Ongoing experiences are a defining feature of the human condition, encompassing the thoughts, feelings, and sensations that underscore how we experience the world and our subjective sense of self from moment to moment. In this review, we show how the Default Mode Network’s structural and functional properties make it uniquely suited to supporting ongoing experiences as a system capable of abstraction, integration, and flexibility. We chart recent methodological advances that use dimensionality reduction techniques to map both neural and experiential states, demonstrating that they are most powerfully leveraged when applied together across both domains. Building on these insights, we propose an attractor-state framework to capture how stable and flexible mental states emerge from complex brain dynamics. Finally, we discuss the implications of our framework for psychopathology, offering a dynamic systems perspective on the interplay between brain function, ongoing experience, and mental health.

When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants’ experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain’s response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.