Feature Inference Learning and Eyetracking
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Besides traditional supervised classification learning, people can learn categories by inferring...
Besides traditional supervised classification learning, people can learn categories by inferring
the missing features of category members. It has been proposed that feature inference
learning promotes learning a category’s internal structure (e.g., its typical features and
interfeature correlations) whereas classification promotes the learning of diagnostic information.
We tracked learners’ eye movements and found in Experiment 1 that inference
learners indeed fixated features that were unnecessary for inferring the missing feature,
behavior consistent with acquiring the categories’ internal structure. However, Experiments
3 and 4 showed that fixations were generally limited to features that needed to
be predicted on future trials. We conclude that inference learning induces both supervised
and unsupervised learning of category-to-feature associations rather than a general motivation
to learn the internal structure of categories.
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