El regreso de la patria: realidad o discurso

Artificial language learning studies offer us a unique window into human language acquisition. However, as with any methodology, these types of studies do have weaknesses that should be acknowledged. This section provides justification for studying distributional learning with artificial language learning experi- ments on adults, while also acknowledging weaknesses of this methodology.

One of the main weaknesses of artificial language learning studies has to do with the unclear sta- tus as to what is being modelled. On one hand, it could be argued that artificial languages are indicative of second language acquisition, since adult participants experience interference from their first language (Schwartz and Sprouse, 1996) and have passed any theorized critical period of language acquisition (Lenneberg, 1967). Even if one assumes that various mechanisms used in language learning function throughout a language learner’s life, it is possible that some mechanisms are stronger at various points in development. For example, Thiessen and Saffran (2003) tested 7-month and 9-month infants in a speech segmentation task. Infants were presented with conflicting cues as to where word boundaries occur. The 9-month old infants used stress to segment speech into words when stress and statistical cues indicated different word boundaries, tending to segment speech into trochaic “words,” while the 7-month old in- fants used statistical cues, tending to segment speech at regions of low transitional probabilities. Thiessen and Saffran suggest that infants rely more heavily on statistical cues early on (at 7 months), and only make use of other cues, such as typical stress patterns, later on (at 9 months). If differences in cue weighting are apparent at 7 months compared to 9 months of age, there must be a number of differences between cue weightings used by adults and those used by infants. On the other hand, one could argue that artificial language learning studies are reflective of first language acquisition, since (although dependent on the particular study’s methodology) artificial language learning studies often do not present learners with the type of explicit instructions that a second language learner might receive (for example, Maye and

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Gerken, 2000; Saffran et al., 1997; Noguchi, 2016; Feldman et al., 2011), which can have an effect on ac- quisition (see Zhang (2013) for an example of the effect of explicit instruction on Chinese tone

acquisition). Some artificial language learning studies draw conclusions about the nature of second lan- guage acquisition (Hayes-Harb, 2007; Escudero et al., 2011), while others draw tentative conclusions about the nature of first language acquisition (Peperkamp et al., 2003; Maye and Gerken, 2001; Noguchi, 2016). A number of adult studies are followed up with a replication study on infants before conclusions regarding first language acquisition are made (e.g. Maye and Gerken (2000) followed by Maye et al. (2002; 2008); Feldman et al. (2011) followed by Feldman et al. (2013)). There is also the very real possi- bility that artificial languages are not indicative of any natural linguistic process (for a discussion of this possibility, see Moreton and Pater, 2012). The ambiguity in what is being modelled in adult artificial lan- guage learning studies is particularly important to keep in mind since the main topic under investigation here, distributional learning, was primarily motivated by observations of language development in infants (Maye and Gerken, 2000), as summarized in the previous section.

With that being said, this dissertation presents a series of artificial language learning experiments conducted on adults. Since no experiments reported here are conducted on infants, this dissertation re- mains agnostic as to whether findings reflect general cognitive mechanisms utilized by both adults and infants in language acquisition, or are only indicative of adult cognition, and will simply refer to “lan- guage learners.” It is still hoped that results can be extended to first language acquisition, since the overall topic under study, distributional learning, has been found in both adult (Maye and Gerken, 2000; Ong et al., 2015; Hayes-Harb, 2007; Maye and Gerken, 2001; Noguchi, 2016) and infant experiments (Maye et al., 2002; Yoshida et al., 2010), and since other phenomena also reliant on statistical tracking have been claimed to occur in both adults and children (Saffran et al., 1997). When available, results from infant studies which appear to corroborate this dissertation’s findings will be mentioned.

34 6. Research Questions and Structure of Dissertation

This dissertation is interested in defining a timeline of early phonological acquisition. Although initially meant as a simple replication study, the data presented in Chapter 3 suggests a necessary distinction be- tween two stages of phonetic category acquisition, a Bias Stage and a Sensitivity Stage. Overall, this dissertation seeks to answer the following question:

(1) What are the stages in early phonological acquisition?

The main goal of this dissertation is to formulate a timeline of phonological acquisition based on experi- mental evidence. In answering this question, this dissertation explores the interaction of distributional learning with various phenomena, indicated below:

(2) How does distributional learning interact with attention? (Chapter 3)

(3) How does distributional learning interact with environmental context? (Chapter 4) (4) How does distributional learning interact with word learning? (Chapter 5)

I argue in this dissertation that phonetic category learning occurs in two stages, a Bias Stage and a Sensi- tivity Stage. This will be supported by a series of experiments, the “A” Experiments, in Chapter 3. Results of the “A” Experiments also indicate an effect of attention on distributional learning. Chapter 4 explores the relationship between phonetic category acquisition and allophony acquisition, and presents experi- mental support for a one-stage model of allophony acquisition (Experiment B), as suggested by Dillon et al. (2013). Finally, this dissertation discusses the gap between phonetic category acquisition and func- tional phonemes that are used to differentiate words in a set of “C” Experiments, presented in Chapter 5. 7. Summary

To summarize, this chapter showed that despite supplemental theories which have been suggested (Feld- man et al., 2013; Thiessen, 2007), distributional learning makes up a large portion of the explanation of how infants come to exhibit language-specific discrimination at such an early age. Experimental support for distributional learning is primarily based on artificial language studies which have been conducted on

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adults; but, importantly, these experiments have also been replicated on infants. This chapter then intro- duced two main concepts from Signal Detection Theory, response bias and sensitivity, and illustrated how these two concepts are independent of one another. Specific guidelines regarding the interpretation of this dissertation’s experimental results were then laid out. Following this, this chapter highlighted seemingly small differences in both methodology and analysis between past distributional learning experiments. The next chapter will further discuss response bias and sensitivity. It will be shown that 1) the seemingly small differences in methodology and analysis of past distributional learning experiments have resulted in the measurement of different things, and 2) past models of distributional learning predict that sensitivity, and not necessarily bias, is directly affected by distributional learning. A set of experiments (the “A” Ex- periments) will be presented which counter these sensitivity models of distributional learning.

36 Chapter 3:

Response Bias and Sensitivity in Distributional Learning, and the Role of Attention

1. Introduction

The original goal of this set of experiments was to determine whether distributional learning, which has been found in in-person experiments, could be replicated through an online platform. In the process of doing so, two main theoretical conclusions were also reached: (1) the distribution of exposure can affect learners’ response bias (also simply “bias”) independently of their sensitivity, and (2) attention plays a role in distributional learning. The first contribution regarding the distinction between response bias and sensitivity in distributional learning is supported with three main “A” Experiments: Experiments A1, A2, and A3. The second theoretical contribution regarding attention in distributional learning will be dis- cussed in two follow-up “Tone” experiments, A2-Tone and A3-Tone. Finally, as all experiments were conducted online using the online participant pool known as Mechanical Turk, this chapter ends with two more contributions which are methodological in nature, by giving suggestions for conducting perceptual experiments online.

Of these four contributions, the primary theoretical contribution of this chapter regards the dis- tinction between response bias and sensitivity. This study argues for a two-stage model of phonetic category acquisition7: a Bias Stage followed by a Sensitivity Stage. This runs contrary to models which base distributional learning in perceptual warping (Guenther and Gjaja, 1996; Boersma et al., 2003), which do not predict a Bias Stage of phonetic category acquisition. Although all experiments conducted

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here were conducted on adults, Section 7 will present evidence that this two-stage model is supported by past infant studies of distributional learning.

Section 2 provides background for this study. Section 3 states this chapter’s main research ques- tions and provides a summary of results from all experiments conducted in this chapter. Sections 4-6 describe the methodology and results of the three main “A” Experiments conducted, A1, A2, and A3. This is followed by a summary and discussion of response bias and sensitivity in distributional learning in Section 7, including possible supporting evidence of a distinction between bias and sensitivity from past distributional learning studies conducted on infants (Yoshida et al., 2010; Maye and Gerken, 2002). Sec- tion 8 presents two “Tone” experiments, Experiment A2-Tone and Experiment A3-Tone. Results of these experiments will be used to argue that attention plays a role in distributional learning in Section 9. Sec- tion 10 provides suggestions for those wishing to conduct perceptual experiments online through platforms such as Mechanical Turk. Section 11 discusses unexpected results involving filler trials and provides possible explanations for these results. Section 12 concludes with a summary of results and an overview of contributions made in this chapter.