Objectives: converging research suggests that mindfulness training exerts its therapeutic effects on depression by reducing rumination. Theoretically, rumination is a multifaceted construct that aggregates multiple neurocognitive aspects of depression, including poor executive control, negative and overgeneral memory bias, and persistence or stickiness of negative mind states. Current measures of rumination, most-often self-reports, do not capture these different aspects of ruminative tendencies, and therefore are limited in providing detailed information about the mechanisms of mindfulness. Methods: we developed new insight into the potential mechanisms of rumination, based on three model-based metrics of free recall dynamics. These three measures reflect the patterns of memory retrieval of valenced information: the probability of first recall (Pstart) which represents initial affective bias, the probability of staying with the same valence category rather than switching, which indicates strength of positive or negative association networks (Pstay), and probability of stopping (Pstop) or ending recall within a given valence, which indicates persistence or stickiness of a mind state. We investigated the effects of Mindfulness-Based Cognitive Therapy (MBCT; N = 29) vs. wait-list control (N = 23) on these recall dynamics in a randomized controlled trial in individuals with recurrent depression. Participants completed a standard laboratory stressor, the Trier Social Stress Test, to induce negative mood and activate ruminative tendencies. Following that, participants completed a free recall task consisting of three word lists. This assessment was conducted both before and after treatment or wait-list. Results: while MBCT participant’s Pstart remained relatively stable, controls showed multiple indications of depression-related deterioration toward more negative and less positive bias. Following the intervention, MBCT participants decreased in their tendency to sustain trains of negative words and increased their tendency to sustain trains of positive words. Conversely, controls showed the opposite tendency: controls stayed in trains of negative words for longer, and stayed in trains of positive words for less time relative to pre-intervention scores. MBCT participants tended to stop recall less often with negative words, which indicates less persistence or stickiness of negatively valenced mental context. Conclusion: MBCT participants showed a decrease in patterns that may perpetuate rumination on all three types of recall dynamics (Pstart, Pstay, and Pstop), compared to controls. MBCT may weaken the strength of self-perpetuating negative associations networks that are responsible for the persistent and “sticky” negative mind states observed in depression, and increase the positive associations that are lacking in depression. This study also offers a novel, objective method of measuring several indices of ruminative tendencies indicative of the underlying mechanisms of rumination.

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Even the most ardent proponents of computational psychiatry admit that the field is far from influencing routine clinical practice. We propose one reason for this is that the field has had difficulty recognizing the variability among mental health problems—and the resulting need to model context and temporal dynamics for many problems. We develop three heuristics for estimating whether time and context are important to a mental health problem. Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry

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