Abstract

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self-paced reading task, we show how individual differences in statistical pattern learning are reflected in readers' knowledge of linguistic co-occurrence patterns and in their exploration and exploitation of content-specific and task-general information. First, we investigated the extent to which an individual's pattern learning correlates with his orher sensitivity to systematic morphological and syntactic co-occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content-sensitive learners in that the more probable the co-occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content-sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition.

Highlights

  • Over the past two decades, evidence has amassed documenting the extent to which language cognition depends on learning spatiotemporal regularities in a probabilistic manner (Jost & Christensen, 2017)

  • We test three hypotheses: learning non-linguistic and linguistic sequences relies on the same mechanism (Section 3.1); individuals differ in their pattern learning and this is reflected in how they read morphological and syntactic patterns (Section 3.2); depending on their pattern learning, individuals differ in their readiness to explore and exploit the environment in order to adapt to the task and improve their performance (Section 3.3)

  • Applying the ideas of Stafford and Dewar (2014) and Milin et al (2017) to the study of English compounds, Schmidtke, Gagne, Kuperman, and Spalding (2018) argued that the diversity of relational knowledge might be reflecting the “explored” space of conceptual knowledge, to enrich the semantic possibilities and “exploit” them by achieving greater semantic precision. These results suggest that the exploration-exploitation hypothesis from Reinforcement Learning (RL) is attractive as a framework for discussing the individual differences in adaptive behavior observed in experimental settings, including settings that focus on language processing

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Summary

Introduction

Over the past two decades, evidence has amassed documenting the extent to which language cognition depends on learning spatiotemporal regularities in a probabilistic manner (Jost & Christensen, 2017). We build on previous work by Milin, Divjak, and Baayen (2017) to demonstrate how individual differences in pattern learning correlate with skilled readers’ handling of morphological and syntactic co-occurrence patterns and how these differences are reflected in their exploration and exploitation of content- and task-related patterns. We test three hypotheses: learning non-linguistic and linguistic sequences relies on the same mechanism (Section 3.1); individuals differ in their pattern learning and this is reflected in how they read morphological and syntactic patterns (Section 3.2); depending on their pattern learning, individuals differ in their readiness to explore and exploit the environment in order to adapt to the task and improve their performance (Section 3.3). Pattern learning influences the information that is gathered and becomes available to be exploited, thereby doubling the effect it has on performance within the domain of language

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