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  • Perspective
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The fundamental importance of method to theory

Nature Reviews Psychology volume 2, pages 55–66 (2023)Cite this article

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Abstract

Many domains of inquiry in psychology are concerned with rich and complex phenomena. At the same time, the field of psychology is grappling with how to improve research practices to address concerns with the scientific enterprise. In this Perspective, we argue that both of these challenges can be addressed by adopting a principle of methodological variety. According to this principle, developing a variety of methodological tools should be regarded as a scientific goal in itself, one that is critical for advancing scientific theory. To illustrate, we show how the study of language and communication requires varied methodologies, and that theory development proceeds, in part, by integrating disparate tools and designs. We argue that the importance of methodological variation and innovation runs deep, travelling alongside theory development to the core of the scientific enterprise. Finally, we highlight ongoing research agendas that might help to specify, quantify and model methodological variety and its implications.

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Fig. 1: Interrelationships between methods used to study the emergence of speech.
Fig. 2: An illustration of methodological expansion to study language and communication.

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References

  1. Hacking, I. Representing and Intervening: Introductory Topics in The Philosophy of Natural Science (Cambridge Univ. Press, 1983).

  2. Mayo, D. G. in PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association Vol. 1994, 270–279 (Cambridge Univ. Press, 1994).

  3. Ackermann, R. The new experimentalism. Brit. J. Philos. Sci. 40, 185–190 (1989).

    Article  Google Scholar 

  4. Simons, M. & Vagelli, M. Were experiments ever neglected? Ian Hacking and the history of philosophy of experiment. Phil. Inq. 9, 167–188 (2021).

    Google Scholar 

  5. Nosek, B. A. et al. Promoting an open research culture. Science 348, 1422–1425 (2015).

    Article  Google Scholar 

  6. Richardson, D. C., Dale, R. & Tomlinson, J. M. Conversation, gaze coordination, and beliefs about visual context. Cogn. Sci. 33, 1468–1482 (2009).

    Article  Google Scholar 

  7. Laidlaw, K. E., Foulsham, T., Kuhn, G. & Kingstone, A. Potential social interactions are important to social attention. Proc. Natl Acad. Sci. USA 108, 5548–5553 (2011).

    Article  Google Scholar 

  8. Richardson, D. C. et al. Joint perception: gaze and social context. Front. Hum. Neurosci. 6, 194 (2012).

    Article  Google Scholar 

  9. Risko, E. F., Richardson, D. C. & Kingstone, A. Breaking the fourth wall of cognitive science: real-world social attention and the dual function of gaze. Curr. Dir. Psychol. Sci. 25, 70–74 (2016).

    Article  Google Scholar 

  10. Levin, I. P., Schneider, S. L. & Gaeth, G. J. All frames are not created equal: a typology and critical analysis of framing effects. Organ. Behav. Hum. Decis. Process. 76, 149–188 (1998).

    Article  Google Scholar 

  11. Pothos, E. M. & Busemeyer, J. R. A quantum probability explanation for violations of ‘rational’ decision theory. Proc. R. Soc. B 276, 2171–2178 (2009).

    Article  Google Scholar 

  12. Pärnamets, P. et al. Biasing moral decisions by exploiting the dynamics of eye gaze. Proc. Natl Acad. Sci. USA 112, 4170–4175 (2015).

    Article  Google Scholar 

  13. Vinson, D. W., Dale, R. & Jones, M. N. Decision contamination in the wild: sequential dependencies in online review ratings. Behav. Res. Methods 51, 1477–1484 (2019).

    Article  Google Scholar 

  14. Longino, H. E. Gender, politics, and the theoretical virtues. Synthese 104, 383–397 (1995).

    Article  Google Scholar 

  15. Hansson, S. O. in Stanford Encyclopedia of Philosophy (ed. Zalta, E. N.) https://plato.stanford.edu/archives/fall2021/entries/pseudo-science/ (Stanford Univ., 2015).

  16. Dupré, J. The Disorder of Things: Metaphysical Foundations of the Disunity of Science (Harvard Univ. Press, 1993).

  17. Cartwright, N. The Dappled World: A Study of the Boundaries of Science (Cambridge Univ. Press, 1999).

  18. McCauley, R. N. & Bechtel, W. Explanatory pluralism and heuristic identity theory. Theory Psychol. 11, 736–760 (2001).

    Article  Google Scholar 

  19. Mitchell, S. D. Integrative pluralism. Biol. Phil. 17, 55–70 (2002).

    Article  Google Scholar 

  20. Abrahamsen, A. & Bechtel, W. in Contemporary Debates in Cognitive Science (ed. Stainton, R.) 159–187 (Blackwell, 2006).

  21. Kellert, S. H., Longino, H. E. & Waters, C. K. Scientific Pluralism (Univ. Minnesota Press, 2006).

  22. Dale, R., Dietrich, E. & Chemero, A. Explanatory pluralism in cognitive science. Cogn. Sci. 33, 739–742 (2009).

    Article  Google Scholar 

  23. Weisberg, M. & Muldoon, R. Epistemic landscapes and the division of cognitive labor. Phil. Sci. 76, 225–252 (2009).

    Article  Google Scholar 

  24. Zollman, K. J. S. The epistemic benefit of transient diversity. Erkenntnis 72, 17–35 (2010).

    Article  Google Scholar 

  25. Horst, S. Beyond reduction: from naturalism to cognitive pluralism. Mind Matter 12, 197–244 (2014).

    Google Scholar 

  26. Open Science Collaboration. Estimating the reproducibility of psychological science. Science 349, 943 (2015).

    Article  Google Scholar 

  27. Lin, H., Werner, K. M. & Inzlicht, M. Promises and perils of experimentation: the mutual-internal-validity problem. Perspect. Psychol. Sci. 16, 854–863 (2021).

    Article  Google Scholar 

  28. van Rooij, I. & Baggio, G. Theory before the test: how to build high-verisimilitude explanatory theories in psychological science. Perspect. Psychol. Sci. 16, 682–697 (2021).

    Article  Google Scholar 

  29. MacLeod, C. M. The Stroop task: the gold standard of attentional measures. J. Exp. Psychol. Gen. 121, 12–14 (1992).

    Article  Google Scholar 

  30. Eriksen, C. W. The flankers task and response competition: a useful tool for investigating a variety of cognitive problems. Vis. Cogn. 2, 101–118 (1995).

    Article  Google Scholar 

  31. Dickter, C. L. & Bartholow, B. D. Ingroup categorization and response conflict: interactive effects of target race, flanker compatibility, and infrequency on N2 amplitude. Psychophysiology 47, 596–601 (2010).

    Article  Google Scholar 

  32. Parris, B. A., Hasshim, N., Wadsley, M., Augustinova, M. & Ferrand, L. The loci of Stroop effects: a critical review of methods and evidence for levels of processing contributing to color-word Stroop effects and the implications for the loci of attentional selection. Psychol. Res. 86, 1029–1053 (2022).

    Article  Google Scholar 

  33. Barzykowski, K., Wereszczyski, M., Hajdas, S. & Radel, R. Cognitive inhibition behavioral tasks in online and laboratory settings: data from Stroop, SART and Eriksen Flanker tasks. Data Brief. 43, 108398 (2022).

    Article  Google Scholar 

  34. Gobel, M. S., Kim, H. S. & Richardson, D. C. The dual function of social gaze. Cognition 136, 359–364 (2015).

    Article  Google Scholar 

  35. Gallup, A. C., Chong, A. & Couzin, I. D. The directional flow of visual information transfer between pedestrians. Biol. Lett. 8, 520–522 (2012).

    Article  Google Scholar 

  36. Lick, D. J. & Johnson, K. L. Straight until proven gay: a systematic bias toward straight categorizations in sexual orientation judgments. J. Pers. Soc. Psychol. 110, 801 (2016).

    Article  Google Scholar 

  37. Alt, N. P., Lick, D. J. & Johnson, K. L. The straight categorization bias: a motivated and altruistic reasoning account. J. Pers. Soc. Psychol. https://doi.org/10.1037/pspi0000232 (2020).

    Article  Google Scholar 

  38. Popper, K. Conjectures and Refutations: The Growth of Scientific Knowledge (Routledge, 2002).

  39. Heit, E. & Hahn, U. Diversity-based reasoning in children. Cogn. Psychol. 43, 243–273 (2001).

    Article  Google Scholar 

  40. Heit, E., Hahn, U. & Feeney, A. in Categorization Inside and Outside the Laboratory: Essays in Honor of Douglas L. Medin 87–99 (American Psychological Association, 2005).

  41. MacWhinney, B. in The Handbook of Linguistics 2nd edn (eds. Aronoff, M. & Rees-Miller, J.) 397–413 (Wiley, 2017).

  42. Stivers, T. et al. Universals and cultural variation in turn-taking in conversation. Proc. Natl Acad. Sci. USA 106, 10587–10592 (2009).

    Article  Google Scholar 

  43. Louwerse, M. M., Dale, R., Bard, E. G. & Jeuniaux, P. Behavior matching in multimodal communication is synchronized. Cogn. Sci. 36, 1404–1426 (2012).

    Article  Google Scholar 

  44. Fusaroli, R., Bjørndahl, J. S., Roepstorff, A. & Tylén, K. A heart for interaction: shared physiological dynamics and behavioral coordination in a collective, creative construction task. J. Exp. Psychol. Hum. Percept. Perform. 42, 1297 (2016).

    Article  Google Scholar 

  45. Rasenberg, M., Özyürek, A. & Dingemanse, M. Alignment in multimodal interaction: an integrative framework. Cogn. Sci. 44, e12911 (2020).

    Article  Google Scholar 

  46. Dunn, M., Greenhill, S. J., Levinson, S. C. & Gray, R. D. Evolved structure of language shows lineage-specific trends in word-order universals. Nature 473, 79–82 (2011).

    Article  Google Scholar 

  47. Hua, X., Greenhill, S. J., Cardillo, M., Schneemann, H. & Bromham, L. The ecological drivers of variation in global language diversity. Nat. Commun. 10, 1–10 (2019).

    Article  Google Scholar 

  48. Christiansen, M. H. & Chater, N. The now-or-never bottleneck: a fundamental constraint on language. Behav. Brain Sci. 39, e62 (2016).

    Article  Google Scholar 

  49. Fitch, W. T., De Boer, B., Mathur, N. & Ghazanfar, A. A. Monkey vocal tracts are speech-ready. Sci. Adv. 2, e1600723 (2016).

    Article  Google Scholar 

  50. Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: what is it, who has it, and how did it evolve? Science 298, 1569–1579 (2002).

    Article  Google Scholar 

  51. Cowley, S. J. Distributed Language (John Benjamins, 2011).

  52. Samuels, R. Nativism in cognitive science. Mind Lang. 17, 233–265 (2002).

    Article  Google Scholar 

  53. Behme, C. & Deacon, S. H. Language learning in infancy: does the empirical evidence support a domain specific language acquisition device? Phil. Psychol. 21, 641–671 (2008).

    Article  Google Scholar 

  54. Chomsky, N. 4. A Review Of BF Skinner’s Verbal Behavior (Harvard Univ. Press, 2013).

  55. Vihman, M. M. Phonological Development: The Origins of Language in the Child (Blackwell, 1996).

  56. Oller, D. K. The Emergence of the Speech Capacity (Psychology Press, 2000).

  57. Clark, E. V. & Casillas, M. First Language Acquisition (Routledge, 2015).

  58. Goldstein, M. H., King, A. P. & West, M. J. Social interaction shapes babbling: testing parallels between birdsong and speech. Proc. Natl Acad. Sci. USA 100, 8030–8035 (2003).

    Article  Google Scholar 

  59. Warlaumont, A. S. Modeling the emergence of syllabic structure. J. Phonet. 53, 61–65 (2015).

    Article  Google Scholar 

  60. VanDam, M. et al. HomeBank: An online repository of daylong child-centered audio recordings. Semin. Speech Lang. 37, 128–142 (2016).

    Article  Google Scholar 

  61. Elmlinger, S. L., Schwade, J. A. & Goldstein, M. H. The ecology of prelinguistic vocal learning: parents simplify the structure of their speech in response to babbling. J. Child Lang. 46, 998–1011 (2019).

    Article  Google Scholar 

  62. Roy, B. C., Frank, M. C., DeCamp, P., Miller, M. & Roy, D. Predicting the birth of a spoken word. Proc. Natl Acad. Sci. USA 112, 12663–12668 (2015).

    Article  Google Scholar 

  63. McClelland, J. L. The place of modeling in cognitive science. Top. Cogn. Sci. 1, 11–38 (2009).

    Article  Google Scholar 

  64. Smaldino, P. E. in Computational Social Psychology (eds Vallacher’, R., Read, S. J. & Nowak, A.) 311–331 (Routledge, 2017).

  65. Guest, O. & Martin, A. E. How computational modeling can force theory building in psychological science. Perspect. Psychol. Sci. 16, 789–802 (2021).

    Article  Google Scholar 

  66. Elman, J. L., Bates, E. A. & Johnson, M. H. Rethinking Innateness: A Connectionist Perspective on Development Vol. 10 (MIT Press, 1996).

  67. Warlaumont, A. S., Westermann, G., Buder, E. H. & Oller, D. K. Prespeech motor learning in a neural network using reinforcement. Neural Netw. 38, 64–75 (2013).

    Article  Google Scholar 

  68. Warlaumont, A. S. & Finnegan, M. K. Learning to produce syllabic speech sounds via reward-modulated neural plasticity. PLoS One 11, e0145096 (2016).

    Article  Google Scholar 

  69. MacWhinney, B. & Snow, C. The child language data exchange system: an update. J. Child. Lang. 17, 457–472 (1990).

    Article  Google Scholar 

  70. MacWhinney, B. The CHILDES Project: Tools for Analyzing Talk 3rd edn (Psychology Press, 2014).

  71. Kachergis, G., Marchman, V. A. & Frank, M. C. Toward a “standard model” of early language learning. Curr. Dir. Psychol. Sci. 31, 20–27 (2022).

    Article  Google Scholar 

  72. Lewis, J. D. & Elman, J. L. A connectionist investigation of linguistic arguments from the poverty of the stimulus: learning the unlearnable. In Proc. Annual Meeting of the Cognitive Science Society Vol. 23, 552–557 (eds. Moore, J. D. & Stenning, K.) (2001).

  73. Regier, T. & Gahl, S. Learning the unlearnable: the role of missing evidence. Cognition 93, 147–155 (2004).

    Article  Google Scholar 

  74. Reali, F. & Christiansen, M. H. Uncovering the richness of the stimulus: structure dependence and indirect statistical evidence. Cogn. Sci. 29, 1007–1028 (2005).

    Article  Google Scholar 

  75. Foraker, S., Regier, T., Khetarpal, N., Perfors, A. & Tenenbaum, J. Indirect evidence and the poverty of the stimulus: the case of anaphoric one. Cognit. Sci. 33, 287–300 (2009).

    Article  Google Scholar 

  76. Saffran, J. R., Aslin, R. N. & Newport, E. L. Statistical learning by 8-month-old infants. Science 274, 1926–1928 (1996).

    Article  Google Scholar 

  77. McMurray, B. & Hollich, G. Core computational principles of language acquisition: can statistical learning do the job? Dev. Sci. 12, 365–368 (2009).

    Article  Google Scholar 

  78. Frost, R., Armstrong, B. C., Siegelman, N. & Christiansen, M. H. Domain generality versus modality specificity: the paradox of statistical learning. Trends Cogn. Sci. 19, 117–125 (2015).

    Article  Google Scholar 

  79. Isbilen, E. S., Frost, R. L. A., Monaghan, P. & Christiansen, M. H. Statistically based chunking of nonadjacent dependencies. J. Exp. Psychol. Gen. https://doi.org/10.1037/xge0001207 (2022).

    Article  Google Scholar 

  80. Ruba, A. L., Pollak, S. D. & Saffran, J. R. Acquiring complex communicative systems: Statistical learning of language and emotion. Top. Cogn. Sci. https://doi.org/10.1111/tops.12612 (2022).

    Article  Google Scholar 

  81. Abney, D. H., Warlaumont, A. S., Oller, D. K., Wallot, S. & Kello, C. T. Multiple coordination patterns in infant and adult vocalizations. Infancy 22, 514–539 (2017).

    Article  Google Scholar 

  82. Mendoza, J. K. & Fausey, C. M. Everyday music in infancy. Dev. Sci. 24, e13122 (2019).

    Google Scholar 

  83. Ritwika, V. et al. Exploratory dynamics of vocal foraging during infant-caregiver communication. Sci. Rep. 10, 10469 (2020).

    Article  Google Scholar 

  84. Mendoza, J. K. & Fausey, C. M. Quantifying everyday ecologies: principles for manual annotation of many hours of infants’ lives. Front. Psychol. 12, 710636 (2021).

    Article  Google Scholar 

  85. Fernald, A., Zangl, R., Portillo, A. L. & Marchman, V. A. in Developmental Psycholinguistics: On-line Methods in Children’s Language Processing (eds. Sekerina, I. A., Fernández, E. M. & Clahsen, H.) Vol. 44, 97 (John Benjamins, 2008).

  86. Weisleder, A. & Fernald, A. Talking to children matters: early language experience strengthens processing and builds vocabulary. Psychol. Sci. 24, 2143–2152 (2013).

    Article  Google Scholar 

  87. Bergelson, E. & Aslin, R. N. Nature and origins of the lexicon in 6-mo-olds. Proc. Natl Acad. Sci. USA 114, 12916–12921 (2017).

    Article  Google Scholar 

  88. Brennan, S. E., Galati, A. & Kuhlen, A. K. in Psychology of Learning and Motivation (ed. Ross, B. H.) Vol. 53, 301–344 (Elsevier, 2010).

  89. Streeck, J., Goodwin, C. & LeBaron, C. Embodied Interaction: Language and Body in the Material World (Cambridge Univ. Press, 2011).

  90. Goodwin, C. Co-operative Action (Cambridge Univ. Press, 2017).

  91. Dale, R., Spivey, M. J. in Eye-Tracking In Interaction. Studies On The Role Of Eye Gaze In Dialogue (eds. Oben, B. and Brône, G.) 67–90 (John Benjamins, 2018).

  92. Richardson, D. C. & Spivey, M. J. in Encyclopedia of Biomaterials and Biomedical Engineering 573–582 (CRC Press, 2004).

  93. Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K. M. & Sedivy, J. C. Integration of visual and linguistic information in spoken language comprehension. Science 268, 1632–1634 (1995).

    Article  Google Scholar 

  94. Spivey, M. J., Tanenhaus, M. K., Eberhard, K. M. & Sedivy, J. C. Eye movements and spoken language comprehension: effects of visual context on syntactic ambiguity resolution. Cogn. Psychol. 45, 447–481 (2002).

    Article  Google Scholar 

  95. Richardson, D. C., Dale, R. & Spivey, M. J. Eye movements in language and cognition. Methods Cogn. Linguist. 18, 323–344 (2007).

    Article  Google Scholar 

  96. Ferreira, F. & Clifton, C. Jr The independence of syntactic processing. J. Mem. Lang. 25, 348–368 (1986).

    Article  Google Scholar 

  97. Kamide, Y., Altmann, G. T. & Haywood, S. L. The time-course of prediction in incremental sentence processing: evidence from anticipatory eye movements. J. Mem. Lang. 49, 133–156 (2003).

    Article  Google Scholar 

  98. Coco, M. I., Keller, F. & Malcolm, G. L. Anticipation in real‐world scenes: the role of visual context and visual memory. Cogn. Sci. 40, 1995–2024 (2016).

    Article  Google Scholar 

  99. Coco, M. I. & Keller, F. Scan patterns predict sentence production in the cross‐modal processing of visual scenes. Cogn. Sci. 36, 1204–1223 (2012).

    Article  Google Scholar 

  100. Kieslich, P. J., Henninger, F., Wulff, D. U., Haslbeck, J. M. & Schulte-Mecklenbeck, M. in A Handbook of Process Tracing Methods 111–130 (Routledge, 2019).

  101. Spivey, M. J., Grosjean, M. & Knoblich, G. Continuous attraction toward phonological competitors. Proc. Natl Acad. Sci. USA 102, 10393–10398 (2005).

    Article  Google Scholar 

  102. Freeman, J., Dale, R. & Farmer, T. Hand in motion reveals mind in motion. Front. Psychol. 2, 59 (2011).

    Article  Google Scholar 

  103. Freeman, J. B. & Johnson, K. L. More than meets the eye: split-second social perception. Trends Cogn. Sci. 20, 362–374 (2016).

    Article  Google Scholar 

  104. Goodale, B. M., Alt, N. P., Lick, D. J. & Johnson, K. L. Groups at a glance: perceivers infer social belonging in a group based on perceptual summaries of sex ratio. J. Exp. Psychol. Gen. 147, 1660–1676 (2018).

    Article  Google Scholar 

  105. Sneller, B. & Roberts, G. Why some behaviors spread while others don’t: a laboratory simulation of dialect contact. Cognition 170, 298–311 (2018).

    Article  Google Scholar 

  106. Atkinson, M., Mills, G. J. & Smith, K. Social group effects on the emergence of communicative conventions and language complexity. J. Lang. Evol. 4, 1–18 (2019).

    Article  Google Scholar 

  107. Raviv, L., Meyer, A. & Lev‐Ari, S. The role of social network structure in the emergence of linguistic structure. Cogn. Sci. 44, e12876 (2020).

    Article  Google Scholar 

  108. Lupyan, G. & Dale, R. Language structure is partly determined by social structure. PLoS One 5, e8559 (2010).

    Article  Google Scholar 

  109. Lupyan, G. & Dale, R. Why are there different languages? The role of adaptation in linguistic diversity. Trends Cogn. Sci. 20, 649–660 (2016).

    Article  Google Scholar 

  110. Wu, L., Waber, B. N., Aral, S., Brynjolfsson, E. & Pentland, A. Mining face-to-face interaction networks using sociometric badges: predicting productivity in an IT configuration task. Inf. Syst. Behav. Soc. Methods https://doi.org/10.2139/ssrn.1130251 (2008).

    Article  Google Scholar 

  111. Paxton, A. & Dale, R. Argument disrupts interpersonal synchrony. Q. J. Exp. Psychol. 66, 2092–2102 (2013).

    Article  Google Scholar 

  112. Alviar, C., Dale, R. & Galati, A. Complex communication dynamics: exploring the structure of an academic talk. Cogn. Sci. 43, e12718 (2019).

    Article  Google Scholar 

  113. Joo, J., Bucy, E. P. & Seidel, C. Automated coding of televised leader displays: detecting nonverbal political behavior with computer vision and deep learning. Int. J. Commun. 13, 4044–4066 (2019).

    Google Scholar 

  114. Metallinou, A. et al. The USC CreativeIT database of multimodal dyadic interactions: from speech and full body motion capture to continuous emotional annotations. Lang. Res. Eval. 50, 497–521 (2016).

    Article  Google Scholar 

  115. Pouw, W., Paxton, A., Harrison, S. J. & Dixon, J. A. Acoustic information about upper limb movement in voicing. Proc. Natl Acad. Sci. USA 117, 11364–11367 (2020).

    Article  Google Scholar 

  116. Enfield, N., Levinson, S. C., De Ruiter, J. P. & Stivers, T. in Field Manual Vol. 10, 96–99 (ed. Majid, A.) (Max Planck Institute for Psycholinguistics, 2007).

  117. Enfield, N. & Sidnell, J. On the concept of action in the study of interaction. Discourse Stud. 19, 515–535 (2017).

    Article  Google Scholar 

  118. Duran, N. D., Paxton, A. & Fusaroli, R. ALIGN: analyzing linguistic interactions with generalizable techNiques — a Python library. Psychol. Methods 24, 419 (2019).

    Article  Google Scholar 

  119. Brennan, S. E. & Clark, H. H. Conceptual pacts and lexical choice in conversation. J. Exp. Psychol. Learn. Mem. Cogn. 22, 1482–1493 (1996).

    Article  Google Scholar 

  120. Hasson, U., Nir, Y., Levy, I., Fuhrmann, G. & Malach, R. Intersubject synchronization of cortical activity during natural vision. Science 303, 1634–1640 (2004).

    Article  Google Scholar 

  121. Huth, A. G., De Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016).

    Article  Google Scholar 

  122. Shain, C. et al. Robust effects of working memory demand during naturalistic language comprehension in language-selective cortex. J. Neurosci. 42, 7412–7430 (2022).

    Article  Google Scholar 

  123. Fedorenko, E., Blank, I. A., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 203, 104348 (2020).

    Article  Google Scholar 

  124. Stephens, G. J., Silbert, L. J. & Hasson, U. Speaker–listener neural coupling underlies successful communication. Proc. Natl Acad. Sci. USA 107, 14425–14430 (2010).

    Article  Google Scholar 

  125. Schilbach, L. et al. Toward a second-person neuroscience. Behav. Brain Sci. 36, 393–414 (2013).

    Article  Google Scholar 

  126. Redcay, E. & Schilbach, L. Using second-person neuroscience to elucidate the mechanisms of social interaction. Nat. Rev. Neurosci. 20, 495–505 (2019).

    Article  Google Scholar 

  127. Riley, M. A., Richardson, M., Shockley, K. & Ramenzoni, V. C. Interpersonal synergies. Front. Psychol. 2, 38 (2011).

    Article  Google Scholar 

  128. Dale, R., Fusaroli, R., Duran, N. D. & Richardson, D. C. in Psychology of Learning and Motivation (ed. Ross, B. H.) Vol. 59, 43–95 (Elsevier, 2013).

  129. Fusaroli, R., Rączaszek-Leonardi, J. & Tylén, K. Dialog as interpersonal synergy. N. Ideas Psychol. 32, 147–157 (2014).

    Article  Google Scholar 

  130. Hadley, L. V., Naylor, G. & Hamilton, A. F. D. C. A review of theories and methods in the science of face-to-face social interaction. Nat. Rev. Psychol. 1, 42–54 (2022).

    Article  Google Scholar 

  131. Cornejo, C., Cuadros, Z., Morales, R. & Paredes, J. Interpersonal coordination: methods achievements and challenges. Front. Psychol. https://doi.org/10.3389/fpsyg.2017.01685 (2017).

    Article  Google Scholar 

  132. Smaldino, P. E. in Computational Social Psychology 311–331 (Routledge, 2017).

  133. Devezer, B., Nardin, L. G., Baumgaertner, B. & Buzbas, E. O. Scientific discovery in a model-centric framework: reproducibility, innovation, and epistemic diversity. PLoS One 14, e0216125 (2019).

    Article  Google Scholar 

  134. Sulik, J., Bahrami, B. & Deroy, O. The diversity gap: when diversity matters for knowledge. Perspect. Psychol. Sci. 17, 752–767 (2022).

    Article  Google Scholar 

  135. O’Connor, C. & Bruner, J. Dynamics and diversity in epistemic communities. Erkenntnis 84, 101–119 (2019).

    Article  Google Scholar 

  136. Longino, H. in The Stanford Encyclopedia of Philosophy (ed. Zalta, E. N.) https://plato.stanford.edu/archives/sum2019/entries/scientific-knowledge-social/ (Stanford Univ., 2019).

  137. Van Rooij, I. The tractable cognition thesis. Cogn. Sci. 32, 939–984 (2008).

    Article  Google Scholar 

  138. Kwisthout, J., Wareham, T. & Van Rooij, I. Bayesian intractability is not an ailment that approximation can cure. Cogn. Sci. 35, 779–784 (2011).

    Article  Google Scholar 

  139. Contreras Kallens, P. & Dale, R. Exploratory mapping of theoretical landscapes through word use in abstracts. Scientometrics 116, 1641–1674 (2018).

    Article  Google Scholar 

  140. Methods for methods’ sake. Nat. Methods https://doi.org/10.1038/nmeth1004-1 (2004).

  141. Oberauer, K. & Lewandowsky, S. Addressing the theory crisis in psychology. Psychon. Bull. Rev. 26, 1596–1618 (2019).

    Article  Google Scholar 

  142. Meehl, P. E. Theory-testing in psychology and physics: a methodological paradox. Phil. Sci. 34, 103–115 (1967).

    Article  Google Scholar 

  143. Klein, O. et al. A practical guide for transparency in psychological science. Collabra Psychol. 4, 20 (2018).

    Article  Google Scholar 

  144. Muthukrishna, M. & Henrich, J. A problem in theory. Nat. Hum. Behav. 3, 221–229 (2019).

    Article  Google Scholar 

  145. Eronen, M. I. & Bringmann, L. F. The theory crisis in psychology: how to move forward. Perspect. Psychol. Sci. https://doi.org/10.1177/1745691620970586 (2021).

    Article  Google Scholar 

  146. Borsboom, D., van der Maas, H. L. J., Dalege, J., Kievit, R. A. & Haig, B. D. Theory construction methodology: a practical framework for building theories in psychology. Perspect. Psychol. Sci. https://doi.org/10.1177/1745691620969647 (2021).

    Article  Google Scholar 

  147. Kyvik, S. & Reymert, I. Research collaboration in groups and networks: differences across academic fields. Scientometrics 113, 951–967 (2017).

    Article  Google Scholar 

  148. Tebes, J. K. & Thai, N. D. Interdisciplinary team science and the public: steps toward a participatory team science. Am. Psychol. 73, 549 (2018).

    Article  Google Scholar 

  149. Falk-Krzesinski, H. J. et al. Mapping a research agenda for the science of team science. Res. Eval. 20, 145–158 (2011).

    Article  Google Scholar 

  150. da Silva, J. A. T. The Matthew effect impacts science and academic publishing by preferentially amplifying citations, metrics and status. Scientometrics 126, 5373–5377 (2021).

    Article  Google Scholar 

  151. Scheel, A. M., Tiokhin, L., Isager, P. M. & Lakens, D. Why hypothesis testers should spend less time testing hypotheses. Perspect. Psychol. Sci. 16, 744–755 (2020).

    Article  Google Scholar 

  152. Jones, M. N. Big Data in Cognitive Science (Psychology Press, 2016).

  153. Paxton, A. & Griffiths, T. L. Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behav. Res. Methods 49, 1630–1638 (2017).

    Article  Google Scholar 

  154. Lupyan, G. & Goldstone, R. L. Beyond the lab: using big data to discover principles of cognition. Behav Res. Methods, 51, 1554–3528 (2019).

    Article  Google Scholar 

  155. Haspelmath, M., Dryer, M. S., Gil, D. & Comrie, B. (eds) The World Atlas of Language Structures (Max Planck Digital Library, 2013).

  156. Eberhard, D. M., Simons, G. F. & Fennig, C. D. Ethnologue: Languages of the World (SIL International, 2021).

  157. MacCorquodale, K. & Meehl, P. E. On a distinction between hypothetical constructs and intervening variables. Psychol. Rev. 55, 95–107 (1948).

    Article  Google Scholar 

  158. van Rooij, I. & Baggio, G. Theory before the test: how to build high-verisimilitude explanatory theories in psychological science. Perspect. Psychol. Sci. https://doi.org/10.1177/1745691620970604 (2021).

  159. Christiansen, M. H. & Chater, N. Creating Language: Integrating Evolution, Acquisition, and Processing (MIT Press, 2016).

  160. Berwick, R. C. & Chomsky, N. Why Only Us: Language and Evolution (MIT Press, 2016).

  161. Gopnik, A. Scientific thinking in young children: theoretical advances, empirical research, and policy implications. Science 337, 1623–1627 (2012).

    Article  Google Scholar 

  162. Pereira, A. F., James, K. H., Jones, S. S. & Smith, L. B. Early biases and developmental changes in self-generated object views. J. Vis. 10, 22–22 (2010).

    Article  Google Scholar 

  163. Fagan, M. K. & Iverson, J. M. The influence of mouthing on infant vocalization. Infancy 11, 191–202 (2007).

    Article  Google Scholar 

  164. Martin, J., Ruthven, M., Boubertakh, R. & Miquel, M. E. Realistic dynamic numerical phantom for MRI of the upper vocal tract. J. Imaging 6, 86 (2020).

    Article  Google Scholar 

  165. Spivey, M. J. & Dale, R. Continuous dynamics in real-time cognition. Curr. Dir. Psychol. Sci. 15, 207–211 (2006).

    Article  Google Scholar 

  166. Publication Manual of the American Psychological Association 3rd edn (American Psychological Association, 1983).

  167. Publication Manual of the American Psychological Association 6th edn (American Psychological Association, 2010).

  168. Ashby, W. R. An Introduction to Cybernetics (Martino, 1956).

  169. de Raadt, J. D. R. Ashby’s law of requisite variety: an empirical study. Cybern. Syst. 18, 517–536 (1987).

    Article  Google Scholar 

  170. Ward, L. M. Dynamical Cognitive Science (MIT Press, 2002).

  171. Regier, T., Carstensen, A. & Kemp, C. Languages support efficient communication about the environment: words for snow revisited. PLoS One 11, e0151138 (2016).

    Article  Google Scholar 

  172. Newell, A. Unified Theories of Cognition (Harvard Univ. Press, 1990).

  173. Rich, P., de Haan, R., Wareham, T. & van Rooij, I. in Proc. Annual Meeting of the Cognitive Science Society Vol. 43, 3034–3040 (eds. Fitch, T., Lamm, C., Leder, H., and Teßmar-Raible, K.) (2021).

  174. Potochnik, A. & Sanches de Oliveira, G. Patterns in cognitive phenomena and pluralism of explanatory styles. Top. Cogn. Sci. 12, 1306–1320 (2020).

    Article  Google Scholar 

  175. Leydesdorff, L. & Schank, T. Dynamic animations of journal maps: indicators of structural changes and interdisciplinary developments. J. Am. Soc. Inf. Sci. Technol. 59, 1810–1818 (2008).

    Article  Google Scholar 

  176. Leydesdorff, L. & Goldstone, R. L. Interdisciplinarity at the journal and specialty level: the changing knowledge bases of the journal Cognitive Science. J. Assoc. Inf. Sci. Technol. 65, 164–177 (2014).

    Article  Google Scholar 

  177. DeStefano, I., Oey, L. A., Brockbank, E. & Vul, E. Integration by parts: collaboration and topic structure in the CogSci community. Top. Cogn. Sci. 13, 399–413 (2021).

    Article  Google Scholar 

  178. Cummins, R. in Explanation And Cognition (eds Keil, F. C. & Wilson, R.) 117–144 (MIT Press, 2000).

  179. Boyd, N. M. & Bogen, J. in Stanford Encyclopedia of Philosophy (ed. Zalta, E. N.) https://plato.stanford.edu/archives/win2021/entries/science-theory-observation/ (Stanford Univ., 2021).

  180. Smaldino, P. E. How to build a strong theoretical foundation. Psychol. Inq. 31, 297–301 (2020).

    Article  Google Scholar 

  181. Chang, H. Inventing Temperature: Measurement and Scientific Progress (Oxford Univ. Press, 2004).

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Acknowledgements

A.S.W. was supported by the National Science Foundation (grants 1529127 and 1539129/1827744) and by the James S. McDonnell Foundation (https://doi.org/10.37717/220020507). K.L.J. was supported by the National Science Foundation (grant 2017245).

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  1. Department of Communication, University of California, Los Angeles, Los Angeles, CA, USA

    Rick Dale, Anne S. Warlaumont & Kerri L. Johnson

  2. Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA

    Kerri L. Johnson

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R.D. discussed the submission theme with the editor and wrote the first draft. A.S.W. and K.L.J. refined and added to this plan and contributed major new sections of writing and revision. All authors contributed to developing the figures.

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Dale, R., Warlaumont, A.S. & Johnson, K.L. The fundamental importance of method to theory. Nat Rev Psychol 2, 55–66 (2023). https://doi.org/10.1038/s44159-022-00120-5

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