Probability, logic and the cognitive foundations of rational belief

Since Pascal introduced the idea of mathematical probability in the 17th century discussions of uncertainty and “rational” belief have been dogged by philosophical and technical disputes. Furthermore, the last quarter century has seen an explosion of new questions and ideas, stimulated by developments in the computer and cognitive sciences. Competing ideas about probability are often driven by different intuitions about the nature of belief that arise from the needs of different domains (e.g., economics, management theory, engineering, medicine, the life sciences etc). Taking medicine as our focus we develop three lines of argument (historical, practical and cognitive) that suggest that traditional views of probability cannot accommodate all the competing demands and diverse constraints that arise in complex real-world domains. A model of uncertain reasoning based on a form of logical argumentation appears to unify many diverse ideas. The model has precursors in informal discussions of argumentation due to Toulmin, and the notion of logical probability advocated by Keynes, but recent developments in artificial intelligence and cognitive science suggest ways of resolving epistemological and technical issues that they could not address.     

TEACHING GENERATIVE USE OF SENTENCE ANSWERS TO THREE FORMS OF QUESTIONS1

Three retarded and four economically disadvantaged children were taught, through modelling and reinforcement procedures, to produce complete sentences in response to three types of questions involving changes in verb inflections. To evaluate generalization of training, new but similar questions were periodically asked, answers to which were never modelled or reinforced. Modelling and reinforcement effectively taught correct sentence answers to training questions and produced new sentence answers to questions for which no specific training had been given.     

Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness.     

人工语法范式下的无意识知识探析

人工语法范式不仅是内隐学习领域广泛运用的范式,同时也是研究无意识知识的主要方法。文章通过对人工语法范式组成材料中的刺激频率、组块等方面进行深入探讨,不仅使人们对无意识知识有了更全面的认识,而且也有利于对内隐学习获得规则知识的假设提出更确定的回答;并通过对无意识知识的神经机制研究以及考察镜射规则学习过程的脑神经激活状况,获得了规则学习对应更为准确的脑神经活动,推动了人工语法范式下无意识知识的深入研究。     

Body configuration as a predictor of centre of mass displacement in a forward reactive step

In balance perturbations that elicit backwards reactive steps, body configuration at stepping contact is related to likelihood of balance recovery. However, less is known about the relationship between body configuration (at stepping contact) and underlying centre of mass (COM) dynamics during dynamic perturbations requiring a forward reactive step. Accordingly, the primary objective of this study was to characterize the potential relationships between body configuration and COM displacement during simulated trips. Towards determining the robustness of these relationships, trips were simulated in both baseline and increased passive joint stiffness conditions. Sixteen healthy adults participated in this study. Trips were simulated using a tether release paradigm where participants were suddenly released, necessitating a forward step (onto a force plate) to recover their balance. Trials were performed in a baseline unconstrained condition, and in a ‘corset’ condition to increase passive stiffness of the trunk and hips. In all trials, whole body kinematics and kinetics were collected. Multiple linear regression models were run to assess the relationship of body angles to COM displacement in both the anteroposterior (AP) and mediolateral (ML) planes. Regression models showed a significant association of sagittal plane body configuration to both COM displacement at stepping contact and maximum COM displacement in the AP plane. Across models, the strongest predictor was the trail leg angle. Associations were stronger in the increased passive stiffness condition (average R² = 0.366) compared to the baseline condition (average R² = 0.266). Poor association of body configuration to COM displacement was found in the ML plane. The significant associations observed between body configuration and COM dynamics in simulated trips supports the potential downstream application of these models in identifying individuals with impaired balance control and increased fall risk.     

Bioinspired decision-making for a socially interactive robot

Nowadays, robots and humans coexist in real settings where robots need to interact autonomously making their own decisions. Many applications require that robots adapt their behavior to different users and remember each user’s preferences to engage them in the interaction. To this end, we propose a decision making system for social robots that drives their actions taking into account the user and the robot’s state. This system is based on bio-inspired concepts, such as motivations, drives and wellbeing, that facilitate the rise of natural behaviors to ease the acceptance of the robot by the users. The system has been designed to promote the human-robot interaction by using drives and motivations related with social aspects, such as the users’ satisfaction or the need of social interaction. Furthermore, the changes of state produced by the users’ exogenous actions have been modeled as transitional states that are considered when the next robot’s action has to be selected. Our system has been evaluated considering two different user profiles. In the proposed system, user’s preferences are considered and alter the homeostatic process that controls the decision making system. As a result, using reinforcement learning algorithms and considering the robot’s wellbeing as the reward function, the social robot Mini has learned from scratch two different policies of action, one for each user, that fit the users’ preferences. The robot learned behaviors that maximize its wellbeing as well as keep the users engaged in the interactions.     

Law,discipline and governance based on cognitive process simulation – analysis of legal forms in the modern rights relationship

People are becoming more and more humanized in the process of understanding the law. According to the right to discipline, the law has its own core setting factors, while some limits can't reach people's desire. Therefore, the legal and illegal mode of transcending rights is very important. In order to analyze the legal form of modern rights, in this paper, the cognitive learning and memory process of human brain were simulated through the artificial neural network and the understanding of human brain structure, and the role of law, discipline and governance was reflected. In the study, the structure and algorithm of the model neural network were optimized, the memory forgetting curve mechanism that can simulate the human brain was introduced, and thus the network recognition rate was improved. And in the algorithm, the calculation of matching degree was avoided, and the computational complexity was reduced to the sample. Then the sample was compared with the SOM, ART1, and PNN algorithms. The experimental simulation results show that the recognition speed of this sample is 1.9 times faster than that of ART1, 58 times than that of SOM, and 1.5 times than that of the PNN network.     

A computational analysis of general intelligence tests for evaluating cognitive development

The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the ‘fluid’ or ‘crystallised’ intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven’s Progressive Matrices and Thurstone’s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive operational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.