Evolutionary approaches to Bilingualism

I recently gave a talk at the University of Edinburgh LEL Postgraduate Conference. It was my first ever talk and it really forced me to figure out what I'm supposed to be studying! Here's a video of my talk:

Frank MC, Goodman ND, & Tenenbaum JB (2009). Using speakers' referential intentions to model early cross-situational word learning. Psychological science : a journal of the American Psychological Society / APS, 20 (5), 578-85 PMID: 19389131

Hunag, Y. (2009). Supporting Meaningful Social Networks Technical Report, ECS, University of Southampton
pdf

Healey, E. and Scarabela, B. (2009). Are children willing to accept two labels for one object? Proceedings of the Child Language Seminar. University of Reading.

Byers-Heinlein K, & Werker JF (2009). Monolingual, bilingual, trilingual: infants' language experience influences the development of a word-learning heuristic. Developmental science, 12 (5), 815-23 PMID: 19702772

Mutual Exclusivity biases in cross-situational learning: A comparison between monolingual and bilingual corpora

This report focuses on models of cross-situational learning and how current models compare when exposed to real monolingual and bilingual input. Several model types were evaluated against two transcribed videos of parent-child interaction, one being monolingual and the other being bilingual. Children have been shown to demonstrate a Mutual Exclusivity (ME) bias (Markman and Wachtel, 1988) during word learning. Frank et al. (2009) showed that their model also exhibited Mutual Exclusivity (ME) behaviour after learning from a monolingual corpus of contexts. The current study takes the same model but with bilingual input and asks whether the same behaviour is exhibited.

Method

Frank et al. (2009) provide a transcribed video of monolingual parent-child interaction coded for use in cross-situational learning. An equivalent bilingual corpus was looked for. The main criterion was a roughly equal number of utterances in both languages. The CHILDES database has suitable resources. A recording from a study by Yip and Matthews was selected (see CHILDES, 2010). The child in question was a native bilingual from birth. Her mother was a native Hong-Kong Cantonese speaker and her father was a native speaker of British English. She was 2;11 in the chosen recording. There are 967 utterances, 48% of which are Cantonese and 52% are English. The objects visible in the video were added to the transcription, along with the mapping between words and objects and the referential intentions of the speakers. The coding scheme was adopted from Frank et al. (2009). The code for the models was supplied by Frank et al. (from Frank,2010).

Results

The performance of different models were analysed in two ways. Firstly, the best estimated lexicon (word-object mappings) of each lexicon was evaluated against a gold-standard lexicon in terms of precision, recall and the resulting F-score.Secondly, the models were asked to guess the intended referent of each utterance-object context.Tables 1 and 2 show the lexicon results for the monolingual and bilingual corpora respectively. Frank et al.’s model returns the highest F-scores in both cases.This is largely due to an advantage in precision, likely stemming from the modeling of non-referential words. Frank et al.’s model returned a word-object mapping for the bilingual corpus with a precision of 0.31 and a recall of 0.27, giving an F-score of 0.29. This is lower than the score for the same model on the monolingual corpus. This could be due to the referential uncertainty (independent from amount of synonymy) in the bilingual corpus being higher.The results for the referential intentions, shown in Tables 3 and 4, have different trends. For the monolingual case, the precision of Frank et al.’s model allows it to outperform the other models. However, it performs relatively poorly in the bilingual case, with the Conditional Probability model performing best. However,all models perform with little precision and recall, suggesting that the task is harder. With more data, results might be different.




Mutual Exclusivity

After the model had processed the corpus, it was presented with a mutual exclusivity task and the relative likelihood of several interpretations were measured.In the task, the model was presented with a context with a new object (e.g. a dax) and a familiar object (a bird for the monolingual model and an orange for the bilingual model) and a new word (e.g. ”dax”). The probabilities were calculated for the model linking the new word with neither object (i.e. it considers the word non-referential), linking it with the new word, linking it with the old word or linking it with both. Figure 1 shows the results of the task with the results for a ’monolingual’ model for comparison.

The monolingual results are re-calculated for this study, so differ slightly from those reported in Frank et al. (2009).The results for the monolingual and bilingual models have the same trend - both rank the possible situations in the same order of likelihood. The most likely situation is the new word being linked to the new object, honouring mutual exclusivity. The second most likely situation is that the word refers to neither object.Intuitively, one would expect a bilingual to be more likely to consider that the new word was another word for the familiar object. Indeed, the bilingual model does consider this possibility relatively more likely than the monolingual model. However, the model still considers neither mapping to be more likely than an extra synonym. This may mean that, given an additional cue (e.g. pragmatic), the bilingual would be more ready to accept a synonymous interpretation. This is an empirical question.

The Prior Probability

The prior probability is simply the number of mappings in the hypothesised lexicon, modulated by a fixed parameter (alpha). This represents a preference for smaller lexicons. This means that a hypothesis which results in the lexicon with fewest mappings will receive the highest prior probability. With the default parameter (alpha = 7), the Mutually Exclusive preference (for DAX-dax) beats the preferences for the original mapping (map neither word to the unfamiliar object),both mappings and the mapping of the unfamiliar object with the familiar name.However, this ranking depends on the lexicon size bias (alpha) parameter. With a low alpha, the most likely mapping is the ME mapping. With a higher alpha, the most likely mapping is the original mapping (see Figure 2).

The same trend also exists between the preference for the ME mapping and both mappings, although the preference for both mappings does not overtake the preference for the ME mapping (see Figure 3).
The explanation is as follows: The original mapping receives a high prior probability because it doesn’t increase the size of the lexicon. However, the likelihood of experiencing a non-referential word is low, leading to a total probability that favours the ME mapping over the original. Assuming a larger lexicon (decreasing alpha), the relative increase in lexicon size is smaller, tipping the balance between the original and ME mapping preferences.Interestingly, the likelihood of choosing both mappings overtakes the original mapping when alpha is less than 1 (see figure 4).
That is, the likelihood of assuming both mappings increases when the prior is set to less than the number of word-object mappings in the lexicon. Such a setting makes sense for a bilingual (who have up to twice as many mappings as bilinguals) because it represents the number of concepts. Put another way, by compensating for the additional synonymy in bilingual input, the likelihood of assuming both mappings increases.The dependence of the ME experiment results on alpha is acknowledged by Frank et al.:

“Note that there is some parameter dependence in our models fit to the mutual exclusivity situation. Depending on the size of the corpus,it might be the case that the prior disadvantage of adding a word to the lexicon would not be outweighed by the increase in corpus likelihood caused by learning a new word. This fact makes a developmental prediction: in early development, when very few words are known,inferences about mutual exclusivity should be weaker.”
Supporting Information for Frank et al. (2009), p. 13.

This prediction is borne out in some studies (Merriman and Bowman, 1989; Frankand Poulin-Dubois, 2002; Merriman et al., 1993). However, Markman and Wachtel (1988) found that the ME constraint weakens over time, with older children showing less of a bias, while Deak et al. (2001) find no change.The issue here is the size of the lexicon. Bilingual children may know more words than monolinguals, but it may be more accurate to judge the lexicon size by the size of one language’s lexicon.The model does not provide a mechanism for modulating the lexicon size prior parameter during learning. Currently the prior is modulated by the alpha parameter and the number of mappings, meaning that adding new mappings is dis-preferred. Bilinguals will have a higher number of mappings, altering their prior probabilities. However, this does not lead to qualitative differences in the mutual exclusivity experiment.The motivation for modulating the prior by the number of mappings is mainly to simplify the model.

“We chose a prior probability distribution that favored parsimony,making lexicons exponentially less probable as they included more word-object pairings ... The choice of a simple prior puts most of the work of the model in the likelihood term ... hence, the likelihood term captures the learners assumptions about the structure of the learning task.”
Frank et al., 2009, p. 579

That is, the decision is driven by the statistical, computational approach to the formal problem rather than being psychologically motivated. Therefore, the interpretation is that mutual exclusivity behaviour stems from the child’s unwillingness to learn new signal-meaning mappings. This seems a little circular - children prefer not to extend mappings from familiar words to unfamiliar objects because they prefer not to extend mappings. It also seems to go against children’s obvious ability and motivation for learning new words and meanings. Several solutions which would make the prior more sensitive to the input involve incorporating the number of concepts, the number of words or the amount of synonymy (proportional to the number of words in the lexicon divided by the number of concepts). However, the nature of the model now changes - we are using it to test specific hypotheses about mutual exclusivity, judged against empirical data,rather than seeing if mutual exclusivity ’falls out’ of more basic assumptions.

Concept-based Prior

The mapping-based prior was biased towards a monolingual mode. The model was altered so that the prior was negatively related to the number of objects in the lexicon. This represents the number of concepts for which the child knows words. The model was run on the bilingual corpus and returned a lexicon with a precision of 0.05, a recall of 0.41 and a resulting F-score of 0.09. The model was also run on the monolingual corpus again, returning a precision of 0.05, a recall of0.79 and an F-score of 0.09. For both monolingual and bilingual corpora, the recall of this model is better than for a mapping-based prior, but the precision is much worse. That is, the model overestimates the number of word-concept mappings. In fact, the models accumulated many hundreds of word-concept mappings for tens of objects (Monolingual: 551 mappings for 22 objects and 419 words; Bilingual:641 mappings for 55 objects and 598 words). The models have failed to acquire a useful vocabulary.However, running the Mutual Exclusivity experiment again, the relative ranking of the preferences has changed. Although the ME mapping is still favoured, the next preferred interpretation is to make both mappings (rather than neither, see Figure 5). However, this difference is exhibited with both monolingual and bilingual input data. By neutralising the difference in the prior, the corpus likelihood now plays a bigger role, leading to a difference in the preferences.




How ’Monolingual’ is the Monolingual corpus?

Although the monolingual corpus is taken from a carer speaking one language, the lexicon the model learns contains synonymy. In fact, for the 15 objects it learned words for, 8 had more than one associated word. For half of these 8 objects, all synonyms were appropriate (e.g. ’bird’ and ’birdie’ to describe the object ’duck’),but half were not appropriate. In other words, the model accommodates synonymy.The original Mutual Exclusivity experiment in Frank et al. was done with the object ’bird’, which had one associated word. The ME experiment was applied for all words that the model learned from the monolingual corpus. There were no significant differences between the posterior probabilities for any of the situations (DAX-dax, Both etc.) for synonymous mappings versus non-synonymous mappings. This holds for both the original and the concept-based prior.

Conclusion

Frank et al.’s model can be used to model word learning in bilinguals. There are some quantitative differences in the ME behaviour of models run on monolingual and bilingual corpora. However, no qualitative differences were found. Even when the prior bias for minimising the number of mappings was neutralised, both models still preferred to map the new object with the new word.

Next Steps
The results are inconclusive, but may reflect the limited data. I suggest that synthetic corpora would make the dynamics more clear. Very simple cross-situational learning corpora could be created with varying amount of ’bilingualism’.

References

Frank MC, Goodman ND, & Tenenbaum JB (2009). Using speakers' referential intentions to model early cross-situational word learning. Psychological science : a journal of the American Psychological Society / APS, 20 (5), 578-85 PMID: 19389131

Byers-Heinlein K, & Werker JF (2009). Monolingual, bilingual, trilingual: infants' language experience influences the development of a word-learning heuristic. Developmental science, 12 (5), 815-23 PMID: 19702772

Deák GO, Yen L, & Pettit J (2001). By any other name: when will preschoolers produce several labels for a referent? Journal of child language, 28 (3), 787-804 PMID: 11797548

Frank, I., & Poulin-Dubois, D. (2002). Young monolingual and bilingual children's responses to violation of the Mutual Exclusivity Principle International Journal of Bilingualism, 6 (2), 125-146 DOI: 10.1177/13670069020060020201

Markman EM, & Wachtel GF (1988). Children's use of mutual exclusivity to constrain the meanings of words. Cognitive psychology, 20 (2), 121-57 PMID: 3365937

Merriman WE, & Bowman LL (1989). The mutual exclusivity bias in children's word learning. Monographs of the Society for Research in Child Development, 54 (3-4), 1-132 PMID: 2608077

Merriman WE, Marazita J, & Jarvis LH (1993). Four-year-olds' disambiguation of action and object word reference. Journal of experimental child psychology, 56 (3), 412-30 PMID: 8301246

Healey, E. and Scarabela, B. (2009). Are children willing to accept two labels for one object? Proceedings of the Child Language Seminar. University of Reading.

LEL Postgraduate Conference

I'm giving a talk at the LEL Postgraduate Conference at the University of Edinburgh, 19th - 21st May. It's not that big of a deal, since I'm required to give a talk, but it is my first talk. A link to the website (which I'm maintaining) with more details and my abstract follow!

LEL Postgraduate Conference 2010

Bilingualism and Social Networks

Children learn language from exposure to speakers in their social network. This learning influences the input that will be given to the next generation. The way languages change over time is dependent on the learning biases of individuals (e.g. Kirby, Dowman & Griffiths, 2007), but also on the dynamics of the social network of those individuals (Gong & Wang, in press; Lupyan & Dale, 2010; Gal, 1979; Govindasamy, 2003).

Bilingualism is often marginalised in theories of language evolution and existing bilingualism is generally seen as the product of contact between two or more monolingual communities. However, I hypothesise that a bilingual ability is a fundamental aspect of language learning: children can learn two languages as easily as learning one. This suggests that human cognition is geared towards handling complex, not homogenous cultural input. This in turn may suggest the kind of social networks in which human cultural transmission evolved. The prevalence of monolingualism in some modern societies may be explained by changes to social structures afforded by communications technology.

This talk will outline my approach to this hypothesis. This involves the idea of cultural transmission as a trade-off between communicative flexibility and expressivity, the use of a comparative approach to bilingualism and methodologies to generate and test hypotheses.

Bilingualism as a preadaptation for Language

This report is the beginnings of an attempt at a comparative approach to bilingualism, in the style of Fitch(2005). Bilingualism is difficult to define, but by asking whether there is evidence for this capacity in non-human species, it's hoped that this question is made clearer.

FLA and FLB
This research project takes an evolutionary approach to Bilingualism. One of the most difficult problems faced so far is identifying the role of bilingualism in the cultural evolution of language. Is it a product or a catalyst? Firstly, I'm not sure whether this has been considered to any great extent. However, I suggest that the implicit assumption in the vast majority of work in both the areas of Bilingualism and Language Evolution has been that bilingualism is a product of the merging of homogenous language communities. This report explicitly asks the question: Which came first - Language or Bilingualism? That is, did the capacity for bilingualism develop from a pressure to learn multiple existing languages or was it a capacity which existed before human languages were established and influenced their arrival?

The latter hypothesis seems to be non-sensical. How can individuals have the ability to learn more than one language when there are no languages to be learned? Here, I'd like to make a distinction between two kinds of bilingualism, following the approach of Hauser Chomsky Fitch (2002). Bilingualism in the narrow sense means the ability to learn several human languages. This is obviously a human-only trait. Bilingualism in the broad sense refers to the general capacity to acquire more than one signalling system. Depending on how one defines signalling systems, this capacity may be shared with many other animals, both closely and distantly related. Of course, defining what constitutes a single signalling system is difficult, let alone defining language or bilingualism. However, it's hoped that the approach taken in this paper will help towards this goal by considering the features of the phenomenon we wish to define.

Before considering this possibility, the comparative approach to language evolution is presented. Fitch (2005 and others) approaches the study of the evolution of language by considering what elements contribute towards the `Faculty of Language'. In the broad sense of the term, this covers all the prerequisite elements that are required for linguistic communication. This involves cognitive capacities such as acoustic string segmentation and semantic processing, but also much more basic features such as memory. That is, features of the Faculty of Language in the broad sense (FLB) are found in humans and animals. The narrow sense of the term (FLN) refers to those capacities which are involved in language alone. There is much more debate about what these elements are. Recursive processing has been suggested as one example.

The comparative approach has been used to answer the question of what belongs to FLN and to FLB. Animals have been shown to be capable of a number of processes required for language, including categorical perception of speech sounds (Kuhl & Miller, 2978) and Mutual Exclusivity (Juliane & Kaminski, 2004). From studies of divergent and convergent evolution of these traits, some important features have been identified. For example, many species which exhibit vocal learning have direct neural connections between the brain and vocal motors, while non-vocal learners do not (see Doupe, 1999).

This report suggests that this approach should be adopted for the study of Bilingualism. Such an approach would seek to answer whether bilingualism is a uniquely human capacity. If it turns out that other animals also have this capacity, then the role of bilingualism in the evolution of language can be re-assessed.

However, there is a large initial problem. Even FLB only consists of capacities that are required for language. Bilingualism in the broad sense, however, is not required in order to speak a language. This problem may be due to the individual-level bias to the idea of the 'Faculty of Language'. Its primary aim is to describe capacities that an individual organism requires, rather than a community. Therefore, bilingualism may not be part of the FLB, and simply a product of cultural interaction. However, the comparative approach can help verify this hypothesis if social animals exhibit the capacity for bilingualism. That is, if bilingualism comes from cultural interaction alone, there should be no non-social animals which have the capacity for it.

Bilingualism in Bengalese Finches
If other species exhibit bilingualism, then this is evidence that bilingualism developed before human language. Takahasi & Okanoya (2010) study the vocal learning patterns of the Bengalese Finch. These are a domesticated breed descended from wild White Backed Munia. The Bengalese Finch exhibits very complex song patterns in comparison to the White Backed Munia.

Takahasi & Okanoya (2010) carry out a cross-fostering experiment where Munias are brought up by Finches, and Finches are brought up by Munias. The Munias tended to have a stronger preference for copying Munia songs, while the Finches are not so disposed towards their own strain's song. That is, Finches have more flexibility in learning. It is hypothesised that this is because there is a pressure on Munias to identify their own strain in the wild where there are mixed flocks, while this pressure has been masked for Finches by domestication and isolation.

However, is this really `Bilingualism'? The problem is that, although there is flexibility in the sources of acquisition, the birds do not have the same flexibility in production. That is, as I understand it, they still develop only one song (i.e. they can't sing elements of A and B's songs in the morning, then elements of C and D's songs in the evening). Furthermore, the idea of 'comprehension' is more difficult to apply, since there is no semantics.

It has been suggested that Bengalese Finches have developed song complexity as a sexual display (Okanoya, 2004). Following from this, Soma et al. (2009) find that chicks select tutors based on their song complexity. Also, Okanoya (2010) presents some evidence to suggest that Benglaese Finches learn from many tutors. That is, they splice whole segments of songs from many other individuals to create their own song. In this sense, learning from multiple tutors increases the complexity of the song and so increases the attractiveness and fitness of the individual.

The ability to learn syntactic sequences from many tutors has apparently occurred in a system with no semantics. This may suggest that bilingualism at the syntactic level emerged before bilingualism at the lexical level, opposite to the order implicitly assumed by many. One big advantage of cultural evolution is that individuals can inherit information from multiple sources, whereas there are a limited number of biological parents. This is the core of what I mean by Bilingualism being a preadaptation for language: part of the acquisition of human languages requires the flexibility afforded by bilingualism.

Counter Arguments
This phenomenon in Bengalese Finches is interesting, but may not help with our question. Although the complexity of the system has increased due to a change in the environment (domestication), whether this was initially enabled by learning from multiple tutors is not clear.

Also, Okanoya has shown that vocal learners who co-inhabit areas with other species of vocal learners have less complex song. That is, song complexity does not help species identification. Therefore, if the capacity for bilingualism developed in humans before language, it's likely that there was little pressure on vocal cues for species identification.

Inter-species semantic communication
Many species also communicate vocally with other species. Vervet monkeys respond to the territorial and alarm calls of superb starlings (Seyfarth Cheney, 1990). Ring-tailed lemurs respond to the alarm calls of Verreaux's sifakas (Oda Masataka, 1996). However, captive ring-tailed lemurs who had never heard the sifakas' alarm calls also responded appropriately to playbacks. Oda and Masataka argue that they are therefore responding to shared acoustic features rather than to an associated meaning. Although in most examples of inter-species communication do not involve the transference of 'concepts', some examples do show evidence for this.

Zuberbuhler (2000) studied communication between Diana monkeys and Campbell's monkeys. Diana monkeys respond appropriately to Campbell's monkeys' alarm calls for leopards and eagles. Furthermore, their responses suggest they are attending to the meaning rather than the acoustic signal. If a Diana monkey hears a leopard or a leopard alarm call, it calls out loudly, but if it hears a second leopard or leopard alarm, it is quieter, presumably because of the risk of predation (the same is true of eagle alarms). Diana monkeys were primed with Campbell alarms for either leopards or eagles then probed with either eagle or leopard sounds (growls and shrieks). They responded loudly to each combination, apart from where the Campbell alarm corresponded to the predator type (e.g. Campbell leopard alarm followed by a leopard sound). In these cases, the Diana monkeys were quieter, suggesting that they thought the predator was already present.

Zuberbuhler concludes that "Diana monkeys can flexibly use and assess information derived from the communication of other species" and that "semantic understanding can be based on arbitrary signals, as it is the
case for word meaning" (Zuberbuhler, 2000, p. 717). Diana monkeys seem to understand the same concept from two different calls. I argue that this is bilingualism in the broad sense at the lexical-like level.

Again, Diana Monkeys are limited by their physiology in terms of production of the Campbell's alarms. However, the information transfer from Campbell's monkeys to Diana monkeys is not 'communication' as defined by MaynardSmith Harper, 2003) (also see Scott-Phillips, 2008). That is, although the Campbell's calls affect the behaviour of the Diana monkeys, they did not evolve to do this (they are cues, not signals). Therefore, I'd like to suggest that the origins of the capacity for bilingualism originates in the evolution from cues to signals.

However, these responses may not be learned. Furthermore, there is no current evidence to suggest that Campbell's reciprocate in their comprehension of Diana Monkey's calls. The latter issue is discussed by Magrath (2009) who study the alarm call responses of 3 ecologically distinct avian species and find that responses may be reciprocal, but not necessarily symmetrical. Different species reacted to each other's alarm calls in proportion to the 'reliability' of the call as a cue to one of the listener's predators. That is, not all predators of species A are predators of species B, so the A's alarms are not always reliable for species B, and species B responds appropriately. In Magrath (2009)'s study, some species responded in the same way to three different calls. Again, this is evidence for bilingualism in the broad sense.

This raises an interesting question of 'reliability' or 'relevance' (as in Relevance theory, Sperber Wilson, 1995) in animal communications. Much of animal communication is limited to and grounded in information relevant to shared survival interests, that is, food, predators and mating. Humans are capable of communicating about topics beyond their immediate survival needs. This difference possibly requires the of 'ungrounding' of signals from the domains in which they evolved (see the next section).

Bilingualism's impact on FLB
Although bilingualism may not be necessary for the acquisition of language, and so could arguably not be part of FLN, learning two languages does seem to have a qualitative impact on capacities in FLB. For example, compared with monolinguals, bilinguals develop better inhibitory control, theory of mind (Goetz, 2003) and task-switching (Bialystok Martin, 2004).

Raphael Nunez's approach to the evolution of language hypothesises that it involved several pre-adapted 'Modules', but these modules coevolved. That is, an advancement in one module (e.g. more stable voice source, see Demolin, 2010) could cause an advancement in another (e.g. vocal learning), which could feed back into the first module.

Nunez sees the evolution of meaning as involving the development of a grounded system, ungrounding this system from its original domain, then re-grounding it in another. His work focuses on how gestural instantiations of space were re-grounded to convey information about time. For example, one might point behind to indicate an event that occurred in the past. Linguistic expressions of time have also adopted this system.

I suggest that bilingualism can be seen in this way. For instance, being able to learn from several tutors has advantages for increasing signal complexity in some situations. If this ability to learn from individuals could be ungrounded to allow learning from contexts, then this would allow a semantic system to develop. In other words, a kind of bilingualism allows the complex vocal learning mechanisms to be deployed over more general domains.

Okanoya has a similar hypothesis which sees string segmentation and context segmentation as necessary preadaptations for a semantic system. Indeed, the Bengalese Finches studied above may not only be doing string segmentation of tutor's songs, but also a king of crude `tutor segmentation'. That is, they select whole sections from different tutors.

The ungrounding theory suggests that the pressure on the original system needs to be lifted by some other mechanism. This may be a change in the environment, or an internal mechanism. It's likely in the case of the Bengalese Finch that its domestication had a large part to play, alleviating the burden of foraging and predation.

Conclusion
Asking whether non-human species have capacities for bilingualism in the broad sense may affect the way we approach bilingualism. This report has reviewed studies which show that animals have capacities compatible with ideas of bilingualism, but without other features of human language. These capacities stem from very basic evolution of cues and being able to learn from multiple tutors.

Further analysis of evidence for bilingual behaviour in animals is required. These include, for example, switching tasks in primates and other animals and the boundaries between different dialects in whale song. Crucially, this analysis, just like for the rest of FLB, relies on a evidence from a great number of studies. If the relevant studies have not been done, the potential for completing them in this project is extremely restricted.

More fundamentally, this report takes an approach to bilingualism that may not be appropriate. The comparative approach was designed to identify and study necessary components of the language faculty. On the other hand, such an approach may show that, from an evolutionary perspective, there is no easy way to define bilingualism, questioning whether there is a difference between monolingualism and bilingualism or even an easy way to distinguish between languages.

References

Bialystok E, & Martin MM (2004). Attention and inhibition in bilingual children: evidence from the dimensional change card sort task. Developmental science, 7 (3), 325-39 PMID: 15595373

D. Demolin (2010). Prosody and recursion in primate vocalisation Proceed- ings of the JAIST International Seminar on the Emergence and Evolution of Linguistic Communication, Kyoto, Japan.

Doupe AJ, & Kuhl PK (1999). Birdsong and human speech: common themes and mechanisms. Annual review of neuroscience, 22, 567-631 PMID: 10202549

Fitch, W. T. (2005). The evolution of language: A comparative review Biology and Philosophy, 20 (2-3), 193-203 : 10.1007/s10539-005-5597-1

Kaminski J, Call J, & Fischer J (2004). Word learning in a domestic dog: evidence for "fast mapping". Science (New York, N.Y.), 304 (5677), 1682-3 PMID: 15192233

Kuhl, P. & Miller, J. D. (1978). Speech perception by the chinchilla: Identification functions for synthetic VOT stimuli The Journal of the Acoustical Society of America, 63 (3) DOI: 10.1121/1.381770

Magrath, R., Pitcher, B., & Gardner, J. (2009). An avian eavesdropping network: alarm signal reliability and heterospecific response Behavioral Ecology, 20 (4), 745-752 DOI: 10.1093/beheco/arp055

Maynard Smith, J., & Harper, D.G.C. (2003). Animal Signals Oxford University Press, Oxford

Oda, R. and Masataka, N. (1996). Interspecific responses of ring-tailed lemurs to playback of antipredator alarm calls given by Verreaux's sifakas. Ethology, 102, 441-453 : 10.1159/000021651

Okanoya, K. (2004). Song syntax in bengalese finches: proximate and ultimate
analyses Advance in the Study of Behaviour, 34, 297-346

Okanoya, K. (2010). Biological preadaptations for language Proceedings of the JAIST International Seminar on the Emergence and Evolution of Linguistic Communication, Kyoto, Japan.

SCOTT-PHILLIPS, T. (2008). Defining biological communication Journal of Evolutionary Biology, 21 (2), 387-395 DOI: 10.1111/j.1420-9101.2007.01497.x

SEYFARTH, R., & CHENEY, D. (1990). The assessment by vervet monkeys of their own and another species' alarm calls Animal Behaviour, 40 (4), 754-764 DOI: 10.1016/S0003-3472(05)80704-3

Soma, M., Hiraiwa-Hasegawa, M., & Okanoya, K. (2009). Song-learning strategies in the Bengalese finch: do chicks choose tutors based on song complexity? Animal Behaviour, 78 (5), 1107-1113 DOI: 10.1016/j.anbehav.2009.08.002

Miki Takahasi, & Kazuo Okanoya (2010). Song Learning in Wild and Domesticated Strains of White-Rumped Munia, Lonchura striata, Compared by Cross-Fostering Procedures: Domestication Increases Song Variability by Decreasing Strain-Specific Bias Ethology

Zuberbühler, K. (2000). Interspecies semantic communication in two forest primates Proceedings: Biological Sciences, 267 (1444), 713-718 DOI: 10.1098/rspb.2000.1061

Levels of Bilingualism - update

I recently went back to my analysis of the Ethnologue into bilingualism. I suggested estimating the level of bilingualism by calculating the minimum number of bilinguals. However, there is already a much better estimation, and it has already been calculated. Greenberg's diversity index calculates the probability of two people who have different mother tongues in the same country meeting. This index has already been calculated for each country in the ethnologue. Below is a map of these indices. Dark colours indicate a higher diversity index (few people have the same mother tongue), lighter colours indicate a lower diversity index (total white = all people have the same mother tongue).

However, this still isn't a good predictor of bilingualism. Instead, it's a measure of diversity of mother tongue. The index still assumes all people only speak one language. I'm trying to figure out how to modify the index to take account of bilinguals - but I was always rubbish at probability.

Cultural Variation and Social Networks

Children learn language from exposure to speakers in their social network. This learning influences the input that will be given to the next generation. The learning biases that an individual has will influence the way the language changes over generations (Kirby, Dowman & Griffiths, 2007). However, language also plays a part in constructing and maintaining social networks. Recent studies have suggested that the structure of the social network also has an effect on the how a language evolves. Gong and Wang (2010) find that different network types influence the evolution of linguistic categories in an artificial categorisation game. Lupyan & Dale (2010) find that the amount of contact with other communities, and a community's spatial dispersion influences the morphological complexity of a language.

I was wondering whether bilingual communities have different social network structures to monolingual communities. Real social networks are very difficult to construct, so I wanted to use some online social networking sites. Twitter seemed like an obvious choice because of it's simple API, and also because 'following' someone has a genuine connection on a user's linguistic input.

I aquired some data for Twitter users. The data includes the number of followers (indegree) and people being followed (outdegree), the user's location, the number of status updates sent by a user and the amount of time since the last update. The last two features can be used to filter out people who are not active participants. The location information is optional and may be as specific as GPS coordinates or as general as a country, or even just a timezone. Following from this, communities were defined by country. Data mining techniques will be used to automatically assign users to countries.

Ideally, we would want the following statistics: Average Degree, Clustering Coefficient, Average shortest Path length. However, this requires information on the specific links between users. However, this requires more time and resources, so this data was not collected for this report. This is not a trivial point, however, because users can follow people in other countries.

Next, data on the linguistic variance is needed. As I showed in a recent post, estimating the amount of bilingualism is difficult. The best source of information is Ethnologue, but numbers of speakers are underestimated, probably due to inadequate data for small linguistic communities. I decided to use two measures of bilingualism: The number of languages spoken in a country and the percentage of the population of a country that speak the majority language. A country's nominal per-capita GDP and number of internet users is also taken into account.

Data was collected for about 31,000 users, about 25,000 of which was usable (there have been databases of up to 2.7 million users with over a billion connections between them). Although Twitter allows very many followers, for practical purposes I filtered out users with over 1,000 followers or following over 1,000 other users. This left data for 17,444 users ffrom 119 countries.

Initial results suggest a negative correlation between the indegree for each country and the percentage of the country's population who speak the majority language (using log indegree, t = 1.88, df = 117, p = 0.06).

Using a linear regression, the indegree and outdegree are significant predictors of linguistic variation, even when the effects of population size and access to the internet are partialled out (R-squared = 0.19, F(4,17439) = 1063, p <0.01; t =" -2.04," p =" 0.04;" t ="2.54," p ="0.01). This was based on data for 17,444 users ffrom 119 countries. Statistics for countries were taken from CIA factbook, 2010. The analysis revealed a negative correlation between linguistic variance and indegree, but a positive correlation between linguistic variance and outdegree. The same qualitative results were found by using the number of languages spoken in a country. However, there is a positive correlation between the number of languages and both indegree and outdegree. I'm not sure how to interpret this yet, or whether any of it makes any sense. In the meantime, here's a pretty uninformative map of the world, coloured by average number of Twitter friends. Darker countries have users with a higher average number of friends.


Lupyan G, & Dale R (2010). Language structure is partly determined by social structure. PloS one, 5 (1) PMID: 20098492
Kirby S, Dowman M, & Griffiths TL (2007). Innateness and culture in the evolution of language. Proceedings of the National Academy of Sciences of the United States of America, 104 (12), 5241-5 PMID: 17360393

How many words for Red? Part 3

This week I've been writing about a bin-packing approach to the bilingual lexicon (here and here). Here's why:

The mutual exclusivity bias is a default approach infants have to learning words which assumes that every object has a single, unique name. However, some studies show that bilingual infants do not follow this bias. The question I'm researching is why assume different things about the world if you're hearing two languages instead of one? Certainly, bilingual children hear more synonyms, but monolinguals also hear many words for the same objects.

The literature has not produced clear-cut results (see my posts here and here), so I've been using a simple model to try and organise my ideas. The model is based on the Categorisation game, where a population of agents try to agree on words for colours. That is, a speaker is presented with a scene of several colours and refers to one of them. A listener must decide which colour the speaker is talking about. Agents begin with no categories and no words, but then divide the colour spectrum into categories and associate words with them, based on verbal interactions. The algorithm is reproduced here.

The algorithm makes some assumptions about mutual exclusivity. The first (what I call Heuristic A) is that, when you see two objects within the same category (e.g. two shades of red), you should divide that category so that there is only one object in each, then assign to each a new unique name. That is, assume that different objects have different names. The second (Heuristic B) is that, when you communicate successfully, delete all other names associated with the category.

These two heuristics limit bilingualism and introduce a mutual exclusivity bias. I ran the categorisation game model without these heuristics to see what would happen. Below are the results of two runs - one with Heuristic A (Black), and one without (Red) (10 runs each, 25 agents, a maximum of 100 perceptual categories, 20,000 rounds). Measures include the number of perceptual categories (both rise and plateau at the same rate), communicative success rate (both similar), the average number of names an agent has, the bin packing depth (bpDepth), bin packing wastage (bpE) and the amount of lexical overlap (overlap function from Baronchelli, Gong, Puglisi and Loreto, 2010).

Removing this heuristic has some interesting consequences for the model. Firstly, the average communicative success is unaffected by removing heuristic A. The number of perceptual categories also increases to the maximum in the same timescale. However, removing the heuristic leads to agents which are more memory-efficient in terms of the number of labels they know, and the efficiency of those names to describe the meaning space. That is, the bin packing metric suggests fewer synonyms and a more efficient coverage of the meaning space. In fact, agents without heuristic A were near optimal in their bin packing.

I suggest that removing heuristic A (each different object has a different name) changes the demands on memory in such a way to favour agents that have several complete descriptions of the meaning space. Dropping heuristic A also reduces the number of lexical items that are stored.

Children exposed to two languages have extra demands on lexical memory. The above analysis suggests that it's a good idea for these children to drop heuristic A in order to save storage space. That is, if you're bilingual, you shouldn't assume that every object has a different name. Indeed, this predicts some of the findings in the experimental literature (i.e. that bilinguals do not apply mutual exclusivity).

However, part of the problem is that this model is a model of emergent structure in labelling perception, it's not a model of acquisition. The method of splitting a perceptual space into categories is also possibly not realistic.

I've just come back from a talk by Kenny Smith, who's been running experiments into Mutual Exclusivity. He trained participants to associate novel words with novel objects, with some participants getting more synonymy than others (objects may have two associated words). After this, participants did a mutual exclusivity task - they were shown two objects, one from the training set and one new object and were asked which one was associated with a novel word. The degree that the participants adhered to mutual exclusivity was proportional to the amount of synonymy they had experienced.

Therefore, it seems that deciding to drop the mutual exclusivity bias may occur on-line. It remains to be seen whether the same results are obtained for children.

How many words for Red?

Just how different are languages in the way they label colours? Since Berlin & Kay's 1969 study of colour terms in many languages, the debate over cross-linguistic similarities has raged. Interestingly, most of the subjects in Berlin & Kay's experiments were bilingual, but they didn't think that other languages could influence the results of individuals.

In the last few years, Dr Panos Athanasopoulos at the ESRC Centre for Research on Bilingualism, Bangor, Wales, has been investigating colour perception in Bilinguals. In 2009, Dr. Athanasopoulos studied Greek-English bilinguals. Greek makes a distinction between dark blue ble and light blue ghalazio. Results suggested that bilinguals' perceptions shift towards those of native speakers of their second language. The study is set to be extended into Japanese this year by two forthcoming publications (in Bilingualism: Language and Cognition and Language and Bilingual Cognition).

Last week I was thinking about models of colour terms and bilinguals. What do we mean when we say someone is 'bilingual'? On a syntactic level, this may be a bit easier to answer, but on the lexical level (where I am at the moment) is a bit more difficult. For example, what's the difference between an having two 'languages', and having one 'language', but many words for the same category?

My current suggestion is that bilinguals differ from monolinguals because they have more sets of categories that span their entire perceptual field. What does that mean? Imagine the colour spectrum (red -> yellow -> green -> blue). A monolingual will be able to label any point in the spectrum with one word, and some points with more than one. For instance, a certain red may be 'red' and 'crimson'. However, a bilingual will be able to label any point with more than one word. That is, the spread of their synonyms will be more even.

Here's an illustration: Below is how a monolingual might break up the colour spectrum. It has several categories in its memory (the colour 'E' spans from red to yellow, 'C' covers greens and 'A' covers a specific kind of green, etc.):


The bottom of the image is labelled 'bin packing'. This represents the results of the bin packing algorithm, which tries to fit the categories into the smallest space possible. The monolingual above has lots of wasted space (striped areas) because it has lots of synonyms for a few categories.

The next illustration is of a bilingual, but note I haven't specified which 'language' a category belongs to. They have synonyms too, but they pack much more efficiently. In fact the speaker below has two options with which to describe any colour. In contrast, the monolingual has only one complete system.

Ok, these examples are set up. But I still predict that bin packing of bilingual categories will be more efficient than for monolingual categories. I'll use this metric to examine the results of a model in an upcoming post.

However, this metric assumes that colour categories within a language are unevenly dispersed. That is, speakers know many words for some colours, but not others. Let's put this to the test. The problem with doing so is that most colour category experiments involve getting people to assign colours to labels, meaning that they can't declare two labels for the same colour. So, I went to the Wikipedia List of Colours page which lists details of all colours mentioned in technical articles on colour. These are mainly standardised names for use in HTML, which is a problem for our current analysis, but let's see what happens.

The names for colours in English and other langauges were gathered along with the Hue angle (i.e. position in the spectrum). Below is the histogram of the number of colour terms in different portions of the spectrum (coloured by average colour of the colour terms, taking saturation and brightness into account) for English, followed by the histograms for some other languages:

First, the histogram for English is certainly not even. There are more names for reds and blues than for greens. This works intuitively - how many types of red can you think of in comparison to types of green?

Let's look cross-linguistically: First of all, the histograms are not identical - the Wikipedia pages are not just translations. Second, they all seem to have lots of names for reds. This may be an artefact of the circular meaning space (from 0 degrees to 360 degrees), but would not entierly explain the imbalances. Let's do an ANOVA of the number of colour terms per section of the spectrum (splitting into 10 even sections) by section position and language.

Colour names were not evenly distributed within languages (F(1,64)=11.35, p<0.01), but were significantly differently distributed across languages (F(7,64)=6.31, p<0.001). Having said this, I'm not completely sure of my stats here.

However, if the analysis is correct, then bilinguals should have a better packing efficiency than monolinguals because acquiring a whole extra 'language' is more likely to normalise the distribution rather than increase the skewness. This may be a useful metric in the analysis of feature-level models of bilingualism.

Linguistic Leniancy

I was once on holiday with a motley crew of tourists when a guy called Dave decided to show off his linguistic skills. He'd lived in China for a few months, and picked up some Mandarin, which he proceeded to flaunt in front two girls from Hong-Kong. However, no matter how hard he tried, the girls had no idea what Dave was saying. After he explained in English what he was trying to convey, the girls did recognise the traces of meaning. He was partly faulty on tone (notoriously difficult for foreigners) and partly on pronunciation.

Dave was very disappointed and a bit embarrassed, but also confused. How had he managed to order all those meals, to greet all those people, to have all those conversations, if he couldn't actually be understood? One possible answer is, instead of Dave learning Chinese, the Chinese had learned Dave. That is, after a few interactions and a lot of contextual help, they had understood that a particular incoherent sound meant that he wanted some noodles/was asking the time/was saying hello.

Recently I've been thinking about what happens when two populations with different languages mix. Several studies have considered this using computational models. Several suggest that one language usually takes over, and that bilingualism is not stable. What the Dave's example suggests is that speakers are extremely forgiving when dealing with people who do not speak their language, especially when gesture and context can make most meanings clear. It's also a good example of the fact that people can communicate despite large discrepancies in their mental representations.

Cymraeg Clir

A scheme at Bangor University offers translation services in Welsh. But this one doesn't translate into English. Instead your documents come back in Cymraeg Clir (Clear Welsh). Ok, so it's more of an editorial service really, but there are some interesting features in its guidelines (The document is titled "Ysgrifennu'n Glir" (Writing Clearly), even though it recommends using 'sgrifenny' instead of 'ysgrifennu'):

Move the subject closer to the start of the sentence by using active verbs, rather than passive. This is general advice for any language, but there are Welsh-specific ones too:

Use verbs instead of nouns. This is slightly confusing at first glance, but refers to the use of phrases like 'tree management' rather than the simpler 'managing trees'. The incursion of such nouns is put down to the influence of English.

Use the 'long form' of the verb (uninflected) to avoid tense ambiguity.

Interestingly, the use of acronyms is discouraged, partly for ease of reading but also because acronyms can be misleading, or even difficult to construct, due to initial consonant mutation (the changing of the first consonant in a word in some syntactic contexts, e.g. 'cat' in welsh is cath, but 'his cat' is ei gath). However, established acronyms and English acronyms along with full Welsh translations can be used. Furthermore, acronyms themselves cannot be mutated.

Mutation is also to be avoided in bullet points by inserting e.g. 'the following' before them (all contiguous nouns after a mutation context are elligable for mutation). Interestingly, foreign place names should not be mutated either.

Users are reminded that mutation applies by agreement, not just in immediate contexts, so in the following sentence, since 'neges' is a feminine noun, the verb 'teipio' (to type) must be mutated using 'aspirate' mutation:

Dyma’r neges y mae’n rhaid i mi ei theipio
Here the message it is must to me it type
This is the message that I must type

Whenever rules like this need to be explained in editorial documents, I wonder whether Welsh mutation has just gone too far.

Contracting the verb 'is' (e.g. 'the building's insured') is recommended. However, the Welsh 'yn' (is) can also mean 'within', in which case it should not be contracted. Instead of explaining the secondary meaning in Welsh, however, the guideline uses English:

"heblaw pan fo’r ‘yn’ yn golygu in yn y Saesneg"
(exept when the 'in' means in in English)

Never use the ampersand (&) when writing in Welsh! This makes sense, since it's a contraction of a Latin phrase anyway.

There is also advice on punctuation, including the use of hyphens to separate double letters, avoiding the possibility of triple and quadruple ls (e.g. alllifo).

Mutual Exlcusivity Diagrams

Lately, I've been reading about experiments on Mutual Exclusivity. I've found it useful to draw up some diagrams to illustrate each. I've posted some of them below. Each diagram represents entities in the Singal, Concept and Environment domains. Circles represent entities (signals, concepts or actual objects). Lines between circles represent a link between them. For instance the word "Dog" would be linked with a concept "Dog" which could be linked to an actual instantiation of a dog in the environment (as long as the observer had learnt these). Dotted circles indicate a novel entity which the observer has not seen before. Dashed lines indicate links trained by the experimenter.

Au & Glusman (1990):
Byers-Heinlein & Werker (2009):

Jaswal & Hansen (2006):


Grassman & Tomasello (2010) - a reaction to Jaswal & Hansen above:

Healey & Scarabela (2009):


Au TK, & Glusman M (1990). The principle of mutual exclusivity in word learning: to honor or not to honor? Child development, 61 (5), 1474-90 PMID: 2245739

Byers-Heinlein, K., & Werker, J. (2009). Monolingual, bilingual, trilingual: infants' language experience influences the development of a word-learning heuristic Developmental Science, 12 (5), 815-823 DOI: 10.1111/j.1467-7687.2009.00902.x

Jaswal VK, & Hansen MB (2006). Learning words: children disregard some pragmatic information that conflicts with mutual exclusivity. Developmental science, 9 (2), 158-65 PMID: 16472316

Grassmann, S., & Tomasello, M. (2010). Young children follow pointing over words in interpreting acts of reference Developmental Science, 13 (1), 252-263 DOI: 10.1111/j.1467-7687.2009.00871.x

Healey, E. and Scarabela, B. (2009). Are children willing to accept two labels for one object? Proceedings of the Child Language Seminar. University of Reading.