Schweppe and Rummer's rejection of the multicomponent model as a basis for multimedia theories rests on a misinterpretation of the working memory–long-term memory relationship¶

Claim · All claims
Evidence · 1 study · theoretical r3 · q1
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The commentary argues that Schweppe and Rummer's rejection of the multicomponent model is based on a misinterpretation, because the model asserts functional rather than structural separation of working memory from long-term memory. → Soemer 2015

Evidence¶

Soemer 2015¶

Soemer, A. (2015). The Multicomponent Working Memory Model, Attention, and Long-term Memory in Multimedia Learning: A Comment on Schweppe and Rummer (2014). Educational Psychology Review. https://doi.org/10.1007/s10648-015-9303-9

q1 · i? · theoretical · r3

Theoretical argument in a commentary on Schweppe and Rummer (2014). The author states the rejection "is based on a misinterpretation of the relationship between working memory and long-term memory in the multicomponent model"; no empirical data are reported.

"the present commentary argues (a) that Schweppe and Rummer's rejection of the multicomponent model as a basis for multimedia theories is based on a misinterpretation of the relationship between working memory and long-term memory in the multicomponent model"

Discussion¶

Related Claims¶

• Multimedia learning principles are not clearly attributable to the multicomponent model or to applied multimedia theories — related
• Whether working memory has a single capacity limit or multiple capacity limits from functional specialization is highly relevant for the design of multimedia materials — related
• Small between-task relative to within-task interference in dual-task studies supports functional separation of phonological and visuospatial working memory, and newer experiments led Cowan's group to add specialized verbal and visual resources to the embedded-processes account — related
• Standard unconstrained neural networks do not naturally separate working memory from long-term memory or impose human-like capacity limits — related