Login / Signup

Effects of feature highlighting and causal explanations on category learning in a natural-science domain.

Brian J MeagherMark A McDanielRobert M Nosofsky
Published in: Journal of experimental psychology. Applied (2021)
Teaching natural-science categories is highly challenging because the objects in such categories are composed of numerous complex dimensions that need to be perceived, evaluated, and integrated. Furthermore, the boundaries separating such categories are often fuzzy. A technique that has been proposed and investigated for enhancing the teaching of natural-science categories is feature highlighting, in which diagnostic features for identifying category members are explicitly described and illustrated. Using rock classification in geology as an example target domain, the present study further investigated the potential benefits of feature highlighting and also of providing causal explanations for the highlighted features. The authors found that feature highlighting did not always lead to improved generalization to novel members of the taught categories. However, robust beneficial effects were seen when the categories were relatively confusable ones and the stated diagnostic features were highly valid for distinguishing among the categories. Finally, at least under the present conditions, supplementing the highlighted features with causal explanations of the reasons for their occurrence did not further enhance the participants' rock-classification learning and generalization. Although the teaching of causal explanations is fundamental to science education, clear evidence that causal explanations enhance classification-learning per se in this domain remains to be demonstrated. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Keyphrases
  • machine learning
  • deep learning
  • public health
  • risk assessment
  • medical students
  • depressive symptoms
  • physical activity
  • atomic force microscopy
  • high resolution
  • mass spectrometry
  • adverse drug