The role of metacognition in recognition of the content of statistical learning
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BRIEF REPORT
The role of metacognition in recognition of the content of statistical learning Mikhail Ordin 1,2 & Leona Polyanskaya 1
# The Psychonomic Society, Inc. 2020
ABSTRACT Despite theoretical debate on the extent to which statistical learning is incidental or modulated by explicit instructions and conscious awareness of the content of statistical learning, no study has ever investigated the metacognition of statistical learning. We used an artificial language-learning paradigm and a segmentation task that required splitting a continuous stream of syllables into discrete recurrent constituents. During this task, statistical learning potentially produces knowledge of discrete constituents as well as about statistical regularities that are embodied in familiarization input. We measured metacognitive sensitivity and efficiency (using hierarchical Bayesian modelling to estimate metacognitive sensitivity and efficiency) to probe the role of conscious awareness in recognition of constituents extracted from the familiarization input and recognition of novel constituents embodying the same statistical regularities as these extracted constituents. Novel constituents are conceptualized to represent recognition of statistical structure rather than recognition of items retrieved from memory as whole constituents. We found that participants are equally sensitive to both types of learning products, yet subject them to varying degrees of conscious processing during the postfamiliarization recognition test. The data point to the contribution of conscious awareness to at least some types of statistical learning content. Keywords Statistical learning . Awareness . Metacognition . Confidence
Statistical learning is a process for extracting statistical regularities from the environment that enables efficient processing of continuous sensory inputs. One of the tasks that relies on statistical learning is segmenting continuous inputs into discrete constituents (Baldwin, Andersson, Saffran, & Meyer, 2008; Gómez, Bion, & Mehler, 2011; Hard, Meyer, & Baldwin, 2019; Siegelman, 2019; Siegelman, Bogaerts, Armstrong, & Frost, 2019). It is generally assumed that statistical learning is incidental and happens without awareness and across modalities (Arciuli, von Koss Torkildsen, Stevens & Simpson, 2014; Aslin & Newport, 2012; Dienes, Broadbent, & Berry, 1991). However, some empirical Mikhail Ordin and Leona Polyanskaya contributed equally to this work. * Mikhail Ordin [email protected] 1
BCBL—Basque Centre on Cognition, Brain and Language, Paseo Mikeletegi 69, 20009 San Sebastián, Spain
2
Ikerbasque—Basque Foundation for Science, Maria Diaz de Hro 3, 48013 Bilbao, Spain
evidence suggests that performance can be modulated by attention (Fernandes, Kolinsky, & Ventura, 2010; Toro, Sinnett & Soto-Faraco, 2005), and that conscious focus on a task improves performance (Alamia & Zenon, 2016; Reber Kassin, Lewis, & Cantor, 1980). Surprisingly, despite theoretical tension regarding how conscious statistical learning may be, studies on metacogn
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