Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect
In many scientific fields, sparseness and indirectness of empirical evidence pose fundamental challenges to theory development. Theories of the evolution of human cognition provide a guiding example, where the targets of study are evolutionary processes that occurred in the ancestors of present-day humans. In many cases, the evidence is both very sparse and very indirect (e.g., archaeological findings regarding anatomical changes that might be related to the evolution of language capabilities); in other cases, the evidence is less sparse but still very indirect (e.g., data on cultural transmission in groups of contemporary humans and non-human primates). From examples of theoretical and empirical work in this domain, we distill five virtuous practices that scientists could aim to satisfy when evidence is sparse or indirect: (i) making assumptions explicit, (ii) making alternative theories explicit, (iii) pursuing computational and formal modelling, (iv) seeking external consistency with theories of related phenomena, and (v) triangulating across different forms and sources of evidence. Thus, rather than inhibiting theory development, sparseness or indirectness of evidence can catalyze it. To the extent that there are continua of sparseness and indirectness that vary across domains and that the principles identified here always apply to some degree, the solutions and advantages proposed here may generalise to other scientific domains.
Authors
- Antonina Kolokolova
- Riccardo Fusaroli (ORCID: https://orcid.org/0000-0003-4775-5219)
- Cory Wright (ORCID: https://orcid.org/0000-0003-2697-966X)
- Patricia Rich (ORCID: https://orcid.org/0000-0003-2284-6798)
- Martin Modrák (ORCID: https://orcid.org/0000-0002-8886-7797)
- Anne S. Warlaumont (ORCID: https://orcid.org/0000-0001-9450-1372)
- Marieke Woensdregt (ORCID: https://orcid.org/0000-0001-5454-4722)
Institutions
- Memorial University of Newfoundland (CA)
- Radboud University Nijmegen (NL)
- University of California, Los Angeles (US)
- Czech Academy of Sciences (CZ)
- Aarhus University (DK)
- Charles University (CZ)
- Max Planck Institute for Psycholinguistics (NL)
- Pennsylvania Academic Library Consortium (US)
- Czech Academy of Sciences, Institute of Microbiology (CZ)
- University of Bayreuth (DE)
- California State University, Long Beach (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Computational Brain & Behavior
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1007/s42113-024-00214-8
- Citations
- 9
- Primary Topic
- Advanced Text Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.13
Funders
- James S. McDonnell Foundation
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Natural Sciences and Engineering Research Council of Canada