Working memory supports inference learning just like classification learning

Craig, Stewart; Lewandowsky, Stephan

doi:10.1080/17470218.2013.818703

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…An influential framework for WM was developed by Baddeley (1986, 2000, 2012) positing four components: (a) the central executive with an (attentional) regulatory function that uses two modality-specific sub-systems; (b) the phonological loop (PL) for verbal information; (c) the visuo-spatial sketchpad for visual information; (d) the episodic buffer for binding information from different modalities. For typically developing children (TD), various studies established positive correlations of WM with academic outcomes (e.g., Alloway and Alloway, 2010; Poloczek et al, 2012; Mähler and Schuchardt, 2016), vocabulary learning (e.g., Gathercole et al, 1997; Baddeley et al, 1998), or thinking patterns such as inferencing and classification (Craig and Lewandowsky, 2013). Thus, a well-functioning WM is seen as an important prerequisite for learning and retaining memory traces until they can be incorporated in long-term memory (LTM).…”

Section: Introductionmentioning

confidence: 99%

Verbal Working Memory Processes in Students With Mild and Borderline Intellectual Disabilities: Differential Developmental Trajectories for Rehearsal and Redintegration

2019

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In verbal working memory, two processes serve to retain a fading memory trace: subvocal rehearsal and lexical redintegration. While recent studies on students with mild and borderline intellectual disabilities (MBID) have yielded mixed results on rehearsal, redintegration has not been researched in MBID, yet. Furthermore, most studies have used a group-matched design which, due to methodological constraints, can only distinguish between two different development patterns. Thus, we study both rehearsal and redintegration in students with MBID using developmental trajectories that have greater potential for identifying differential developmental patterns than traditional group-matching approaches. We investigate whether three aspects in working memory develop differently in students with MBID in comparison to typically developing students: (a) the general capacity of the phonological loop, and the effectiveness of (b) rehearsal, and (c) redintegration. We use three different developmental indicators to compare trajectories: chronological age, cognitive capacity, and vocabulary size. N = 210 students (87 students with MBID, 123 typically developing students) completed working memory span tasks with short and long (1- vs. 3-syllable) real words and pseudowords. The effect for word length (short vs. long) measures rehearsal, and the lexicality effect (real words vs. pseudowords) measures redintegration. Results show that developmental trajectories reveal an intercept difference but no slowed rate in rehearsal, and no impairment in redintegration. However, concerning the developmental relation between redintegration and vocabulary size, students with MBID reveal a differential pattern as redintegration appears higher for students with small vocabulary size, but unexpectedly decreases as vocabulary size increases. We conclude that students with MBID show a delayed onset in the development of capacity of the phonological loop and rehearsal and that they do not catch up in their development. Redintegration does not seem to be impaired in relation to age and cognitive capacity. However, the differential relation of redintegration with vocabulary size calls for further research. While impaired subvocal rehearsal appears to be connected to the developmental problems of students with MBID, lexical redintegration seems to be intact in relation to chronological age and cognitive capacity, making it a possible area of strength.

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Section: Introductionmentioning

confidence: 99%

Verbal Working Memory Processes in Students With Mild and Borderline Intellectual Disabilities: Differential Developmental Trajectories for Rehearsal and Redintegration

2019

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“…Results from recent empirical studies indicate a positive relationship between executive control and the efficiency of rule learning (e.g., Craig & Lewandowsky, 2013;DeCaro, Thomas, & Beilock, 2008;Rabi & Minda, 2014;Ropovik, 2014;Wang, Ren, & Schweizer, 2015). For example, Wang et al (2015) investigated the relationship between executive control and rule-based category learning.…”

Section: Introductionmentioning

confidence: 99%

Executive Control in Learning: Evidence for the Dissociation of Rule Learning and Associative Learning

Wang

Schweizer

Ren³

2019

ACP

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The dual-process account and the propositional account of learning hold opposite views regarding the recruitment of higher-level processes in rule learning and associative learning. Taking an individual differences perspective, the current study focused on the relationship between rule learning and associative learning and investigated to what extent executive control accounts for rule learning and associative learning. Two studies were conducted. In Study 1, a sample of 184 university students completed paired associative learning and rule learning tasks, as well as measures of working memory capacity, short-term storage, and executive control. Theory-based bifactor models were used to achieve a purified representation of executive control. The results showed that the latent correlation between associative learning and rule learning was rather small. Executive control showed a substantial relationship with rule learning, whereas no significant link was found with associative learning. In Study 2, a sample of 211 university students completed a three-term contingency learning task and an executive control task. The results replicated the finding that executive control was not significantly related to associative learning. Taken together, these results suggest a dissociation between rule learning and associative learning in terms of their underlying processes, which supports the dual-process account of learning.

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Feedback Discounting in Probabilistic Categorization: Converging Evidence from EEG and Cognitive Modeling

et al. 2018

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Working memory supports inference learning just like classification learning

Cited by 5 publications

References 18 publications

Verbal Working Memory Processes in Students With Mild and Borderline Intellectual Disabilities: Differential Developmental Trajectories for Rehearsal and Redintegration

Verbal Working Memory Processes in Students With Mild and Borderline Intellectual Disabilities: Differential Developmental Trajectories for Rehearsal and Redintegration

Executive Control in Learning: Evidence for the Dissociation of Rule Learning and Associative Learning

Feedback Discounting in Probabilistic Categorization: Converging Evidence from EEG and Cognitive Modeling

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