Critical Consideration of Science Convergence
There has been a lot of discussion for years now over the value and necessity of science convergence in research progress. Science convergence is defined as the confluence of several disciplines aiming to address difficult problems1. In previous work, we documented the triumphant role of science convergence in the Human Genome Project (HGP)2 . In recent work, we highlighted the inconclusive role of convergence in brain science3 , identifying suboptimal polymathic convergence as a culprit. In contrast to multidisciplinary convergence, where the different disciplines partaking in research are represented by corresponding disciplinary experts, in polymathic convergence collaborating experts venture out of their disciplinary competence zone4. As a result, understrength convergent teams bite off more than they can chew on, with negative implications in research performance. This nuanced consideration stood in sharp contrast to the simplistic treatment and all-round glorification of science convergence by research funding agencies.
Presently, it is also believed that multi-disciplinary knowledge synthesis (aka convergent scholarship) benefits the most from prior multi-disciplinary research products5. Contrary to this prevailing wisdom, in our newest paper in Communications Psychology6 we advance the thesis that convergent research products are not prerequisites for the flourishing of convergent scholarship. We show that even mono-disciplinary research products can have much stronger multi-disciplinary appeal than convergent research products, fostering strong downstream convergence.
Upstream and Downstream Convergence in the Affective and Cognitive Literature
We ran the study on the affective and cognitive literature in psychological sciences and allied fields. We chose these two domains because they both relate to methods aiming to explain human behaviors as follows:
The cognitive literature focuses on the central role of cognitive representation and information processing in explaining behavior, while largely ignores the role of affect. This scholarly trend is known as cognitivism.
The affective literature focuses on the singular or combined role of affect in explaining behavior. This scholarly trend is known as affectivism and features two prominent sub-branches: one focusing solely on affective considerations, and one mixing both affective and cognitive considerations.
Both cognitivism and affectivism claim convergence pedigree; cognitivism because of its association with brain science – a well-known disciplinary melting pot3; affectivism because of a consensus testimonial of over 60 prominent researchers in Nature Human Behavior7. The question is which of these two partly competing and partly complementary scholarly trends is more convergent than the other, and if this convergence differential is associated with any citation impact differential.
Two things render our study unique: First, it affords meaningful comparison between two `sister domains’ – a rare opportunity in science convergence research, which typically focuses on one domain at a time. Second, it examines disciplinary confluence not only in the content of publications (upstream convergence), but also in the content of their citations (downstream convergence). Hence, by design, this is a bibliometric study capable of answering the following profound question: `Do you need multi-disciplinary content to reach an analogous multi-disciplinary audience?’ Or, expressed differently, `Do you need convergence to breed convergence?’ The answer to this question is a resounding `No!’
Analyzing over half a million relevant publication records from PubMed, we found that although affectivism publications have significantly less convergent content (Dp) than cognitivism publications, they have stronger and much broader impact (Dc) , exerting influence on almost every other known field, including medicine, technoscience, and humanities. For instance, data from published affective studies like the “Netherlands Study of Anxiety and Depression” (NESDA)8 feed medical studies on the etiology of migraines9, machine learning studies in predicting anxiety disorders10, and socioeconomic studies on the depressive role of perceived financial strain11.
Implications and Future Research
Science convergence is important and its contribution to science advances, like genomics, is undeniable4. Nevertheless, convergence is neither a one tune song nor a panacea. There are different ways to operationalize convergence, not all of which are equally successful3 . Furthermore, our paper shows that convergence is not always needed to formulate broadly applicable solutions. Results from pure psychological studies like NESDA, can percolate to a broad spectrum of research fields, spanning medicine, computer science, and economics, something that multi-disciplinary brain studies can never hope to achieve. Hopefully, research funding agencies will take note of these insights and replace blanket encouragement of convergence with a more thoughtful approach that includes promotion of either convergence or mono-disciplinary work, depending on the circumstances.
Future research should focus on expanding these solid leads, shedding more light to the non-linear mechanisms of science convergence and science advances. In this direction, AI – a mono-disciplinary development coming out of computer science – has an apparent pattern of downstream convergence that calls for a similar analytic investigation. It appears that the most consequential scientific advancement of this decade has little to do with convergence science in its composition, but a lot to do with convergence science in its consequences – a phenomenon that testifies to the value and timeliness of our framework.
References
National Research Council. Convergence: Facilitating transdisciplinary integration of life sciences, physical sciences, engineering, and beyond (National Academies Press, Washington, D.C., 2014).
Petersen, A. M., Majeti, D., Kwon, K., Ahmed, M. E. & Pavlidis, I. Cross-disciplinary evolution of the genomics revolution. Sci. Adv. 4, eaat4211, DOI: https://doi.org/10.1126/sciadv.aat4211 (2018).
Petersen, A. M., Ahmed, M. E. & Pavlidis, I. Grand challenges and emergent modes of convergence science. Humanit. Soc. Sci. Commun. 8, 1–15, DOI: https://doi.org/10.1057/s41599-021-00869-9 (2021).
Pavlidis, I., Akleman, E. & Petersen, A. M. From Polymaths to Cyborgs – Convergence Is Relentless. Am. Sci. 110, 196–200 (2022).
Arnold, A. & Bowman, K. (eds.). Fostering the culture of convergence in research: Proceedings of a Workshop (National Academies Press, 2019).
Zhukov, V. et al. Science convergence in affective research is associated with impactful multidisciplinary appeal rather than multidisciplinary content. Commun. Psychol. 2, DOI: https://doi.org/10.1038/s44271-024-00129-x (2024).
Dukes, D. et al. The rise of affectivism. Nat. Hum. Behav. 5, 816–820, DOI: https://doi.org/10.1038/s41562-021-01130-8(2021).
Penninx, B. W. et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int. J. Methods Psychiatr. Res. 17, 121–140, DOI: https://doi.org/10.1002/mpr.256 (2008).
Gormley, P. et al. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat. Genet. 48, 856–866, DOI: https://doi.org/10.1038/ng.3598 (2016).
Jacobson, N. C., Lekkas, D., Huang, R. & Thomas, N. Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17–18 years. J. Affect. Disord. 282, 104–111, DOI: https://doi.org/10.1016/j.jad.2020.12.086 (2021).
Dijkstra-Kersten, S. M., Biesheuvel-Leliefeld, K. E., van der Wouden, J. C., Penninx, B. W. & van Marwijk, H. W. Associations of financial strain and income with depressive and anxiety disorders. J Epidemiol Community Heal. 69, 660–665 (2015).