The social brain: allowing humans to boldly go where no other species has been

The biological basis of complex human social interaction and communication has been illuminated through a coming together of various methods and disciplines. Among these are comparative studies of other species, studies of disorders of social cognition and developmental psychology. The use of neuroimaging and computational models has given weight to speculations about the evolution of social behaviour and culture in human societies. We highlight some networks of the social brain relevant to two-person interactions and consider the social signals between interacting partners that activate these networks.Wemake a case for distinguishing between signals that automatically trigger interaction and cooperation and ostensive signals that are used deliberately.We suggest that this ostensive signalling is needed for ‘closing the loop’ in two-person interactions, where the partners each know that they have the intention to communicate. The use of deliberate social signals can serve to increase reputation and trust and facilitates teaching. This is likely to be a critical factor in the steep cultural ascent ofmankind

Mindfulness meditation modulates reward prediction errors in a passive conditioning task

Reinforcement learning models have demonstrated that phasic activity of dopamine neurons during reward expectation encodes information about the predictability of reward and cues that predict reward. Self-control strategies such as those practiced in mindfulness-based approaches is claimed to reduce negative and positive reactions to stimuli suggesting the hypothesis that such training may influence basic reward processing. Using a passive conditioning task and fMRI in a group of experienced mindfulness meditators and age-matched controls, we tested the hypothesis that mindfulness meditation influence reward and reward prediction error (PE) signals. We found diminished positive and negative PE-related blood-oxygen level-dependent (BOLD) responses in the putamen in meditators compared with controls. In the meditator group this decrease in striatal BOLD responses to reward PE was paralleled by increased activity in posterior insula, a primary interoceptive region. Critically, responses in the putamen during early trials of the conditioning procedure (run 1) were elevated in both meditators and controls. Overall, these results provide evidence that experienced mindfulness meditators are able to attenuate reward prediction signals to valenced stimuli, which may be related to interoceptive processes encoded in the posterior insula

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

Search Process as Transitions Between Neural States

Search is one of the most performed activities on the World Wide Web. Various conceptual models postulate that the search process can be broken down into distinct emotional and cognitive states of searchers while they engage in a search process. These models significantly contribute to our understanding of the search process. However, they are typically based on self-report measures, such as surveys, questionnaire, etc. and therefore, only indirectly monitor the brain activity that supports such a process. With this work, we take one step further and directly measure the brain activity involved in a search process. To do so, we break down a search process into five time periods: a realisation of Information Need, Query Formulation, Query Submission, Relevance Judgment and Satisfaction Judgment. We then investigate the brain activity between these time periods. Using functional Magnetic Resonance Imaging (fMRI), we monitored the brain activity of twenty-four participants during a search process that involved answering questions carefully selected from the TREC-8 and TREC 2001 Q/A Tracks. This novel analysis that focuses on transitions rather than states reveals the contrasting brain activity between time periods – which enables the identification of the distinct parts of the search process as the user moves through them. This work, therefore, provides an important first step in representing the search process based on the transitions between neural states. Discovering more precisely how brain activity relates to different parts of the search process will enable the development of brain-computer interactions that better support search and search interactions, which we believe our study and conclusions advance

Neuronal Distortions of Reward Probability without Choice

Reward probability crucially determines the value of outcomes. A basic phenomenon, defying explanation by traditional decision theories, is that people often overweigh small and underweigh large probabilities in choices under uncertainty. However, the neuronal basis of such reward probability distortions and their position in the decision process are largely unknown. We assessed individual probability distortions with behavioral pleasantness ratings and brain imaging in the absence of choice. Dorsolateral frontal cortex regions showed experience dependent overweighting of small, and underweighting of large, probabilities whereas ventral frontal regions showed the opposite pattern. These results demonstrate distorted neuronal coding of reward probabilities in the absence of choice, stress the importance of experience with probabilistic outcomes and contrast with linear probability coding in the striatum. Input of the distorted probability estimations to decision-making mechanisms are likely to contribute to well known inconsistencies in preferences formalized in theories of behavioral economics