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A significant amount of research in artificial grammar learning and especiallychunk learning

has shed light on many aspects of language learning processes (Ellis, 2015). This might seem somewhat odd as the statistical regularities inherent in the training patterns are void of any phonetic, morphological or syntactic information,¹³ unlike natural languages. To see how the above results can relate to language acquisition, we need to go beyond the non-

¹³The finite-state grammar used in Fig. 1.1 implies the existence of ‘syntactic’ rules in the presented strings. Although an fsg can provide an adequate approximation of language on many Natural Language Processing (nlp) tasks (Kaplan, 2003), it cannot be considered an accurate description of any human language (Chomsky, 1957).

sensical patterns the participants come in contact with and look at them assequentially related combinations of strings. The idea that language is the sequential concatenation of strings is quite old in linguistics (de Saussure, 1916) and although debated heavily by Chomsky (1957) has had a profound influence on cognitive psychology and especially studies on speech segmentation (Aslin, Saffran & Newport, 1998; Saffran, Aslin & Newport, 1996), learning of phonotactic constraints (Dell, Reed, Adams & Meyer, 2000; Warker & Dell, 2006) or orthographic regularities (Pacton, Perruchet, Fayol & Cleeremans, 2001).

Consider, for example, the study by Durrant & Doherty (2010); if humans are endowed with a chunking mechanism such as the ggj2006 which can span beyond the boundaries of a single word, then high-frequency collocations will be assigned a higher probability or even considered as a single chunk. Durrant & Doherty (2010) presented participants with low-frequency collocations (e.g., ‘famous saying’), moderate-frequency (e.g., ‘greater concern’), high-frequency (e.g., ‘mental picture’) and psychologically associated collocations (e.g., ‘card game’). For the computational models presented above, these collocations would maximise the probability of appearance of the second word (i.e.,P(game∣card)) but do not occur as

much so as to be considered a single chunk (i.e., ‘card_game’). Regarding the behavioural results, Durrant & Doherty (2010) indeed found that for the last two conditions there was significant priming for the second word in the collocation indicating that the participants were sensitive to the transitional probabilities from the first to the second word (see also, §5.2). The low probability cases, on the other hand, there was no priming on the second word, probably because the first word was not as predictive. In other words, if we construct a probability distribution of the words that can follow the first word in the collocation, in the low-frequency cases this would be more uniform, whereas in the high-frequency cases the probability mass would be placed on a single word.

The computational models presented above (especially the ggj2006) make the assumption that frequent enough collocations would receive high-transitional probabilities (cf. (1.1)) butveryfrequent collocations would merge into a single chunk. In a collocational priming experiment as in Durrant & Doherty (2010) this would slow down the reaction to the second word as it would be harder to recognise it as a component part. Indeed, Kapatsinski & Radicke (2009) performed an experiment where the participants were instructed to monitor the word

upin phrases such asgive uporkeep up. Priming effects were correlated with the transitional probability for frequent collocations, whereas for thehighest-frequency collocations (e.g., set up) there was a slowdown as predicted by the above computational models. Presumably, the slowdown happens because collocations such as ‘set up’ have merged through frequent usage into a single chunk (i.e., ‘set_up’) which participants have to segment and then parse its component parts separately.

Learning of frequencies and transitional probabilities are two prime examples of implicit learning in natural languages. Acquiring these association statistics is an unconscious process which happens only through exposure to ‘raw’ linguistic input by gradually changing the synaptic connections in the brain. Indeed, by now we have evidence from developmental psychology that the speed of word recognition at infancy can be a strong predictor of linguistic abilities during late childhood (Marchman & Fernald, 2008). Further to that, a substantial amount of theoretical work has linked frequency and statistical based effects to language ac- quisition (Bod, Hay & Jannedy, 2001; Bybee & Hopper, 2001; Diessel, 2007, for comprehensive reviews).

Ellis (2005) reviewing a bulk of evidence on l2 form-meaning connections in relation to implicit and explicit learning highlights a significant interaction between the two modes of learning. Explicit learning, such as classroom instruction, can be used to guide the focus of language learners to certain form-meaning constructions. Once this happens, however, unconscious, implicit learning mechanisms constantly update the frequencies and probabilities of this mapping to facilitate subsequent processing and learning. Indeed, there is abundant literature that l2 learners also utilise frequency distributions and co-occurrence statistics in a similar manner to native speakers when processing formulaic phrases in their second language (e.g., Conklin & Schmitt, 2007; Jiang & Nekrasova, 2007).

Despite the differences between Artificial Grammar Learning (agl) and natural language acquisition, we see that the former provides a reasonable abstraction on which we can study the micro-processes that guide the latter. Indeed, the agl studies along with the idea that we usechunkingas a fast and efficient way to retain in memory linguistic information have formed the basis of a recent theory put forward by Christiansen & Chater (2016a) (also in Christiansen & Chater, 2016b). According to this, learning a language means learning to efficiently process the continual deluge of linguistic input. The solution in this theory is given by the assumption that the mind constantly chunks its incoming input. Although the frameworks differ, this underlying idea thatlanguage acquisition is nothing more than learning to process(Christiansen & Chater, 2016a, p. 114) is inherent in the connectionist framework presented above.