Egocentric Projection is a Rational Strategy for Accurate Emotion Prediction

People need to accurately understand and predict others’ emotions in order to build and maintain meaningful social connections. However, when they encounter new social partners, people often do not have enough information about them to make accurate inferences. Rather, they often resort to an egocentric heuristic, and make predictions about a target by using their own self-knowledge as a proxy. Is this egocentric heuristic a form of cognitive bias, or is it a rational strategy for real-world social prediction? If egocentrism provides a rational and effective solution to the challenging task of social prediction in naturalistic contexts, we should expect that a) egocentric predictions tend to be more accurate, and b) people rely on self-knowledge to a greater extent when it’s more likely to be a good proxy. Using an emotion prediction task and personality measures, we assessed similarity and predictive accuracy between first year college students and their new acquaintance roommate. Results demonstrated that, when people need to make predict an unfamiliar target’s emotions, self-knowledge can often effectively approximate knowledge about others, and thus support accurate predictions. Moreover, participants that were typical of the sample, whose self-knowledge can better approximate information about the target, relied more on self-knowledge in their predictions, and thus achieved higher accuracy. These findings suggest that people rationally tune their use of egocentrism based on whether it is likely to pay off. Overall, these findings demonstrate a rational side to a cognitive phenomenon usually framed as a cognitive pitfall, namely egocentric projection, when its natural decision context is taken into consideration.

Social prediction: a new research paradigm based on machine learning

Sociology is a science concerned with both the interpretive understanding of social action and the corresponding causal explanation, process, and result. A causal explanation should be the foundation of prediction. For many years, due to data and computing power constraints, quantitative research in social science has primarily focused on statistical tests to analyze correlations and causality, leaving predictions largely ignored. By sorting out the historical context of "social prediction," this article redefines this concept by introducing why and how machine learning can help prediction in a scientific way. Furthermore, this article summarizes the academic value and governance value of social prediction and suggests that it is a potential breakthrough in the contemporary social research paradigm. We believe that through machine learning, we can witness the advent of an era of a paradigm shift from correlation and causality to social prediction. This shift will provide a rare opportunity for sociology in China to become the international frontier of computational social sciences and accelerate the construction of philosophy and social science with Chinese characteristics.

Social clumsiness

To acquire age-appropriate social skills, certain parts of the brain need to develop normally. Children with ADHD may experience social difficulties and experience what is called a social cognition deficit. This chapter outlines social clumsiness in ADHD. It discusses social cognition as a function of the brain, specific social competence deficits (social blindness, egocentricity, lack of appropriate inhibition, insatiability, insensitivity to style and convention, lack of responsiveness, over-talkativeness, difficulties reading facial expression, aggressive tendencies, lack of judgment, poor understanding of group dynamics, misinterpretation of feedback, poor social prediction, poor social memory, lack of awareness of image, poor behaviour-modification strategies), management of social clumsiness, and autism spectrum disorder.

An active inference perspective on the negative symptoms of schizophrenia

Predictive coding has played a transformative role in the study of psychosis, casting delusions and hallucinations as statistical inference in an abnormally imprecise system. However, the negative symptoms of schizophrenia, such as affective blunting, avolition and asociality, remain poorly understood. We propose a computational framework for emotional expression that is based on active inference – namely that affective behaviours such as smiling are driven by predictions about the social consequences of smiling. Just as delusions and hallucinations can be explained by predictive uncertainty in sensory circuits, negative symptoms naturally arise from uncertainty in social prediction circuits. This perspective draws on computational principles to explain blunted facial expressiveness and apathy-anhedonia in schizophrenia. Its phenomenological consequences also shed light on the content of paranoid delusions and indistinctness of self-other boundaries. Close links are highlighted between social prediction, facial affect mirroring, and the fledgling study of interoception. Advances in automated analysis of facial expressions and acoustic speech patterns will allow empirical testing of these computational models of the negative symptoms of schizophrenia.

Study on Generation and Development of Social Prediction System

Prediction means predicting future scientifically which has a great relationship with human beings. Based on the development history of prediction system, this paper discusses establishment of modern prediction system and its importance in leadership structure, especially when national prediction system has become a major factor of national security among modern prediction systems. It’s just because each country has built a gradually improved prediction system that international political relation could form and stay stabilized. Modern prediction system is now steering to the tendency of controlling the whole social system. In modern society, all the decisions made by institutions depend more and more on prediction and the reform of social system is the soul of predicting the new paradigm of institutional behaviors.

Social prediction modulates activity of macaque superior temporal cortex

The ability to attribute thoughts to others, also called theory of mind (TOM), has been extensively studied. Computationally, the basis of TOM in humans has been interpreted within the predictive coding framework and associated with activity in the temporo-parietal junction (TPJ). However, the evolutionary origins of these human mindreading abilities have been challenged since the concept was coined. Here we identify a brain region in the Rhesus macaque that shares computational properties with the human TPJ. We revealed, using a non-linguistic task and functional magnetic resonance imaging, that activity in a region of the macaque middle superior temporal cortex was specifically modulated by the predictability of social interactions. As in human TPJ, this region could be distinguished from other temporal regions involved in face processing. Our result suggests the existence of a precursor for the theory of mind ability in the last common ancestor of human and old-world monkeys.

Neuropsychological assessment and virtual reality training of social prediction in patients with cerebellar malformation

It has been proposed that impairments of the predictive function exerted by the cerebellum may account for social cognition deficits. Here, we integrated cerebellar functions in a predictive coding framework to elucidate how cerebellar alterations could affect the predictive processing of others’ behavior. Experiment 1 demonstrated that cerebellar patients were impaired in relying on contextual information during action prediction, and this impairment was significantly associated with social cognition abilities. Experiment 2 indicated that patients with cerebellar malformation showed a domain-general deficit in using contextual information to predict both social and physical events. Experiment 3 provided first evidence that a social-prediction training in virtual reality could boost the ability to use context-based predictions to understand others’ intentions. These findings shed new light on the predictive role of the cerebellum and its contribution to social cognition, paving the way for new approaches to the rehabilitation of the Cerebellar Cognitive Affective Syndrome.