An important topic in the evolution of language is the kinds of grammars that can be computed by humans and other animals. Fitch and Hauser () approached this question by assessing the ability of different species to learn 2 grammars, (AB)n and An Bn. An Bn was taken to indicate a phrase structure grammar, eliciting a center‐embedded pattern. (AB)n indicates a grammar whose strings entail only local relations between the categories of constituents. F&H's data suggest that humans, but not tamarin (...) monkeys, learn an An Bn grammar, whereas both learn a simpler (AB)n grammar (). In their experiments, the A constituents were syllables pronounced by a female voice, whereas the B constituents were syllables pronounced by a male voice. This study proposes that what characterizes the An Bn exemplars is the distributional regularities of the syllables pronounced by either a male or a female rather than the underlying, more abstract patterns. This article replicates F&H's data and reports new controls using either categories similar to those in F&H or less salient ones. This article shows that distributional regularities explain the data better than grammar learning. Indeed, when familiarized with An Bn exemplars, participants failed to discriminate A3 B2 and A2 B3 from An Bn items, missing the crucial feature that the number of As must equal the number of Bs. Therefore, contrary to F&H, this study concludes that no syntactic rules implementing embedded nonadjacent dependencies were learned in these experiments. The difference between human linguistic abilities and the putative precursors in monkeys deserves further exploration. (shrink)

Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non-adjacent relations. On the surface, these different aspects share few commonalities. Despite this, (...) we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain-general characteristics of memory. (shrink)

Wilson et al. focus on one class of AGL tasks: the cognitively demanding task of detecting non‐adjacent dependencies (NADs) among items. They provide a typology of the different types of NADs in natural languages and in AGL tasks. A range of cues affect NAD learning, ranging from the variability and number of intervening elements to the presence of shared prosodic cues between the dependent items. These cues, important for humans to discover non‐adjacent dependencies, are also found to facilitate NAD learning (...) in several nonhuman animal species. (shrink)

Mueller et al. discuss the role of acoustic cues in detecting language structure more generally. Across languages, there are clear links between acoustic cues and syntactic structure. They show that AGL experiments implementing analogous links demonstrate that prosodic cues, as well as various auditory biases, facilitate the learning of structural rules. Some of these biases, e.g. for auditory grouping, are also present in other species.

Wilson et al. focus on one class of AGL tasks: the cognitively demanding task of detecting non‐adjacent dependencies (NADs) among items. They provide a typology of the different types of NADs in natural languages and in AGL tasks. A range of cues affect NAD learning, ranging from the variability and number of intervening elements to the presence of shared prosodic cues between the dependent items. These cues, important for humans to discover non‐adjacent dependencies, are also found to facilitate NAD learning (...) in several nonhuman animal species. (shrink)

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can (...) be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. (shrink)

Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics (...) influenced learning of both adjacent and non-adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants’ knowledge of the deterministic non-adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition. (shrink)

Some empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional rule-following mechanism in order to induce grammatical regularities. In this article, we present a set of neural network studies demonstrating (...) that a single statistical mechanism can mimic the apparent discovery of structural regularities, beyond the segmentation of speech. We argue that our results undermine one argument for the MOM hypothesis. (shrink)