Many events that humans and other organisms experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co-occurrences between temporarily adjacent stimuli, others involve tracking the co-occurrences between temporarily distant stimuli (i.e., non-adjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here we report on four experiments that examined NAD learning from various types of visual stimuli. The results suggest that continuous movements aid the acquisition of NADs. We also found that human motion leads to more robust NAD learning compared to object motions, perhaps because of a richer representation. This richer representation could result in better memory and recall, and provide a stronger signal for NAD learning.

Many studies of human sequential pattern learning demonstrate that learners detect adjacent and non-adjacent dependencies in many kinds of sequences. However, it is often assumed that the computational mechanisms behind extracting these dependencies are the same. We replicate the seminal finding that adults are capable of learning dependencies between non-adjacent words (Gómez, 2002). When we eliminate the positional information about the statistical structures by embedding the structure in phrases, learners can no longer learn the dependencies. Our methods allow us to study the learning mechanisms that are more representative of the patterns in natural languages, and show that when directly compared, adjacent and non-adjacent dependencies are not equally learnable. We suggest that learning non-adjacent dependencies in language involves a different computational mechanism from learning adjacent dependencies

Many grammatical dependencies in natural language involve elements that are not adjacent, such as between the subject and verb in the child always runs. To date, most experiments showing evidence of learning non- adjacent dependencies have used artificial languages in which the to-be-learned dependencies are presented in isolation by presenting the minimal sequences that contain the dependent elements. However, dependencies in natural language are not typically isolated in this way. In this study we exposed learners to non-adjacent dependencies in long sequences of words. We accelerated the speed of presentation and learners showed evidence for learning of non-adjacent dependencies. The previous pause-based positional mechanisms for learning of non-adjacent dependency are challenged.

Grammatical dependencies often involve elements that are not adjacent. However, most experiments in which non-adjacent dependencies are learned bracketed the dependent material with pauses, which is not how dependencies appear in natural language. Here we report successful learning of embedded NAD without pause bracketing. Instead, we induce learners to compute structure in an artificial language by entraining them through processing English sentences. We also found that learning becomes difficult when grammatical entrainment causes learners to compute boundaries that are misaligned with NAD structures. In sum, we demonstrated that grammatical entrainment can induce boundaries that can carry over to reveal structures in novel language materials, and this effect can be used to induce learning of non-adjacent dependencies.

Many grammatical dependencies in natural language involve elements that are not adjacent, such as between thesubject and verb in ”the dog always barks”. We recently showed that non-adjacent dependencies are easily learnable withoutpauses in the signal when speech is presented rapidly. In this study, we used an online measure to look at the relationshipbetween online parsing and the learning performance from the offline assessment of non-adjacent dependency learning. Wefound that participants who showed current parsing of the language online also learned the dependencies better. However, thispattern disappeared when they are explicitly told where the boundaries are before parsing. Theories of non-adjacent dependencylearning are discussed.