|
|
- Network science in experimental psychology.
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: This introduction to the special issue entitled “Network Science in Experimental Psychology” describes how complex networks are used by experimental psychologists to examine questions from a range of topics in psychology. Complex networks use nodes to represent individual entities and connections between nodes that are related in some way. The overall weblike structure that emerges influences the processes that operate in that system. The articles summarized here illustrate the various definitions of nodes (e.g., people, words, parts of the brain) and connections between nodes (e.g., friendships, semantic similarity, coactivation of brain regions) and also illustrate a wide range of metrics that reveal information that could not be found using contemporary and conventional approaches. The guest editors and authors hope that these examples encourage other researchers to apply the computational techniques from network science to their questions of interest to make new and interesting discoveries. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Thu, 13 Mar 2025 00:00:00 GMT
DOI: 10.1037/cep0000367
- Predicting individual vocabulary learning: The importance of approximating
toddlers’ linguistic environment.-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Using network representations of the lexicon has expanded our understanding of vocabulary growth processes and vocabulary structure during early development. These models of vocabulary development have used multiple types of sources to create lexical representations. More recently, Weber and Colunga (2022) demonstrated that predictions of early vocabulary norms can be improved by using network representations based on a corpus incorporating language a young child might typically hear. The present work goes a step further by evaluating the accuracy of network representations for predicting individual children’s word learning that are based on embeddings that are readily available or embeddings gathered from the same child language corpus. We predicted the specific words that individual children add to their vocabulary over time, using a longitudinal data set of 86 monolingual English-speaking toddler’s changing vocabulary from 18 to 30 months of age. The toddler-based network predicted word learning more accurately than the off-the-shelf network. Further, there was an advantage for prediction methods that took into account the individual child’s particular network structure rather than overall network connectivity. These results highlight the importance of tailoring representational and processing choices to the population of interest. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Thu, 13 Mar 2025 00:00:00 GMT
DOI: 10.1037/cep0000364
- Leveraging social network data to ground multilingual background measures:
The case of general and socially based language entropy.-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Recent research on multilingualism highlights the role of language diversity in modulating the cognitive capacities of communication and suggests a gap in available measures for quantifying socially realistic language experience. One questionnaire-based measure that potentially fills this gap is Language Entropy (e.g., Gullifer & Titone, 2018, 2020), which quantifies the balance between compartmentalised and integrated language use. However, an open question is whether questionnaire-based Language Entropy is a valid reflection of socially realistic language behaviours. To address this question, we grounded questionnaire-based Language Entropy using personal social network data for a linguistically diverse sample of speakers of French and English in the city of Montréal (n = 95). Specifically, we used exploratory factor analysis to characterise the factor structures resulting from questionnaire-based and social network-based Entropy. In addition, we examined the generalisability and stability of the relationship between both entropies across three bilingual groups with different social network compositions: simultaneous, English-dominant, and French-dominant. Our findings indicated that both questionnaire-based and social network-based entropies loaded onto the same factors and that the relationship between them was not affected by group differences in social network composition or by context. This suggests that questionnaire-based Language Entropy aligns well with social network-based Entropy and that this relationship is stable across different sociolinguistic realities, validating Language Entropy as a useful tool for quantifying language diversity. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Mon, 30 Dec 2024 00:00:00 GMT
DOI: 10.1037/cep0000352
- Collective memory and fluency tasks: Leveraging network analysis for a
richer understanding of collective cognition.-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Collective memory broadly refers to the memories shared by a group of people. Interest in collective memory among cognitive psychologists has boomed in recent years, with many studies leveraging fluency tasks to probe what events and people come to mind given a prompt. As other research using fluency tasks has benefitted greatly from network analysis (e.g., semantic memory research), it seems there is an opportunity to deepen our understanding of collective cognition and changes in collective cognition by adopting a network perspective. In the current article, we ask whether collective memory investigations could be enriched by harnessing the tools of network science. We start by reviewing the relevant collective memory literature and touch on the deep semantic memory literature to the extent it provides ties to network analysis for present goals. Our novel contributions to the topic include the introduction of a large fluency data set collected over the course of a decade as part of a task embedded within several research projects. We conduct several descriptive analyses and initial, proof-of-concept network analyses examining collective memory for U.S. cities. Some cities—those that are recalled most frequently—are recalled at similar rates and in similar output positions across time and task contexts. Our network approach suggests that recall transitions (e.g., recalling Los Angeles and San Francisco in adjacent positions) are made at similar rates as well. Together, these complementary approaches suggest a striking stability in both what people recall and their ordering, providing a window into the composition of collective memories. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Mon, 30 Dec 2024 00:00:00 GMT
DOI: 10.1037/cep0000353
- Evidence of community structure in phonological networks of multiple
languages.-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Thousands of phonological word forms known to a speaker can be organised as a lexical network using the tools of network science. In these networks, nodes represent words and edges are placed between phonological neighbours. Previous work has shown that phonological networks of various languages have similar macrolevel network properties. The present study aimed to investigate if phonological networks of different languages also have similar mesolevel properties, specifically, the presence of robust community structure. Prior community detection analyses revealed robust community structure for English. Community detection analyses conducted on French, German, Dutch, and Spanish networks indicate that all networks showed strong evidence of community structure—mesolevel clustering of word forms whereby larger communities tended to contain shorter, frequent words with many phonological neighbours. Words of the same community tended to share similar phonotactic structures. Results suggest that the organisation of phonological word forms in language are governed by similar principles that could have important implications for lexical processing. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Mon, 21 Oct 2024 00:00:00 GMT
DOI: 10.1037/cep0000357
- Modelling the bilingual lexicon as a multiplex phonological network.
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Phonological word form networks of the mental lexicon are of psycholinguistic relevance, offering insights into the efficiency of lexical access. While much research has concentrated on first languages, there is growing evidence suggesting that phonological networks of second languages are equally significant for lexical processes. Bilingual language processing is proposed to involve the integration of first and second languages, with lexical activation spreading between similar word forms in both languages. Multiplex networks provide a framework to combine different phonological networks, allowing for the analysis of the integrated lexical system’s behaviour during lexical processing. In the context of the present study, which focusses on German learners of English as a second language, a multiplex network analysis was constructed to model the interactive complexity of the bilingual mental lexicon. The study tested cross-linguistic effects in a word recognition task using English stimuli. Results revealed that during lexical processing in their second language English, German speakers also activate phonological neighbours from German. In addition, the bilinguals are attuned to the interconnectedness (i.e., clustering) of the German and English neighbours with one another in the phonological neighbourhood of the English target words. These findings can contribute to the ongoing debate on the degree of integration in the bilingual mental lexicon and shed light on the role that phonological networks can play in modelling bilingual lexical processing. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Mon, 21 Oct 2024 00:00:00 GMT
DOI: 10.1037/cep0000351
- Complex meanings shape early noun and verb vocabulary structure and
learning.-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Verbs and nouns vary in many ways—including in how they are used in language and in the timing of their early learning. We compare the distribution of semantic features that comprise early acquired verb and noun meanings and measure their effect on learning. First, couched in prior literature, we use semantic feature data to establish that features pattern on a hierarchy of complexity, with perceptual features being less complex than other features like encyclopaedic features. Second, given overall semantic and syntactic differences between nouns and verbs, we hypothesize that the preference for directly perceptible features observed for nouns will be attenuated for verbs. Building on prior work using semantic features and semantic networks in nouns, we find that compared to early learned nouns (N = 359), early learned verbs (N = 103) have meanings disproportionately built from complex information inaccessible to the senses. Third, we find that 16- to 30-month-old children’s early noun and verb vocabularies (N = 3,804) show semantic relationships that differ in their use of this complex information from the beginning of vocabulary development. Last, we find that the complexity of nouns’ and verbs’ meanings affects their typical order of learning in early vocabulary development. Complexity differs in early noun and verb meanings, affects the semantic structure of children’s vocabularies, and shapes the course of word learning. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Mon, 21 Oct 2024 00:00:00 GMT
DOI: 10.1037/cep0000355
- Dynamics of second-language learners’ semantic memory networks: Evidence
from a snowball sampling paradigm.-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: This article provides an analysis of structural changes in second-language (L2)-based semantic memory networks—graphs composed of L2 words as nodes and semantic relations between them as edges, during L2 learning. We used snowball sampling paradigm to create individual semantic networks of participants divided into two groups differing in L2 learning time and then compare their structural characteristics cross-sectionally. The results showed that as L2 learning progresses, semantic memory networks tend to become more connected (by increasing the average node degree), more efficient (by decreasing the average shortest path length), less fragmented (by decreasing the modularity), less centralized (by decreasing the centralization), less dense (by decreasing the density), and no more “small-worlded” (by similar average clustering coefficients and small-world indices). The findings provide quantitative evidence of how the duration of L2 learning shapes the structure of L2-based semantic memory networks generated in the snowball sampling paradigm. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Thu, 17 Oct 2024 00:00:00 GMT
DOI: 10.1037/cep0000350
- Some challenges in using multilayer networks to bridge brain and mind.
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: The application of techniques from network science to create single-layer networks of the brain and mind has resulted in significant advances in the neuro- (i.e., structural and functional brain networks) and cognitive sciences (i.e., cognitive network science). Recent advances in network science on multilayer networks increase the possibility that a “network of networks” might finally connect the physical brain to the intangible mind, much like physical fibre optic cables and wires connect to other layers of the internet to allow intangible social networks to form in various social media platforms. Several advances in structural brain networks, functional brain networks, cognitive networks, and multilayer networks are briefly reviewed. The possibility that these single-layer networks can be connected in a multilayer network to connect the brain to the mind is discussed, as well as some of the challenges that face such an ambitious endeavour. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Mon, 29 Jul 2024 00:00:00 GMT
DOI: 10.1037/cep0000341
- How retrieval processes change with age: Exploring age differences in
semantic network and retrieval dynamics.-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: This study investigated the impact of age on semantic memory networks and retrieval dynamics using a single-list free recall paradigm, involving 318 participants. The younger group, with 175 participants aged 25–55 years (M = 46.68 years; SD = 10.69), and the older group, consisting of 143 participants aged 61–88 years (M = 68.71 years; SD = 6.09), completed a word recall test to assess delayed recall performance. Semantic memory networks were constructed from recall data by analyzing the co-occurrence and sequence of recalled words. We observed significant differences in network structure, where the older group displayed higher average shortest path length and modularity values, indicative of less integrated networks, while the younger group exhibited a higher clustering coefficient, suggesting a more interconnected network. In terms of retrieval dynamics, both groups showed a temporal contiguity effect with forward asymmetry. However, this effect was less pronounced in older adults. The study also identified participants that diverted from the average dynamic curves: one subgroup relied on nontemporal mechanisms, and the other employed a backward direction in memory search. Participants utilizing forward temporal associations demonstrated the highest recall performance. Overall, our findings suggest that lower free recall performance in older adults may be related to a diminished capacity to reinstate temporal context for retrieval and distinct differences in their semantic memory network structure. Specifically, older adults appear to exhibit networks with a less flexible, small-world-like structure. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
PubDate: Thu, 25 Jul 2024 00:00:00 GMT
DOI: 10.1037/cep0000332
|
Subscription journal
