Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring ne
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BRIEF REPORT
Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning Matthew H. C. Mak 1 & Hope Twitchell 2
# The Author(s) 2020
Abstract Here, we view the mental lexicon as a semantic network where words are connected if they are semantically related. Steyvers and Tenenbaum (Cognitive Science, 29, 41–78, 2005) proposed that the growth of semantic networks follows preferential attachment, the observation that new nodes are more likely to connect to preexisting nodes that are more well connected (i.e., the rich get richer). If this is the case, well-connected known words should be better at acquiring new links than poorly connected words. We tested this prediction in three paired-associate learning (PAL) experiments in which participants memorized arbitrary cue–response word pairs. We manipulated the semantic connectivity of the cue words, indexed by the words’ free associative degree centrality. Experiment 1 is a reanalysis of the PAL data from Qiu and Johns (Psychonomic Bulletin & Review, 27, 114– 121, 2020), in which young adults remembered 40 cue–response word pairs (e.g., nature–chain) and completed a cued recall task. Experiment 2 is a preregistered replication of Qiu and Johns. Experiment 3 addressed some limitations in Qiu and Johns’s design by using pseudowords as the response items (e.g., boot–arruity). The three experiments converged to show that cue words of higher degree centrality facilitated the recall/recognition of the response items, providing support for the notion that betterconnected words have a greater ability to acquire new links (i.e., the rich do get richer). Importantly, while degree centrality consistently accounted for significant portions of variance in PAL accuracy, other psycholinguistic variables (e.g., concreteness, contextual diversity) did not, suggesting that degree centrality is a distinct variable that affects the ease of verbal associative learning. Keywords Preferential attachment . Semantic network . Degree centrality . Paired-associate learning (PAL) . Adult free association
Network science can be applied to any structure made up of nodes connected to each other through links (Hills, Maouene, Riordan, & Smith, 2010). For example, nodes might be people, and links might represent friendship or sexual contact. In recent years, network science has been applied to the study of complex cognitive systems, including the mental lexicon. Some of these studies (e.g., Griffiths, Steyvers, & Tenenbaum, 2007) view the mental lexicon as semantic
* Matthew H. C. Mak [email protected] 1
Department of Experimental Psychology, Division of Medical Sciences, University of Oxford, Oxford, UK
2
College of Behavioral & Social Sciences, Southeastern University, Lakeland, FL, USA
networks, where word nodes are linked together by semantic relatedness. In their seminal paper, Steyvers and Tenenbaum (2005) reported that adult semantic networks possess structural properties that are belie
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