapes Abstract Concepts. Psychological Science 22, 419–422.

Casasanto, D., Jasmin, K., Brookshire, G., & Gijssels, T. (2014). The QWERTY Effect: How typing shapes word meanings and baby names. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Jasmin, K., & Casasanto, D. (2012). The QWERTY Effect: How Typing Shapes the Meanings of Words. Psychonomic Bulletin & Review 19, 499–504.

Littauer, R., Roberts, S., Winters, J., Bailes, R., Pleyer, M., & Little, H. (2014). From the Savannah to the Cloud. Blogging Evolutionary Linguistics Research. In L. McCrohon, B. Thompson, T. Verhoef, & H. Yamauchi, The Past, Present, and Future of Language Evolution Research. Student Volume following the 9th International Conference on the Evolution of Language (pp. 121–131).

Nübling, D. (2009). Von Monika zu Mia, von Norbert zu Noah. Zur Androgynisierung der Rufnamen seit 1945 auf prosodisch-phonologischer Ebene. Beiträge zur Namenforschung 44.