Distant Connectivity and Multiple-Step Priming in Large-Scale Semantic Networks

Autor/inn/en	Kumar, Abhilasha A.; Balota, David A.; Steyvers, Mark
Titel	Distant Connectivity and Multiple-Step Priming in Large-Scale Semantic Networks
Quelle	In: Journal of Experimental Psychology: Learning, Memory, and Cognition, 46 (2020) 12, S.2261-2276 (16 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kumar, Abhilasha A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0278-7393
DOI	10.1037/xlm0000793
Schlagwörter	Semantics; Networks; Correlation; Semitic Languages; Task Analysis; Associative Learning; Decision Making; Models; Comparative Analysis; College Students; Reaction Time; Missouri (Saint Louis) + Suchen Sie Ihr Suchwort? Semantik; Korrelation; Arabisch; Hebräisch; Aufgabenanalyse; Decision-making; Entscheidungsfindung; Analogiemodell; Collegestudent; Reaktionsvermögen
Abstract	We examined 3 different network models of representing semantic knowledge (5,018-word directed and undirected step distance networks, and an association-correlation network) to predict lexical priming effects. In Experiment 1, participants made semantic relatedness judgments for word pairs with varying path lengths. Response latencies for judgments followed a quadratic relationship with network path lengths, replicating and extending a recent pattern reported by Kenett, Levi, Anaki, and Faust (2017) for an 800-word association-correlation network in Hebrew. In Experiment 2, participants identified target words in a progressive demasking task, immediately following a briefly presented prime (120 ms). Response latencies to identify the target showed a linear trend for all network path lengths. Importantly, there were statistically significant differences between relatively distant words in the step distance networks, for example, path lengths 4 and beyond, suggesting that association networks can indeed capture distant functional semantic relationships. Additional comparisons with 2 distributional models (LSA and "word2vec") suggested that distributional models also successfully predicted response latencies, although there appear to be fundamental differences in the types of semantic relationships captured by the different models. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01