Practicing theory building in a many modelers hackathon: A proof of concept

Dongen, Noah N'Djaye Nikolai van; Finnemann, Adam; Ron, Jill de; Tiokhin, Leonid; Wang, Shirley B.; Algermissen, Johannes; Altmann, Elena C.; Chuang, Li-Ching; Dumbravă, Andrei; Bahník, Štěpán; Fuenderich, Jens; Geiger, Sandra J.; Gerasimova, Daria; Golan, Aidai; Herbers, Judith; Lin, Yih-Shiuan; Moreau, David; Oberholzer, Yvonne; Peetz, Hannah Katharina; Rohrer, Julia M.; Rothers, Adrian; Schönbrodt, Felix D.; Seetahul, Yashvin; Szabelska, Anna; Tonge, Natasha April; Walasek, Nicole; Werner, Marlene; Borsboom, Denny

doi:10.31234/osf.io/r5yfz

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Progress toward a more precise, mechanistic understanding of creative cognition cannot be made by modeling alone, but will require the cooperation of theorists, modelers, and experimenters (Hitchcock et al, 2022; van Dongen et al, 2022; Wiggins & Bhattacharya, 2014). How might greater integration between NCR and computational modeling look?…”

Section: Toward Greater Integration Between Ncr and Computational Mod...mentioning

confidence: 99%

Toward greater computational modeling in neurocognitive creativity research.

Lloyd-Cox,

Pickering,

Beaty

et al. 2023

Psychology of Aesthetics, Creativity, and the Arts

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Creative cognition is the driving force behind all cultural and scientific progress. In recent years, the field of neurocognitive creativity research (NCR) has made considerable progress in revealing the neural and psychological correlates of creative cognition. However, a detailed understanding of how cognitive processes produce creative ideas, and how these processes interact differently across tasks and individuals, remains elusive. In this article, we argue that the increased adoption of computational modeling can help greatly in achieving this goal. While the verbal theories guiding NCR have evolved from broader accounts into more specific descriptions of neurocognitive processes, they remain more open to interpretation and harder to falsify than formal models. Translating theories into computational models can make them more concrete, accessible, and easier to compare, and helps researchers to develop causal hypotheses for how variation in cognitive factors leads to variation in creative outcomes. Currently, however, computational modeling of creativity is conducted almost entirely separately from NCR, and few attempts have been made to embody the cognitive theories of NCR in models that can simulate performance on common lab-based tasks. In this article, we discuss theories of creative cognition and how they might benefit from the wider adoption of formal modeling. We also examine recent computational models of creativity and how these might be improved and better integrated with NCR. Finally, we describe a pathway toward a mechanistic understanding of creative cognition through the integration of computational modeling, psychological theory, and empirical research, outlining an example model based on dual-process accounts. Public Significance StatementThis review argues that creativity research would benefit greatly from the wider adoption of computational modeling. We discuss how translating verbal theories of creative cognition into formal computational models can make them more rigorous, accessible, and communicable, and can highlight questions for future research. We examine previous models of creativity and explain how these can be improved to benefit our understanding of human creative cognition and the development of artificial creative systems.

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Section: Toward Greater Integration Between Ncr and Computational Mod...mentioning

confidence: 99%

Toward greater computational modeling in neurocognitive creativity research.

Lloyd-Cox,

Pickering,

Beaty

et al. 2023

Psychology of Aesthetics, Creativity, and the Arts

View full text Add to dashboard Cite

show abstract

“…The arbitrariness of modeling-decisions and multi-explicability of auxiliary assumptions can be revealed in a many-modeler approach (e.g., van Dongen, Finnemann, et al, 2022), where multiple research teams construct a model from the same verbal theory independently of each other. For instance, a table that sums up the choices that are derived from the theory and the arbitrary modeling decisions could then serve as a proxy of precision.…”

Section: Precisionmentioning

confidence: 99%

Productive Explanation: A Framework for Evaluating Explanations in Psychological Science

Dongen¹,

Bork²,

Finnemann³

et al. 2022

Preprint

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he explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains the phenomena it purports to explain. We aim to address this shortcoming in psychology by characterizing the nature of explanation and proposing a framework in which to evaluate explanation. We present a productive account of explanation: a theory explains a phenomenon if and only if it can produce a statistical pattern representing the phenomenon $P$ (bottom panel of Figure 1). Using this account, we outline a workable methodology of explanation. This methodology entails (a) translating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we explicate a number of methodological criteria for evaluating the goodness of this explanatory relation between theory and empirical phenomenon and conclude with a discussion about how this productive explanation methodology can be used within the broader aim of constructing and developing psychological theories.

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Strategic ambiguity in the social sciences

Frankenhuis,

Panchanathan,

Smaldino

2023

Soc. Psychol. Bull.

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In the wake of the replication crisis, there have been calls to increase the clarity and precision of theory in the social sciences. Here, we argue that the effects of these calls may be limited due to incentives favoring ambiguous theory. Intentionally or not, scientists can exploit theoretical ambiguities to make support for a claim appear stronger than it is. Practices include theory stretching, interpreting an ambiguous claim more expansively to absorb data outside of the scope of the original claim, and post-hoc precision, interpreting an ambiguous claim more narrowly so it appears more precisely aligned with the data. These practices lead to the overestimation of evidence for the original claim and create the appearance of consistent support and progressive research programs, which may in turn be rewarded by journals, funding agencies, and hiring committees. Selection for ambiguous research can occur even when scientists act in good faith. Although ambiguity might be inevitable or even useful in the early stages of theory construction, scientists should aim for increased clarity as knowledge advances. Science benefits from transparently communicating about known ambiguities. To attain transparency about ambiguity, we provide a set of recommendations for authors, reviewers, and journals. We conclude with suggestions for research on how scientists use strategic ambiguity to advance their careers and the ways in which norms, incentives, and practices favor strategic ambiguity. Our paper ends with a simple mathematical model exploring the conditions in which high-ambiguity theories are favored over low-ambiguity theories, providing a basis for future analyses.

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Practicing theory building in a many modelers hackathon: A proof of concept

Cited by 3 publications

References 9 publications

Toward greater computational modeling in neurocognitive creativity research.

Toward greater computational modeling in neurocognitive creativity research.

Productive Explanation: A Framework for Evaluating Explanations in Psychological Science

Strategic ambiguity in the social sciences

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