Learning From Refutation and Standard Expository Science Texts: The Contribution of Inhibitory Functions in Relation to Text Type

Lucia Mason, Erika Borella, Irene Anna N. Diakidoy, Reese Butterfuss, Panayiota Kendeou, Barbara Carretti

Research output: Contribution to journalArticlepeer-review

Abstract

Inhibition is thought to help suppress interference from misconceptions in science learning. Using a pre-, post-, and delayed posttest design, we examined the influence on learning from science texts of three inhibitory-related functions—prepotent response inhibition, resistance to distractor interference, and resistance to proactive interference. Children in the fourth and fifth grades (N = 110) read two texts, one about energy and one about the food chain. One of the texts was a standard expository text; the other was a refutation text. We found that, regardless of text type, students’ conceptual knowledge increased between pretest and posttest, a result retained at delayed posttest. Prepotent response inhibition uniquely predicted conceptual learning but only from refutation texts at both posttests after controlling for prior knowledge and reading comprehension skills. Inhibiting proactive interference also contributed to long-term conceptual learning but only in refutation text readers. These findings suggest that specific inhibitory functions play a role in learning from science texts depending on text structure.

Original languageEnglish (US)
Pages (from-to)921-939
Number of pages19
JournalDiscourse Processes
Volume57
Issue number10
DOIs
StatePublished - 2020

Bibliographical note

Funding Information:
This work was carried out within the scope of the project “Use-inspired basic research,” for which the Department of General Psychology of the University of Padova has been recognized as “Dipartimento di eccellenza” by the Italian Ministry of University and Research. The authors are grateful to Noemi Di Blasio and Federica Peretta for their help in data collection. They also extend their gratitude to Lucia Ronconi for her help in statistical analyses.

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