Reconceptualizing Altered States of Consciousness Using Network-Based Tools

Scientific study of altered states of consciousness (ASCs) has been hampered by disagreements over measurement tools and by conceptual problems with existing typologies—trigger-based, phenomenology-based, attribution-based, neurobiological, and psychopathological—which struggle to clearly demarcate different states. The authors propose a network-based classification method that treats ASCs as a network of nodes, where clusters of states can be identified. This approach offers flexibility: nodes can include experiential, contextual, social, and biological features. Network models allow calculation of properties like centrality and outperform current conceptual frameworks for organizing research, enabling more precise results and better interdisciplinary communication.

Study at a glance

Abstract

Abstract Progress in the scientific study of altered states of consciousness (ASCs) has been hindered by methodological and conceptual problems. At a methodological level there is a lack of agreement on the scales and instruments used to measure and categorize ASCs. At a conceptual level, existing typologies (such as trigger-based, phenomenology-based, attribution-based, neurobiological, and psychopathological approaches) face difficulties with demarcating different states and in organizing the field of investigation in a clear and productive way. The authors propose a new method of classification for this research field that will mitigate these problems by providing better typologies to study ASCs. In this chapter the authors lay out the epistemological foundations of such an approach, arguing that scientific progress can be made through an iterative process of conceptual refinement. On this approach, ASCs can be conceived of as a network of nodes, in which different clusters of states can be identified. At a methodological level, network-based models offer the potential to calculate network properties (e.g., centrality measures), while at a conceptual level they offer flexibility in terms of the nodes that can be included in the network (these could encompass features of experience, as well as contextual, social and biological features). The authors argue that a network-based model outperforms existing conceptual approaches for fostering current scientific research. By doing so, the authors also offer methodological tools to organize the research field of ASCs, which helps scientists to produce more precise scientific results and to foster interdisciplinary transmission of results.