Computational Theories of Mindfulness
By Liza Tatishev
Mindfulness involves non-judgmental moment-to-moment self-awareness of physical and mental states– it is the ability to be fully conscious and aware. Within the past decade, scientific interest in mindfulness practices has increased. Mindfulness has a long history: originating over 2,500 years ago in Indian Buddhist tradition [1]. By the mid-twentieth century, mindfulness became secularized and incorporated into psychological intervention methods to improve emotional well-being [2]. Today, mindfulness-based intervention methods are recognized as successful approaches in treating a variety of psychological disorders, including depression, anxiety, and addiction. A more precise understanding of mindfulness can benefit the refinement of mindfulness-based intervention methods.
A more precise understanding of mindfulness can benefit the refinement of mindfulness-based intervention methods. Artificial neural networks within the field of computational neuroscience offer a method to examine the neuroscience of mindfulness.
Psychological Mechanisms
As a psychological intervention method, mindfulness practices– such as Tibetan Buddhist imagery meditation [3], Zen meditation [4], and Kundalini yoga [5]– are grounded in training and refining attention skills. Attentional control is a key cognitive process of executive functioning (also referred to as cognitive control) necessary for emotional and behavioral regulation [6].
Cognitive control Mindfulness training has been shown to enhance cognitive control in children and adults [7]. On a phenomenological level, the process of mindfulness training engages with the self-regulation of cognitive states that are interrelated with attentional networks.
