Dataset Information

Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning.

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 better-connected 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.

SUBMITTER: Mak MHC

PROVIDER: S-EPMC7546987 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning.

Mak Matthew H C MHC Twitchell Hope H

Psychonomic bulletin & review 20201001 5

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 ...[more]

PMID: 32638328

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Project description:Semantic processing of visually presented words can be identified both on behavioral and neurophysiological evidence. One of the major discoveries of the last decades is the demonstration that these signatures of semantic processing, initially observed for consciously perceived words, can also be detected for masked words inaccessible to conscious reports. In this context, the distinction between conscious and unconscious verbal semantic processing constitutes a challenging scientific issue. A prominent view considered that while conscious representations are subject to executive control, unconscious ones would operate automatically in a modular way, independent from control and top-down influences. Recent findings challenged this view by revealing that endogenous attention and task-setting can have a strong influence on unconscious processing. However, one of the major arguments supporting the automaticity of unconscious semantic processing still stands, stemming from a seminal observation reported by Marcel in 1980 about polysemous words. In the present study we reexamined this evidence. We present a combination of behavioral and event-related-potentials (ERPs) results that refute this view by showing that the current conscious semantic context has a major and similar influence on the semantic processing of both visible and masked polysemous words. In a classical lexical decision task, a polysemous word was preceded by a word which defined the current semantic context. Crucially, this context was associated with only one of the two meanings of the polysemous word, and was followed by a word/pseudo-word target. Behavioral and electrophysiological evidence of semantic priming of target words by masked polysemous words was strongly dependent on the conscious context. Moreover, we describe a new type of influence related to the response-code used to answer for target words in the lexical decision task: unconscious semantic priming constrained by the conscious context was present both in behavior and ERPs exclusively when right-handed subjects were instructed to respond to words with their right hand. The strong and respective influences of conscious context and response-code on semantic processing of masked polysemous words demonstrate that unconscious verbal semantic representations are not automatic.

Project description:BackgroundMany biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks.ResultsThe connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in betagamma-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment.ConclusionHere, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate that E. coli has adjusted its metabolic network to a changing environment by replacing the relatively central enzymes for better adapted orthologs from other prokaryotic species.

Dataset Information

Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning.

Publications

Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning.

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