How computational modeling can force theory building in psychological science

Guest, Olivia; Martin, Andrea E.

doi:10.31234/osf.io/rybh9

Cited by 91 publications

(105 citation statements)

References 24 publications

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“…Clinicians are required to make more rigorous decisions in specifying relations in the case conceptualization, which makes the formalization of idiographic theories a promising avenue to foster more scientific practices in designing patient-tailored treatment. This reasoning is in line with a growing body of literature indicating the need for more rigorous theory development in clinical and social sciences [25,35,[42][43][44][45]. The left part of Fig.…”

Section: The Role Of Computational Models In Bridging the Scientist-psupporting

confidence: 78%

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Burger

Veen

Robinaugh

et al. 2020

BMC Med

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Background: The past decades of research have seen an increase in statistical tools to explore the complex dynamics of mental health from patient data, yet the application of these tools in clinical practice remains uncommon. This is surprising, given that clinical reasoning, e.g., case conceptualizations, largely coincides with the dynamical system approach. We argue that the gap between statistical tools and clinical practice can partly be explained by the fact that current estimation techniques disregard theoretical and practical considerations relevant to psychotherapy. To address this issue, we propose that case conceptualizations should be formalized. We illustrate this approach by introducing a computational model of functional analysis, a framework commonly used by practitioners to formulate case conceptualizations and design patient-tailored treatment. Methods: We outline the general approach of formalizing idiographic theories, drawing on the example of a functional analysis for a patient suffering from panic disorder. We specified the system using a series of differential equations and simulated different scenarios; first, we simulated data without intervening in the system to examine the effects of avoidant coping on the development of panic symptomatic. Second, we formalized two interventions commonly used in cognitive behavioral therapy (CBT; exposure and cognitive reappraisal) and subsequently simulated their effects on the system. Results: The first simulation showed that the specified system could recover several aspects of the phenomenon (panic disorder), however, also showed some incongruency with the nature of panic attacks (e.g., rapid decreases were not observed). The second simulation study illustrated differential effects of CBT interventions for this patient. All tested interventions could decrease panic levels in the system.

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Section: The Role Of Computational Models In Bridging the Scientist-psupporting

confidence: 78%

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Burger

Veen

Robinaugh

et al. 2020

BMC Med

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“…As Cummins put it: "In psychology, we are overwhelmed with things to explain, and somewhat underwhelmed by things to explain them with" (Cummins, 2000). For this reason, many scholars have argued that psychology's attention to statistics and replicability has distracted from a problem that runs much deeper: a crisis of theory (Borsboom, 2013;Borsboom et al, 2020;Cummins, 2000;Guest & Martin, 2020;Haslbeck et al, 2019;Meehl, 1990b;Muthukrishna & Henrich, 2019;Phaf, 2020;Robinaugh, Haslbeck, et al, 2019;Smaldino, 2019;Szollosi et al, 2019;Vaidyanathan et al, 2015;Van Rooij & Baggio, 2020).…”

Section: Data Rich and Theory Poormentioning

confidence: 99%

“…Somewhat surprisingly, since starting to work on this paper in early 2019, numerous contributions in different areas of psychology have identified this crisis of theory as a crucial challenge moving forward (Borsboom et al, 2020;Burger et al, 2019;Gershman, 2019;Guest & Martin, 2020;Haslbeck et al, 2019;Kellen, 2019;Lakens & Debruine, 2020;Muthukrishna & Henrich, 2019;Oberauer & Lewandowsky, 2019;Robinaugh, Haslbeck, et al, 2019;Savi et al, 2019;Smaldino, 2019;Szollosi et al, 2019;Van Rooij & Baggio, 2020), and both the Journal of Abnormal Psychology and Perspective of Psychological Science have opened calls for special issues on theory.…”

Section: Putting the Theory Back Into Psychologymentioning

confidence: 99%

Lack of theory building and testing impedes progress in the factor and network literature

Fried¹

2020

Preprint

112

138

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The last decade has brought reforms to improve methodological practices, with the goal to increase the reliability and replicability of effects. However, explanations of effects remain scarce, and a growing chorus of scholars argues that the replicability crisis has distracted from a crisis of theory. In the same decade, the empirical literature using factor and network models has grown rapidly. I discuss three ways in which this literature falls short of theory building and testing. First, statistical and theoretical models are conflated, leading to invalid inferences such as the existence of psychological constructs based on factor models, or recommendations for clinical interventions based on network models. I demonstrate this inferential gap in a simulation study on statistical equivalence: excellent model fit does little to corroborate a theory, regardless of quality or quantity of data. Second, researchers fail to explicate theories about psychological constructs, but use implicit causal beliefs to guide inferences. These latent theories have led to problematic best practices in psychological research where inferences are drawn based on one specific causal model that is assumed, but not explicated. Third, explicated theories are often weak theories: narrative and imprecise descriptions vulnerable to hidden assumptions and unknowns. They fail to make clear predictions, and it remains unclear whether statistical effects corroborate such theories or not. Weak theories are immune to refutation or revision. I argue that these three challenges to theory building and testing are common and harmful, and impede theory formation, failure, and reform. A renewed focus on theoretical psychology and formal models offers a way forward.

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“…They are mental prostheses that let us check our intuitions against simplified universes. They force us to make crucial assumptions explicit so they can be openly evaluated for plausibility (Guest & Martin, 2020). They can let us specify potentially necessary and sufficient conditions to generate specific (often terrible) states-of-the-world (O'Connor, 2019b;O'Connor & Weatherall, 2018;Smaldino, 2017), suggesting interventions that might be more or less likely to succeed.…”

Section: Modeling Scientific Desideratamentioning

confidence: 99%

“…We should pay attention to models, especially when they violate our intuitions or cherished notions. Models are stupid (Smaldino, 2017) and rely on deliberately unrealistic, yet transparent (Guest & Martin, 2020), assumptions. Yet they can offer guidance about possible forces that could have created worlds like ours, and highlight logical patterns that can shape our expectations about the scientific world we desire.…”

Section: Metascience Needs Theorymentioning

confidence: 99%

Practical Methodological Reform Needs Good Theory

Gervais¹

2020

Preprint

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In the face of unreplicable results, statistical anomalies, and outright fraud, introspection and changes in the psychological sciences have taken root. Vibrant reform and meta-science movements have emerged. These are exciting developments, and may point towards practical improvements in the future. Yet there is nothing so practical as good theory. This paper outlines aspects of reform and meta-science that appear ripe for an injection of theory, including a lot of excellent and overlooked theoretical work from different disciplines. I focus on theories of meta-science and scientific forensics, theories of scientific aspiration, and theories of cultural change in science. There is much room for theoretical growth -- a growth that can only benefit emerging movements -- and highlight some, focusing especially on the potential of cultural evolutionary theories to help direct reform efforts. We have unwittingly evolved a toxic scientific ecosystem; theory may help us intelligently design a better one.

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How computational modeling can force theory building in psychological science

Cited by 91 publications

References 24 publications

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Lack of theory building and testing impedes progress in the factor and network literature

Practical Methodological Reform Needs Good Theory

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