Investigation of the Sense of Agency in Social Cognition, based on frameworks of Predictive Coding and Active Inference: A simulation study on multimodal imitative interaction

When agents interact socially with different intentions, conflicts are difficult to avoid. Although how agents can resolve such problems autonomously has not been determined, dynamic characteristics of agency may shed light on underlying mechanisms. The current study focused on the sense of agency (SoA), a specific aspect of agency referring to congruence between the agent's intention in acting and the outcome. Employing predictive coding and active inference as theoretical frameworks of perception and action generation, we hypothesize that regulation of complexity in the evidence lower bound of an agent's model should affect the strength of the agent's SoA and should have a critical impact on social interactions. We built a computational model of imitative interaction between a robot and a human via visuo-proprioceptive sensation with a variational Bayes recurrent neural network, and simulated the model in the form of pseudo-imitative interaction using recorded human body movement data. A key feature of the model is that each modality's complexity can be regulated differently with a hyperparameter assigned to each module. We first searched for an optimal setting that endows the model with appropriate coordination of multimodal sensation. This revealed that the vision module's complexity should be more tightly regulated than that of the proprioception module. Using the optimally trained model, we examined how changing the tightness of complexity regulation after training affects the strength of the SoA during interactions. The results showed that with looser regulation, an agent tends to act more egocentrically, without adapting to the other. In contrast, with tighter regulation, the agent tends to follow the other by adjusting its intention. We conclude that the tightness of complexity regulation crucially affects the strength of the SoA and the dynamics of interactions between agents.Comment: 23 pages, 8 figure

Link Prediction in the Stochastic Block Model with Outliers

The Stochastic Block Model is a popular model for network analysis in the presence of community structure. However, in numerous examples, the assumptions underlying this classical model are put in default by the behaviour of a small number of outlier nodes such as hubs, nodes with mixed membership profiles, or corrupted nodes. In addition, real-life networks are likely to be incomplete, due to non-response or machine failures. We introduce a new algorithm to estimate the connection probabilities in a network, which is robust to both outlier nodes and missing observations. Under fairly general assumptions, this method detects the outliers, and achieves the best known error for the estimation of connection probabilities with polynomial computation cost. In addition, we prove sub-linear convergence of our algorithm. We provide a simulation study which demonstrates the good behaviour of the method in terms of outliers selection and prediction of the missing links

Empathy predicts false belief reasoning ability: evidence from the N400

Interpreting others’ actions relies on an understanding of their current mental state. Emerging research has begun to identify a number of factors that give rise to individual differences in this ability. We report an event-related brain potential study where participants ( N = 28) read contexts that described a character having a true belief (TB) or false belief (FB) about an object’s location. A second sentence described where that character would look for the object. Critically, this sentence included a sentence-final noun that was either consistent or inconsistent with the character’s belief. Participants also completed the Empathy Quotient questionnaire. Analysis of the N400 revealed that when the character held a TB about the object’s location, the N400 waveform was more negative-going for belief inconsistent vs belief consistent critical words. However, when the character held an FB about the object’s location the opposite pattern was found. Intriguingly, correlations between the N400 inconsistency effect and individuals’ empathy scores showed a significant correlation for FB but not TB. This suggests that people who are high in empathy can successfully interpret events according to the character’s FB, while low empathizers bias their interpretation of events to their own egocentric view

Book Chapter in Computational Demography and Health

Recent developments in computing, data entry and generation, and analytic tools have changed the landscape of modern demography and health research. These changes have come to be known as computational demography, big data, and precision health in the field. This emerging interdisciplinary research comprises social scientists, physical scientists, engineers, data scientists, and disease experts. This work has changed how we use administrative data, conduct surveys, and allow for complex behavioral studies via big data (electronic trace data from mobile phones, apps, etc.). This chapter reviews this emerging field's new data sources, methods, and applications

Link Prediction and Denoising in Networks

Network data represent connections between units of interests, but are often noisy and/or include missing values. This thesis focuses on denoising network data via inferring underlying network structure from an observed noisy realization. The observed network data can be viewed as a single random realization of an unobserved latent structure, and our general approach to estimating this latent structure is based factorizing it into a product of interpretable components, with structural assumptions on the components determined by the nature of the problem. We first study the problem of predicting links when edge features are available, or node features that can be converted into edge features. We propose a regression-type model to combine information from network structure and edge features. We show that estimating parameters in this model is straightforward and the estimator enjoys excellent theoretical performance guarantees. Another direction we study is predicting links in time-stamped dynamic networks. A common approach to modeling networks observed over time is aggregating the networks to a few snapshots, which reduces computational complexity, but also loses information. We address this limitation through a dynamic network model based on tensor factorization, which simultaneously captures time trends and the graph structure of dynamic networks without aggregating over time. We develop an efficient algorithm to fit this model and demonstrate the method performs well numerically. The last contribution of this thesis is link prediction for ego-networks. Ego-networks are constructed by recording all friends of a particular user, or several users, which is widely used in survey-based social data collection. There are many methods for filling in missing data in a matrix when entries are missing independently at random, but here it is more appropriate to assume that whole rows of the matrix are missing (corresponding to users), whereas other rows are observed completely. We develop an approach to estimate missing links in this scenario via subspace estimation, exploiting potential low-rank structure common in networks. We obtain theoretical bounds on the estimator's performance and demonstrate it significantly outperforms many widely used benchmarks in both simulated and real networks.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138596/1/yjwu_1.pd