Self representation is fundamental to mental functions. While the self has mostly been studied in traditional psychophilosophical terms ('self as subject'), recent laboratory work suggests that the self can be measured quantitatively by assessing biases towards self-associated stimuli ('self as object'). Here, we summarize new quantitative paradigms for assessing the self, drawn from psychology, neuroeconomics, embodied cognition, and social neuroscience. We then propose a neural model of the self as an emerging property of interactions between a core 'self network' (e.g., medial prefrontal cortex; mPFC), a cognitive control network [e.g., dorsolateral (dl)PFC], and a salience network (e.g., insula). This framework not only represents a step forward in self research, but also has important clinical significance, resonating recent efforts in computational psychiatry.
Keywords: computational psychiatry; objective measures; other; self; self network.
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