Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Social learning refers to the belief updates of others’ personal attributes and intentions as well as social norms under various circumstances during social interactions, which helps to optimize social decision-making and maintain positive social interactions. Due to its critical role in human decisions and social interactions, the past years have witnessed a growing body of studies that examine computational and neural basis of social learning combining computational models and human brain imaging techniques. The current literature indicates that human social learning can be well captured by reinforcement learning model and Bayesian model. In the framework of reinforcement learning models, an active agent adaptively adjusts his behaviors according to the feedback in social interactions to achieve a certain goal, with positive feedback will increasing the possibility of the previous behavior and negative feedback weakening it. Accordingly, social learning mainly engages the computation of subjective expectation and prediction error. Consistent with the findings in nonsocial learning, these computations involve brain regions associated with reward and punishment processing (e.g., the ventral striatum and ventromedial prefrontal cortex). Notably, in social situations, brain regions associated with social cognition (e.g., the dorsomedial prefrontal cortex and the temporal-parietal junctions) are also involved due to the inference of the traits and intentions of others. Although reinforcement learning models provide powerful explanations for social learning processes, they did not account for the representation of social uncertainty. Instead, the Bayesian models assume that the social learning process follows the Bayesian information updating, and the perceived uncertainty is represented in the posterior distribution of psychological variables. Therefore, the Bayesian models can depict the representation of uncertainty. People represent their prior beliefs about others and calculate the deviation between actual feedback and prior beliefs, which is similar to the representation of subjective expectations and expected errors respectively in reinforcement learning style. In addition, representation of uncertainty and information integration are involved, engaging brain regions associated with reward and punishment processing, social cognition, and cognitive control (e.g., dorsolateral prefrontal cortex). However, it should be noted that there is no one-to-one mapping between computational processes and brain regions, rather, it is in a many-to-many-pattern, that is, a single cognitive process involves multiple brain regions, and a specific brain region can be involved in multiple calculations. Therefore, multivoxel pattern analysis and brain network analysis should be utilized in future studies to reveal how different computational processes are implemented in large-scale networks according to systems neuroscience. Moreover, future studies should try to increase the ecological validity by creating real social interactions between people and by leveraging novel neuroimaging approaches (e.g. hyperscanning techniques). Finally, more efforts are needed to unravel the neural and computational signatures of implicit social learning.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Auditory language comprehension plays an important role in interpersonal communication in daily life, however, we still do not fully understand its underlying neural mechanisms. Electrical brain stimulation (EBS) is an experimental technique with a very long history but has only recently been widely used on human subjects. By performing electrical stimulation, analyzing the induced transient behavioral functional changes and recording the neural activity, it is possible to directly reveal the functional roles within brain regions and the effective connections between brain areas during auditory language processing. Electrical brain stimulation offers a very high spatial and temporal resolution and employs recording electrodes that can reach deep into subcortical areas. Given these unique advantages, electrical brain stimulation has received increasing research interest in recent years. Auditory language processing is a fairly complex process and involves a wide range of brain areas. In general, the process of auditory language processing in the brain is as follows: incoming speech from the external environment enters the thalamus, which then passes to the auditory cortex (AC) for primary processing of acoustic-phonological information, followed by more advanced language processing in the temporal and frontal language areas. In addition, frontal language areas may also generate speech-related predictions that feedback to temporal language areas to facilitate auditory language processing. Electrical brain stimulation allows relatively flexible cortical or subcortical stimulation in subjects who were performing an auditory language task. By comparing the differences in task performance before and after electrical stimulation, the relationship between stimulated brain areas and cognitive function could be analyzed and thus the distribution of functionally relevant areas could be mapped. Besides, electrical brain stimulation, as a means to reflect effective connections between brain areas, can also reveal the functional connections during auditory language processing. Therefore, this paper, from the perspective of auditory language processing, is divided into three parts: thalamus and auditory cortex, auditory language processing within auditory cortex, and higher language cortex and auditory cortex. By reviewing the available studies on electrical brain stimulation during auditory language processing, the functional characteristics of the brain areas involved in auditory language processing and the information transfer mechanisms between different brain areas are summarized, providing a new perspective for further exploring the mechanisms of auditory language processing and the application of electrical brain stimulation techniques in the study of brain function. Electrical brain stimulation has broad application prospects in auditory language research, and the increased application of this technique will also bring more causal evidence on the brain function and connectivity, providing the possibility of further understanding the neural mechanisms of auditory language processing.
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