36. Implicit learning in developmental dyslexia as demonstrated by the Serial Reaction Time (SRT) and the Artificial Grammar Learning (AGL) tasks

2018 ◽  
pp. 753-764
Keyword(s):  
Reaction Time ◽  
Implicit Learning ◽  
Developmental Dyslexia ◽  
Artificial Grammar Learning ◽  
Artificial Grammar ◽  
Serial Reaction Time ◽  
Grammar Learning ◽  
Serial Reaction

Implizites Lernen und LRS: Spielen Defizite im impliziten Lernen eine Rolle bei der Entstehung von Schwierigkeiten im Lesen und Rechtschreiben?

2012 ◽  
Vol 1 (2) ◽  
pp. 079-097 ◽  
Author(s):  
Elena Ise ◽  
Gerd Schulte-Körne
Keyword(s):  
Reaction Time ◽  
Artificial Grammar Learning ◽  
Artificial Grammar ◽  
Serial Reaction Time ◽  
Grammar Learning ◽  
Serial Reaction

Zusammenfassung:Zirka fünf Prozent aller Kinder und Jugendlichen in Deutschland leiden an einer Lese-Rechtschreibstörung (LRS). Bisher ist kaum bekannt, auf welche Art und Weise das Lesen- und Schreibenlernen bei Kindern mit einer LRS beeinträchtigt ist. Studien zeigen, dass der Erwerb der Schriftsprache nicht nur explizit durch Unterrichtung stattfindet, sondern auch implizit (unbewusst) durch häufigen Kontakt mit geschriebenen Wörtern. D. h. Kinder lernen implizit, welche Buchstabenkombinationen häufig vorkommen und wie oft und unter welchen Bedingungen Laute und Buchstaben miteinander assoziiert sind. Möglicherweise können Schwierigkeiten im Lesen und Rechtschreiben dadurch erklärt werden, dass diese impliziten Lernprozesse beeinträchtigt sind. In aktuellen Studien wurden daher anhand von Serial Reaction Time (SRT) und Artificial Grammar Learning (AGL) Aufgaben implizite Lernprozesse bei Kindern und Erwachsenen mit einer LRS untersucht. In der vorliegenden Arbeit werden die Ergebnisse dieser Studien systematisch zusammengefasst und kritisch diskutiert. Die Mehrzahl der Studien weist darauf hin, dass Kinder mit einer LRS sowohl im impliziten Lernen von Reihenfolgen (gemessen mit SRT-Aufgaben) als auch im impliziten Lernen von Regeln und Fragmenthäufigkeiten (gemessen mit AGL-Aufgaben) beeinträchtigt sind. Implikationen für die Praxis werden vorgestellt.

scholarly journals Characteristics of dissociable human learning systems

1994 ◽  
Vol 17 (3) ◽  
pp. 367-395 ◽  
Author(s):  
David R. Shanks ◽  
Mark F. St. John
Keyword(s):  
Implicit Learning ◽  
Computational Models ◽  
Rule Learning ◽  
Human Learning ◽  
Artificial Grammar Learning ◽  
Learning Systems ◽  
Artificial Grammar ◽  
Stimulus Selection ◽  
Grammar Learning ◽  
Unconscious Learning

AbstractA number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning.

Measuring Individual Differences in Implicit Learning with Artificial Grammar Learning Tasks

2017 ◽  
Vol 225 (1) ◽  
pp. 5-19 ◽  
Author(s):  
Daniel Danner ◽  
Dirk Hagemann ◽  
Joachim Funke
Keyword(s):  
Individual Differences ◽  
Implicit Learning ◽  
Artificial Grammar Learning ◽  
Artificial Grammar ◽  
General Intelligence ◽  
Knowledge Test ◽  
Learning Tasks ◽  
Grammar Learning ◽  
Grammar Knowledge ◽  
The Impact

Abstract. Implicit learning can be defined as learning without intention or awareness. We discuss conceptually and investigate empirically how individual differences in implicit learning can be measured with artificial grammar learning (AGL) tasks. We address whether participants should be instructed to rate the grammaticality or the novelty of letter strings and look at the impact of a knowledge test on measurement quality. We discuss these issues from a conceptual perspective and report three experiments which suggest that (1) the reliability of AGL is moderate and too low for individual assessments, (2) a knowledge test decreases task consistency and increases the correlation with reportable grammar knowledge, and (3) performance in AGL tasks is independent from general intelligence and educational attainment.

Sign in / Sign up

Export Citation Format

Share Document