Parsing state mindfulness effects on neurobehavioral markers of cognitive control: A within-subject comparison of focused attention and open monitoring

解析正念状态对认知控制神经行为指标的影响:专注式注意与开放式监测的被试内比较

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Abstract

Over the past two decades, scientific interest in understanding the relationship between mindfulness and cognition has accelerated. However, despite considerable investigative efforts, pervasive methodological inconsistencies within the literature preclude a thorough understanding of whether or how mindfulness influences core cognitive functions. The purpose of the current study is to provide an initial "proof-of-concept" demonstration of a new research strategy and methodological approach designed to address previous limitations. Specifically, we implemented a novel fully within-subject state induction protocol to elucidate the neurobehavioral influence of discrete mindfulness states-focused attention (FA) and open monitoring (OM), compared against an active control-on well-established behavioral and ERP indices of executive attention and error monitoring assessed during the Eriksen flanker task. Bayesian mixed modeling was used to test preregistered hypotheses pertaining to FA and OM effects on flanker interference, the stimulus-locked P3, and the response-locked ERN and Pe. Results yielded strong but unexpected evidence that OM selectively produced a more cautious and intentional response style, characterized by higher accuracy, slower RTs, and reduced P3 amplitude. Follow-up exploratory analyses revealed that trait mindfulness moderated the influence of OM, such that individuals with greater trait mindfulness responded more cautiously and exhibited higher trial accuracy and smaller P3s. Neither FA nor OM modulated the ERN or Pe. Taken together, our findings support the promise of our approach, demonstrating that theoretically distinct mindfulness states are functionally dissociable among mindfulness-naive participants and that interactive variability associated with different operational facets of mindfulness (i.e., state vs. trait) can be modeled directly.

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