Using variability to guide dimensional weighting: associative mechanisms in early word learning

At 14 months, children appear to struggle to apply their fairly well-developed speech perception abilities to learning similar sounding words (e.g., bih/dih; Stager & Werker, 1997). However, variability in nonphonetic aspects of the training stimuli seems to aid word learning at this age. Extant theories of early word learning cannot account for this benefit of variability. We offer a simple explanation for this range of effects based on associative learning. Simulations suggest that if infants encode both noncontrastive information (e.g., cues to speaker voice) and meaningful linguistic cues (e.g., place of articulation or voicing), then associative learning mechanisms predict these variability effects in early word learning. Crucially, this means that despite the importance of task variables in predicting performance, this body of work shows that phonological categories are still developing at this age, and that the structure of noninformative cues has critical influences on word learning abilities.     

Prominence vs. aboutness in sequencing: A functional distinction within the left inferior frontal gyrus

Prior research on the neural bases of syntactic comprehension suggests that activation in the left inferior frontal gyrus (lIFG) correlates with the processing of word order variations. However, there are inconsistencies with respect to the specific subregion within the IFG that is implicated by these findings: the pars opercularis or the pars triangularis. Here, we examined the hypothesis that the dissociation between pars opercularis and pars triangularis activation may reflect functional differences between clause-medial and clause-initial word order permutations, respectively. To this end, we directly compared clause-medial and clause-initial object-before-subject orders in German in a within-participants, event-related fMRI design. Our results showed increased activation for object-initial sentences in a bilateral network of frontal, temporal and subcortical regions. Within the lIFG, posterior and inferior subregions showed only a main effect of word order, whereas more anterior and superior subregions showed effects of word order and sentence type, with higher activation for sentences with an argument in the clause-initial position. These findings are interpreted as evidence for a functional gradation of sequence processing within the left IFG: posterior subportions correlate with argument prominence-based (local) aspects of sequencing, while anterior subportions correlate with aboutness-based aspects of sequencing, which are crucial in linking the current sentence to the wider discourse. This proposal appears compatible with more general hypotheses about information processing gradients in prefrontal cortex (Koechlin & Summerfield, 2007).     

The effect of sonority on word segmentation: evidence for the use of a phonological universal

It has been well documented how language-specific cues may be used for word segmentation. Here, we investigate what role a language-independent phonological universal, the sonority sequencing principle (SSP), may also play. Participants were presented with an unsegmented speech stream with non-English word onsets that juxtaposed adherence to the SSP with transitional probabilities. Participants favored using the SSP in assessing word-hood, suggesting that the SSP represents a potentially powerful cue for word segmentation. To ensure the SSP influenced the segmentation process (i.e., during learning), we presented two additional groups of participants with either (a) no exposure to the stimuli prior to testing or (b) the same stimuli with pauses marking word breaks. The SSP did not influence test performance in either case, suggesting that the SSP is important for word segmentation during the learning process itself. Moreover, the fact that SSP-independent segmentation of the stimulus occurred (in the latter control condition) suggests that universals are best understood as biases rather than immutable constraints on learning.     

Exploring the robustness of cross-situational learning under Zipfian distributions

Cross-situational learning has recently gained attention as a plausible candidate for the mechanism that underlies the learning of word-meaning mappings. In a recent study, Blythe and colleagues have studied how many trials are theoretically required to learn a human-sized lexicon using cross-situational learning. They show that the level of referential uncertainty exposed to learners could be relatively large. However, one of the assumptions they made in designing their mathematical model is questionable. Although they rightfully assumed that words are distributed according to Zipf's law, they applied a uniform distribution of meanings. In this article, Zipf's law is also applied to the distribution of meanings, and it is shown that under this condition, cross-situational learning can only be plausible when referential uncertainty is sufficiently small. It is concluded that cross-situational learning is a plausible learning mechanism but needs to be guided by heuristics that aid word learners with reducing referential uncertainty.     

Spreading Activation in an Attractor Network With Latching Dynamics: Automatic Semantic Priming Revisited

Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assume a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations, we demonstrated how findings that challenged attractor‐based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments.     

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment ( Culbertson, Smolensky, & Legendre, 2012 ) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals.     

Exclusion constraints facilitate statistical word learning

The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech-object pairings with detailed sensitivity in stochastic learning environments (Vouloumanos, 2008). Here, we replicate this statistical work with nonspeech sounds and compare the results with the previous speech studies to examine whether exclusion constraints contribute equally to the statistical learning of speech-object and nonspeech-object associations. In environments in which performance could benefit from exclusion, we find a learning advantage for speech over nonspeech, revealing an interaction between statistical and exclusion processes in associative word learning.     

Cognitive control and word recognition speed influence the Stroop effect in bilinguals

Bilinguals have been shown to be less susceptible to Stroop interference in their first language than monolinguals, though the cause is currently being debated. In two experiments, we explored how cognitive control and word recognition contribute to the Stroop effect by contrasting cognitive control (via a Simon arrow task), word recognition speed (via a Chinese/English word recognition task) and Stroop susceptibility (via a verbal Stroop task) between proficient and non‐proficient Chinese–English bilinguals. Compared to non‐proficient bilinguals, proficient bilinguals showed better cognitive control at inhibiting irrelevant information, and they were slower at recognising Chinese words but quicker at recognising English words. Critically, we also showed that proficient bilinguals showed a smaller Stroop effect than non‐proficient bilinguals in Chinese but a comparable Stroop effect as non‐proficient bilinguals in English. The results cannot be accounted for by cognitive control or word recognition speed alone; instead, they are best accommodated by assuming that cognitive control and word recognition speed jointly determine the Stroop effect. Thus, we conclude that enhanced cognitive control and delayed word recognition combine to reduce Stroop effect in bilinguals as compared to monolinguals.     

Sublexical and Lexical Influences on Gender Assignment in French

Two experiments showed that French native speakers rely on sublexical and lexical cues to allocate gender during word recognition. Sublexical cues were based on whether the word ending was typical for a particular gender rather than neutral with regard to gender. Lexical cues were based on whether the associated definite article was informative (for words beginning with a consonant) or uninformative (for words beginning with a vowel). Classification of single nouns and verification of grammatical combinations of indefinite article and noun led to longer times when both sublexical and lexical cues were uninformative compared with when one or both cues were informative. Verification of ungrammatical combinations of indefinite article and noun yielded separate effects of both cues, though only when monitoring for both semantic and syntactic unacceptability in meaningful phrases did people attend to both cues independently. It was argued that people became more cautious in their gender assignments as task requirements became deeper. If strategic changes as a function of task demands are incorporated, the results are compatible with connectionist models proposing that gender decisions are computed from strength of past associations of the word and gender-specifying elements.     

Word category and verb--argument structure information in the dynamics of parsing

One of the core issues in psycholinguistic research concerns the relationship between word category information and verb-argument structure (e.g. transitivity) information of verbs in the process of sentence parsing. In two experiments (visual versus auditory presentation) using event-related brain potentials (ERPs), we addressed this question by presenting sentences in which the critical word simultaneously realized both a word category and a transitivity violation. ERPs for sentences with both types of violation clustered with the patterns for sentences with a word category violation only, but were different from the patterns elicited by argument structure violations in isolation, since only the latter elicited an N400 ERP component. The finding that an argument structure violation evoked an N400 only if the phrase structure of the respective sentence was correct suggests that a successful integration of the word category information of a verb functionally precedes the application of its argument structure information.