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Zormpa, E., Brehm, L., Hoedemaker, R. S., & Meyer, A. S. (2019). The production effect and the generation effect improve memory in picture naming. Memory, 27(3), 340-352. doi:10.1080/09658211.2018.1510966.
Abstract
The production effect (better memory for words read aloud than words read silently) and the picture superiority effect (better memory for pictures than words) both improve item memory in a picture naming task (Fawcett, J. M., Quinlan, C. K., & Taylor, T. L. (2012). Interplay of the production and picture superiority effects: A signal detection analysis. Memory (Hove, England), 20(7), 655–666. doi:10.1080/09658211.2012.693510). Because picture naming requires coming up with an appropriate label, the generation effect (better memory for generated than read words) may contribute to the latter effect. In two forced-choice memory experiments, we tested the role of generation in a picture naming task on later recognition memory. In Experiment 1, participants named pictures silently or aloud with the correct name or an unreadable label superimposed. We observed a generation effect, a production effect, and an interaction between the two. In Experiment 2, unreliable labels were included to ensure full picture processing in all conditions. In this experiment, we observed a production and a generation effect but no interaction, implying the effects are dissociable. This research demonstrates the separable roles of generation and production in picture naming and their impact on memory. As such, it informs the link between memory and language production and has implications for memory asymmetries between language production and comprehension.Additional information
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Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O'Donnell, T. J. O., Sainburgh, T., & Gentner, T. Q. (2020). Five ways in which computational modeling can help advance cognitive science: Lessons from artificial grammar learning. Topics in Cognitive Science, 12(3), 925-941. doi:10.1111/tops.12474.
Abstract
There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space. -
Zuidema, W., & Fitz, H. (2019). Key issues and future directions: Models of human language and speech processing. In P. Hagoort (
Ed. ), Human language: From genes and brain to behavior (pp. 353-358). Cambridge, MA: MIT Press. -
Zwitserlood, I. (2009). Het Corpus NGT. Levende Talen Magazine, 6, 44-45.
Abstract
The Corpus NGT -
Zwitserlood, I. (2009). Het Corpus NGT en de dagelijkse lespraktijk (1). Levende Talen Magazine, 8, 40-41.
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