Literature DB >> 35344099

Temporal learning in the suprasecond range: insights from cognitive style.

Alice Teghil1,2, Fabrizia D'Antonio3, Antonella Di Vita3, Cecilia Guariglia4,5, Maddalena Boccia4,5.   

Abstract

The acquisition of information on the timing of events or actions (temporal learning) occurs in both the subsecond and suprasecond range. However, although relevant differences between participants have been reported in temporal learning, the role of dimensions of individual variability in affecting performance in such tasks is still unclear. Here we investigated this issue, assessing the effect of field-dependent/independent cognitive style on temporal learning in the suprasecond range. Since different mechanisms mediate timing when a temporal representation is self-generated, and when it depends on an external referent, temporal learning was assessed in two conditions. Participants observed a stimulus across six repetitions and reproduced it. Unbeknownst to them, in an internally-based learning (IBL) condition, the stimulus duration was fixed within a trial, although the number of events defining it varied; in an externally-cued learning (ECL) condition, the stimulus was defined by the same number of events within each trial, although its duration varied. The effect of the reproduction modality was also assessed (motor vs. perceptual). Error scores were higher in IBL compared to ECL; the reverse was true for variability. Field-independent individuals performed better than field-dependent ones only in IBL, as further confirmed by correlation analyses. Findings provide evidence that differences in dimensions of variability in high-level cognitive functioning, such as field dependence/independence, significantly affect temporal learning in the suprasecond range, and that this effect depends on the type of temporal representation fostered by the specific task demands.
© 2022. The Author(s).

Entities:  

Year:  2022        PMID: 35344099     DOI: 10.1007/s00426-022-01667-x

Source DB:  PubMed          Journal:  Psychol Res        ISSN: 0340-0727


  54 in total

1.  Effect of Cognitive Style on Topographical Learning Across Life Span: Insights From Normal Development.

Authors:  Maddalena Boccia; Francesca Vecchione; Antonella Di Vita; Simonetta D'Amico; Cecilia Guariglia; Laura Piccardi
Journal:  Child Dev       Date:  2018-11-09

2.  Raven's coloured progressive matrices: normative values on 305 adult normal controls.

Authors:  A Basso; E Capitani; M Laiacona
Journal:  Funct Neurol       Date:  1987 Apr-Jun

3.  Restructuring the navigational field: individual predisposition towards field independence predicts preferred navigational strategy.

Authors:  Maddalena Boccia; Laura Piccardi; Adele D'Alessandro; Raffaella Nori; Cecilia Guariglia
Journal:  Exp Brain Res       Date:  2017-03-10       Impact factor: 1.972

4.  Statistical learning: From acquiring specific items to forming general rules.

Authors:  Richard N Aslin; Elissa L Newport
Journal:  Curr Dir Psychol Sci       Date:  2012-06-01

5.  Learning and generalization of time production in humans: rules of transfer across modalities and interval durations.

Authors:  Ramon Bartolo; Hugo Merchant
Journal:  Exp Brain Res       Date:  2009-06-19       Impact factor: 1.972

6.  The human hippocampus is sensitive to the durations of events and intervals within a sequence.

Authors:  Alexander J Barnett; Edward B O'Neil; Hilary C Watson; Andy C H Lee
Journal:  Neuropsychologia       Date:  2014-09-16       Impact factor: 3.139

7.  Incidental learning of temporal structures conforming to a metrical framework.

Authors:  Melissa Brandon; Josephine Terry; Catherine J Stevens; Barbara Tillmann
Journal:  Front Psychol       Date:  2012-08-23

8.  Effect of Cognitive Style on Learning and Retrieval of Navigational Environments.

Authors:  Maddalena Boccia; Francesca Vecchione; Laura Piccardi; Cecilia Guariglia
Journal:  Front Pharmacol       Date:  2017-07-25       Impact factor: 5.810

9.  Infants discriminate the source of social touch at stroking speeds eliciting maximal firing rates in CT-fibers.

Authors:  Marie Aguirre; Auriane Couderc; Justine Epinat-Duclos; Olivier Mascaro
Journal:  Dev Cogn Neurosci       Date:  2019-03-19       Impact factor: 6.464

10.  Learning to predict: exposure to temporal sequences facilitates prediction of future events.

Authors:  Rosalind Baker; Matthew Dexter; Tom E Hardwicke; Aimee Goldstone; Zoe Kourtzi
Journal:  Vision Res       Date:  2013-11-11       Impact factor: 1.886

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