According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology—the distribution of linguistic patterns across the world's languages—and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapting techniques from computational approaches to statistical learning, we develop a Bayesian model of cognitive biases and show that it accounts for the detailed pattern of results of artificial (...) grammar experiments on noun-phrase word order (Culbertson, Smolensky, & Legendre, 2012). Our proposal has several novel properties that distinguish it from prior work in the domains of linguistic theory, computational cognitive science, and machine learning. This study illustrates how ideas from these domains can be synthesized into a model of language learning in which biases range in strength from hard (absolute) to soft (statistical), and in which language-specific and domain-general biases combine to account for data from the macro-level scale of typological distribution to the micro-level scale of learning by individuals. (shrink)

Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones because long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram (...) models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant-harmony pattern in which the first consonant of a five-syllable word was [s] or [∫] (“sh”) and triggered a suffix that was either [-su] or [-∫u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance. (shrink)

Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a (...) label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference. (shrink)

Cognitive Science, Volume 46, Issue 3, March 2022.

Cognitive Science, Volume 46, Issue 2, February 2022.

One of the most common ways of morphological marking is affixation, morphemes are classified according to their position. In languages with affixal morphology, suffixes and prefixes are the most common types of affixes. Despite several proposals, it has been impossible to identify solid generalisations about the behaviour of prefixes, in opposition to suffixes. This article argues that the reason is that our traditional definitions of suffix and prefix are based on pre-theoretical, surface criteria that have been given up in other (...) areas of linguistics: defining a morpheme as a prefix does not tell us anything about its grammatical nature, as that label does not take into consideration the structural configuration underlying the morpheme. Once the structural configuration is taken into account, solid generalisations begin to emerge. The article illustrates the advantages of this approach through a study of the interaction between vowel harmony and affixes. (shrink)

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language learning experiment, (...) we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non‐confusable. Regarding levels of abstraction, we find that premarking of super‐ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation‐based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre‐ and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system. (shrink)

Across languages of the world, some grammatical patterns have been argued to be more common than expected by chance. These are sometimes referred to as (statistical) language universals. One such universal is the correlation between constituent order freedom and the presence of a case system in a language. Here, we explore whether this correlation can be explained by a bias to balance production effort and informativity of cues to grammatical function. Two groups of learners were presented with miniature artificial languages (...) containing optional case marking and either flexible or fixed constituent order. Learners of the flexible order language used case marking significantly more often. This result parallels the typological correlation between constituent order flexibility and the presence of case marking in a language and provides a possible explanation for the historical development of Old English to Modern English, from flexible constituent order with case marking to relatively fixed order without case marking. In addition, learners of the flexible order language conditioned case marking on constituent order, using more case marking with the cross‐linguistically less frequent order, again mirroring typological data. These results suggest that some cross‐linguistic generalizations originate in functionally motivated biases operating during language learning. (shrink)

Across languages of the world, some grammatical patterns have been argued to be more common than expected by chance. These are sometimes referred to as language universals. One such universal is the correlation between constituent order freedom and the presence of a case system in a language. Here, we explore whether this correlation can be explained by a bias to balance production effort and informativity of cues to grammatical function. Two groups of learners were presented with miniature artificial languages containing (...) optional case marking and either flexible or fixed constituent order. Learners of the flexible order language used case marking significantly more often. This result parallels the typological correlation between constituent order flexibility and the presence of case marking in a language and provides a possible explanation for the historical development of Old English to Modern English, from flexible constituent order with case marking to relatively fixed order without case marking. In addition, learners of the flexible order language conditioned case marking on constituent order, using more case marking with the cross-linguistically less frequent order, again mirroring typological data. These results suggest that some cross-linguistic generalizations originate in functionally motivated biases operating during language learning. (shrink)

Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross‐situational learning paradigm to test whether English speakers were able to use co‐occurrences to learn word‐to‐object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership (...) than English, were able to learn words and form object categories when trained with the same type of structures. The results indicate that both groups of learners successfully extracted multiple levels of co‐occurrence and used them to learn words and object categories simultaneously. However, marked individual differences in performance were also found, suggesting possible interference and competition in processing the two concurrent streams of regularities. (shrink)

Psychologists have used experimental methods to study language for more than a century. However, only with the recent availability of large-scale linguistic databases has a more complete picture begun to emerge of how language is actually used, and what information is available as input to language acquisition. Analyses of such “big data” have resulted in reappraisals of key assumptions about the nature of language. As an example, we focus on corpus-based research that has shed new light on the arbitrariness of (...) the sign: the longstanding assumption that the relationship between the sound of a word and its meaning is arbitrary. The results reveal a systematic relationship between the sound of a word and its meaning, which is stronger for early acquired words. Moreover, the analyses further uncover a systematic relationship between words and their lexical categories—nouns and verbs sound differently from each other—affecting how we learn new words and use them in sentences. Together, these results point to a division of labor between arbitrariness and systematicity in sound-meaning mappings. We conclude by arguing in favor of including “big data” analyses into the language scientist's methodological toolbox. (shrink)

Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error‐driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of categories better than learners exposed to input where labels preceded objects. We sought to replicate this finding in two online experiments employing the same tests used originally: A four pictures test (match a label (...) to one of four pictures) and a four labels test (match a picture to one of four labels). In our study, only findings from the four pictures test were consistent with the original result. Additionally, the effect sizes observed were smaller, and participants over‐generalized high‐frequency category labels more than in the original study. We suggest that although Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) feature‐label order predictions were derived from error‐driven learning, they failed to consider that this mechanism also predicts that performance in any training paradigm must inevitably be influenced by participant prior experience. We consider our findings in light of these factors, and discuss implications for the generalizability and replication of training studies. (shrink)