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Three experiments with 88 college-aged participants explored how unlabeled experiences—learning episodes in which people encounter objects without information about their category membership—influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then made a large number of unsupervised categorization decisions about new items. In all three experiments, the unsupervised experience altered participants’ implicit and explicit mental category boundaries, their explicit beliefs about the most representative members of each category, and even their memory (...) for the items encountered during the supervised learning phase. These changes were influenced by both the range and frequency distribution of the unlabeled stimuli: mental category boundaries shifted toward the middle of the range and toward the trough of the bimodal distribution of unlabeled items, whereas beliefs about the most representative category members shifted toward the modes of the unlabeled distribution. One consequence of this shift in representations is a false-consensus effect (Experiment 3) where participants, despite receiving very disparate training experiences, show strong agreement in judgments about representativeness and boundary location following unsupervised category judgments. (shrink)

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A common practice in textbooks is to introduce concepts or strategies in association with specific people. This practice aligns with research suggesting that using “real-world” contexts in textbooks increases students’ motivation and engagement. However, other research suggests this practice may interfere with transfer by distracting students or leading them to tie new knowledge too closely to the original learning context. The current study investigates the effects on learning and transfer of connecting mathematics strategies to specific people. A total of 180 (...) college students were presented with an example of a problem-solving strategy that was either linked with a specific person or presented without a person. Students who saw the example without a person were more likely to correctly transfer the novel strategy to new problems than students who saw the example presented with a person. These findings are the first evidence that using people to present new strategies is harmful for learning and transfer. (shrink)

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How do people use past experience to generalise to novel cases? This paper reports four experiments exploring the significance on one class of past experiences: encounters with negative or contrasting cases. In trying to decide whether all ravens are black, what is the effect of learning about a non-raven that is not black? Two experiments with preschool-aged, young school-aged, and adult participants revealed that providing a negative example in addition to a positive example supports generalisation. Two additional experiments went on (...) to ask which kinds of negative examples offer the most support for generalisations. These studies contrasted similarity-based and category-based accounts of inductive generalisation. Results supported category-based predictions, but only for preschool-aged children. Overall, the younger children showed a greater reliance on negative evidence than did older children and adults. Most things we encounter in the world are negative evidence for our generalisations. Understanding the role of negative evidence is central for psychological theories of inductive generalisation. (shrink)

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Three experiments with preschool- and young school-aged children (N = 75 and 53) explored the kinds of relations children detect in samples of instances (descriptive problem) and how they generalize those relations to new instances (inferential problem). Each experiment initially presented a perfect biconditional relation between two features (e.g., all and only frogs are blue). Additional examples undermined one of the component conditional relations (not all frogs are blue) but supported another (only frogs are blue). Preschool-aged children did not distinguish (...) between supported and undermined relations. Older children did show the distinction, at least when the test instances were clearly drawn from the same population as the training instances. Results suggest that younger children’s difficulties may stem from the demands of using imperfect correlations for predictions. Older children seemed sensitive to the inferential problem of using samples to make predictions about populations. (shrink)

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