31 research outputs found
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
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Schrödinger’s Category: Active Learning in the Face of Label Ambiguity
Research on active category learning—i.e., where the learnermanipulates continuous feature dimensions of novel objects andreceives labels for their self-generated exemplars—has routinelyshown that people prefer to sample from regions of the space withhigh class uncertainty (near category boundaries). Prevailingaccounts suggest that this strategy facilitates an understanding of thesubtle distinctions between categories. However, prior work hasfocused on situations where category boundaries are rigid. Inactuality, the boundaries between natural categories are often fuzzyor unclear. Here, we ask: do learners pursue uncertainty samplingwhen labels at the boundary are themselves uncertain? To answerthis question, we introduce a fuzzy buffer around a target categorywhere conflicting labels are returned from two ‘teachers,’ then weevaluate how sampling and representation are affected. We find that,relative to the rigid boundary case, learners avoid uncertainty,opting to sample densely from highly certain regions of the targetcategory as opposed to its boundary. Subsequent generalization testsreveal that the sampling strategies encouraged by the fuzzyboundary negatively affected participants' grasp of categorystructure, even outside the fuzzy buffer zone
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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
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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
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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
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Sensitivity to Temporal Community Structure in the Language Domain
The interrelatedness of lexical items, typically defined in termsof semantic or phonological overlap, has been shown toinfluence language learning. Given that language also containssequential structure, we investigate here whether temporaloverlap among words, formalized in graph theoretical terms asdisplaying the property of community structure, might alsohave consequences for learning. We create a graph organizedinto clusters of densely interconnected nodes with relativelysparse external connections. After assigning a novelpseudoword to each node in the graph, we generate acontinuous sequence of visually-presented items by walkingalong its edges. Word-by-word reading times suggest thatlearners are indeed sensitive to temporal overlap.Compellingly, we also demonstrate that prior exposure tosequences organized into temporal communities influencesperformance on a subsequent word recognition task