2020
DOI: 10.1111/cogs.12835
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Exploring and Exploiting Uncertainty: Statistical Learning Ability Affects How We Learn to Process Language Along Multiple Dimensions of Experience

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 r… Show more

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Cited by 4 publications
(5 citation statements)
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“…However, as discussed earlier, the group difference is not statistically significant. A similar pattern can be seen when performance is assessed using the slope measure, which, according to Divjak and Milin (2020) , is more sensitive: participants in the HAA groups showed, as expected, negative slopes (i.e., their RTs got shorter as the task progressed), whereas the LAA group had an average slope that was very close to zero (and in fact positive). In this case, the between-group comparison was marginally significant.…”
Section: Discussionsupporting
confidence: 68%
See 1 more Smart Citation
“…However, as discussed earlier, the group difference is not statistically significant. A similar pattern can be seen when performance is assessed using the slope measure, which, according to Divjak and Milin (2020) , is more sensitive: participants in the HAA groups showed, as expected, negative slopes (i.e., their RTs got shorter as the task progressed), whereas the LAA group had an average slope that was very close to zero (and in fact positive). In this case, the between-group comparison was marginally significant.…”
Section: Discussionsupporting
confidence: 68%
“…This measures how much RT decreased as a function of exposure to the repeating sequence. SRT slopes were calculated following the procedure outlined in Divjak and Milin (2020). On this measure, lower (i.e., more negative) scores correspond to a steeper decrease in RTs and hence evidence a stronger impact of exposure to the sequence.…”
Section: Discussionmentioning
confidence: 99%
“…In our final analytic step, we analyzed the dynamic aspects of the participants’ behavior and modeled the variation in the time taken to reach a decision across experimental trials (in order of presentation) in both ST and CT Conditions and across four grammatical Types (Syntax, Case, Collocation, and Aspect). Following Milin, Divjak, and Baayen ( 2017 ) and Divjak and Milin ( 2020 ), we used moving (or rolling) standard deviations (SDs); the rolling SD correlates perfectly ( r = 0.99) with the older coefficient of variation of lexical decision RT (CV RT )—the SD of RT divided by mean RT—proposed by Segalowitz and Segalowitz ( 1993 ) as a measure of automaticity. These moving SDs were calculated over three consecutive trial latencies, which maximizes the number of available datapoints (moving SDs).…”
Section: Resultsmentioning
confidence: 99%
“…We evaluated the resulting model at the median (quantile = 0.5), the typical evaluation point (cf. Divjak & Milin, 2020 ; Schmidtke, Matsuki, & Kuperman, 2017 ; Tomaschek, Tucker, Fasiolo, & Baayen, 2018 ). As with the analysis of speed of judgment (i.e., RTs), we confirmed the model against its Bayesian alternative, using the brms package (Bürkner, 2017 , 2018).…”
Section: Resultsmentioning
confidence: 99%
“…However, usage-based approaches posit that human skills are highly plastic and shaped by experience (Evans and Levinson, 2009;Dąbrowska, 2015;Dąbrowska, 2018). Recent research has highlighted that language attainment within adult native speakers is modulated by both endogenous constraints (e.g., executive functions, statistical learning abilities, personality traits) and exogenous, experience-related variables (notably, the quality and quantity of the input) (Andringa and Dąbrowska, 2019;Dąbrowska, 2019;Frost et al, 2019;Medimorec et al, 2019;Divjak and Milin, 2020;Kidd and Donnelly, 2020;Ryskin et al, 2020;Kerz and Wiechmann, 2021).…”
Section: Experience-based Individual Differencesmentioning
confidence: 99%