Semantic determinants of memorability.
Human memory
Memorability predictions
Psycholinguistics
Semantic representations
Journal
Cognition
ISSN: 1873-7838
Titre abrégé: Cognition
Pays: Netherlands
ID NLM: 0367541
Informations de publication
Date de publication:
10 2023
10 2023
Historique:
received:
19
01
2022
revised:
26
03
2023
accepted:
11
05
2023
medline:
14
8
2023
pubmed:
14
7
2023
entrez:
13
7
2023
Statut:
ppublish
Résumé
We examine why some words are more memorable than others by using predictive machine learning models applied to word recognition and recall datasets. Our approach provides more accurate out-of-sample predictions for recognition and recall than previous psychological models, and outperforms human participants in new studies of memorability prediction. Our approach's predictive power stems from its ability to capture the semantic determinants of memorability in a data-driven manner. We identify which semantic categories are important for memorability and show that, unlike features such as word frequency that influence recognition and recall differently, the memorability of semantic categories is consistent across recognition and recall. Our paper sheds light on the complex psychological drivers of memorability, and in doing so illustrates the power of machine learning methods for psychological theory development.
Identifiants
pubmed: 37442022
pii: S0010-0277(23)00131-2
doi: 10.1016/j.cognition.2023.105497
pii:
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
105497Informations de copyright
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