Literature DB >> 21477186

Statistical learning in typically developing children: the role of age and speed of stimulus presentation.

Joanne Arciuli1, Ian C Simpson.   

Abstract

It is possible that statistical learning (SL) plays a role in almost every mental activity. Indeed, research on SL has grown rapidly over recent decades in an effort to better understand perception and cognition. Yet, there remain gaps in our understanding of how SL operates, in particular with regard to its (im)mutability. Here, we investigated whether participant-related variables (such as age) and task-related variables (such as speed of stimulus presentation) affect visual statistical learning (VSL) in typically developing children. We tested 183 participants ranging in age from 5 to 12 years and compared three speeds of presentation (using stimulus durations of 800, 400 and 200 msecs). A multiple regression analysis revealed significant effects of both age and speed of presentation - after attention during familiarization and gender had been taken into consideration. VSL followed a developmental trajectory whereby learning increased with age. The amount of learning increased with longer presentation times (as shown by Turk-Browne, Jungé & Scholl, 2005, in their study of adults). There was no significant interaction between the two variables. These findings assist in elucidating the nature of statistical learning itself. While statistical learning can be observed in very young children and at remarkably fast presentation times, participant- and task-related variables do impact upon this type of learning. The findings reported here may serve to enhance our understanding of individual differences in the cognitive and perceptual processes that are thought to rely, at least in part, on SL (e.g. language processing and object recognition).

Entities:  

Mesh:

Year:  2011        PMID: 21477186     DOI: 10.1111/j.1467-7687.2009.00937.x

Source DB:  PubMed          Journal:  Dev Sci        ISSN: 1363-755X


  35 in total

1.  Brief report: a comparison of statistical learning in school-aged children with high functioning autism and typically developing peers.

Authors:  Jessica Mayo; Inge-Marie Eigsti
Journal:  J Autism Dev Disord       Date:  2012-11

2.  Prediction in infants and adults: A pupillometry study.

Authors:  Felicia Zhang; Sagi Jaffe-Dax; Robert C Wilson; Lauren L Emberson
Journal:  Dev Sci       Date:  2018-12-27

3.  Linguistic entrenchment: Prior knowledge impacts statistical learning performance.

Authors:  Noam Siegelman; Louisa Bogaerts; Amit Elazar; Joanne Arciuli; Ram Frost
Journal:  Cognition       Date:  2018-04-26

Review 4.  The multi-component nature of statistical learning.

Authors:  Joanne Arciuli
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2017-01-05       Impact factor: 6.237

5.  Statistical learning as an individual ability: Theoretical perspectives and empirical evidence.

Authors:  Noam Siegelman; Ram Frost
Journal:  J Mem Lang       Date:  2015-05-01       Impact factor: 3.059

6.  Neurocognitive Correlates of Statistical Learning of Orthographic-Semantic Connections in Chinese Adult Learners.

Authors:  Xiuhong Tong; Yi Wang; Shelley Xiuli Tong
Journal:  Neurosci Bull       Date:  2020-05-12       Impact factor: 5.203

7.  Comparing statistical learning across perceptual modalities in infancy: An investigation of underlying learning mechanism(s).

Authors:  Lauren L Emberson; Jennifer B Misyak; Jennifer A Schwade; Morten H Christiansen; Michael H Goldstein
Journal:  Dev Sci       Date:  2019-07-02

8.  Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities.

Authors:  Louisa Bogaerts; Noam Siegelman; Ram Frost
Journal:  Psychon Bull Rev       Date:  2016-08

9.  Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.

Authors:  Shafali S Jeste; Natasha Kirkham; Damla Senturk; Kyle Hasenstab; Catherine Sugar; Chloe Kupelian; Elizabeth Baker; Andrew J Sanders; Christina Shimizu; Amanda Norona; Tanya Paparella; Stephanny F N Freeman; Scott P Johnson
Journal:  Dev Sci       Date:  2014-05-13

10.  Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

Authors:  Noam Siegelman; Louisa Bogaerts; Ofer Kronenfeld; Ram Frost
Journal:  Cogn Sci       Date:  2017-10-07
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.