CHIELD: the causal hypotheses in evolutionary linguistics database

CHIELD: the causal hypotheses in evolutionary linguistics database Sea´n G. Roberts 1 , Anton Killin 2,3 , Angarika Deb 4 , Catherine Sheard 5 , Simon J. Greenhill 6,7 , Kaius Sinnema¨ki 8 , Jose´ Segovia-Martı´n 9 , Jonas No¨ lle 10 , Aleksandrs Berdicevskis 11 , Archie Humphreys-Balkwill 1 , Hannah Little 12 , Christopher Opie 1 , Guillaume Jacques 13 , Lindell Bromham 14 , Peeter Tinits 15 , Robert M. Ross 16 , Sean Lee 17 , Emily Gasser 18 , Jasmine Calladine 1 , Matthew Spike 10 , Stephen Francis Mann 19 , Olena Shcherbakova 7 , Ruth Singer 20 , Shuya Zhang 13 , Antonio Benı´tez-Burraco 21 , Christian Kliesch 22,23 , Ewan Thomas-Colquhoun 1 , Hedvig Skirga˚rd 24 , Monica Tamariz 25 , Sam Passmore 1 , Thomas Pellard 13 and Fiona Jordan 1 1 Department of Anthropology and Archaeology, EXCD.LAB, University of Bristol, 43 Woodland Rd, Bristol, BS8 1TH, UK, 2 School of Philosophy and Centre of Excellence for the Dynamics of Language, Australian National University, Canberra, ACT, Australia, 3 Department of Philosophy, Mount Allison University, Sackville, New Brunswick, E4L 1G9, Canada, 4 Department of Cognitive Science, Central European University, Oktober 6 street 7, 1st floor, Budapest, 1051, Hungary, 5 School of Earth Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol, UK, 6 ARC Centre of Excellence for the Dynamics of Language, ANU College of Asia and the Pacific, Australian National University, Canberra, 2600, Australia, 7 Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Jena, 07743, Germany, 8 Department of Languages, University of Helsinki, 00014, University of Helsinki, Helsinki, Finland, 9 Cognitive Science and Language (CCiL), Universitat Auto`noma de Barcelona, C/Montalegre, 6, 4 a planta, despatx, Barcelona 400908001, 10 Centre for Language Evolution, The University of Edinburgh, Dugald Stewart Building, 3 Charles St, Edinburgh, EH8 9AD, UK, 11 The Swedish Language Bank, University of Gothenburg, Gothenburg, SE, 405 30, Sweden, 12 Science Communication Unit, Department of Applied Sciences, University of the West of England, Bristol, UK, 13 CNRS-EHESS-INALCO, Centre de recherches linguistiques sur l’Asie orientale, 105 Boulevard Raspail 75006, Paris, France, 14 Research School of Biology, Australian National University, 134 Linnaeus Way, Acton ACT, 2601, Australia, 15 Department of Social Science, University of Tartu, Salme 1a–29, Tartu, 50103, Estonia, 16 Department of Philosophy, Macquarie University, Level 2 North Australian Hearing Hub, NSW, 2109, Australia, 17 Graduate School of Asia-Pacific Studies, Waseda University, 1 Chome-21-1 Nishiwaseda, Shinjuku City, Tokyo, 169-0051, Japan, 18 Linguistics Department, Swarthmore College, 500 College Avenue, Swarthmore, PA, 19081, USA, 19 School of Philosophy and ARC Centre of Excellence for the Dynamics of Language, Australian National University, H.C. Coombs Building, ACT, 2601, Australia, 20 School of Languages and Linguistics, University of Melbourne, Babel (Building 139), Parkville, 3010, VIC, V CThe Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.pe[email protected] Journal of Language Evolution, 2020, 101–120 doi: 10.1093/jole/lzaa001 Advance Access Publication Date: 16 April 2020 Research article Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 Australia, 21 Department of Spanish, Linguistics, and Theory of Literature (Linguistics), University of Seville. Palos de la Frontera, 41004, Seville, Spain, 22 Department of Psychology, Lancaster University, Lancaster, LA1 4YF, UK, 23 Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, Leipzig, 04103, Germany, 24 School of Culture, History and Language, Australian National University, Canberra, ACT, Australia and 25 Psychology, Heriot-Watt University, Edinburgh, EH14 4AS, UK *Corresponding author: E-mail: sean.robert[email protected] Abstract Language is one of the most complex of human traits. There are many hypotheses about how it originated, what factors shaped its diversity, and what ongoing processes drive how it changes. We present the Causal Hypotheses in Evolutionary Linguistics Database (CHIELD, https://chield.excd.org/), a tool for expressing, exploring, and evaluating hypotheses. It allows researchers to integrate multiple theories into a coherent narrative, helping to design future research. We present design goals, a formal specification, and an implementation for this database. Source code is freely available for other fields to take advantage of this tool. Some initial results are presented, including identifying conflicts in theories about gossip and ritual, comparing hypotheses relating population size and morphological complexity, and an author relation network. Key words: database, causal graphs, causal inference 1. Introduction Evolutionary linguistics is a field that uses evolutionary principles to explain the origins of complex communication systems, as well as the similarities and differences between them (e.g., Knight, Studdert-Kennedy and Hurford 2000;Wray 2002;Botha 2003;Christiansen and Kirby 2003;Hurford, 2007;Kinsella 2009;Fitch 2010;Berwick and Chomsky 2016;Progovac 2019). Scott-Phillips and Kirby (2010) identified four phases in human language evolution that are studied within this field: •Pre-adaptation—the preconditions for a language ability, often related to genetic evolution (e.g., Lieberman 1984;Corballis 1999;Hurford 2003; Slocombe and Zuberhu¨hler 2005;Cheney and Seyfarth 2005;Fehe´r 2017;Vernes 2017). •co-evolution—how the first human communication systems and these pre-adaptations evolved together (e.g., Deacon 1997;Dor, Knight and Lewis 2014; Berwick and Chomsky 2013;Pakendorf 2014; Vigliocco, Perniss and Vinson 2014;Power, Finnegan and Callan 2016;Falk 2016). •cultural evolution—the initial emergence of new linguistic structures (e.g., Nowak and Krakauer 1999; Tallerman 2007;Smith and Kirby 2008;Culbertson and Newport 2015;Progovac 2015;Kempe, Gauvrit and Forsyth 2015;Tamariz and Kirby 2016;Goldin- Meadow and Yang 2016;Piantadosi and Fedorenko 2017). •language change—the ongoing change in languages (e.g., Mufwene 2001;Ritt 2004;Croft 2008; Sampson and Trudgill 2009;Dunn et al. 2011; Gavin et al. 2013;Majid, Jordan and Dunn, 2015; Bowern 2015;Bybee 2015;Coelho et al. 2019). There have been many exciting developments in recent decades, making it perhaps possible to join them into larger theories. However, synthesis has become difficult as there now exists a mountain of theories and evidence, in increasingly specialised sub-fields. Jim Hurford once moderated a discussion between four plenary speakers who had presented four different theories of language evolution. His first question was ‘What do you disagree about?’. Nobody had a reply. This showed that, although the theories were internally consistent, they weren’t connected to each other. This characterises a problem in many fields—it is possible to have nearly as many theories as there are researchers, and debate is often limited to dogmatic acceptance or complete rejection of these theories, rather than trying to systematically compare, evaluate, and synthesise. Progress in evolutionary linguistics will be made by working towards building a chain of causal links that join theories together. Since there are many aspects of language evolution that cannot be tested directly, each link should be tested with multiple methods and sources of data—a ‘robust’ approach (Irvine, Roberts and Kirby 2013;Roberts 2018). In order to combine these different 102 Journal of Language Evolution, 2020, Vol. 5, No. 2 Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 strands of evidence (experiments, models, simulations, comparative work), researchers must coherently express how these results relate to each other and to the real world (Vogt and De Boer 2010). There have been previous calls for this kind of approach; for example, Zuidema and de Boer (2010,2013) suggest that computational modelling should aim for greater ‘model parallelisation’ (qualitatively comparing models against each other) and greater ‘model sequencing’ (building chains of models that feed into each other). However, practical solutions are difficult to produce, due to challenges that relate to expression, exploration, evaluation, and extension. Expression: Expressing complex causal hypotheses in prose is difficult, and many hypotheses are underspecified. Without deep knowledge of a theory’s particular sub-field, it is often difficult to identify the assumptions and claims of a theory. In addition, different sub-fields may use different terms to refer to very similar concepts, or use the same term to refer to different concepts. A classic example of this is the word ‘language’ itself, which can be interpreted as anything relating to human communication or only a specific syntactic ability. All this can lead to researchers talking past each other, along with a general lack of connection between subfields. How can we better express hypotheses to avoid these problems? Exploration: Evolutionary linguistics now includes many subfields within linguistics (Bergmann and Dale 2016;Berwick and Chomsky 2016), and also relates to other larger interdisciplinary fields of research, such as learning or cooperation (Kirby and Christiansen 2003; Progovac 2019). The methods are diverse, ranging from molecular genetics (e.g., Enard et al., 2002;Hitchcock, Paracchini and Gardner 2019), to archaeology (e.g., Noble and Davidson 1996;Currie and Killin 2019), to computational simulation (Steels 1997;Cangelosi and Parisi 2012;Jon-And and Aguilar 2019). It is therefore increasingly hard to keep up to date with all the developments in the field. How can we make theories searchable so that researchers can access work from other fields that relates to their own? Furthermore, how can we formally relate these hypotheses to each other, in order to find similarities and differences? Evaluation: After relating theories to each other, how do we then evaluate them? How do we identify the claims that are supported by evidence, and those that require further investigation? How can we get an overview of research conducted on a topic? Studies are getting more complex, and there is more emphasis on largescale tests that can evaluate multiple competing models. Systematically collecting these hypotheses, storing them and converting them into statistical models is hard (Bareinboim and Pearl 2016). Extension: Language evolution is a field that has inspired much debate, and even reaching a consensus on interpretations of hypotheses is difficult. How can we support researchers in the continuing process of refining and extending them? How can we ensure that the tools to do this will be useful into the future? One possible solution is to harness the power of causal graphs. A causal graph is a graphical tool which breaks a complex hypothesis into individual causal links. We present the Causal Hypotheses in Evolutionary Linguistics Database (CHIELD, pronounced like ‘shield’, https://chield.excd.org/), a database of hypotheses expressed as causal graphs. It allows users to apply computational search and visualisation methods, in order to express, explore, and evaluate hypotheses. This article describes the design and presents three case studies to demonstrate its functionality. Case Study 1 demonstrates how CHIELD can be used to explore connections between theories. Case Study 2 attempts to evaluate competing explanations of the connection between population size and morphological complexity, and demonstrates some issues with vocabulary. Of course, problems such as converging on the same vocabulary requires more than a database to solve, but CHIELD may at least provide a space for spotting potential areas of disagreement. Case Study 3 demonstrates extended uses such as constructing networks of authors working on the same topics. To be clear, the aim is not to build a list of theories that have been accepted by the scientific community as ‘correct’, that are somehow more ‘prestigious’ or supposedly have no counterevidence. It is, of course, very difficult to prove a causal effect as being distinct from a correlation. However, at the heart of any research trying to explain a phenomenon is an idea about some kind of causal relationship. The aim therefore is to faithfully represent these ideas such that researchers can plan future work. Furthermore, the aim of this database is not to highlight one view over another, but to simply present them on an accessible platform. Its aim is description, not prescription. Finally, the database aims to be editable and maintainable into the future. We hope that future studies in language evolution will be enhanced by the insights provided by causal graphs. 2. Causal inference and causal graphs Causal inference is an approach to thinking about causality (Pearl 2000;Pearl and Mackenzie 2018;Rohrer 2018) that uses graphical tools—causal graphs—to Journal of Language Evolution, 2020, Vol. 5, No. 2 103 Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 depict models of causal processes. Causal graphs consist of nodes that represent measurable quantities or concepts, and arrows which represent the causal influences between these nodes. For example, consider why we might see more shirt stains on hot days. Prof. Whippy suggests a causal explanation: ‘High temperatures cause more ice-cream consumption, and more ice-cream consumption leads to more shirt stains’. So we can draw the following causal graph: Temperature !Ice cream consumption !Shirt stains This causal graph has three measurable quantities (nodes e.g., ‘Temperature’) and two causal links. The links are interpreted according to an interventionist interpretation of causality (see e.g., Woodward 2003, 2016). For example, the first causal link states that if one were to ‘intervene’ by changing the temperature sufficiently, then the amount of ice cream consumption would also change (and the second link states that if one were to increase the amount of ice cream consumption sufficiently, then the number of shirt stains would increase). That is, it makes a counterfactual claim about a possible world where the measurable quantities were different. Interventionist causality is often interpreted in experimental terms: if one were to experimentally manipulate the temperature, then there would be a change in ice cream consumption. Relationships encoded by the causal arrow could be fully deterministic or just probabilistic, and could be between continuous, discrete, or categorical variables. This is a widely used approach for which many helpful tools are available. Note that the graph above also makes some more claims. First, there is no link from ice cream consumption to temperature, which is interpreted as there being no causal effect of ice cream consumption on temperature. That is, if we were to intervene by forcing people to consume ice cream, then the temperature would not increase. Secondly, since there is no direct causal link between the first and the third node, the number of shirt stains is causally independent of the temperature such that temperature only influences shirt stains via ice cream consumption. This hypothesis could be experimentally tested, for example by raising the temperature (maybe within a shopping centre) and simultaneously banning the sale of ice cream, to see if the number of shirt stains decreases. A key thing to understand about causal graphs is that they do not necessarily reflect what has been proven to be true, but instead reflect a particular researcher’s hypothesis. In other words, causal graphs represent ideas about how the world works, they are not proof that the world really is like that. There are many other possible hypotheses (and causal chains) to explain the same phenomenon. For example, Prof. Whippy suggested that higher temperature causes more ice cream consumption, which in turn causes more shirt stains. Let’s imagine that, in another paper, Prof. Gelato attacks Prof. Whippy’s hypothesis and suggests a different explanation, namely that ice cream consumption has no effect on shirt stains, and instead seeing a shirt stain reminds people of ice cream and so they seek it out. In yet another paper, Prof. Sorbet studies climate change and suggests that refrigerated ice cream vans are contributing to greenhouse gases, therefore affecting the temperature. We can draw the three hypotheses in a single graph (Fig. 1). By representing multiple hypotheses on a single causal graph, we can identify relationships between them. For example, Profs Whippy and Gelato agree on the effect of temperature on ice cream consumption. Furthermore, Prof. Sorbet’s climate change mechanism is not necessarily in conflict with the effect of temperature on ice cream consumption. Both could be operating to create a feedback loop. However, Prof. Whippy and Prof. Gelato disagree on the relationship between ice cream consumption and shirt stains. Intuitively, Prof. Gelato’s theory seems much less likely to be true, but the causal graph is still a valid representation of Prof. Gelato’s theory. Drawing out the two theories has therefore shown where the conflict lies, and further suggests a future empirical test: Prof. Gelato would predict that staining people’s shirts would cause ice cream consumption to increase, while Prof. Whippy would predict that it would make no difference. As this example shows, expressing different hypotheses as causal graphs allows researchers to express, explore, evaluate, and extend the relationships between them (see Ho¨fler et al. 2018). Constructing a causal graph is also an Figure 1. Causal graph showing three hypotheses of the relationship between temperature, ice cream consumption, and shirt stains. Nodes represent measurable quantities, and lines between them represent hypothesised causal links. Lines with arrows reflect causal effects and the line with a bar head is interpreted as ‘no causal effect’. Lines are coloured according to their source publication. The grey dotted rectangle highlights a conflict between two theories. 104 Journal of Language Evolution, 2020, Vol. 5, No. 2 Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 excellent way to identify underspecified mechanisms. Causal graphs can help readers to understand papers (Easterday, Aleven and Scheines 2007;Cao, Sun and Zhuge 2018;Ho¨fler et al. 2018;althoughTubau 2008, suggests that they may not aid reasoning in certain domains) and facilitate debate (Easterday et al. 2009). They also help communicate theories indirectly by providing a roadmap for writing (e.g., authors can check if they have provided justification or evidence for each causal link, or identify arguments that are tangential to the central claim, see Section 4.2 below). Expression of theories is an important part of teaching, and a unified approach to expressing causal hypotheses in language evolution could improve student understanding. For example, students could point to a particular causal link that they do not understand, and the database could give them a quote from the paper. Causal graphs also aid understanding in high-school students (Hsu et al. 2015). Of course, carrying out the experimental manipulations mentioned above might be impractical. In this case, researchers might depend on converging evidence from multiple sources, analogies with simulations or ‘natural experiments’ (see e.g., Steels 1997;Pyers et al. 2010;De Boer and Verhoef 2012;Morgan, 2013; Irvine, Roberts and Kirby 2013). For example, in the case above, researchers could seek locations where the temperature does not change, or where the temperature varies but there is no ice-cream. However, many recent developments in causal inference have allowed researchers to estimate causal effects with purely observational data, even in cases where researchers cannot directly measure a variable of interest (Pearl 2000;Bareinboim and Pearl 2012). Another advantage is in identifying conditioning sets: which variables to control for in an analysis. Standard methods about how to choose control variables are often vague (Pearl and Mackenzie 2018), and many assume that controlling for more variables makes the central test more robust. However, controlling for some variables can create spurious correlations due to colliders. A collider is a node on a causal path with two causal links (arrows) pointing into it (see Supplementary Material S5). In the example above, Prof. Gelato predicts that ‘ice cream consumption’ is a collider along the path involving temperature and shirt stains. Gelato would predict that temperature and shirt stains both contribute to ice cream consumption. If this were true, then temperature and shirt stains should be uncorrelated, except when controlling for ice cream consumption, at which point they would become correlated (see e.g., Elwert 2013). This property of colliders means that spurious correlations can sometimes be introduced by statistical controls (see Elwert and Winship 2014;Ding and Miratrix 2015;Middleton et al. 2016;Westfall and Yarkoni 2016;Rohrer 2018;York 2018). Drawing a causal graph helps to identify these cases, and therefore helps make effective decisions in study design and analysis. These benefits make causal graphs a very powerful tool for research in the social sciences. However, causal graphs are not substitutes for careful thought and design. They are tools for helping researchers do their job. CHIELD aims to make these tools more accessible. Some recent developments in causal inference theory go further by attempting to estimate the most likely causal graph directly from observational data on large number of variables (Kalisch et al. 2012;Hauser and Bu¨ hlmann 2012; see Heinze-Deml, Maathuis and Meinshausen 2018, for applications in linguistics see Roberts and Winters 2013;Baayen, Milin and Ramscar 2016;Blasi and Roberts 2017). Analysing the entire causal network, rather than just explaining the variation in a single target variable, makes it possible to study much more complex relationships, and to form a more comprehensive picture of complex systems such as those found in social sciences. However, there are many possible ways of applying this method (parameters of the algorithm, treatment of the data, assumptions of the statistical tests), and these can lead to big differences in the results and interpretation. What is needed is a way of compiling and organising existing knowledge about causal effects within one domain, in order to evaluate the automatically derived causal graph. CHIELD aims to provide this prior knowledge in order to protect researchers from making hasty post-hoc hypotheses from the output of automatic methods. 3. Design of the database We reviewed various existing applications (see Supplementary Material S1) to check whether they address the problems discussed above. There appears to be no single tool that allows researchers to express hypotheses (visually), explore the way they interact, and evaluate them. CHIELD aims to fill this gap. In this section, we cover the design of CHIELD. The principle is to store data in a simple spreadsheet format, with scripts that convert them to a database that can be accessed and edited through a customised web interface. 3.1 Expression The aim of CHIELD is to allow researchers to express causal graphs for a particular theory, in a simple and Journal of Language Evolution, 2020, Vol. 5, No. 2 105 Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 intuitive format. CHIELD is based around ‘documents’ (usually a peer-reviewed, published paper), each of which have three associated files: bibliographic information stored as a bibtex file, a simple text file listing who contributed the data, and a spreadsheet file which stores information about the causal links in the document. These files are kept together in a folder, which is named after the unique bibtex key for the document. In order to make finding particular files easier, we borrow the design of Glottolog: document folders are sorted into parent folders for each year of publication, and then each year is sorted into parent folders for each decade of publication (see https://github.com/CHIELDOnline/CHIE LD/tree/master/data/tree/documents). For example, the files for Dunbar’s (2004) paper on gossip is stored in ‘documents/2000s/2004/dunbar2004gossip/’. This organisation is largely for the convenience of the database developer. For most users, the online interface for coding papers will automatically create files in the correct location, and documents can be searched using a userfriendly interface. Within the causal links spreadsheet, a single observation (a single row) represents a single causal link between two variables. The causal link has a number of associated properties, shown in Table 1. The types of causal relation are shown in Table 2. While a standard causal graph does not encode the direction of the relationship, this information can be encoded in CHIELD via the ‘Cor’ (correlation) field, including positive, negative and non-monotonic relationships. The format is easiest to explain with an example (Table 3). Lupyan and Dale (2010) suggested a link between the number of speakers a language has, and the language’s morphological complexity. In their paper, they hypothesise that: ‘Speakers of languages [with large numbers of speakers] are more likely to use the language to speak to outsiders—individuals from different ethnic and/or linguistic backgrounds.’ This could be coded as the first row of Table 3. They hypothesise a causal effect (‘>’) and they support this with references to the literature (type ¼‘review’). The main quantitative results from the paper demonstrate a negative correlation between population size and morphological complexity. This can be coded separately as a ‘statistical’ type of support. See Section 5.3 or the entry in CHIELD (https://chield.excd.org/document.html?key lupyan2010language) for more information. More detailed specifications for each field are provided in the supporting materials. Defining hierarchies of variables is possible by using a colon character in the variable name. For example, in a paper on morphological complexity, Nettle (2012) distinguishes ‘paradigmatic complexity’ from ‘syntagmatic complexity’. Ideally, these concepts should link to the ‘morphological complexity’ node of other hypotheses while maintaining their specificity. Therefore, a coder might use ‘morphological complexity: paradigmatic’ and ‘morphological complexity: syntagmatic’. Various settings in the visualisation allow users to switch from connecting nodes only if their full labels match, and connecting them if their higher order category matches. It would also be possible to allow empirical measures of causal strength to be stored for each causal link, which are important for causal studies in fields like medicine. However, they are not part of the core design goals here, since many studies of evolutionary linguistics do not estimate these kinds of measures directly, but use experiments, models or case studies to make analogies. Table 1. Fields in the database Field Description Values Var1 The source variable Any lowercase string. Hierarchies can be separated by ‘:’ Relation The type of causal relation See Table 2 Var2 The destination variable Same as Var1 Cor The direction of correlation blank, pos, neg, nm (non-monotonic) Topic The topic of study Lowercase strings separated by ‘;’ Stage The evolutionary stage Preadaptation, co-evolution, cultural evolution, language change (see Scott-Phillips and Kirby 2010) Type The type of evidence used to support the link Experiment, review, model, simulation, statistical, qualitative, logical, hypothesis, other Confirmed Whether the evidence supported the link Blank, yes, no Notes For context, e.g., a quote from the paper that supports the observation Any string See the Supplementary Material S2 for the full specification. 106 Journal of Language Evolution, 2020, Vol. 5, No. 2 Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 The most important design feature here is a low barrier to adding data, and causal strength estimates may not be very helpful for many researchers in evolutionary linguistics. Having said this, it would be easy to add this kind of information to a future version of the database. 3.1 Exploration The web interface for CHIELD includes several ways of searching the data. PHP scripts fetch data from the compiled SQLite version of the database and serve them up into a dynamically searchable tabulated format (using the Datatables library, https://datatables.net). For example, users can view a list of documents, and search by title, author or year. Each document links to a document page, which displays the bibliographic information, the list of causal links (with the specification information above) and a list of other documents that have variables in common (found using live queries of the SQLite database). CHIELD also includes an interactive visualisation of the causal graph using the vis.js javascript library (http://visjs.org). Users can view tables which list all the causal links or all variables. These link to similar pages which give overviews of the variable (e.g., all causal links involving ‘linguistic diversity’). These search features make it easy to find documents or causal links, and to explore links between documents. Collaboration is an important part of language evolution research (see Bergmann and Dale 2016; Youngblood and Lahti 2018). There are now various approaches for automatically suggesting collaborations between authors based on network models (e.g., Lopes et al. 2010;Xu et al. 2010;Yan and Guns 2014;Guns and Rousseau 2014;Kong et al. 2017). By combining the bibliographic information with the causal links data, CHIELD may be able to find connections between authors beyond co-authorship. Users can search a list of authors, with links to author pages showing all documents and causal links by an author, a list of their co-authors and a list of potential collaborators. The potential collaborators are defined following a method similar to the AXON database (see Supplementary Materials S1): find authors whose causal graphs overlap (have nodes in common), but who have not published any papers together. CHIELD includes an “Explore” mode, where users can load multiple causal graphs into a single visualisation. This includes various tools: •An interface for combining multiple causal graphs into a single visualisation. •Interactive manipulation, allowing adding a node from the database, adding all nodes from a particular document, or removing nodes or edges from the current visualisation. •Expand links from the currently selected node (display all causal links that connect to a particular node). •Find evidence from other documents for the currently displayed links. •Display sub-variables under a single higher order variable •Find causal pathways between two given variables, in order to discover alternative hypotheses. Table 2. Syntax for expressing the relationship between the source and destination variable Syntax Meaning Symbol X>Y A change in X causes a change in Y ! X<¼>Y X and Y co-evolve $ XY X and Y are correlated ---- X id="721" />Y X does not causally influence Y —j XY X is a necessary precondition for Y !(red) X¼ Y X is an indicator of (measured by) Y —䊉 X ^ Y X exerts an evolutionary selection pressure on Y ----" Table 3. Example coding for two links in Lupyan and Dale (2010) Var1 Relation Var2 Cor Topic Stage Type Confirmed Notes Population size >Population contact pos Contact Language change Review Speakers of lang ... ... Population size Morphological complexity neg Morphology Language change Statistical Yes Population was ... Journal of Language Evolution, 2020, Vol. 5, No. 2 107 Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 •Exporting the causal graphs as ‘dot’ format files. These can be used in visualisation tools, like GraphViz (Ellson et al. 2004) or Gephi (Bastian, Heymann and Jacomy 2009) to produce images for use in publications (e.g. pdf, svg). These tools can help a researcher find “upstream” explanations that feed into their own hypothesis, and also see how their hypothesis can generate predictions for other “downstream” work. CHIELD finds causal pathways using a variant of Dijkstra’s algorithm (Dijkstra, 1959), which finds all possible paths between two nodes. This can become complicated if there are loops or multiple connections between variables. However, the algorithm does not need to find all possible paths, only the set of nodes along all possible paths (the SQL query will find all paths connecting these nodes later). Therefore, the algorithm only follows each edge once. Another issue is which types of causal connection should be considered in the search. Considering only standard causal links (‘>’) can lead to missing some connections, but including all links can lead to very large causal networks which are not useful. The current implementation, therefore, only considers the following connections: ‘>’, ‘’, ‘<¼>’ and ‘¼’. In the future, this search process could be customisable. 3.2 Evaluation The explore mode allows control over the visualisation in order to support evaluation and effective design decisions for future research. Parameters of the causal graph can be manipulated (e.g., to display hierarchical layout or a dynamic ‘spring’ layout). For example, the colours of edges and node positions can be manipulated to reflect the source publication, type of evidence, type of causal effect, or direction of correlation (communicated through a hideable legend). The colour schemes are designed to be distinguishable by colourblind users. The explore mode also includes a tool for highlighting differences between hypotheses. If causal links from more than one document are loaded, an algorithm finds edges where the two documents disagree. At the moment, this only involves cases where one document claims that there is a causal connection (‘>’, ‘’, ‘<¼>’) and the other claims that there is no causal connection (‘/>’), but this could be expanded in the future. If conflicts are detected, the relevant edges are highlighted and the visualisation zooms in to display them. The final causal graph can be exported to the DAGitty web interface (Textor, Hardt and Knu¨ppel 2011) or as model definition code for the R package phylopath (von Hardenberg and Gonzalez-Voyer 2013; van der Bijl 2018), which performs phylogenetic path analysis on multiple competing models (each document is listed as a different model). The full database is openly available in a range of formats, for further manipulation by statistical software. 3.3 Extension Constructing a comprehensive database of theories of language evolution is not a feasible task for a single person. Consequently, CHIELD has been designed to be extendable by large numbers of contributors. This makes it important to be able to curate contributions and keep track of changes. It is not expected that everyone will agree on interpretations, but these issues are worth debating, and the database should include space for discussion and revisions. If the database is to be useful in the long-term, there are also the practical questions about how best to store the data. Moreover, we want the basic application to be extendable to other fields and by other developers. Therefore, the design goals here are: •Integration with version control software for keeping track of changes. •Tools for discussion and curation. •Simple file formats that can be easily edited. •Non-proprietary data formats for longevity. •Open source code for extension to other fields. CHIELD is integrated with Git and GitHub for keeping track of changes, and for managing public contributions, issues, and discussion (https://github.com/ CHIELDOnline/CHIELD). This also provides open access to the source code if other fields of research want to develop their own version. The file formats (bib, txt, and csv) are open-source, non-proprietary, and simple to edit. All processing scripts and external libraries are free and open source. Data can be added to CHIELD by coders through the GitHub repository, but the website also includes a simpler customised interface for adding data (using the javascript library js-grid, http://js-grid.com/). This guides coders through the process of contributing a document to CHIELD, including how to draw the causal graph visually or to upload a causal links template spreadsheet from their computer (more information at https://chield. excd.org/Help_AddingData.html). The interface suggests existing variable names for the coder to re-use, in order to maximise convergence on variable names. Once the coder has entered their data, a script creates the standard file formats for CHIELD and sends them to 108 Journal of Language Evolution, 2020, Vol. 5, No. 2 Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 the GitHub repository (it creates a new branch, commits the new files to it, and then makes a pull request to merge these into the main branch). This sends an alert to the owner of the repository who can review the contribution and accept it into the database. The web manager can now pull these changes down to the server, and run the compiling script that creates an updated SQL database and deploys it to the website. This system is perhaps one of the more successful parts of the database design that allows anyone to contribute without needing to know how to use version control software. This system also takes advantage of the existing tools of GitHub, including user logins, bug reports, discussion threads, and tracking changes which reduces the development load. Finally, this system also allows rapid turnover: after the coder has submitted their data, the process of adding it to the database and updating the website takes just a few minutes. 3.3.1 Scope of data The condition for entry into the database is that the document proposes a hypothesis with causal claims that relates to some part of the evolution of communication and that it is published in a peer-reviewed publication (e.g., journal paper, peer-reviewed conference proceedings, book chapter). This could be either biological or cultural evolution (or both) at any evolutionary stage. As a rule of thumb, the document should relate directly to language, or be ‘one link away’ from a relevant language document. Entry into the database does not mean that the hypothesis is correct or widely accepted, or even empirically supported. The aim is not that the database be a single theory of the evolution of communication, but a reflection of the whole field. Potential sources for documents include the Language Evolution and Computation Bibliography (https://langev.com, including over 2,500 papers), the Journal of Language Evolution, the Journal of Interaction Studies,EvoLang conferences, and so on. 3.3.2 Moderating contributions CHIELD aims to be open and editable. Anyone can contribute documents to CHIELD or edit any existing documents (as long as they have a free GitHub account). This necessitates moderation for quality control. This is done through GitHub, with a central administrator being able to review each contribution or edit (as a pull request) before it is added to the database. An advantage of this system is that it creates a record of every change to the database. The document pages of the website include buttons for raising issues with the coding (through a pre-filled GitHub issues page), which may be taken up by other users or an administrator. There are various administrator tools for aggregate tasks, like replacing all occurrences of a particular variable label with another. 4. Results The CHIELD website is live (http://chield.excd.org/) and is updated continuously (https://github.com/CHI ELDOnline/CHIELD). The results in this article are based on version 1.1. In general, we found coding of causal graphs challenging but productive. We found that a paper might take between 10 minutes and an hour to code, depending on the complexity of the theory, the methods used and the coder’s familiarity with the subject. Version 1.1 of CHIELD includes 400 documents and 3,406 causal links between 1,700 variables. These were contributed by 41 coders (see https://chield.excd. org/about.html). The ratio of unique variables to links is high, suggesting that many documents introduce new variables. Ideally, if the database was approaching a ‘full picture’ of the field, the number of new variables being added would decrease over time. Figure 2 shows the relationship between the number of documents and the number of unique variables (points are average number of unique labels from 1,000 random orderings of documents). The curve is growing slower than strictly linear, and if we assume a quadratic function, then we estimate that a plateau will be reached when around 650 documents have been coded. Of course, at the moment, the database reflects the research interests of its contributors and might under-represent many sub-fields, so coverage of the whole field might require many more documents. However, another sign of convergence is that documents are highly connected. The largest component of the network includes around 75% of all documents (Fig. 3). 4.1 Case study 1: gossip, ritual, and language The first example of CHIELD’s functionality demonstrates how theories can be compared against each other, and how CHIELD can be used to explore empirical evidence that might help resolve debates. Figure 4 shows two theories about the coevolution of group organisation and communication in human language emergence. The first theory, Dunbar (2004) relates population size, brain size, and gossip: risk of predation drives individuals into larger groups for safety, but these groups require more time dedicated to social bonding in order to maintain alliances. Since gossip is more efficient at maintaining a larger number of alliances than one-on- Journal of Language Evolution, 2020, Vol. 5, No. 2 109 Downloaded from https://academic.oup.com/jole/article/5/2/101/5821004 by Universidad de Sevilla user on 12 April 2023 add statistical results to edges (though there is the question of how to allow multiple types of statistic to one edge), and add more flexibility in searching (e.g., limit the results to a particular stage of language evolution). There is also the possibility of converting to database formats that explicitly support network structures for advanced querying (e.g., GraphML). We would like to add support for large touchscreens and interactive debate so that CHIELD could be used as a tool for live discussion between researchers. As research methods increasingly involve collaboration with multiple researchers, this could help people check how their understanding of terms and concepts match. We would also like to develop a way for CHIELD to automatically suggest studies (e.g., for student projects). For example, randomly choosing a causal link that currently has no empirical evidence, detecting colliders that might require alternative explanations, or showing a link between two theories that might not have been noticed. It would also be possible to link nodes to open source data, so that CHIELD could try to discover links that currently have no empirical support, but for which there was existing data that could be utilised. Of course, automated procedures may be susceptible to bias (e.g., publication biases), and they can be no replacement for careful research practice. But we hope CHIELD can help make research more systematic. In particular, we are keen to explore how the data in CHIELD can be used as prior biases in more automated machine-learning processes, such as automatic causal graph inference. In conclusion, clearly expressing complex scientific hypotheses is hard. There is a need for formal tools, such as causal graphs, to help researchers communicate their ideas. CHIELD provides easier access to these tools and creates a space for clarifying theories. It demonstrates that the field of evolutionary linguistics is more connected than we thought, and there are potentially many links between theories that are waiting to be discovered. We suspect the same is true for many other fields that would also benefit from building their own database of causal graphs. Supplementary data Supplementary data is available at JOLEV online. Acknowledgements The following researchers contributed data to CHIELD without contributing to this article: Lachlan Walmsley, Limor Raviv, Andrew Buskell, Cara Evans, Michael Dunn, Robin Dunbar, Isobel Clifford, James Winters, and Simon Kirby. Many thanks to Robert Forkel, Tessa Alexander, Jon Hallett, and Simon Greenhill for additional code review. Funding A.D., C.S., F.J., and E.T.C. were funded from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 639291, Starting Grant VARIKIN). J.N. was supported by a Principal’s Career Development Scholarship from the School of Philosophy, Psychology & Language Sciences at Edinburgh. K.S. was supported by an Academy of Finland grant (296212) and by an ERC starting grant (805371). R.S. was supported by the Australian Research Council (FL130100111). 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