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Mapping Mental Representations With Free Associations: A Tutorial Using the R Package associatoR Cover

Mapping Mental Representations With Free Associations: A Tutorial Using the R Package associatoR

Open Access
|Jan 2025

References

  1. 1Aeschbach, S., & Wulff, D. U. (2023). associatoR. GitHub. https://github.com/samuelae/associatoR
  2. 2Atari, M., Xue, M. J., Park, P. S., Blasi, D., & Henrich, J. (2023). Which humans? PsyArXiv. 10.31234/osf.io/5b26t
  3. 3Avis, M., Forbes, S., & Ferguson, S. (2014). The brand personality of rocks: A critical evaluation of a brand personality scale. Marketing Theory, 14(4), 451475. 10.1177/1470593113512323
  4. 4Barone, B., Rodrigues, H., Nogueira, R. M., Guimarães, K. R. L. S. L. D. Q., & Behrens, J. H. (2020). What about sustainability? Understanding consumers’ conceptual representations through free word association. International Journal of Consumer Studies, 44(1), 4452. 10.1111/ijcs.12543
  5. 5Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), Article P10008. 10.1088/1742-5468/2008/10/P10008
  6. 6Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904911. 10.3758/s13428-013-0403-5
  7. 7Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Tulio Ribeiro, M., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv. 10.48550/arXiv.2303.12712
  8. 8Bullinaria, J. A., & Levy, J. P. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39(3), 510526. 10.3758/BF03193020
  9. 9Bullinaria, J. A., & Levy, J. P. (2012). Extracting semantic representations from word co-occurrence statistics: Stop-lists, stemming, and SVD. Behavior Research Methods, 44(3), 890907. 10.3758/s13428-011-0183-8
  10. 10Cassani, G., Günther, F., Attanasio, G., Bianchi, F., & Marelli, M. (2023). Meaning modulations and stability in large language models: An analysis of BERT embeddings for psycholinguistic research. PsyArXiv. 10.31234/osf.io/b45ys
  11. 11Cave, S., & Dihal, K. (2019). Hopes and fears for intelligent machines in fiction and reality. Nature Machine Intelligence, 1(2), 7478. 10.1038/s42256-019-0020-9
  12. 12Chari, T., & Pachter, L. (2023). The specious art of single-cell genomics. PLOS Computational Biology, 19(8), e1011288. 10.1371/journal.pcbi.1011288
  13. 13Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 2229. https://aclanthology.org/J90-1003
  14. 14Coane, J. H., Cipollini, J., Barrett, T. E., Kavaler, J., & Umanath, S. (2023). Lay definitions of intelligence, knowledge, and memory: Inter- and independence of constructs. Journal of Intelligence, 11(5), Article 84. 10.3390/jintelligence11050084
  15. 15De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2019). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods, 51(3), 9871006. 10.3758/s13428-018-1115-7
  16. 16De Deyne, S., Navarro, D. J., & Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations. Behavior Research Methods, 45(2), 480498. 10.3758/s13428-012-0260-7
  17. 17Dubossarsky, H., De Deyne, S., & Hills, T. T. (2017). Quantifying the structure of free association networks across the life span. Developmental Psychology, 53(8), 15601570. 10.1037/dev0000347
  18. 18Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., & Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, Article 104743. 10.1016/j.engappai.2022.104743
  19. 19Feinerer, I., Hornik, K., & Meyer, D. (2008). Text mining infrastructure in R. Journal of Statistical Software, 25(5), 154. 10.18637/jss.v025.i05
  20. 20File, B., Keczer, Z., Vancsó, A., Böthe, B., Tóth-Király, I., Hunyadi, M., Ujhelyi, A., Ulbert, I., Góth, J., & Orosz, G. (2019). Emergence of polarized opinions from free association networks. Behavior Research Methods, 51(1), 280294. 10.3758/s13428-018-1090-z
  21. 21Galton, F. (1883). Inquiries into human faculty and its development. MacMillan. 10.1037/14178-000
  22. 22Gao, C., Shinkareva, S. V., & Desai, R. H. (2023). SCOPE: The South Carolina psycholinguistic metabase. Behavior Research Methods, 55(6), 28532884. 10.3758/s13428-022-01934-0
  23. 23Haslbeck, J. M. B., & Wulff, D. U. (2020). Estimating the number of clusters via a corrected clustering instability. Computational Statistics, 35(4), 18791894. 10.1007/s00180-020-00981-5
  24. 24Hennig, C. (2015). Clustering strategy and method selection. arXiv. 10.48550/arXiv.1503.02059
  25. 25Hertwig, R., Wulff, D. U., & Mata, R. (2019). Three gaps and what they may mean for risk preference. Philosophical Transactions of the Royal Society B, 374(1766), Article 20180140. 10.1098/rstb.2018.0140
  26. 26Hussain, Z., Binz, M., Mata, R., & Wulff, D. U. (2024). A tutorial on open-source large language models for behavioral science. Behavior Research Methods, 124. 10.3758/s13428-024-02455-8
  27. 27Hussain, Z., Mata, R., Newell, B. R., & Wulff, D. U. (2024). Probing the contents of text, behavior, and brain data toward improving human-LLM alignment [Manuscript in preparation].
  28. 28Hussain, Z., Mata, R., & Wulff, D. U. (2024). Novel embeddings improve the prediction of risk perception. EPJ Data Science, 13, Article 38. 10.1140/epjds/s13688-024-00478-x
  29. 29Jung, C. G. (1910). The association method. The American Journal of Psychology, 21(2), 219269. 10.2307/1413002
  30. 30Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8, Article 407. 10.3389/fnhum.2014.00407
  31. 31Kiss, G. R., Armstrong, C., Milroy, R., & Piper, J. (1973). An associative thesaurus of English and its computer analysis. In A. J. Aitken (Ed.), The computer and literary studies (pp. 153165). University Press.
  32. 32Koll, O., von Wallpach, S., & Kreuzer, M. (2010). Multi-method research on consumer–brand associations: Comparing free associations, storytelling, and collages. Psychology & Marketing, 27(6), 584602. 10.1002/mar.20346
  33. 33Lakens, D. (2024). When and how to deviate from a preregistration. Collabra: Psychology, 10(1), Article 117094. 10.1525/collabra.117094
  34. 34Mata, R., Frey, R., Richter, D., Schupp, J., & Hertwig, R. (2018). Risk preference: A view from psychology. Journal of Economic Perspectives, 32(2), 155172. 10.1257/jep.32.2.155
  35. 35McInnes, L., Healy, J., & Melville, J. (2018). UMAP: uniform manifold approximation and projection for dimension reduction. arXiv. 10.48550/arXiv.1802.03426
  36. 36Morais, A. S., Olsson, H., & Schooler, L. J. (2013). Mapping the structure of semantic memory. Cognitive Science, 37(1), 125145. 10.1111/cogs.12013
  37. 37Nelson, D. L., Mcevoy, C. L., & Dennis, S. (2000). What is free association and what does it measure? Memory & Cognition, 28(6), 887899. 10.3758/BF03209337
  38. 38Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (2004). The University of South Florida free association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & Computers, 36(3), 402407. 10.3758/BF03195588
  39. 39OpenAI. (2023). GPT-4 technical report. arXiv. 10.48550/arXiv.2303.08774
  40. 40R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
  41. 41Richie, R., & Bhatia, S. (2021). Similarity judgment within and across categories: A comprehensive model comparison. Cognitive Science, 45(8), Article e13030. 10.1111/cogs.13030
  42. 42Schnabel, K., & Asendorpf, J. B. (2013). Free associations as a measure of stable implicit attitudes. European Journal of Personality, 27(1), 3950. 10.1002/per.1890
  43. 43Selwyn, N., & Gallo Cordoba, B. (2022). Australian public understandings of artificial intelligence. AI & Society, 37(4), 16451662. 10.1007/s00146-021-01268-z
  44. 44Stella, M., De Nigris, S., Aloric, A., & Siew, C. S. (2019). Forma mentis networks quantify crucial differences in STEM perception between students and experts. PloS one, 14(10), e0222870. 10.1371/journal.pone.0222870
  45. 45Sternberg, R. J., Conway, B. E., Ketron, J. L., & Bernstein, M. (1981). People’s conceptions of intelligence. Journal of Personality and Social Psychology, 41(1), 3755. 10.1037/0022-3514.41.1.37
  46. 46Steyvers, M., & Tenenbaum, J. B. (2005). The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science, 29(1), 4178. 10.1207/s15516709cog2901_3
  47. 47Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Grant, E., Groen, I., Achterberg, J., Tenenbaum, J. B., Collins, K. M., Hermann, K. L., Oktar, K., Greff, K., Hebart, M. N., Jacoby, N., Zhang, Q., Marjieh, R., … Griffiths, T. L. (2023). Getting aligned on representational alignment. arXiv. 10.48550/arXiv.2310.13018
  48. 48Szalay, L. B., & Brent, J. E. (1967). The analysis of cultural meanings through free verbal associations. The Journal of Social Psychology, 72(2), 161187. 10.1080/00224545.1967.9922313
  49. 49Szollosi, A., & Newell, B. R. (2020). People as intuitive scientists: Reconsidering statistical explanations of decision making. Trends in Cognitive Sciences, 24(12), 10081018. 10.1016/j.tics.2020.09.005
  50. 50Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411423. 10.1111/1467-9868.00293
  51. 51Vankrunkelsven, H., Verheyen, S., Storms, G., & De Deyne, S. (2018). Predicting lexical norms: A comparison between a word association model and text-based word co-occurrence models. Journal of Cognition, 1(1), Article 45. 10.5334/joc.50
  52. 52Wang, Y., Huang, H., Rudin, C., & Shaposhnik, Y. (2021). Understanding how dimension reduction tools work: An empirical approach to deciphering t-SNE, UMAP, TriMAP, and PaCMAP for data visualization. The Journal of Machine Learning Research, 22(1), 91299201.
  53. 53Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45(4), 11911207. 10.3758/s13428-012-0314-x
  54. 54Wickham, H. (2023). Stringr: Simple, consistent wrappers for common string operations [R package version 1.5.1, https://github.com/tidyverse/stringr]. https://stringr.tidyverse.org
  55. 55Wijffels, J. (2023). Udpipe: Tokenization, parts of speech tagging, lemmatization and dependency parsing with the ‘UDPipe’ ‘NLP’ toolkit [R package version 0.8.11]. https://CRAN.R-project.org/package=udpipe
  56. 56Wulff, D. U., Aeschbach, S., Deyne, S. D., & Mata, R. (2022). Data from the MySWOW proof-of-concept study: Linking individual semantic networks and cognitive performance. Journal of Open Psychology Data, 10(1), Article 5. 10.5334/jopd.55
  57. 57Wulff, D. U., De Deyne, S., Aeschbach, S., & Mata, R. (2022). Using network science to understand the aging lexicon: Linking individuals’ experience, semantic networks, and cognitive performance. Topics in Cognitive Science, 14(1), 93110. 10.1111/tops.12586
  58. 58Wulff, D. U., De Deyne, S., Jones, M. N., & Mata, R. (2019). New perspectives on the aging lexicon. Trends in Cognitive Sciences, 23(8), 686698. 10.1016/j.tics.2019.05.003
  59. 59Wulff, D. U., Hills, T. T., & Mata, R. (2022). Structural differences in the semantic networks of younger and older adults. Scientific Reports, 12(1), Article: 21459. 10.1038/s41598-022-11698-4
  60. 60Wulff, D. U., & Mata, R. (2022). On the semantic representation of risk. Science Advances, 8(27), Article eabm1883. 10.1126/sciadv.abm1883
  61. 61Wulff, D. U., & Mata, R. (2023). Automated jingle–jangle detection: Using embeddings to tackle taxonomic incommensurability. PsyArXiv. 10.31234/osf.io/9h7aw
  62. 62Zaller, J., & Feldman, S. (1992). A simple theory of the survey response: Answering questions versus revealing preferences. American Journal of Political Science, 36(3), 579616. 10.2307/2111583
DOI: https://doi.org/10.5334/joc.407 | Journal eISSN: 2514-4820
Language: English
Submitted on: Mar 22, 2024
Accepted on: Oct 3, 2024
Published on: Jan 6, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Samuel Aeschbach, Rui Mata, Dirk U. Wulff, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.