Elucidating an implicit learning network in healthy adults during artificial grammar tasks

Implicit learning is the unconscious extraction of rules governing complex stimuli, measured through experiments such as artificial grammar tasks, and is directly related to natural language learning. While several theories address the underlying framework for implicit learning, few studies have shed light on a consensus neural network involved in implicit learning. The shortterm goal of this thesis is to further elucidate the brain regions involved in implicit learning of linguistic stimuli. The long-range goal of this research program is to understand how implicit learning and the brain regions associated with it relate to language learning and treatment outcomes in individuals with aphasia. A coordinate-based meta-analysis of 25 studies using implicit language learning tasks was completed. Activation likelihood estimate (ALE) results show significant activation in the bilateral inferior frontal gyri, bilateral insula, left supplemental motor area, right precentral gyrus, right middle cingulate, right middle occipital gyrus, and right inferior parietal lobule. The inferior frontal gyrus is discussed as a general rule-processing and error detection mechanism, and other regional activations are discussed related to their involvement in a cognitive control network. Cognitive control may be seen as an underlying mechanism for successful implicit learning and may be clinically relevant as a target for language intervention to scaffold syntax comprehension. IMPLICIT LEARNING NETWORK 1 Elucidating an implicit learning network in healthy adults during artificial grammar tasks CHAPTER I: INTRODUCTION Implicit learning is learning that occurs unconsciously and automatically. Implicit knowledge is not explicitly taught, but is stored in long-term memory and retrieved when appropriate, such as the rules underlying language. Implicit learning is a term first coined by Reber (1967) and is defined as the ability to learn the lawfulness of stimulus sequences to efficiently respond to stimuli without explicit knowledge of the rules or explicit strategy use. The ability to implicitly learn has been shown in artificial grammar or serial reaction time tasks in which an increase in accuracy implies that a participant has implicitly learned the underlying rules of the task. Artificial grammar tasks are composed of two parts: a learning phase and a testing phase. In the learning phase, participants are exposed to stimuli through multiple trials. Stimuli in the learning phase consist of grammatical sentences from which participants will implicitly extract rules about how sentences can be composed. Participants may or may not receive instructions for the task, but rules are never explicitly taught. Following the learning phase, participants enter the testing phase during which they make grammaticality judgments about grammatical and ungrammatical sentences in the artificial language. These sentences consist of grammatical examples heard in the learning phase, random sentences, and sentences that partially follow a rule as a foil. As established by Reber (1967), participants are able to efficiently and accurately judge the grammaticality of sentences created from an artificial grammar while being unaware of the underlying rules or of strategies they may have used. Serial reaction time tasks are a non-linguistic implicit learning task. In serial reaction time tasks, participants respond to an icon that appears in several locations on a screen. The IMPLICIT LEARNING NETWORK 2 participants are required to press a button when the icon appears in a new location and reaction time is recorded. The task consists of random and sequenced blocks, during which the icon moves either following a pattern or randomly. Participants begin with a set of random trials, then sequenced trials, followed by another block of random trials. A reduction in reaction time during the sequenced block, followed by an increase in reaction time for the final random block, indicates that the participants implicitly learned the rules underlying the movement of the icon. While implicit learning is not language-specific, one type of implicit learning called statistical learning, involves hierarchical rule learning which relates directly to language. Statistical learning is the ability to track regularities, or statistical probabilities, in the environment and extract rules from them. Statistical learning is a necessary process for language learning (Saffran, 2001; Saffran, Aslin, & Newport, 1996). While the study of statistical learning of language has focused on infants, adults are also able to listen to unfamiliar languages or artificial grammars and extract regularities, allowing them to make judgements about the grammaticality of an utterance and perform above chance (Bahlmann et al., 2008; Batterink et al., 2015; Lieberman et al., 2004; Petersson et al., 2012; Petersson et al., 2004; Plante et al., 2015; Reber, 1967; Saffran, 2001). What is unique about this type of learning, and implicit learning in general, is that it happens automatically and unconsciously, without specific instructions to learn the grammar or rules. The ability to implicitly learn is utilized in memory intervention for patients with amnesia in the form of errorless learning. In a meta-analysis by Kessels and de Haan (2003), errorless learning, which utilizes minimization of errors to access the implicit learning system through repetition of correct targets, produced a large effect size of 0.87 across 11 studies. A more recent study by Hart et al. (2020) also found success for errorless learning and minimized reliance on IMPLICIT LEARNING NETWORK 3 explicit memory in patients with post-traumatic amnesia. However, there are no known language interventions that employ implicit learning strategies and only one experimental study that has used implicit learning treatment with individuals with aphasia. Schuchard et al. (2017) implemented an implicit sentence comprehension treatment utilizing errorless learning strategies with five adults with agrammatic aphasia. The training took place over five sessions, with each session lasting around 20 minutes. Schuchard et al. (2017) found no significant increases in passive sentence comprehension, and three participants showed decreased comprehension scores from baseline. However, the short duration of treatment and difficulty of the task should be noted as they may have prevented treatment gains. Additionally, other studies show that individuals with non-fluent aphasia can perform above chance on serial reaction time tasks involving nonlinguistic stimuli, and therefore learn implicitly (Schuchard &Thompson, 2014; Schuchard et al., 2017). Moreover, Christiansen et al. (2010) found that seven individuals with agrammatic aphasia were able to learn an artificial grammar with 91% accuracy during a learning phase, but the average test performance was only 51% when ungrammatical stimuli were introduced, which was not significantly above chance. The ability to implicitly learn has not been examined in other forms of aphasia and has only been minimally tested using artificial grammar tasks to measure implicit learning, thus warranting more research. In addition to using implicit learning to promote intervention success, the automaticity involved in implicit learning may also assist in generalization of intervention. Generalization requires information to be extracted from stimuli, encoded in memory, and consolidated for automatic retrieval and use. As generalization is the ultimate goal of speech and language intervention, the focus of this study is on the automaticity underlying implicit learning, and specifically statistical learning. One way to better understand implicit learning is by examining IMPLICIT LEARNING NETWORK 4 the neural correlates of this type of learning and the connections between regions involved in the implicit learning network. Currently, a meta-analysis that clarifies which brain regions are involved in implicit learning does not exist, although two recent meta-analyses have been published examining language learning in adults (Tagarelli et al., 2019) and non-linguistic sequence learning in adults (Janacsek et al., 2020). While Tagarelli et al. (2019) examined artificial grammar learning in adults, their main goal was to look at language learning in general, and not specifically implicit language learning. Additionally, their literature search ended in 2015 and more implicit learning studies have been published since the end of their search. This thesis aims to extend the findings of Tagarelli et al. (2019) by identifying the brain regions involved in implicit learning in healthy adults to serve as a reference for future research.