Non-linguistic pattern learning uses distinct implicit and explicit processes, which differ in behavioural signatures, inductive biases and proposed model architectures. This study asked whether both processes are available in phonotactic learning in the lab. Five Internet experiments collected generalisation, learning curves, response times and detailed debriefings from 671 valid participants. Implicit and explicit learners were found in all conditions and experiments. Objective measures of implicit vs. explicit learning were correlated with introspective self-report. Participants spontaneously discovered and named phonetic features. These findings contradict the common (usually tacit) assumption that ‘artificial-language’ participants learn only implicitly. Learning mode also affected inductive bias: Implicit learning improved performance on family-resemblance patterns relative to biconditionals (if-and-only-if, exclusive-or) in two experiments. The direction of this effect is unexpected under many current theories of how implicit and explicit concept learning differ, and is consistent with models of explicit learning which take pattern-irrelevant features into account.

Thomas Reid distinguished between natural and artificial language and argued that natural language has a very specific sort of priority over artificial language. This paper critically interprets Reid's discussion, extracts a Reidian explanatory argument for the priority of natural language, and places Reid's thought in the broad tradition of Cartesian linguistics.

Artificial linguistic systems can offer researchers test tube-like models of second language (L2) acquisition through which specific questions can be examined under tightly controlled conditions. This paper examines what research with artificial linguistic systems has revealed about the neural mechanisms involved in L2 grammar learning. It first considers the validity of meaningful and non-meaningful artificial linguistic systems. Then it contextualizes and synthesizes neural artificial linguistic system research related to questions about age of exposure to the L2, type of exposure, and online L2 learning mechanisms. Overall, using artificial linguistic systems seems to be an effective and productive way of developing knowledge about L2 neural processes and correlates. With further validation, artificial linguistic system paradigms may prove an important tool more generally in understanding how individuals learn new linguistic systems as they become bilingual.

This article reviews work that has employed artificial languages to investigate the learning and processing of additional language grammar in bilinguals, with a focus on morphosyntactic processing in sentence contexts. The article first discusses research that has utilized artificial languages to elucidate two central issues in research on bilingual third language learning and processing: the role of prior language-learning experience and cross-linguistic transfer from the native and second languages to the third. Then, research that has compared bilingual third language to monolingual second language grammar processing is discussed, with specific consideration of hypothesized bilingual advantages at language learning. Finally, future directions in artificial language learning research on bilingual morphosyntactic processing are considered.

This study investigates how the fine-grained phonetic realization of tonal cues impacts speech segmentation when the cues signal the same word boundary in the native and unfamiliar languages but do so differently. Korean listeners use the phrase-final high (H) tone and the phrase-initial low (L) tone to segment speech into words (Kim, Broersma, & Cho, 2012; Kim & Cho, 2009), but it is unclear how the alignment of the phrase-final H tone and the scaling of the phrase-initial L tone modulate their speech segmentation. Korean listeners completed three artificial-language (AL) tasks (within-subject): (a) one AL without tonal cues; (b) one AL with later-aligned phrase-final H cues (non-Korean-like); and (c) one AL with earlier-aligned phrase-final H cues (Korean-like). Three groups of Korean listeners heard (b) and (c) in three phrase-initial L scaling conditions (between-subject): high (non-Korean-like), mid (non-Korean-like), or low (Korean-like). Korean listeners’ segmentation improved as the L tone was lowered, and (b) enhanced segmentation more than (c) in the high- and mid-scaling conditions. We propose that Korean listeners tune in to low-level cues (the greater H-to-L slope in [b]) that conform to the Korean intonational grammar when the phrase-initial L tone is not canonical phonologically.