Network constraints on learnability of probabilistic motor sequences

Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.Comment: 29 pages, 4 figure

Now or Later: Representational Convergence in Simulated Simultaneous and Sequential Bilingual Learning Contexts

In bilinguals, certain concepts across languages come to be represented similarly—a semantic convergence effect that reflects interactivity between languages. The causal factors that affect semantic convergence are not fully understood; this gap may be due to limitations of the correlative methods used in extant work, which assesses the representations of real-world bilinguals. Here, we utilize an artificial language learning paradigm—inspired by the study of category learning—to elucidate causal influences on semantic convergence. We contrast simulated simultaneous bilingual learners with simulated sequential bilingual learners before assessing the representations of both. Bilingual groups are additionally compared to simulated monolingual controls from each language. We report on the pattern of semantic convergence and conclude with implications for theories of bilingual representation

sj-docx-1-qjp-10.1177_17470218221124869 – Supplemental material for Noise-induced differences in the complexity of spoken language

Supplemental material, sj-docx-1-qjp-10.1177_17470218221124869 for Noise-induced differences in the complexity of spoken language by Catherine T Pham and Elisabeth A Karuza in Quarterly Journal of Experimental Psychology</p

Language Experience Modulates L2-Related Representational Change when Learning Novel Categories

When learners are exposed to multiple languages, semantic categories have been shown to undergo a process of convergence wherein concepts that overlap across natural languages come to be represented more similarly. Recently, we replicated this convergence effect using a simulated bilingual language learning paradigm in which participants learn one language (i.e., category boundary and associated labels) before then learning a second language with a shifted category boundary. This work, however, only assessed English-speaking monolinguals. In the present study, we extend this paradigm to bilinguals—asking whether extensive experience maintaining different label mapping systems modulates degree of semantic convergence when learners face two novel (artificial) languages. We first assessed the language experience of Polish-English bilinguals then measured representational change via the simulated bilingual language learning paradigm. We report on evidence that language history moderates the extent, and direction, of representational change, and we conclude with implications for theories of bilingual representation

Human Sensitivity to Community Structure Is Robust to Topological Variation

Despite mounting evidence that human learners are sensitive to community structure underpinning temporal sequences, this phenomenon has been studied using an extremely narrow set of network ensembles. The extent to which behavioral signatures of learning are robust to changes in community size and number is the focus of the present work. Here we present adult participants with a continuous stream of novel objects generated by a random walk along graphs of 1, 2, 3, 4, or 6 communities comprised of N = 24, 12, 8, 6, and 4 nodes, respectively. Nodes of the graph correspond to a unique object and edges correspond to their immediate succession in the stream. In short, we find that previously observed processing costs associated with community boundaries persist across an array of graph architectures. These results indicate that statistical learning mechanisms can flexibly accommodate variation in community structure during visual event segmentation

Combining fMRI and behavioral measures to examine the process of human learning

Prior to the advent of fMRI, the primary means of examining the mechanisms underlying learning were restricted to studying human behavior and non-human neural systems. However, recent advances in neuroimaging technology have enabled the concurrent study of human behavior and neural activity. We propose that the integration of behavioral response with brain activity provides a powerful method of investigating the process through which internal representations are formed or changed. Nevertheless, a review of the literature reveals that many fMRI studies of learning either (1) focus on outcome rather than process or (2) are built on the untested assumption that learning unfolds uniformly over time. We discuss here various challenges faced by the field and highlight studies that have begun to address them. In doing so, we aim to encourage more research that examines the process of learning by considering the interrelation of behavioral measures and fMRI recording during learning

Learning across space, time, and input modality : towards an integrative, domain-general account of the neural substrates underlying visual and auditory statistical learning

Thesis (Ph. D.)--University of Rochester. Department of Brain and Cognitive Sciences, 2014.In the present work, we detail a set of experiments aimed at elucidating the neural mechanisms underpinning statistical learning across space, time, and input modality. Specifically, we have employed functional magnetic resonance imaging (fMRI) to test directly the hypothesis that distributional learning recruits a common set of neural substrates, regardless of the domain of the structure to be acquired. In experiment 1, we made use of an intermittent testing design in order to monitor changes in learning over time during a word segmentation task. By relating fluctuations in behavioral performance with differences in the magnitude of neural response across exposure runs, we demonstrated the involvement of a fronto-subcortical network of regions supporting statistical learning, with peak activation in the left inferior frontal gyrus. In experiment 2, we investigated the brain basis of learning when we shifted not only the modality of the input, but also its spatiotemporal properties. In contrast to the sequentially-ordered segmentation task in the previous study, experiment 2 sought to uncover the regions recruited during the acquisition of simultaneously-presented visuospatial patterns. Again capitalizing on inter and intra-subject variability in behavior, we revealed involvement of a parallel fronto-subcortical circuit that additionally encompassed bilateral amygdala. A further connectivity analysis using seeds within this network made clear a striking pattern: for each univariate activation peak, functional coupling was stronger in the first exposure run relative to the last exposure run. Finally, experiment 3 combined sequential learning in the auditory and visual modalities. We exposed participants to one of two carefully matched conditions. In the auditory condition, they completed a word segmentation task similar to the one described in experiment 1. In the visual condition, participants were exposed to an identical language, but one in which each syllable was replaced with a shape. Intermittent test scores showed behavioral performance that was slower to reach above-chance levels and less robust than in the segmentation task of the first study. Neuroimaging analyses revealed hippocampal, not fronto-subcortical, involvement correlated with changes in performance, and we discussed this finding in light of crucial differences between the rates of learning in experiments 1 and 3. Similar to the results of experiment 2, a subsequent functional connectivity analysis suggested greater interregional coherence in the earliest phases of learning. Linking together results from the three experiments, we propose a two-part mechanism to the neural basis of statistical learning. We posit that the brain, when confronted with structured stimuli, immediately engages widespread network of frontal, subcortical, and hippocampal regions. Over time, this network narrows, and the substrates best suited to perform the computations required of task at hand assume the processing burden. With influence from well-known proposals of the computational architecture underlying learning in the brain (e.g., Atallah, Frank, & O’Reilly, 2004; McClelland, McNaughton, & O’Reilly, 1995), we suggest that prefrontal cortex and basal ganglia form a complementary circuit best suited for the maintenance and updating of internal representations, while medial temporal regions are best suited for calculating the rapid element-to-element associations crucial to the earliest stages of a statistical learning task