@article{siewSpreadrPackageSimulate2019,
  title = {Spreadr: {{An R}} Package to Simulate Spreading Activation in a Network},
  shorttitle = {Spreadr},
  author = {Siew, Cynthia S. Q.},
  date = {2019-04-01},
  journaltitle = {Behavior Research Methods},
  shortjournal = {Behav Res},
  volume = {51},
  pages = {910--929},
  issn = {1554-3528},
  doi = {10.3758/s13428-018-1186-5},
  url = {https://doi.org/10.3758/s13428-018-1186-5},
  urldate = {2020-12-19},
  abstract = {The notion of spreading activation is a central theme in the cognitive sciences; however, the tools for implementing spreading activation computationally are not as readily available. This article introduces the spreadr R package, which can implement spreading activation within a specified network structure. The algorithmic method implemented in the spreadr subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (Frontiers in Psychology, 2, 369, 2011), who viewed activation as a fixed cognitive resource that could “spread” among connected nodes in a network. Three sets of simulations were conducted using the package. The first set of simulations successfully reproduced the results reported in Vitevitch et al. (Frontiers in Psychology, 2, 369, 2011), who showed that a simple mechanism of spreading activation could account for the clustering~coefficient effect in spoken word recognition. The second set of simulations showed that the same mechanism could be extended to account for higher false alarm rates for low~clustering~coefficient words in a false memory task. The final set of simulations demonstrated how spreading activation could be applied to a semantic network to account for semantic priming effects. It is hoped that this package will encourage cognitive and language scientists to explicitly consider how the structures of cognitive systems such as the mental lexicon and semantic memory interact with the process of spreading activation.},
  number = {2}
}

@article{chanInfluencePhonologicalNeighborhood2009,
  title = {The {{Influence}} of the {{Phonological Neighborhood Clustering}}-{{Coefficient}} on {{Spoken Word Recognition}}},
  author = {Chan, Kit Ying and Vitevitch, Michael S.},
  date = {2009-12},
  journaltitle = {Journal of experimental psychology. Human perception and performance},
  shortjournal = {J Exp Psychol Hum Percept Perform},
  volume = {35},
  pages = {1934--1949},
  issn = {0096-1523},
  doi = {10.1037/a0016902},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2791911/},
  urldate = {2021-01-17},
  abstract = {Clustering coefficient—a measure derived from the new science of networks—refers to the proportion of phonological neighbors of a target word that are also neighbors of each other. Consider the words bat, hat, and can, all of which are neighbors of the word cat; the words bat and hat are also neighbors of each other. In a perceptual identification task, words with a low clustering coefficient (i.e., few neighbors are neighbors of each other) were more accurately identified than words with a high clustering coefficient (i.e., many neighbors are neighbors of each other). In a lexical decision task, words with a low clustering coefficient were responded to more quickly than words with a high clustering coefficient. These findings suggest that the structure of the lexicon, that is the similarity relationships among neighbors of the target word measured by clustering coefficient, influences lexical access in spoken word recognition. Simulations of the TRACE and Shortlist models of spoken word recognition failed to account for the present findings. A framework for a new model of spoken word recognition is proposed.},
  eprint = {19968444},
  eprinttype = {pmid},
  number = {6},
  pmcid = {PMC2791911}
}

@article{vitevitchSimulatingRetrievalHighly2011,
  title = {Simulating {{Retrieval}} from a {{Highly Clustered Network}}: {{Implications}} for {{Spoken Word Recognition}}},
  shorttitle = {Simulating {{Retrieval}} from a {{Highly Clustered Network}}},
  author = {Vitevitch, Michael S. and Ercal, Gunes and Adagarla, Bhargav},
  date = {2011},
  journaltitle = {Frontiers in Psychology},
  shortjournal = {Front. Psychol.},
  volume = {2},
  publisher = {{Frontiers}},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2011.00369},
  url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2011.00369/full},
  urldate = {2021-01-17},
  abstract = {Network science describes how entities in complex systems interact, and argues that the structure of the network influences processing. Clustering coefficient, C—one measure of network structure—refers to the extent to which neighbors of a node are also neighbors of each other. Previous simulations suggest that networks with low C dissipate information (or disease) to a large portion of the network, whereas in networks with high C information (or disease) tends to be constrained to a smaller portion of the network (Newman, 2003). In the present simulation we examined how C influenced the spread of activation to a specific node, simulating retrieval of a specific lexical item in a phonological network. The results of the network simulation showed that words with lower C had higher activation values (indicating faster or more accurate retrieval from the lexicon) than words with higher C. These results suggest that a simple mechanism for lexical retrieval can account for the observations made in Chan and Vitevitch (2009), and have implications for diffusion dynamics in other fields.},
  file = {/home/ning/Zotero/storage/YELTV7EQ/Vitevitch et al. - 2011 - Simulating Retrieval from a Highly Clustered Netwo.pdf},
  keywords = {clustering coefficient,diffusion dynamics,mental Lexicon,Network Science,simulation,word recognition},
  langid = {english}
}