In their paper “Recognizing a zebra from its stripes and the stripes from ‘zebra’: the role of verbal labels in select- ing category relevant information”, Perry and Lupyan (P&L) argue that sparse categories impose high selective attention demands, requiring one to choose what to attend to, compared to dense categories for which several dimensions can or must be used. Furthermore, P&L argue that labels are more useful for sparse categories because they “tune” perception towards the dis- criminative properties of objects. P&L show that categories for which there is substantial agreement by participants on what the common feature of that category is, have lower selective attention demands and benefit more from the inclusion of a label. In this commentary we focus on what constitutes a sparse category. We will attempt to make the case for the importance of considering both how many discriminative features as well as how many common but not discriminative features a category has in order to evaluate category sparsity. We propose that for a complete understanding of the selective attention processes and labelling effects on category learning one must take the categorisation space, rather than each isolated category, into account.
Zebras and antelopes: category sparsity as the result of the relations between objects and within categories
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