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Although syntactic priming has been well studied and is commonly assumed to involve implicit learning, the mechanisms behind this phenomenon are still under debate. Recent studies have suggested that exposure to nonlinguistic statistical patterns may influence language users' relative clause attachment biases, but whether the priming effect comes from implicit learning or explicit experimental task demands has remained unclear. In this study, we tested whether implicit learning of adjacent and nonadjacent sequences occurs in a nonlinguistic task (the serial reaction time task), and if so, whether these implicitly learned dependencies can cause syntactic priming in the linguistic domain. We found no priming effects even though dependencies in the serial reaction time task were successfully learned: learning of nonlinguistic dependencies did not prime relative clause attachment biases when dependency learning was strictly implicit in nature. This work provides novel evidence that implicit knowledge from a domain-general sequencing task alone does not necessarily induce syntactic priming.

Existing models of statistical learning involve computation ofconditional probabilities over discrete, categorical items in asequence. We propose an alternative view that learning occursthrough a process of tracking changes along physicaldimensions from one stimulus to the next within a “perceptualsimilarity space.” To test this alternative, we examined asituation where it is difficult or impossible to label stimuli inreal time, and where the two assumptions lead to conflictinghypotheses. We conducted two experiments in which humanparticipants passively listened to a familiarization sequence offrequency-modulated tones and were then asked to makefamiliarity judgments on a series of test bigrams. Behavioralresults were broadly consistent with a conceptualization oflearning as tracking trajectories through perceptual similarityspace. We also trained a neural network that codes stimuli asvalues along two continuous dimensions to predict the nextstimulus given the current stimulus, and show that it capturedkey features of the human data.

Many grammatical dependencies in natural languageinvolve elements that are not adjacent, such as betweenthe subject and verb in the child always runs. To date,most experiments showing evidence of learning non-adjacent dependencies have used artificial languages inwhich the to-be-learned dependencies are presented inisolation by presenting the minimal sequences thatcontain the dependent elements. However,dependencies in natural language are not typicallyisolated in this way. In this study we exposed learnersto non-adjacent dependencies in long sequences ofwords. We accelerated the speed of presentation andlearners showed evidence for learning of non-adjacentdependencies. The previous pause-based positionalmechanisms for learning of non-adjacent dependencyare challenged.

Although syntactic priming is well studied andcommonly assumed to involve implicit learning, themechanisms behind this phenomenon are still underdebate. We tested whether implicit learning of adjacentand non-adjacent sequences occurs in a non-linguistic,finger sequence task (Serial Reaction Time task), and ifso, whether these implicitly-learned dependencies cancause syntactic priming in the linguistic domain. Wefollowed the logic that exposure to statistical patterns inthe SRT task may influence language users’ relativeclause (RC) attachment biases, and trained participantson SRT sequences with adjacent or non-adjacentdependencies. Participants then wrote completions torelative clause fragments in a situation where theycould opt for adjacent or non-adjacent linguisticstructures. Participants successfully learned the adjacentand non-adjacent dependency implicitly during the SRTtask, but, strikingly, their RC continuations did notexhibit priming effects. Implications for theories ofsyntactic priming and its relations to implicit learningare discussed.

Grammatical dependencies often involve elements thatare not adjacent. However, most experiments in whichnon-adjacent dependencies are learned bracketed thedependent material with pauses, which is not howdependencies appear in natural language. Here wereport successful learning of embedded NAD withoutpause bracketing. Instead, we induce learners tocompute structure in an artificial language by entrainingthem through processing English sentences. We alsofound that learning becomes difficult when grammaticalentrainment causes learners to compute boundaries thatare misaligned with NAD structures. In sum, wedemonstrated that grammatical entrainment can induceboundaries that can carry over to reveal structures innovel language materials, and this effect can be used toinduce learning of non-adjacent dependencies.

Many studies of human sequential pattern learningdemonstrate that learners detect adjacent and non-adjacentdependencies in many kinds of sequences. However, it isoften assumed that the computational mechanisms behindextracting these dependencies are the same. We replicate theseminal finding that adults are capable of learningdependencies between non-adjacent words (Gómez, 2002).When we eliminate the positional information about thestatistical structures by embedding the structure in phrases,learners can no longer learn the dependencies. Our methodsallow us to study the learning mechanisms that are morerepresentative of the patterns in natural languages, and showthat when directly compared, adjacent and non-adjacentdependencies are not equally learnable. We suggest thatlearning non-adjacent dependencies in language involves adifferent computational mechanism from learning adjacentdependencies

The phenomenon of syntactic priming is well studied in theliterature, but the mechanisms behind it are still under debate.In this study, we trained English-speaking participants inartificial language sequences with dependencies that are eitheradjacent or non-adjacent. The participants then wrotecompletions to relative clause (RC) fragments. We found thatparticipants who learn non-adjacent dependencies in theartificial language, exhibit a bias to write high-attachment(non-adjacent) continuations for RCs, when compared toparticipants in a control condition who exhibit low-attachment(adjacent) biases in RCs. The implications for theories ofsyntactic priming and its relations to implicit learning arediscussed.

The structure of natural languages give rise to many dependencies in the linear sequences of words, and within words themselves. Detecting these dependencies is arguably critical for young children in learning the underlying structure of their language. There is considerable evidence that human adults and infants are sensitive to the statistical properties of sequentially adjacent items. However, the conditions under which learners detect nonadjacent dependencies (NADs) appears to be much more limited. This has resulted in proposals that the kinds of learning mechanisms learners deploy in processing adjacent dependencies are fundamentally different from those deployed in learning NADs. Here we challenge this view. In 4 experiments, we show that learning both kinds of dependencies is hindered in conditions when they are embedded in longer sequences of words, and facilitated when they are isolated by silences. We argue that the findings from the present study and prior research is consistent with a theory that similar mechanisms are deployed for adjacent and nonadjacent dependency learning, but that NAD learning is simply computationally more complex. Hence, in some situations NAD learning is only successful when constraining information is provided, but critically, that additional information benefits adjacent dependency learning in similar ways.

In word learning, learners need to identify the referent of words by leveraging the fact that the same word may co‐occur with different sets of objects. This raises the question, what do children remember from "in the moment" that they can use for cross‐situational learning? Furthermore, do children represent pictures of familiar animals versus drawings of non‐existent novel objects as potential referents differently? This study examined these questions by creating learning scenarios with only two potential referents, requiring the least amount of memory to represent all co‐present objects. Across three experiments (n > 250) with 4‐ and 6‐year‐old children, children reliably selected the intended referent from learning at test, though the learning of novel objects was better than familiar objects. When asked for a co‐present object, children of all ages in the study performed at chance in all of the conditions. We discuss the developmental differences in cross‐situational word learning capabilities with regard to representing different stimuli as potential referents. Importantly, all children used a propose‐but‐verify procedure for learning novel words even in the simplest of the learning scenarios given repeated exposure. [ABSTRACT FROM AUTHOR]