Embracing Computational Approaches Can Stimulate Clinical Psychology Research Editorial Embracing Computational Approaches Can Stimulate Clinical Psychology Research Omer Van den Bergh a, Nadine Lehnen b [a] Health Psychology, University of Leuven, Leuven, Belgium. [b] Department of Psychosomatic Medicine and Psychotherapy, Technical University of Munich, Munich, Germany. Clinical Psychology in Europe, 2019, Vol. 1(3), Article e39237, https://doi.org/10.32872/cpe.v1i3.39237 Published (VoR): 2019-09-20 Corresponding Author: Omer Van den Bergh, University of Leuven, Health Psychology, Tiensestraat 102, 3000 Leuven, Belgium. E-mail: omer.vandenbergh@kuleuven.be Clinical psychology is predominantly a “verbal” science: we derive most clinically useful information from what people say and talking is a critical means in the preferred unit of intervention: person-to-person interaction. Psychologists often tend to believe that num‐ bers are a poor means of capturing and representing what goes on in the individual’s mind and sometimes consider attempts to do so as naïve, if not offensive, to the essence of human nature and existence. One of the arguments advanced cites “complexity”: the human mind is simply too rich and complex to reduce it to numbers. Interestingly, com‐ plexity in other sciences and clinical specialties is often cited as one of the main reasons to use computing and to develop mathematical models and apply simulations. Should clinical psychology consider going down this path? As a scientific endeavor, clinical psychology is (and should be) rooted in empirical da‐ ta and validated theoretical models that allow prediction. Indeed, in a broad sense, both diagnostic and therapeutic steps (implicitly) involve a probabilistic prediction about fu‐ ture behavior. One way to validate models is to carry out experiments. However, reality in experiments is artificially reduced and controlled in order to test the effect of one or only a small set of independent (manipulated) variables on some variable of interest. The benefit is that they allow us to detect causal relationships and develop heuristics to un‐ derstand behavior. This is why experiments should be simple: our human mind can hard‐ ly grasp a 2-way interaction, let alone a 3- or 4-way interaction1. However, since multiple higher order interactions and recursive effects (effects feed back on causes) are the rule in life, experiments do not allow us to predict actual behavior in a real context. 1) Courtesy for this statement to my old professor of statistics, OVdB This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License, CC BY 4.0, which permits unrestricted use, distribution, and reproduction, provided the original work is properly cited. https://crossmark.crossref.org/dialog/?doi=10.32872/cpe.v1i3.39237&domain=pdf&date_stamp=2019-09-20 https://cpe.psychopen.eu/ https://www.psychopen.eu/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ This is no different to natural sciences: Just like experimentally investigating the rela‐ tionship between pressure, temperature and volume of a gas is important to eventually understand weather systems, the equations generated in experiments will not enable us to predict the weather across the next few days. The latter implies more complicated computational models with deterministic and stochastic variables in which lab-based equations act as building blocks that are fed with initial data and that are continuously updated as new information unfolds. Eventually, our human mind may not be able to fully grasp all the higher-order interactions, but nevertheless we may become quite good at predicting the weather. Computational science as an interdisciplinary field develops concepts, methods and tools to mathematically model and analyze complex problems and systems. It is, by itself, rather content-free. Computational approaches have been successfully used in neuro‐ sciences for a long time (Sejnowski, Koch, & Churchland, 1988; Huys, Maia, & Frank, 2016) and have been promoted in psychiatry (Friston, Stephan, Montague, & Dolan, 2014; Petzschner, 2017) and psychosomatics (Petzschner, Weber, Gard, & Stephan, 2017). Com‐ putational approaches are advocated, for example, to bridge the gap between neural pat‐ tern activity and behavioral data (Stephan & Mathys, 2014), to improve (data-driven) phe‐ notyping of patients (Patzelt, Hartley, & Gershman, 2018), and to develop, test and im‐ prove theoretical explanatory models through simulation (Lehnen et al., submitted). A re‐ cent first attempt at the latter approach, combining mathematical formulization of an ex‐ isting explanatory model with experiments, has proven useful to deepen our understand‐ ing of the complex mechanisms underlying persistent physical symptoms (Lehnen, Schröder, Henningsen, Glasauer, & Ramaioli, 2019). How relevant is this for clinical psychology in practice? Several important new devel‐ opments will probably force us to go in this direction. First, ecological momentary assess‐ ments will undoubtedly become increasingly standard to measure self-reported variables of cognitive and affective processes and social interactions while they are occurring. Sec‐ ond, it will increasingly become standard to concurrently collect psychophysiological and behavioral data through unobtrusive body sensors. Both sources of information in real life will generate large multilevel sets of data per person in multiple conditions. Since clinical psychology is primarily concerned with care for an individual patient in a partic‐ ular context, this is exactly the kind of data that is relevant for personalized care. Individ‐ ualized functions comprising deterministic and stochastic variables that model observa‐ tions registered across multiple occasions in multiple relevant contexts actually represent a theory of an individual that may act as an empirically based tool to expect/predict (and understand) behavior. In addition, such functions can be used to assess step by step change over time in a therapeutic process. Aggregation of single-case data may enable us to generalize and develop data-driven models and theories and/or to test and refine exist‐ ing theoretical models. Such an approach, which has already been successfully applied in other clinical specialties (for a recent example see Glasauer, Dieterich, & Brandt, 2018), Editorial 2 Clinical Psychology in Europe 2019, Vol.1(3), Article e39237 https://doi.org/10.32872/cpe.v1i3.39237 https://www.psychopen.eu/ turns the current situation upside down: rather than using heuristics that are based on experiments to intuitively predict/understand behavior of an individual patients in a par‐ ticular context, the reverse sequence might result in quite different models that, for ex‐ ample, attribute much more weight to contextual variables. Obviously, this may require clinical psychologists to be trained in a completely different way, as well as may require much more interdisciplinary collaboration. R e f e r e n c e s Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: The brain as a phantastic organ. Lancet Psychiatry, 1(2), 148-158. https://doi.org/10.1016/S2215-0366(14)70275-5 Glasauer, S., Dieterich, M., & Brandt, T. (2018). Neuronal network-based mathematical modeling of perceived verticality in acute unilateral vestibular lesions: From nerve to thalamus and cortex. Journal of Neurology, 265(Suppl 1), 101-112. https://doi.org/10.1007/s00415-018-8909-5 Huys, Q. J., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3), 404-413. https://doi.org/10.1038/nn.4238 Lehnen, N., Radziej, K., Weigel, A., von Känel, R., Glasauer, S., Pitron, V., … Henningsen, P. on behalf of the EURONET-SOMA Group (submitted). Computational-experimental psychosomatics for a better understanding of persistent somatic symptoms. Manuscript submitted for publication. Lehnen, N., Schröder, L., Henningsen, P., Glasauer, S., & Ramaioli, C. (2019). Deficient head motor control in functional dizziness: Experimental evidence of central sensory-motor dysfunction in persistent physical symptoms. Progress in Brain Research, 249, 385-400. https://doi.org/10.1016/bs.pbr.2019.02.006 Patzelt, E. H., Hartley, C. A., & Gershman, S. J. (2018). Computational phenotyping: Using models to understand individual differences in personality, development, and mental illness. Personality Neuroscience, 1, Article e18. https://doi.org/10.1017/pen.2018.14 Petzschner, F. H. (2017). Stochastic dynamic models for computational psychiatry and computational neurology. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(3), 214-215. https://doi.org/10.1016/j.bpsc.2017.03.003 Petzschner, F. H., Weber, L. A., Gard, T., & Stephan, K. E. (2017). Computational psychosomatics and computational psychiatry: Toward a joint framework for differential diagnosis. Biological Psychiatry, 82(6), 421-430. https://doi.org/10.1016/j.biopsych.2017.05.012 Sejnowski, T. J., Koch, C., & Churchland, P. S. (1988). Computational neuroscience. Science, 241(4871), 1299-306. https://doi.org/10.1126/science.3045969 Stephan, K. E., & Mathys, C. (2014). Computational approaches to psychiatry. Current Opinion in Neurobiology, 25, 85-92. https://doi.org/10.1016/j.conb.2013.12.007 Van den Bergh & Lehnen 3 Clinical Psychology in Europe 2019, Vol.1(3), Article e39237 https://doi.org/10.32872/cpe.v1i3.39237 https://doi.org/10.1016/S2215-0366(14)70275-5 https://doi.org/10.1007/s00415-018-8909-5 https://doi.org/10.1038/nn.4238 https://doi.org/10.1016/bs.pbr.2019.02.006 https://doi.org/10.1017/pen.2018.14 https://doi.org/10.1016/j.bpsc.2017.03.003 https://doi.org/10.1016/j.biopsych.2017.05.012 https://doi.org/10.1126/science.3045969 https://doi.org/10.1016/j.conb.2013.12.007 https://www.psychopen.eu/ Clinical Psychology in Europe (CPE) is the official journal of the European Association of Clinical Psychology and Psychological Treatment (EACLIPT). PsychOpen GOLD is a publishing service by Leibniz Institute for Psychology Information (ZPID), Germany. Editorial 4 Clinical Psychology in Europe 2019, Vol.1(3), Article e39237 https://doi.org/10.32872/cpe.v1i3.39237 https://www.psychopen.eu/