Inner speech

Self-talk: research challenges and opportunities

In this review, we discuss major measurement and methodological challenges to studying self-talk. We review the assessment of self-talk frequency, studying self-talk in its natural context, personal pronoun usage within self-talk, experiential sampling methods, and the experimental manipulation of self-talk. We highlight new possible research opportunities and discuss recent advances such as brain imaging studies of self-talk, the use of self-talk by robots, and measurement of self-talk in aphasic patients.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

Believing and social interactions: effects on bodily expressions and personal narratives

The processes of believing integrate external perceptual information from the environment with internal emotional states and prior experience to generate probabilistic neural representations of events, i.e., beliefs. As these neural representations manifest mostly below the level of a person's conscious awareness, they may inadvertently affect the spontaneous person's bodily expressions and prospective behavior. By yet to be understood mechanisms people can become aware of these representations and reflect upon them. Typically, people can communicate the content of their beliefs as personal statements and can summarize the narratives of others to themselves or to other people. Here, we describe that social interactions may benefit from the consistency between a person's bodily expressions and verbal statements because the person appears authentic and ultimately trustworthy. The transmission of narratives can thus lay the groundwork for social cooperation within and between groups and, ultimately, between communities and nations. Conversely, a discrepancy between bodily expressions and narratives may cause distrust in the addressee(s) and eventually may destroy social bonds.