Abstract
Recency effects are well documented in the adult and infant literature: recognition and recall memory are better for recently occurring events. We explore recency effects in infant categorization, which does not merely involve memory for individual items, but the formation of abstract category representations. We present a computational model of infant categorization that simulates category learning in 10-month-olds. The model predicts that recency effects outweigh previously reported order effects for the same stimuli. According to the model, infant behaviour at test should depend mainly on the identity of the most recent training item. We evaluate these predictions in a series of experiments with 10-month-old infants. Our results show that infant behaviour confirms the model’s prediction. In particular, at test infants exhibited a preference for a category outlier over the category average only if the final training item had been close to the average, rather than distant from it. Our results are consistent with a view of categorization as a highly dynamic process where the end result of category learning is not the overall average of all stimuli encountered, but rather a fluid representation that moves depending on moment-to-moment novelty. We argue that this is a desirable property of a flexible cognitive system that adapts rapidly to different contexts.
Highlights
The ability to form categories is a core cognitive ability that allows us to structure the world
The result of our simulations clearly shows that the end stimulus type is the dominant factor in determining responses at test
This best-matching unit (BMU) is either updated to match the incoming stimulus or, if there is a different, better-matching unit, that other unit is chosen as the BMU
Summary
The ability to form categories is a core cognitive ability that allows us to structure the world. Infant category learning is often studied in familiarization paradigms, where a sequence of objects from the target category is presented as a learning phase, prior to one or several test trials during which the established representation is probed. Such familiarization procedures can be used to tap into already existing category representations [10,11], where the assumption is that they activate previously acquired knowledge. The focus in the current paper is, by contrast, on learning novel categories where prior representations cannot be used for grouping objects. Implicit in this logic is the idea that the category representation is, by that point, fixed, or at least converging to a category centroid
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
