This study investigates the ability of individuals with psychopathy to perform passive avoidance learning and whether this ability is modulated by level of reinforcement/punishment. Nineteen psychopathic and 21 comparison individuals, as defined by the Hare Psychopathy Checklist Revised (Hare, 1991), were given a passive avoidance task with a graded reinforcement schedule. Response to each rewarding number gained a point reward specific to that number (i.e., 1, 700, 1400 or 2000 points). Response to each punishing number lost a (...) point punishment specific to that number (i.e., the loss of 1, 700, 1400 or 2000 points). In line with predictions, individuals with psychopathy made more passive avoidance errors than the comparison individuals. In addition, while the performance of both groups was modulated by level of reward, only the performance of the comparison population was modulated by level of punishment. The results are interpreted with reference to a computational account of the emotional learning impairment in individuals with psychopathy. (shrink)

To determine the effects of early undernutrition on learning and long-term memory, Swiss mice were cross-fostered randomly at birth to produce litter sizes of 6 and 16, thereby providing “normally nourished” and “undernourished” conditions. At 28 days of age, the pups were housed in groups of 3 and 4, with ad-lib access to food and water until testing began at 60 days of age. Each mouse received 50 active-avoidance training trials daily for 14 consecutive days. Retention testing began (...) 14 days after the end of original training and consisted of 50 trials daily for 5 days. Overall, mice reared in large litters suffered deficits in learning but not in long-term memory. (shrink)

Democracies are calling on schools to respond to a rise in extremist ideologies and actions. In this article Sigal Ben-Porath situates the rise in extremism within the broader context of political polarization. She suggests that the latter is a more appropriate target for school intervention than the former. She further suggests that addressing polarization can result in a reduction in extremism, and that polarization can be addressed by refocusing the use of existing teaching and learning tools, rather than by (...) instituting new forms of intervention such as the Prevent strategy used in the UK. Tackling polarization through media literacy and the development of democratic habits can help rectify false beliefs, which contribute both to broad political polarization and to individuals' slide toward extremism. Focusing on strengthening knowledge as well as social ties can fortify individuals' and communities' resilience against extremism, as well as build bridges and connect people to a sense of shared fate across political divides. These practices are more effective and more justified than targeting individual students who are suspected of holding radical beliefs. (shrink)

ABSTRACTWe used an immersive virtual environment to examine avoidance learning in spider-fearful participants. In 3 experiments, participants were asked to repeatedly lift one of 3 virtual boxes, under which either a toy car or a spider appeared and then approached the participant. Participants were not told that the probability of encountering a spider differed across boxes. When the difference was large, spider-fearfuls learned to avoid spiders by lifting the few-spiders-box more often and the many-spiders-box less often than non-fearful (...) controls did. However, they hardly managed to do so when the probability differences were small, and they did not escape from threat more quickly. In contrast to the observed performance differences, spider-fearfuls and non-fearfuls showed equal competence, that is comparable post-experimental knowledge about the probability to encounter spiders under the 3 boxes. The limitations and implications of the present study are discussed. (shrink)

The use of predictive systems has become wider with the development of related computational methods, and the evolution of the sciences in which these methods are applied Solon and Selbst and Pedreschi et al.. The referred methods include machine learning techniques, face and/or voice recognition, temperature mapping, and other, within the artificial intelligence domain. These techniques are being applied to solve problems in socially and politically sensitive areas such as crime prevention and justice management, crowd management, and emotion analysis, (...) just to mention a few. However, dissimilar predictions can be found nowadays as the result of the application of these methods resulting in misclassification, for example for the case of conviction risk assessment Office of Probation and Pretrial Services or decision-making process when designing public policies Lange. The goal of this paper is to identify current gaps on fairness achievement within the context of predictive systems in artificial intelligence by analyzing available academic and scientific literature up to 2020. To achieve this goal, we have gathered available materials at the Web of Science and Scopus from last 5 years and analyzed the different proposed methods and their results in relation to the bias as an emergent issue in the Artificial Intelligence field of study. Our tentative conclusions indicate that machine learning has some intrinsic limitations which are leading to automate the bias when designing predictive algorithms. Consequently, other methods should be explored; or we should redefine the way current machine learning approaches are being used when building decision making/decision support systems for crucial institutions of our political systems such as the judicial system, just to mention one. (shrink)

A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making (...) neural networks. To achieve higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft. (shrink)

An important topic in evolutionary art is the development of systems that can mimic the aesthetics decisions made by human begins, e.g., fitness evaluations made by humans using interactive evolution in generative art. This paper focuses on the analysis of several datasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since these datasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluation methods were considered, one derived from (...) the ratings available at DPChallenge.com and two obtained under experimental conditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, which had more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state-of-the-art ones, in order to predict these three values. The obtained results were similar to those using state-of-the-art procedures. (shrink)

Machines that learn and think like people must be able to learn from others. Social learning speeds up the learning process and – in combination with language – is a gateway to abstract and unobservable information. Social learning also facilitates the accumulation of knowledge across generations, helping people and artificial intelligences learn things that no individual could learn in a lifetime.

ABSTRACT A trivalent theory of indicative conditionals automatically enforces Stalnaker's thesis – the equation between probabilities of conditionals and conditional probabilities. This result holds because the trivalent semantics requires, for principled reasons, a modification of the ratio definition of conditional probability in order to accommodate the possibility of undefinedness. I explain how this modification is motivated and how it allows the trivalent semantics to avoid a number of well-known triviality results, in the process clarifying why these results hold for many (...) bivalent theories. In short, the slew of triviality results published in the last 40-odd years need not be viewed as an argument against Stalnaker's thesis: it can be construed instead as an argument for abandoning the bivalent requirement that indicative conditionals somehow be assigned a truth-value in worlds in which their antecedents are false. (shrink)