This year’s Nijmegen lectures were given by David Poeppel on his work linking language processing to low-level neural mechanisms. He called for more “muscular” linguists to step up and propose a “parts list” of linguistic primitives that neuro researchers could try and detect in the brain. In this post, I cover the generativist answer to this, as proposed by Norbert Hornstien, who appeared as a panelist at the Nijmegen lectures, and why it bothered me (TLDR: I think there’s more to language science than syntax, and other areas can also draw up a “parts list”).
This year, the proceedings of the Evolution of Language conference will appear online. The first group of papers are already up:
The move to self-publishing is a bit of an experiment, but hopefully it’ll mean that the papers are more accessible to a wider audience. To aid this, the papers are published under Creative Commons licenses. Some papers also include supplementary materials.
The full list of papers will be updated as revisions come in, but here are some interesting papers available so far:
The MPI for Psycholinguistics in Nijmegen, Netherlands is inviting scholars to visit in 2016 and 2017 to participate in Language Evolution research.
Under the running guest programme of the Institute, special opportunities are available in 2016-2017 for visitors – both senior scholars and post-docs – participating in the research theme “Evolution of Language”, starting at any time in 2016. The theme will be hosted by the Language & Cognition department (run by Stephen Levinson) in collaboration with the other departments at the Institute.
The MPI for Psycholinguistics and its partners in Nijmegen offer an exciting environment in which to explore the evolution of language, both in its biological and cultural dimensions, and the interactions between them. The MPI has departments in language and genetics, the neurobiology of language, the psychology of language, language and cognition, and (in development) the acquisition of language. The MPI is a partner in the Donders Institute for Brain, Cognition & Behaviour, Centre for Language Studies, and the Language in Interaction consortium, which bring together many different perspectives on language evolution.
Senior Visitorships are available for any period from one month up to one year until the end of 2017, and Postdocs are available for up to one year starting at any time in 2016.
More details here.
Special Issue on the Emergence of Phonetics and Phonology Call For Papers: Special issue of the Journal of Language Evolution
In recent years, there has been a resurgence in research in the evolution of language and speech. This special issue will focus on recent work addressing the evolution of speech apparatus, and the emergence of phonetic and phonological structure. The focus will be on the interaction between biological and cultural evolution, and the interaction between the cognitive evolution of language, and the biological evolution of speech. We are interested in submissions that consider how the physical aspects of a linguistic modality might shape our language, and how our phonetic capabilities at the speech level may influence our phonology at the language level.
The primary goal of the special issue is to exchange the latest advances in the study of the evolution of speech. We are interested in computational and mathematical modeling, experimental studies, brain and vocal tract imaging, corpus analysis and comparative data from animal studies, especially nonhuman primates. These techniques have allowed us to address questions relevant to the evolution of our phonetic capabilities, and the special issue will aim to open an interdisciplinary discourse.
Submissions must provide relevant empirical insight within the remit of this special issue.
Authors should prepare their manuscript according to the Instructions for Authors available from the online submission page of the Journal of Language Evolution at http://jole.oxfordjournals.org/for_authors/index.html. All the papers will be peer-reviewed.
Submission deadline: 17th April 2016
Guest Editor: Hannah Little (firstname.lastname@example.org)
This week the workshops appearing at EvoLang have been announced. Participation is free to those attending the main conference.
Gregory Mills and I are running a workshop on language evolution and interaction. Details are on the workshop website, including a call for papers.
Language Adapts to Interaction
Language has been shown to be adapted to constraints from many domains such as production, transmission, memory, processing and acquisition. These adaptations and constraints have formed the basis for theories of language evolution, but arguably the primary ecology of language is interaction – face-to-face conversation (Levinson, 2006). Taking turns at talk, repairing problems in communication and organising conversation into contingent sequences seem completely natural to us, but are in fact highly organised, tightly integrated systems (Sacks et al., 1974) which are not shared by any other species. Therefore, the infrastructure for interaction may provide an insight into the origins of our unique communicative abilities (Mills, 2014a; Micklos, 2014).
Indeed, recent studies on interaction have shown that an approach that emphasises interaction can sharpen our understanding of linguistic universals (Kendrick et al., 2014; Dingemanse et al., 2015), ontogeny and acquisition (Hilbrink, et al. 2015; Vogt, 2014), on-line processing constraints (Bögels et al., 2015) and animal signalling (Levinson & Holler, 2014).
The emerging picture is that the infrastructure for interaction is an evolutionary old requirement for the emergence of a complex linguistic system, and for a cooperative, cumulative culture more generally. These issues are also being integrated into computational models of the cultural evolution of linguistic systems (Vogt and Haasdijk, 2010; Roberts, & Levinson, 2015) and are being explored through studies of experimental semiotics (Mills, 2014b; Christensen et al., 2016).
This workshop is interested in addressing the following questions:
- How did the infrastructure for interaction emerge and how did it shape the emergence of language?
- What evidence is there that language structures show adaptation to interaction?
- How do interactional constraints interact with other domains such as processing?
- What are the limits of interactional abilities in non-human animals?
For instructions on how to submit, please see the Abstract Submissions page.
Bögels, S., Magyari, L., & Levinson, S. C. (2015). Neural signatures of response planning occur midway through an incoming question in conversation. Scientific Reports, 5: 12881.
Christensen, P., Fusaroli, R., and Tylén, K. (2016). Environmental constraints shaping constituent order in emerging communication systems: Structural iconicity, interactive alignment and conventionalization. Cognition, 146:67–80.
Dingemanse, M., Roberts, S. G., Baranova, J., Blythe, J., Drew, P., Floyd, S., Gisladottir, R. S., Kendrick, K. H., Levinson, S. C., Manrique, E., Rossi, G., & Enfield, N. J. (2015). Universal Principles in the Repair of Communication Problems. PLoS One, 10(9).
Hilbrink, E., Gattis, M., & Levinson, S. C. (2015). Early developmental changes in the timing of turn-taking: A longitudinal study of mother-infant interaction. Frontiers in Psychology, 6: 1492.
Kendrick, K. H., Brown, P., Dingemanse, M., Floyd, S., Gipper, S., Hayano, K., Hoey, E., Hoymann, G., Manrique, E., Rossi, G., & Levinson, S. C. (2014). Sequence organization: A universal infrastructure for action. 4th International Conference on Conversation Analysis. UCLA, CA.
Levinson, S. (2006). On the human interaction engine. In Enfield, N. and Levinson, S., editors, Roots of Human Sociality: Culture, Cognition and Human Interaction, pages 39–69. Oxford: Berg.
Levinson, S. C., & Holler, J. (2014). The origin of human multi-modal communication. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 369(1651): 2013030.
Micklos, A. (2014) The Nature of Language in Interaction. Proceedings of the 10th Evolution of Language conference. World Scientific: Vienna, Austria.
Mills, G. (2014a) Establishing a communication system: Miscommunication drives Abstraction. Proceedings of the 10th Evolution of Language conference. World Scientific: Vienna, Austria.
Mills, G. J. (2014b). Dialogue in joint activity: complementarity, convergence and conventionalization. New Ideas in Psychology, 32:158–173.
Roberts, S. G. & Levinson (2015) On-line pressures for turn-taking constrain the cultural evolution of word order. Workshop on Cognitive Linguistics and the Evolution of Language, International Cognitive Linguistics Conference, Newcastle University, UK.
Sacks, H., Schegloff, E. A., and Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 40(4):696–735.
Vogt, P. (2014) Language acquisition through interactions: A cross-cultural corpus of multimodal interactions with 1-to 2-years old infants. 8th International Conference on Construction Grammar. Osnabrück University, Germany.
Vogt, P. and Haasdijk, E. (2010). Modeling social learning of language and skills. Artificial Life, 16(4):289–309.
This weekend I appeared in an NPR article about Ian Maddieson and Christophe Coupe’s work on the effects of climate and ecology on the sound systems of languages. I haven’t read the study itself, but I did get to see the slides that Maddieson and Coupe presented this week at the Acoustical Society of America. Essentially, they find that speech sounds have high efficacy – adaptation to being transmitted and received in the local ecology. Specifically, languages tend to be more sonorous (less ‘consonant heavy’) in warmer places with more tree cover. This makes sense, since these kind of sounds are better at cutting through these obstacles.
I was interviewed for about an hour, but the quotes from me in the piece are actually a bit out of context. For instance, I claim that this is the first study of its kind, when there have been several studies which looks at climate and language (including one I co-authored with Caleb Everett and Damian Blasi). But, to be fair, Maddieson and Coupe’s study is probably the one with the greatest range of ecological variables and most sophisticated linguistic measure (although I’m not sure yet how they control for historical relatedness).
You can read about the study in the article above, but I wanted to address another thing I’m quoted as saying. The interviewer asked if it was possible to see these effects in a single language or a single speaker, and I said that it was very unlikely, but that I’d tried to do this with transcripts of Larry King. I went on to say that I absolutely wouldn’t publish this because it was a crazy idea that nobody would believe.
But now the cat’s out of the bag, so here’s what I found.
Does Larry King use more vowels when it’s warmer?
If we see language change as natural selection operating on individual utterances in conversation (a la Croft and others), then we should be able to see selective pressures at work in the utterances of an individual speaker. This should also apply to the influence of climate. Given enough data, you should be able to see an individual adapting over the seasons. In light of Maddieson and Coupe’s hypothesis, speakers should use proportionately more vowels compared to consonants when it’s warmer.
CNN provides transcripts for every show broadcast between 2000 and 2012. Larry King has done an interview practically every day on the show ‘Larry King Live’ (which has been used before in linguistic studies), so I extracted Larry King’s speech transcripts (excluding the guest’s speech, mentions of the location of the studio and the guest’s names). For each transcript I counted the frequency of each letter, then calculated the ratio of vowels (aeiouy) to consonants. Then, for each air date, I found the actual temperature and humidity data for that date and the location of the show (CNN studios in LA). The show is occasionally recorded in Washington DC or New York. I tried to detect these automatically and matched them with the climate measures for the CNN studios in those cities.
There are about 3,500 transcripts over 11 years, about 90% of which were aired on consecutive days. (I know nothing about this show, and am a bit surprised by its frequency! I’ll have to check whether the transcripts include repeats).
Here is a depiction of the results for temperature and humidity:
The Black lines show King’s vowel ratio (higher = more vowels) and bars are 95% confidence intervals around the mean for each week of the year. The maximum and minimum temperature are shown in red and blue. Below is a similar graph for specific humidity.
Surprisingly, there is some variation in proportion of vowels, and it looks like there’s a trend in the right direction.
To analyse the data, I used a linear mixed effects model, predicting vowel ratio by (log) text length and maximum temperature, with random effects for year and each week (580 separate weeks, to try to control for topical issues).
Maximum temperature significantly improves the fit of the model over a null model with text length (Chi Squre = 23.7, df = 1, p < 0.00001).
Model estimates: Estimate Std. Error t value (Intercept) 4.100e-01 8.114e-04 505.3 maxTempC.loc 9.628e-05 1.973e-05 4.9 text.total.log.center 5.340e-03 8.400e-04 6.4
King uses proportionately more vowels when it’s warmer. The effect is very small: On average, there is a difference of about 15 vowels used in an hour of conversation between summer and winter. A model with location-specific maximum temperature improves the model fit over one with just LA-specific maximum temperature (Chi Sqaured = 11.59, df=1 , p=0.0007).
Of course, the temperature is not independent from day to day, so I also tried a lagged regression, predicting vowel ratio by text length (total) and maximum temperature of the recording location (maxTempC.loc). Lagging back in time by days.
Coefficients: Estimate Std. Error t value Pr(>
|t|) (Intercept) 4.076e-01 7.342e-04 555.073 < 2e-16 *** d[lag.0, ]$textLength 3.772e-07 6.715e-08 5.618 2.08e-08 *** d[lag.0, ]$maxTempC.loc 1.008e-04 3.300e-05 3.055 0.00227 ** d[lag.1, ]$maxTempC.loc 5.009e-05 3.812e-05 1.314 0.18893 d[lag.2, ]$maxTempC.loc 1.549e-07 3.840e-05 0.004 0.99678 d[lag.3, ]$maxTempC.loc -7.405e-05 3.809e-05 -1.944 0.05195 . d[lag.4, ]$maxTempC.loc -2.418e-06 3.300e-05 -0.073 0.94159
The temperature of actual day is still significant, taking into account previous days. Note that the coefficient is negative after 3 lagged days. (please forgive the rough analysis- it’s all I have left after my computer broke recently).
I’m not really sure what to make of this. Given the data above, there is an argument that King is adapting the way he speaks to the climate. However, a LOT more needs to be done in order to show this. There are several confounding factors, such as the show being recorded in an air conditioned studio, the topics or guests might be different, there might be seasonal topics or key-words which affect the results (though one might argue that the lexicon for things related to cold climates has adapted). The graphs show big jumps around week 32, which I can’t explain yet. Then there is the question of the mechanism – how exactly is King adapting? By choice of alternative words, or constructions? And, of course, the transcripts are orthorgraphic. And, of course, the idea is crazy.
Still, I think it’s amazing that we’re now in a position where we can even start asking these kind of questions with data.
This is a guest post by Cole Robertson.
Readers of this blog are likely to be familiar with controversy surrounding Keith Chen’s (2013) findings suggesting that the way a language deals with the future tense affects speakers’ economic decisions. A new study by a group of Austrian and German economists has recently added experimental evidence to the growing body of research on the issue.
The study focuses on German and Italian. As with English, Italian requires a speaker to mark the future tense, e.g. “it will rain tomorrow” not simply “it rains tomorrow.” German, on the other hand, requires no such marking—speakers are free to say the equivalent of “it rains tomorrow.”
Chen hypothesized that speaking about future event using the present tense, as in languages like German, could make the future seem more immediate. He suggested this might cause speakers to value future events more highly than speakers of languages like Italian. Indeed, using large-scale statistical methods, Chen found that speakers of languages like German (with no separate future tense) tend to smoke less, be less obese, engage in safer sex practices, retire with more money, and save more per year.
His findings have been criticized on various grounds, from potential inconsistencies in the data, to small number bias in the original dataset, to the fact that the hypothesis could easily have been formulated the other way around, to the fact that Chen’s classification of how languages refer to the future may be overly simplistic.
However, most criticism has centred around the potential spuriousness of the correlation between future tense marking and future orientated behaviour. Commentators have been quick to point out that since cultural traits tend to be inherited in packages, it’s likely that future orientation and future tense reference are causally unrelated. In fact, Roberts, Winters and Chen (2015) found that the correlation dropped below significance when controlling for cultural relatedness (for some if not all their statistical tests). As such, the jury may yet be hung until experimental evidence confirms (or doesn’t) Chen’s findings.
The new study attempts to do exactly this. Though not yet peer reviewed, it was recently opened to comments and criticism as a discussion paper, as apparently is common in the discipline of economics. You can read it here.
The paper takes advantage of bilingualism in the city of Meran in the autonomous province of South Tyrol in Northern Italy, where roughly 50% of the population speaks German and the other 50% speaks Italian. Hoping to mitigate the cultural confounds that plagued Chen’s (2013) findings, the authors argue that since the German- and Italian-speaking citizens of Meran share a home city, they also share a similar enough cultural milieu that experimenting on them can isolate the effects of their language on how they value future events.
The experiment worked like this: children were presented with three choices. In each of the choices they could either accept two tokens at the time of the test or opt instead to receive a greater number of tokens four weeks in the future (the ‘patient’ choice). The future rewards were three, four, or five tokens. So in other words, children had to choose three times, between: 1) two tokens now and three tokens in four weeks, 2) two tokens now and four tokens in four weeks, and 3) two tokens now and five tokens in four weeks.
One of these choices was randomly selected for their actual pay-out, and they could then trade the tokens for a selection of small toys and items the experimenters had on offer. As control variables, they also measured IQ relative to cohort, the income and education of the parents, the children’s risk-taking propensity, average housing prices in the area of the school, and whether the children had friends who spoke another language.
In presenting their results they begin with a sample of just those students both of whose parents speak either German or Italian (n=860). They then progressively add students from different linguistic groups to their sample. They start with (n=203) students from bilingual German-Italian households, then add (n=261) students from household where both parents speak a language which has a future tense (like Italian) but which is not Italian. They then treat these delineations as categorical variables and use both regressions and non-parametric tests to see whether a child’s language category affects their tendency to wait for future rewards.
Their results suggest that it does. In the first sample, the German-speaking children were more likely to wait for the future reward than the Italian-speaking children, across all age groups and when including the host of control variables. Moreover, when they added the German-Italian bilingual group to the sample, their tendency to wait for future rewards was intermediate between the all-Italian and all-German children. Finally, they find that the (n=261) student who speak a language which marks the future tense, but which is not Italian, are significantly less likely to wait for future rewards compared to German speaking children, but are not significantly different from the Italian cohort.
They also separately analyse (n=91) students one of whose parents speak German or Italian and the other of whose parents (except for 5 cases) speaks a language which, like Italian, mandates marking of the future tense. They find that children with one German-speaking parent and one future-tense-marking parent are more likely to wait for future rewards than children with one Italian-speaking parent and another future-tense-marking parent.
Should these results be trusted? There are some strange statistics in the paper; throughout, they use multiple Mann-Whitney U Tests when comparing more than two groups, but never report any kind of post-hoc correction for multiple comparisons. With some p-values reported at the < .05 level, this could be problematic for some of their findings. Whilst their regressions would not be affected, I have to assume they present non-parametric tests because at least some of their data violate normality assumptions.
However, their results are corroborated by a second experiment which uses a slightly different discounting task and which only has two groups (all German and all Italian) and is therefore not affected by any apparent failure to correct for multiple comparisons.
They also include several other robustness tests. They make sure risk attitudes (which they find predict propensity to wait for future pay outs) are not correlated with language group (they aren’t). Although, whether this reveals anything more than their multiple regressions is unclear, since they already revealed a result whilst controlling for any correlation between the two variables. Additionally, they survey (n=177) Meraners to check whether there are any differences between the Italian and German populations in terms of their attitudes towards the importance of “thrift” and “patience.” Finding no such differences, they argue that this supports the conclusions that there are no cultural differences confounding their findings.
However, can we really trust self-reported estimations of the importance of thrift and patience? Most people know that they should say these are important, so is it really possible to rule out implicit demand characteristics that might undermine actual differences between the two language groups?
In fact, even according to the authors themselves, the German- and Italian-speaking populations in Meran remain linguistically and culturally “fairly segregated, with different media (like newspapers or TV channels) and leisure activities (like different football clubs)” (Sutter et al, 2015 p. 5). Schools are evidently also segregated by language, teaching either in Italian or German, and no schools to-date have an equal number of Italian and German students.
Indeed, relations between the Italian- and German-speaking people in South Tyrol may not be as copasetic as they are portrayed to be by the authors. In fact, there is evidence that tensions have been simmering since Mussolini attempted to “Italianise” the area in the 1930s by mass relocating Southern Italians northwards. Activism in the 1960s (including violent conflict) eventually lead to a bilingual language accord, and significant autonomy from Rome in 1972. Today, German speakers accuse Italian authorities of racism and “linguistic imperialism”, whilst Italians accuse Germans of receiving preferential treatment. There have even been arguments over the language of signage on alpine hiking routes , which eventually sparked a felt-tipped graffiti war that quickly degenerated into racial profanity. As such, is it really possible to rule out cultural confounds? As a Canadian who has lived in Montreal, the epicentre of Canadian French-English bilingual conflict, I can personally attest to the fact that geography and culture are not the same thing.
Moreover, the experiment does not actually manipulate children’s responses; it merely applies the same test to children from different language groups. What is strange is that there may actually be an unreported manipulation in the experiment. Since the authors only state that the test prompts were given in the child’s “mother tongue”, I would assume that some of the (n=203) bilingual children were tested in Italian and some in German. This means that the researchers hopefully have (or might be able to get) data on whether the language of the test prompt affected the children’s propensity to wait for future rewards.
Until they include these results in the analysis, their findings seem to be subject to the same criticisms levelled against Chen (2013). In other words, they may be picking up on a spurious correlation resulting from the fact that thriftiness and tense structure, whilst causally unrelated, could have been co-inherited from antecedent cultural groups.
If the Italian and German populations in South Tyrol are as alienated and segregated as they seem to be, it’s entirely possible that the German-speakers inherited increased thriftiness as well as a language without a future tense, whilst the Italian-speakers inherited decreased thriftiness as well as a language with a future tense. They need not be causally related, and the speakers’ geographical proximity may not have been enough to override these packages of traits. Until we see a language-based manipulation of future-orientated behaviour, the issue of whether tense affects how we value future events will remain unresolved.
Chen, M. K. (2013). “The Effect of Language on Economic Behavior: Evidence from Savings Rates, Health Behaviors, and Retirement Assets.” American Economic Review 103(2): 690-731.
Roberts, S. G., Winters, J. & Chen, M. K. (2015). “Future Tense and Economic Decisions: Controlling for Cultural Evolution.” PLoS One 10(7): e0132145.
Sutter, M., et al. (2015). “The Effect of Language on Economic Behavior: Experimental Evidence from Children’s Intertemporal Choices.” IZA Discussion Paper Series 9383.
Cole Robertson lives and works in Oxford, where he is completing a PhD focusing on inter-linguistic tense differences and future-orientated behavior, but he’s generally interested in how language interfaces with other cognitive, perceptual, and behavioural systems, as well as language revitalization and language evolution. He’s an avid rock climber and mountaineer and once spent two days eating nothing but sausages cooked over a candle whilst waiting out a storm. He grew up in Vancouver, Canada.
This is a guest post by Christine Cuskley
TL;DR: Please and thank you play this thing for science.
Insofar as the field of language evolution has even had time to spill a lot of ink about anything – if you take Pinker & Bloom’s seminal 1990 paper as the starting point, the field is only just a carefree twenty-something – the focus of the field has primarily been about the finer points of language: syntax, the lexicon, the physiology of speech production, etc. But one’s mid-twenties are a time for exploration, so, with my colleagues at the Social Dynamics Unit, we’re looking towards something relatively unexplored in language evolution: stories.
It’s almost impossible to imagine telling a story without language – how would you even begin? (Even Emoji Dick had to be translated from the original, and might not even be successful, and emojis arguably are language…but I digress…) So the questions emerge: why and how do we tell stories? Do stories simply take advantage of our ability to speak about things that are not present and/or are not concrete (or even real), or are they a key part in how language evolved the ability to do this? How do stories evolve over time and respond to cultural pressures? What kinds of features of stories survive and replicate, and what features peter off and die? What selection pressures underlie this?
This is, of course a whole host of questions, none of which we can expect to find definitive answers to anytime soon – a feature shared by a lot of work in language evolution (and an exciting one, in my opinion). And of course, we’re not jumping into a void: already there is work that focuses on the phylogeny of stories, the potential evolutionary function of stories, and also a fair bit of work on evolution in literature more generally, some of it featured right here on Replicated Typo. But we’re taking a new experimental approach: we’re crowd sourcing collaborative stories. We hope this will contribute to answering the last two questions in particular: how do stories evolve over time and respond to different constraints, and what features survive and replicate?
We’re doing this using an experimental game called CreaStoria – and the more players we have, the better! So please play! The game is a hybrid of choose your own adventure, Twitter, and a creative writing workshop. It works like this: we start with a bunch of single-sentence “story prompts” created in collaboration with Piano Piano Book Bakery in Rome, and these stories become the “root” for collaborative story trees. At each turn, a player is presented with three potential stories and has to choose which one to continue with their own short story, creating an element of preferential selection. After you’ve played, or between bouts of play (I hear it’s great for procrastination, so feel free to come and go as you need it), you can look at the growing story tree and vote on stories you like (or don’t).
The inner workings are a bit more complicated than this, of course – I could tell you, but I’d have to “know your IP address and exclude any stories you write from our data” (if you know what I mean). We’ve had the opportunity to exhibit the game at a couple of live events in Italy, so the tree of Italian stories is pretty well populated – but we would really like more English data (and having a lot of both could lead to some interesting contrasts), so play now and tell your friends! If you’re curious as to how the data pans out, like the game on Facebook or follow @creastoria on Twitter to get updates.
Christine Cuskley is a linguist/psychologist/nerd type who currently researches the evolution of language and communication in the Social Dynamics Unit at the Institute for Scientific Interchange in Turin, Italy, and will take up a position as a British Academy Postdoctoral Fellow at the University of Edinburgh from January 2016. She mostly retweets at @nerdpro.
Another working paper, links:
Abstract, contents, and introduction below.
* * * * *
Abstract: In his work on memetics Daniel Dennett does a poor job of negotiating the territory between philosophy and science. The analytic tools he has as a philosopher aren’t of much use in building accounts of the psychological and social mechanisms that underlie cultural processes. The only tool Dennett seems to have at his disposal is analogy. That’s how he builds his memetics, by analogy from biology on the one hand and computer science on the other. These analogies do not work very well. To formulate an evolutionary account of culture one needs to construct one’s gene and phenotype analogues directly from the appropriate materials, neurons and brains in social interaction. Dennett doesn’t do that. Instead of social interaction he has an analogy to apps loading into computers. Instead of neurons he has homuncular agents that are suspiciously like his other favorite homuncular agents, memes. It doesn’t work.
Introduction: Too many analogies, no construction 2
Watch Out, Dan Dennett, Your Mind’s Changing Up on You! 5
The Memetic Mind, Not: Where Dennett Goes Wrong 11
Turtles All the Way Down: How Dennett Thinks 16
A Note on Dennett’s Curious Comparison of Words and Apps 21
Has Dennett Undercut His Own Position on Words as Memes? 23
Dennet’s WRONG: the Mind is NOT Software for the Brain 27
Follow-up on Dennett and Mental Software 31
Introduction: Too many analogies, no construction
Just before the turn of the millennium Dennet gave an interview in The Atlantic in which he observed:
In the beginning, it was all philosophy. Aristotle, whether he was doing astronomy, physiology, psychology, physics, chemistry, or mathematics — it was all the same. It was philosophy. Over the centuries there’s been a refinement process: in area after area questions that were initially murky and problematic became clearer. And as soon as that happens, those questions drop out of philosophy and become science. Mathematics, astronomy, physics, chemistry — they all started out in philosophy, and when they got clear they were kicked out of the nest.
Philosophy is the mother. These are the offspring. We don’t have to go back a long way to see traces of this. The eighteenth century is quite early enough to find the distinction between philosophy and physics not being taken very seriously. Psychology is one of the more recent births from philosophy, and we only have to go back to the late nineteenth century to see that.
My sense is that the trajectory of philosophy is to work on very fundamental questions that haven’t yet been turned into scientific questions.
This is a standard view, and it’s one I hold myself, though it’s not clear to me just how it would look when the historical record is examined closely.
But I do think that, in his recent work, Dennett’s been having troubles negotiating the difference between philosophy, in which he has a degree, and science. For he is also a cognitive scientist in good standing, and that phrase – “cognitive science” – stretches all over the place, leaving plenty of room to get tripped up over the difference between philosophy and science.
Dennett has spent much of his career as a philosopher of artificial intelligence, neuroscience, and cognitive psychology. That is to say, he’s looked at the scientific work in those disciplines and considered philosophical implications and foundations. More recently he’s done the same thing with biology.
Now, it is one thing to apply the analytic tools of philosophy to the fruits of those disciplines. But Dennett has also been interested in memetics, a putative evolutionary account of culture. The problem is that there is no science of memetics for Dennett to analyze. So, when he does memetics, just what is he doing?
The analytic tools he has as a philosopher aren’t of much use in building accounts of the psychological and social mechanisms that might underlie cultural processes. The only tool Dennett seems to have at his disposal is analogy. And so that’s how he builds his memetics, by analogy from biology on the one hand and computer science on the other.
Alas, these analogies do not work very well. That’s what I examine in the posts I’ve gathered into this working paper. What Dennett, or anyone else, needs to do to formulate an evolutionary account of culture is to construct one’s gene and phenotype analogues (if that’s what you want to do) directly from the appropriate materials, neurons and brains in social interaction. Dennett doesn’t do that. Instead of social interaction he has an analogy to apps loading into computers. Instead of neurons he has homuncular agents that are suspiciously like his other favorite homuncular agents, memes. It doesn’t work. It’s incoherent. It’s bad philosophy or bad science, or both. Continue reading
What kind of information do children and infants take into account when learning new words? And to what extent do they need to rely on interpreting a speakers intention to extract meaning? A paper by Morse, Cangelosi and Smith (2015), published in PLoS One, suggests that bodily states such as body posture might be used by infants to acquire word meanings in the absence of the object named. To test their hypothesis, the authors ran a series of experiments using a word learning task with infants—but also a self-learning robot, the iCub.