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Here, we review these functions of network analysis in the context of three types of application in psychological science, illustrating them with examples taken from personality, attitude research and mental health.
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The combination of network representations in data analytics and theory formation is remarkably fruitful in forging connections between different fields and research programmes. One important connection is that between the study of inter-individual differences and intra-individual mechanisms. More than half a century ago, Cronbach famously diagnosed psychological science to be a deeply divided discipline129. With one camp of psychological scientists concerned with mechanistic explanations and another camp primarily focused on the study of individual differences, that dichotomy is still prevailing. Some argue that in order to overcome this division, psychological scientists should rethink their widespread practice of detaching statistical practice from substantive theory130,131,132. One reason for this detachment, however, has been the long-standing lack of an intuitive modelling framework that facilitates both theory construction and process-based computations and simulation, so that it can connect the two disciplines129. But this gap is exactly what makes network approaches fall on fertile soil. Networks readily accommodate the multivariate architecture of psychological systems and also offer a toolbox to develop formal theories of the dynamical processes that act on them. In this manner, models of intra-individual dynamics can serve as explanations of systems of inter-individual differences, bridging the gap between intra-individual and inter-individual modelling129.
Network models are not only useful to create bridges from data analysis to theory formation but also to connect different scientific disciplines to each other. In recent years, network science and associated complex systems approaches have led to an active interdisciplinary research area in which researchers from many fields collaborate. Network approaches in psychology, as discussed here, have similarly broadened the horizon of relevant candidate methodologies relevant to psychological research questions; for instance, it is remarkable that the first network model fitted to psychopathology data16 was based on modelling approaches developed to study atomic spins133,134, whereas subsequent studies into the research dynamics of psychopathology135 investigated sudden transitions using methodology developed in ecology136 and, finally, recent studies of interventions in such networks are based on control theory137. Clearly, network representations create a situation where scientists with different disciplinary backgrounds find a common vocabulary.
Artificial intelligence powered by deep neural networks has reached a level of complexity where it can be difficult or impossible to express how a model makes its decisions. This black-box problem is especially concerning when the model makes decisions with consequences for human well-being. In response, an emerging field called explainable artificial intelligence (XAI) aims to increase the interpretability, fairness, and transparency of machine learning. In this paper, we describe how cognitive psychologists can make contributions to XAI. The human mind is also a black box, and cognitive psychologists have over 150 years of experience modeling it through experimentation. We ought to translate the methods and rigor of cognitive psychology to the study of artificial black boxes in the service of explainability. We provide a review of XAI for psychologists, arguing that current methods possess a blind spot that can be complemented by the experimental cognitive tradition. We also provide a framework for research in XAI, highlight exemplary cases of experimentation within XAI inspired by psychological science, and provide a tutorial on experimenting with machines. We end by noting the advantages of an experimental approach and invite other psychologists to conduct research in this exciting new field.
Producing performant AI is the domain of ML research, where aspects of model design such as architecture, loss functions, learning and optimization algorithms are studied extensively. Reviewing these topics is outside the scope of this article, but a cursory understanding (at least) of ML principles is required to be making inferences about how machines make decisions, in the same way that neuroscience is part of every education in human psychology. To that end, we will recommend some resources here for psychologists to familiarize themselves with the principles of ML. Luckily, ML has a strong culture of making educational resources free and open-source. 2ff7e9595c
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