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134 results on '"Valentina Gliozzi"'

1. A Simple Computational Model of Semantic Priming in 18-Month-Olds

Author

Valentina Gliozzi

Abstract

We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying the effect of Inter Stimulus Interval on these priming effects. Our model explains taxonomic priming between words by "semantic feature overlap," whereas associative priming between words is explained by Hebbian links between semantic representations derived from "co-occurrence relations" between words (or their referents). From a developmental perspective, any delay in the emergence of taxonomic priming compared to associative priming during infancy seems paradoxical since feature overlap "per se" need not be learned. We address this paradox in the model by showing that "feature overlap" itself is an emergent process. The model successfully replicates infant data related to Inter Stimulus Interval effects in priming experiments and makes testable predictions.

Published
2024
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2. Infant categorization as a dynamic process linked to memory

Author

Nadja Althaus, Valentina Gliozzi, Julien Mayor, and Kim Plunkett

Subjects
infant categorization, computational models, memory, infant learning, Science
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.

Published
2020
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31. Rational Closure in SHIQ.

Author

Laura Giordano 0001, Valentina Gliozzi, Nicola Olivetti, and Gian Luca Pozzato

Published
2014

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