An information-theoretic approach to build hypergraphs in psychometrics.
Journal
Behavior research methods
ISSN: 1554-3528
Titre abrégé: Behav Res Methods
Pays: United States
ID NLM: 101244316
Informations de publication
Date de publication:
30 Jul 2024
30 Jul 2024
Historique:
accepted:
30
06
2024
medline:
31
7
2024
pubmed:
31
7
2024
entrez:
30
7
2024
Statut:
aheadofprint
Résumé
Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to visualise and conceptualise the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting the representation to pairwise relationships can neglect potentially critical information shared by groups of three or more variables (higher-order statistical interdependencies). To overcome this important limitation, here we propose an information-theoretic framework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account on the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for future investigation.
Identifiants
pubmed: 39080122
doi: 10.3758/s13428-024-02471-8
pii: 10.3758/s13428-024-02471-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Ministero dell'Istruzione, dell'Universitá e della Ricerca
ID : PRIN 2017WZFTZP
Informations de copyright
© 2024. The Psychonomic Society, Inc.
Références
Armour, C., Fried, E. I., Deserno, M. K., Tsai, J., & Pietrzak, R. H. (2017). A network analysis of dsm-5 posttraumatic stress disorder symptoms and correlates in U.S. military veterans. Journal of Anxiety Disorders, 45, 49–59.
Armour, C., Greene, T., Contractor, A. A., Weiss, N., Dixon-Gordon, K., & Ross, J. (2020). Posttraumatic stress disorder symptoms and reckless behaviors: A network analysis approach. Journal of Traumatic Stress, 33(1), 29–40.
pubmed: 32086982
doi: 10.1002/jts.22487
Bairey, E., Kelsic, E. D., & Kishony, R. (2016). High-order species interactions shape ecosystem diversity. Nature Communications, 7(1), 1–7.
doi: 10.1038/ncomms12285
Barnett, L., Barrett, A. B., & Seth, A. K. (2009). Granger causality and transfer entropy are equivalent for gaussian variables. Physical Review Letters, 103(23), 238701.
pubmed: 20366183
doi: 10.1103/PhysRevLett.103.238701
Barrett, A. B. (2015). Exploration of synergistic and redundant information sharing in static and dynamical gaussian systems. Physical Review E, 91(5), 052802.
doi: 10.1103/PhysRevE.91.052802
Battiston, F., Amico, E., Barrat, A., Bianconi, G., Ferraz de Arruda, G., Franceschiello, B., Iacopini, I., Kéfi, S., Latora, V., Moreno, Y., et al. (2021). The physics of higher-order interactions in complex systems. Nature Physics, 17(10), 1093–1098.
doi: 10.1038/s41567-021-01371-4
Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J.-G., & Petri, G. (2020). Networks beyond pairwise interactions: Structure and dynamics. Physics Reports, 874, 1–92.
Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., & Ay, N. (2014). Quantifying unique information. Entropy, 16(4), 2161–2183.
doi: 10.3390/e16042161
Bianconi, G. (2021). Higher-order networks. Cambridge University Press.
doi: 10.1017/9781108770996
Bick, C., Gross, E., Harrington, H. A., & Schaub, M. T. (2023). What are higher-order networks? SIAM Review, 65(3), 686–731.
doi: 10.1137/21M1414024
Bisson, J. I., Wright, L. A., Jones, K. A., Lewis, C., Phelps, A. J., Sijbrandij, M., Varker, T., & Roberts, N. P. (2021). Preventing the onset of post traumatic stress disorder. Clinical Psychology Review, 86, 102004.
pubmed: 33857763
doi: 10.1016/j.cpr.2021.102004
Boccaletti, S., De Lellis, P., Del Genio, C., Alfaro-Bittner, K., Criado, R., Jalan, S., & Romance, M. (2023). The structure and dynamics of networks with higher order interactions. Physics Reports, 1018, 1–64.
doi: 10.1016/j.physrep.2023.04.002
Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121.
pubmed: 23537483
doi: 10.1146/annurev-clinpsy-050212-185608
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., et al. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), 1–18.
doi: 10.1038/s43586-021-00055-w
Briganti, G., Kempenaers, C., Braun, S., Fried, E. I., & Linkowski, P. (2018). Network analysis of empathy items from the interpersonal reactivity index in 1973 young adults. Psychiatry Research, 265, 87–92.
pubmed: 29702306
doi: 10.1016/j.psychres.2018.03.082
Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., Wigman, J. T., & Snippe, E. (2019). What do centrality measures measure in psychological networks? Journal of Abnormal Psychology, 128(8), 892.
pubmed: 31318245
doi: 10.1037/abn0000446
Brooks, M., Graham-Kevan, N., Robinson, S., & Lowe, M. (2019). Trauma characteristics and posttraumatic growth: The mediating role of avoidance coping, intrusive thoughts, and social support. Psychological Trauma: Theory, Research, Practice, and Policy, 11(2), 232.
pubmed: 29723030
doi: 10.1037/tra0000372
Brown, M. M., Thibodeau, R. B., Pierucci, J. M., & Gilpin, A. T. (2017). Supporting the development of empathy: The role of theory of mind and fantasy orientation. Social Development, 26(4), 951–964.
doi: 10.1111/sode.12232
Chodrow, P. S., Veldt, N., & Benson, A. R. (2021). Generative hypergraph clustering: From blockmodels to modularity. Science Advances, 7(28), eabh1303.
pubmed: 34233880
doi: 10.1126/sciadv.abh1303
Christensen, A. P., Garrido, L. E., & Golino, H. F. (2020). Unique variable analysis: A novel approach for detecting redundant variables in multivariate data.
Cover, T. M., & Thomas, J. A. (2012). Elements of information theory. John Wiley & Sons.
Cramer, A. O., Waldorp, L. J., Van Der Maas, H. L., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33(2–3), 137–150.
pubmed: 20584369
doi: 10.1017/S0140525X09991567
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge University Press.
doi: 10.1017/CBO9780511802843
DiCiccio, T. J., & Efron, B. (1996). Bootstrap confidence intervals. Statistical Science, 11(3), 189–228.
doi: 10.1214/ss/1032280214
Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. CRC Press.
doi: 10.1201/9780429246593
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212.
pubmed: 28342071
doi: 10.3758/s13428-017-0862-1
Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., Borsboom, D., et al. (2012). Qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1–18.
doi: 10.18637/jss.v048.i04
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617.
Faes, L., Mijatovic, G., Antonacci, Y., Pernice, R., Bará, C., Sparacino, L., Sammartino, M., Porta, A., Marinazzo, D., & Stramaglia, S. (2022). A framework for the time-and frequency-domain assessment of high-order interactions in brain and physiological networks. arXiv:2202.04179 .
Forbes, M. K., Wright, A. G., Markon, K. E., & Krueger, R. F. (2021). Quantifying the reliability and replicability of psychopathology network characteristics. Multivariate Behavioral Research, 56(2), 224–242.
pubmed: 31140875
doi: 10.1080/00273171.2019.1616526
Foygel, R., & Drton, M. (2010). Extended bayesian information criteria for gaussian graphical models. Advances in Neural Information Processing Systems, 23.
Fried, E. I., Papanikolaou, F., & Epskamp, S. (2022). Mental health and social contact during the covid-19 pandemic: An ecological momentary assessment study. Clinical Psychological Science, 10(2), 340–354.
doi: 10.1177/21677026211017839
Fried, E. I., van Borkulo, C. D., Cramer, A. O., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2017). Mental disorders as networks of problems: A review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 52(1), 1–10.
pubmed: 27921134
doi: 10.1007/s00127-016-1319-z
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432–441.
pubmed: 18079126
doi: 10.1093/biostatistics/kxm045
Golino, H. F., Christensen, A. P., Moulder, R., Kim, S., & Boker, S. M. (2021). Modeling latent topics in social media using dynamic exploratory graph analysis: The case of the right-wing and left-wing trolls in the 2016 us elections. Psychometrika, 1–32.
Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PloS One, 12(6), e0174035.
pubmed: 28594839
pmcid: 5465941
doi: 10.1371/journal.pone.0174035
Golino, H. F., Moulder, R., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2021). Entropy fit indices: New fit measures for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research, 56(6), 874–902.
pubmed: 32634057
doi: 10.1080/00273171.2020.1779642
Grasso, M., Albantakis, L., Lang, J. P., & Tononi, G. (2021). Causal reductionism and causal structures. Nature Neuroscience, 24(10), 1348–1355.
Hallquist, M. N., Wright, A. G., & Molenaar, P. C. (2021). Problems with centrality measures in psychopathology symptom networks: Why network psychometrics cannot escape psychometric theory. Multivariate Behavioral Research, 56(2), 199–223.
pubmed: 31401872
doi: 10.1080/00273171.2019.1640103
Haslbeck, J. M., Borsboom, D., & Waldorp, L. J. (2021). Moderated network models. Multivariate Behavioral Research, 56(2), 256–287.
pubmed: 31782672
doi: 10.1080/00273171.2019.1677207
Hindriks, R., Broeders, T. A., Schoonheim, M. M., Douw, L., Santos, F., van Wieringen, W., & Tewarie, P. K. (2024). Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants. Human Brain Mapping, 45(5), e26663.
pubmed: 38520377
pmcid: 10960559
doi: 10.1002/hbm.26663
Iacopini, I., Petri, G., Barrat, A., & Latora, V. (2019). Simplicial models of social contagion. Nature Communications, 10(1), 1–9.
doi: 10.1038/s41467-019-10431-6
Ince, R. A., Giordano, B. L., Kayser, C., Rousselet, G. A., Gross, J., & Schyns, P. G. (2017). A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Human Brain Mapping, 38(3), 1541–1573.
pubmed: 27860095
doi: 10.1002/hbm.23471
James, R. G., & Crutchfield, J. P. (2017). Multivariate dependence beyond shannon information. Entropy, 19(10), 531.
doi: 10.3390/e19100531
James, R. G., Emenheiser, J., & Crutchfield, J. P. (2018). Unique information via dependency constraints. Journal of Physics A: Mathematical and Theoretical, 52(1), 014002.
doi: 10.1088/1751-8121/aaed53
Jones, P. J., Ma, R., & McNally, R. J. (2019). Bridge centrality: A network approach to understanding comorbidity. Multivariate Behavioral Research, 1–15.
Kamiński, B., Prałat, P., & Théberge, F. (2020). Community detection algorithm using hypergraph modularity. International Conference on Complex Networks and Their Applications, 152–163.
Landry, N. W., Lucas, M., Iacopini, I., Petri, G., Schwarze, A., Patania, A., & Torres, L. (2023). Xgi: A python package for higher-order interaction networks. Journal of Open Source Software, 8(85), 5162.
doi: 10.21105/joss.05162
Lizier, J. T., Bertschinger, N., Jost, J., & Wibral, M. (2018). Information decomposition of target effects from multi-source interactions: Perspectives on previous, current and future work.
Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the depression anxiety stress scales (dass) with the beck depression and anxiety inventories. Behaviour Research and Therapy, 33(3), 335–343.
pubmed: 7726811
doi: 10.1016/0005-7967(94)00075-U
Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R. V., Waldorp, L. J., Maas, H. V. D., & Maris, G. (2018). An introduction to network psychometrics: Relating ising network models to item response theory models. Multivariate Behavioral Research, 53(1), 15–35.
pubmed: 29111774
doi: 10.1080/00273171.2017.1379379
McDonald, R. P. (2013). Test theory: A unified treatment. Psychology Press
McGill, W. (1954). Multivariate information transmission. Transactions of the IRE Professional Group on Information Theory, 4(4), 93–111.
doi: 10.1109/TIT.1954.1057469
Mediano, P. A., Rosas, F. E., Luppi, A. I., Carhart-Harris, R. L., Bor, D., Seth, A. K., & Barrett, A. B. (2021). Towards an extended taxonomy of information dynamics via integrated information decomposition. arXiv:2109.13186 .
Medina-Mardones, A. M., Rosas, F. E., Rodriguez, S. E., & Cofre, R. (2021). Hyperharmonic analysis for the study of high-order information-theoretic signals. Journal of Physics: Complexity, 2(3), 035009.
Meinshausen, N., & Bühlmann, P. (2006). High-dimensional graphs and variable selection with the lasso. The Annals of Statistics, 34(3), 1436–1462.
doi: 10.1214/009053606000000281
Milojević, S. (2014). Principles of scientific research team formation and evolution. Proceedings of the National Academy of Sciences, 111(11), 3984–3989.
doi: 10.1073/pnas.1309723111
Nomura, K., & Akai, S. (2012). Empathy with fictional stories: Reconsideration of the fantasy scale of the interpersonal reactivity index. Psychological Reports, 110(1), 304–314.
pubmed: 22489396
doi: 10.2466/02.07.09.11.PR0.110.1.304-314
Olbrich, E., Bertschinger, N., & Rauh, J. (2015). Information decomposition and synergy. Entropy, 17(5), 3501–3517.
doi: 10.3390/e17053501
Praggastis, B., Arendt, D., Joslyn, C., Purvine, E., Aksoy, S., & Monson, K. (2019). Hypernetx.
Quax, R., Har-Shemesh, O., & Sloot, P. M. (2017). Quantifying synergistic information using intermediate stochastic variables. Entropy, 19(2), 85.
doi: 10.3390/e19020085
Ressler, K., Berretta, S., Bolshakov, V. Y., Rosso, I. M., Meloni, E. G., Rauch, S. L., Carlezon, W. A., et al. (2022). Post-traumatic stress disorder: Clinical and translational neuroscience from cells to circuits. Nature Reviews Neurology, 18(5), 273–288.
pubmed: 35352034
pmcid: 9682920
doi: 10.1038/s41582-022-00635-8
Rosas, F. E., Mediano, P., Jensen, H. J., Seth, A. K., Barrett, A. B., Carhart-Harris, R. L., & Bor, D. (2020). Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. PLoS Computational Biology, 16(12), e1008289.
pubmed: 33347467
pmcid: 7833221
doi: 10.1371/journal.pcbi.1008289
Rosas, F. E., Mediano, P., Ugarte, M., & Jensen, H. J. (2018). An information-theoretic approach to self-organisation: Emergence of complex interdependencies in coupled dynamical systems. Entropy, 20(10), 793.
pubmed: 33265882
pmcid: 7512355
doi: 10.3390/e20100793
Rosas, F. E., Mediano, P. A. M., Gastpar, M., & Jensen, H. J. (2019). Quantifying high-order interdependencies via multivariate extensions of the mutual information. Physical Review E, 100, 032305.
pubmed: 31640038
doi: 10.1103/PhysRevE.100.032305
Rosas, F. E., Mediano, P. A., Luppi, A. I., Varley, T. F., Lizier, J. T., Stramaglia, S., Jensen, H. J., & Marinazzo, D. (2022). Disentangling high-order mechanisms and high-order behaviours in complex systems. Nature Physics, 1–2.
Rosas, F. E., Mediano, P. A., Rassouli, B., & Barrett, A. B. (2020). An operational information decomposition via synergistic disclosure. Journal of Physics A: Mathematical and Theoretical, 53(48), 485001.
doi: 10.1088/1751-8121/abb723
Rosas, F. E., Ntranos, V., Ellison, C. J., Pollin, S., & Verhelst, M. (2016). Understanding interdependency through complex information sharing. Entropy, 18(2), 38.
doi: 10.3390/e18020038
Rosseel, Y. (2012). Lavaan: An r package for structural equation modeling and more. version 0.5–12 (beta). Journal of Statistical Software, 48(2), 1–36.
doi: 10.18637/jss.v048.i02
Santoro, A., Battiston, F., Petri, G., & Amico, E. (2023). Higher-order organization of multivariate time series. Nature Physics, 19(2), 221–229.
Scagliarini, T., Marinazzo, D., Guo, Y., Stramaglia, S., & Rosas, F. E. (2022). Quantifying high-order interdependencies on individual patterns via the local o-information: Theory and applications to music analysis. Physical Review Research, 4(1), 013184.
doi: 10.1103/PhysRevResearch.4.013184
Scagliarini, T., Nuzzi, D., Antonacci, Y., Faes, L., Rosas, F. E., Marinazzo, D., & Stramaglia, S. (2022). Gradients of o-information: Low-order descriptors of high-order dependencies. arXiv:2207.03581 .
Schipper, M., & Petermann, F. (2013). Relating empathy and emotion regulation: Do deficits in empathy trigger emotion dysregulation? Social Neuroscience, 8(1), 101–107.
pubmed: 23327297
doi: 10.1080/17470919.2012.761650
Schmittmann, V. D., Cramer, A. O., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43–53.
doi: 10.1016/j.newideapsych.2011.02.007
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.
doi: 10.1002/j.1538-7305.1948.tb01338.x
Skardal, P. S., & Arenas, A. (2020). Higher order interactions in complex networks of phase oscillators promote abrupt synchronization switching. Communications Physics, 3(1), 1–6.
doi: 10.1038/s42005-020-00485-0
St-Onge, G., Iacopini, I., Latora, V., Barrat, A., Petri, G., Allard, A., & Hébert-Dufresne, L. (2022). Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs. Communications Physics, 5(1), 1–16.
doi: 10.1038/s42005-021-00788-w
Stotland, E., et al. (1978). Empathy, fantasy and helping. Sage.
Stramaglia, S., Angelini, L., Wu, G., Cortes, J. M., Faes, L., & Marinazzo, D. (2016). Synergetic and redundant information flow detected by unnormalized granger causality: Application to resting state fmri. IEEE Transactions on Biomedical Engineering, 63(12), 2518–2524.
pubmed: 27875123
doi: 10.1109/TBME.2016.2559578
Stramaglia, S., Scagliarini, T., Daniels, B. C., & Marinazzo, D. (2021). Quantifying dynamical high-order interdependencies from the o-information: An application to neural spiking dynamics. Frontiers in Physiology, 1784.
Tabar, M. R. R., Nikakhtar, F., Parkavousi, L., Akhshi, A., Feudel, U., & Lehnertz, K. (2024). Revealing higher-order interactions in high-dimensional complex systems: A data-driven approach. Physical Review X, 14(1), 011050.
doi: 10.1103/PhysRevX.14.011050
Te Sun, H. (1978). Nonnegative entropy measures of multivariate symmetric correlations [Publisher: Elsevier]. Information and Control, 36, 133–156.
doi: 10.1016/S0019-9958(78)90275-9
Thompson, N. M., Uusberg, A., Gross, J. J., & Chakrabarti, B. (2019). Empathy and emotion regulation: An integrative account. Progress in Brain Research, 247, 273–304.
pubmed: 31196438
doi: 10.1016/bs.pbr.2019.03.024
Torres, L., Blevins, A. S., Bassett, D., & Eliassi-Rad, T. (2021). The why, how, and when of representations for complex systems. SIAM Review, 63(3), 435–485.
doi: 10.1137/20M1355896
Tuccitto, D. E., Giacobbi, P. R., Jr., & Leite, W. L. (2010). The internal structure of positive and negative affect: A confirmatory factor analysis of the panas. Educational and Psychological Measurement, 70(1), 125–141.
doi: 10.1177/0013164409344522
Tudisco, F., & Higham, D. J. (2021). Node and edge nonlinear eigenvector centrality for hypergraphs. Communications Physics, 4(1), 1–10.
Van den Bergh, N., Marchetti, I., & Koster, E. H. (2021). Bridges over troubled waters: Mapping the interplay between anxiety, depression and stress through network analysis of the dass-21. Cognitive Therapy and Research, 45(1), 46–60.
Vasiliauskaite, V., & Rosas, F. E. (2020). Understanding complexity via network theory: A gentle introduction. arXiv:2004.14845 .
Vijayakumar, R., Choi, J. Y., & Jung, E. H. (2022). A Unified Neural Network Framework for Extended Redundancy Analysis. Psychometrika
Waldorp, L., & Marsman, M. (2022). Relations between networks, regression, partial correlation, and the latent variable model. Multivariate Behavioral Research, 57(6), 994–1006.
pubmed: 34397314
doi: 10.1080/00273171.2021.1938959
Watanabe, S. (1960). Information theoretical analysis of multivariate correlation [Publisher: IBM]. IBM Journal of Research and Development, 4(1), 66–82.
doi: 10.1147/rd.41.0066
Williams, P. L. (2011). Information dynamics: Its theory and application to embodied cognitive systems [Doctoral dissertation, Indiana University].
Williams, P. L., & Beer, R. D. (2010). Nonnegative decomposition of multivariate information. arXiv:1004.2515 .
Zaki, J. (2020). Integrating empathy and interpersonal emotion regulation. Annual Review of Psychology, 71, 517–540.
pubmed: 31553672
doi: 10.1146/annurev-psych-010419-050830
Zhang, Y., Lucas, M., & Battiston, F. (2023). Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes. Nature Communications, 14(1), 1605.
pubmed: 36959174
pmcid: 10036330
doi: 10.1038/s41467-023-37190-9