Relating Neural and Behavioural Measures of Statistical Learning to Children's Reading Abilities

The process of reading is guided by statistical regularities between orthographic and phonological properties embedded in the English language system. Reliance on these probabilistic cues suggest statistical learning (SL) may be an underlying mechanism in reading ability and variance in SL could explain why those with reading impairments struggle to become proficient readers. Previous research has found a relationship between SL and reading ability; however, majority of these studies use offline measures of SL that are sensitive to secondary processes. The current study examined the link between SL and reading ability in 32 children using traditional measures of SL and a relatively new online measure of electroencephalogram neural entrainment for a pure assessment of SL. Participants showed evidence of SL as measured by online and offline methods; however, no relation between SL and reading ability was found. The implications of this study may inform the ways in which children are taught to read such as implementing more explicit compared to implicit reading activities and instructions. STATISTICAL LEARNING AND READING 3 Relating Neural and Behavioural Measures of Statistical Learning to Children’s Reading Ability From the time we are infants, humans are able to learn statistical regularities in their environment and make predictions based off of those patterns (Saffran, Aslin, & Newport, 1996). This powerful learning mechanism, known as statistical learning (SL), has been shown to take place during passive exposure to complex patterns (Yang & Flombaum, 2015). While SL can occur during a variety of learning processes, it is most commonly understood through the context of speech segmentation. For example, when listening to the phrase “what a pretty baby”, English speakers are able to implicitly identify higher transitional probabilities within words (e.g., pretty) and lower transitional probabilities across word boundaries (e.g., ty-ba). Despite the fact that there are no reliable pauses between words in spoken language, utilizing this transitional probability information allows one to distinguish boundaries between words (e.g., “pretty” and “baby”; Romberg & Saffran, 2010). Moreover, previous research has found typically developing (TD) children are not only able to identify transitional probabilities from an artificial speech stream of non-sense words but they can also recognize newly learned non-words when compared to highly similar non-words (Raviv & Arnon, 2018). Notably, this precise ability to segment speech is an important reading skill when decoding words (White, Mattys, & Wiget, 2012). Reading as Statistical Learning When applying SL to the context of reading acquisition, Arcuili (2018) explained that children learn orthographic (letter combinations) to phonemic (sound) mapping regularities, such as identifying a grapheme (single letter) “a” with the specific phoneme /a/. English is a deep orthographical language system meaning each grapheme can correspond to multiple different phonemes compared to a shallow language system (e.g., Spanish, Croatian) which has a one to one correspondence of graphemes to phonemes (Miller, Kargin, & Guldenoglu, 2014). For STATISTICAL LEARNING AND READING 4 example, the grapheme “a” has inconsistent phonemic representations among words like, “case”, “cat”, and “car” resulting in different orthographic combinations that follow different phonetic patterns, or rule exceptions. Arcuili (2018) explained that the patterns within the English language system follow a number of exceptions and while the majority of these rules are taught using explicit instruction within the classroom, a large portion of language learning involves passive exposure and implicit learning. It could be that certain aspects of SL relate to certain areas within the reading process. More specifically, the complex regularities and exceptions involved in orthographic to phonetic mappings suggests individuals with a precise ability to identify these patterns could have stronger underlying SL abilities. Arciuli and Simpson (2012) showed that variability in SL is related to variability in reading for TD participants. They used a visual SL (VSL) paradigm where a stream of 12 aliens were arranged in four groups of three. Each group formed a specific pattern and aliens were presented on a computer screen one at a time in a continuous stream. Following this exposure phase, participants saw two groups of aliens: a group following the pattern from the stream and a group in an unfamiliar pattern. They had to choose the pattern that was more familiar. Accuracy on this task had a significant correlation to participants standardized scores of reading. Notably, the relationship between VSL and reading suggests that SL ability may be domain general mechanism opposed to domain specific as shown with the non-linguistic stimulus in this study. As further support, native English speakers learning Hebrew showed VSL abilities that were correlated with second language reading ability (Frost, Siegelman, Narkiss, & Afek, 2013). In Hebrew there are two forms of spelling: “pointed” where vowels are included and “unpointed” where no vowels are included (Kutscher & Kutscher, 1982). Better performance on the VSL task resulted in higher un-pointed non-word and pointed word reading ability. Together, STATISTICAL LEARNING AND READING 5 these findings highlight the idea that an ability to distinguish non-linguistic patterns aids in reading ability. As noted previously, language provides probabilistic cues to orthographical and phonological mappings (Arciuli, 2018); it could be that SL is an underlying tool used to learn complexities within the reading process. While it is clear that variability in SL is related to variability in reading for TD participants, past research has not yet looked at SL in children with a range of reading abilities. Reading Impairments and Statistical Learning SL abilities have been known to differ significantly between TD participants and those with reading impairments such as developmental language disorder (DLD; also known as Specific Language Impairment) or developmental dyslexia (DD; Evans, Saffran, & Robe-Torres, 2009; Gabay, Thiessen & Holt, 2015). As a general overview, DLD is defined as difficulties or delays in receptive and expressive language that cannot be explained by another disorder or cause (Powers, Brooks, Obeid, Gillespie-Lynch, & Lum, 2016). In comparison, DD is defined as difficulties with correct word recognition and spelling in the presence of normal IQ and sensory abilities (Snowling, 2000). While these are two separate disorders, both have been implicated in deficits during SL tasks (Bishop & Snowling, 2004; Tomblin, Mainela-Arnold, Zhang, 2007; Gabay, Schiff, & Vakil, 2012). Previous work by Evans et al. (2009) found a deficit in auditory SL (ASL) ability for those with DLD. Participants listened to an artificial speech stream consisting of six tri-syllabic non-sense words that were formed so transitional probabilities were higher within words than between words. After the familiarization phase, children distinguished which word was more familiar between a word from the stream and an unfamiliar word. TD participants performed better than children with DLD; however, when presented with a longer familiarization phase, STATISTICAL LEARNING AND READING 6 children in the DLD also showed equal SL ability as reflected on post measure tasks. Moreover, impaired SL in those with DD has also been found during similar ASL experiments (Gabay et al., 2015). Participants with DD performed significantly worse compared to TD controls during an ASL experiment. These results were correlated with reading ability. The deficits in SL for those with DLD is not only limited to ASL but VSL as well (Sigurdardottir, Danielsdottir, Gudmundsdottir, Hjartarson, Thorarinsdottir, & Kristjánsson, 2017). During a VSL paradigm, 12 shapes grouped into two pairs were presented on at a time in a continuous stream on a computer screen. The shape pairs were counter-balanced so that each pair was always seen together but the pairs were presented in a random order. After being familiarized to the shape patterns, TD participants and participants with DLD were asked to choose which pair of shapes was the most familiar from the stream. Participants with DLD identified a significantly smaller quantity of correct pairs than TD participants. The results of these studies go on to suggest that domain general impairments in SL may explain why those with reading impairments struggle to become proficient readers. Neural Entrainment as a Measure of Statistical Learning The majority of research on SL has used behavioural outcomes such as a familiarization phase followed by a behavioural task; these measurements target long term memory and SL ability (Vlach & Sandhofer, 2014). One way of teasing apart these two mechanisms is by introducing online measurements of neural activity to assess SL through neural entrainment. This occurs when the brain naturally synchronizes brain wave frequency to the rhythm of an external stimulus such as visual or speech patterns (Schroeder & Lakatos, 2008;2009). For example, Paller and Batterink (2017) used electroencephalogram (EEG) data to show that participants perceived tri-syllabic words while listening to an artificial speech stream as whole words STATISTICAL LEARNING AND READING 7 compared to individual syllables. They examined this through participants’ ability to neutrally entrainment the word frequencies compared to the syllable frequencies. Participants were also able to learn these novel words by using high and low transitional probabilities. This was further demonstrated by using a Target Detection Task (TDT) where participants were able to predict final syllables in a word faster than the first syllable, indicating learning. Traditional behavioural methods of assessing SL do so after the learning ha