Remarkably little is known regarding what people talk to themselves about (inner speech use) in their everyday lives. Existing self-directed speech measures (e.g., thought sampling and questionnaires) either uniquely capture inner speech frequency and neglect its content or classify selfreported thoughts instances in overly simplistic categories determined by the researchers. In the current study, we describe an open-format thought listing procedure as well as a refined coding scheme and present detailed inner speech content self-generated by 76 university students.The most frequently self-reported inner speech activities were self-regulation (e.g., planning and problem solving), self-reflection (e.g., emotions, self-motivation, appearance, behavior/performance, and autobiography), critical thinking (e.g., evaluating, judging, and criticizing), people in general, education, and current events. Inner speech occurred most commonly while studying and driving. These results are consistent with the self-regulatory and self-referential functions of inner speech often emphasized in the literature. Future research avenues using the open-format inner speech listing procedure and coding scheme are proposed.

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The experience of inner speech is a common one. Such a dialogue accompanies the introspection of mental life and fulfills essential roles in human behavior, such as self-restructuring, self-regulation, and re-focusing on attentional resources. Although the underpinning of inner speech is mostly investigated in psychological and philosophical fields, the research in robotics generally does not address such a form of self-aware behavior. Existing models of inner speech inspire computational tools to provide a robot with this form of self-awareness. Here, the widespread psychological models of inner speech are reviewed, and a cognitive architecture for a robot implementing such a capability is outlined in a simplified setup.

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