Abstract
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.
Highlights
Segmentation processes, such those involved in extracting words from continuous speech, are the backbone of much of human learning
Before examining the influence of variation in community structure on this measure, the following steps were taken to clean the data: removal of incorrect or no response trials (7.4% data loss), removal of rotated trials, removal of implausible reaction times, and removal of outlier data points greater than 3 standard deviations from the average RT per subject. These preprocessing steps were identical to those used in prior work [14], and we note that the pattern of significant results reported below holds without the removal of implausible and outlier data points
We ran two regression models to answer the following questions: First, do previously reported increases in RTs at community boundaries vary by community size and number (Model 1)? Second, are general processing times, separate from the hypothesized cross-community RT increases, influenced by these same
Summary
Segmentation processes, such those involved in extracting words from continuous speech, are the backbone of much of human learning. Foundational work by Saffran and colleagues demonstrated that segmentation in the absence of semantic or acoustic cues to word boundaries is driven by the transition probabilities between syllables [3, 4] They found that the successful extraction of structure was due to the relative difference in transition probabilities throughout streams of nonsense syllables, characterized by high probabilities within words and low probabilities between words. This simple statistic has since been linked to parsing behavior in both visual and motor learning tasks, suggesting that sensitivity to transition probabilities, or statistical learning, extends beyond a single cognitive domain [5,6,7,8]
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