The Impact of Cooperative Learning on University Students’ Academic Goals

Cooperative learning encourages the development of interpersonal skills and motivates students to participate more actively in the teaching and learning process. This study explores the impact of cooperative learning on the academic goals influencing university students’ behavior and leading to the attainment of a series of academic objectives. To this end, a quasi-experimental pretest-posttest control group design was used, with a sample of 509 university students from Preschool, Primary and Social Education undergraduate degree courses. Using the Academic Goals Questionnaire (AGQ), pretest and posttest measures were taken via self-reports to evaluate three types of academic goals: learning goals, social reinforcement goals and achievement goals. The results show that cooperative learning is an effective tool for encouraging university students to develop academic goals that motivate them to fully engage with the tasks they are set in order to acquire knowledge and skills (learning goals). In addition, when students are asked to work as part of a team on an autonomous basis without the structure and supervision necessary to ensure a minimum standard of cooperation, they display a greater tendency toward social reinforcement goals than toward learning and achievement goals. These findings contribute new knowledge to the conceptual framework on cooperative learning. Goals may be considered one of the most important variables influencing students’ learning and the use of cooperative learning techniques in university classrooms creates the necessary conditions for encouraging students to develop goals oriented toward learning.

Optimal Control of ISTR Rumor Propagation Model with Social Reinforcement in Heterogeneous Network

The wide spread of rumor is undoubtedly harmful to social stability; we should try to lower the effect of rumor on society. Therefore, it is reasonable to put forward the rumor control strategy on the basis of the study of the law of rumor propagation. Firstly, the ISTR model of rumor is established by including influencing factors of true information spreader and social reinforcement. And by using the next generation matrix method, the basic reproduction number of rumor is obtained. Then, in order to minimize the adverse effects of rumors, through introducing two control strategies of scientific knowledge popularization and refutation of rumors, the optimal control problem is established. And through using Pontryagin’s Minimum Principle, the optimal solution of the rumor propagation model is solved. Finally, through theoretical analysis and numerical simulation, some results can be obtained. The results show that adding true information spreaders into the rumor model can effectively control the rumor propagation, and social reinforcement plays a significance role in rumor. The results also prove that these two control strategies can effectively inhibit the propagation of rumors. With the addition of control strategies, the number of true information spreaders increases, while the number of rumor spreaders decreases.

An opinion on the interpretation of social release in rats

This commentary concerns a controversial animal model in rodent social release research wherein one rat releases another rat from entrapment in a plastic tube. Release from the plastic tube has been proposed as a model to study empathically motivated behaviour. However, empathic motivations have been contested by others who have provided evidence for social reinforcement motivating release behaviour. Furthermore, helping, or other forms of pro-social behaviour could exist independent of empathy or empathetic motivation and the stimuli occasioning this helping behaviour are not known. In addition, there is a dearth in the citations of published studies whose results fail to support this model. In other words, the controversial aspect of the rodent social release model is often overlooked. This controversy is described in the current opinion piece.

Social Contact Reinforces Cocaine Self-Administration in Young Adult Male Rats: The Role of Social Reinforcement in Vulnerability to Drug Use

Drug-using peers are recognized as a leading factor influencing drug use among adolescents and young adults. One mechanism by which peers influence drug use is by providing social reinforcement for using drugs. Social reinforcement may be provided in multiple ways, including by making social contact contingent on drug use (i.e., an individual must use drugs to gain/maintain access to a peer). The purpose of this study was to develop a preclinical model in which intravenous cocaine self-administration was positively reinforced by access to a social partner. Young adult male rats were trained to self-administer cocaine in operant conditioning chambers with a guillotine door that could be opened to an adjacent compartment housing either a social partner or a non-social stimulus. Once cocaine self-administration was established, the guillotine door was activated, and cocaine intake was reinforced by brief access to either a social (age- and sex-matched peer) or non-social (black-and-white athletic sock) stimulus. Contingent access to a social partner rapidly increased cocaine self-administration. Total cocaine intake was 2- to 3-fold greater in rats assigned to the social versus non-social condition across a 100-fold dose range. Cocaine intake rapidly increased when rats in the original non-social group were later provided with social partners, whereas cocaine intake resisted change and remained elevated when rats in the original social group had their partners removed. These data indicate that contingent access to a social partner increases drug intake and suggest that social reinforcement may represent a vulnerability factor that is particularly resistant to psychosocial interventions.

Infants Learn to Follow Gaze in Stages: Evidence Confirming a Robotic Prediction

Gaze following is an early-emerging skill in infancy argued to be fundamental to joint attention and later language development. However, how gaze following emerges is a topic of great debate. Representational theories assume that in order to follow adults’ gaze, infants must have a rich sensitivity to adults’ communicative intention from birth. In contrast, learning-based theories hold that infants may learn to gaze follow based on low-level social reinforcement, without the need to understand others’ mental states. Nagai et al. (2006) successfully taught a robot to gaze follow through social reinforcement and found that the robot learned in stages: first in the horizontal plane, and later in the vertical plane—a prediction that does not follow from representational theories. In the current study, we tested this prediction in an eye-tracking paradigm. Six-month-olds did not follow gaze in either the horizontal or vertical plane, whereas 12-month-olds and 18-month-olds only followed gaze in the horizontal plane. These results confirm the core prediction of the robot model, suggesting that children may also learn to gaze follow through social reinforcement coupled with a structured learning environment.

Modeling variation in empathic sensitivity using go/no-go social reinforcement learning

Empathic experiences shape social behaviors and display considerable individual variation. Recent advances in computational behavioral modeling can help rigorously quantify individual differences, but remain understudied in the context of empathy and antisocial behavior. We adapted a go/no-go reinforcement learning task across social and non-social contexts such that monetary gains and losses explicitly impacted the subject, a study partner, or no one. Empathy was operationalized as sensitivity to others’ rewards, sensitivity to others’ losses, and as the Pavlovian influence of empathic outcomes on approach and avoidance behavior. Results showed that 61 subjects learned for a partner in a way that was computationally similar to how they learned for themselves. Results supported the psychometric value of individualized model parameters such as sensitivity to others’ loss, which was inversely associated with antisociality. Modeled empathic sensitivity also mapped onto motivation ratings, but was not associated with self-reported trait empathy. This work is the first to apply a social reinforcement learning task that spans affect and action requirement (go/no-go) to measure multiple facets of empathic sensitivity.

A 5-Factor Framework for Assessing Tobacco Use Disorder

Cigarette use is the leading cause of preventable death in the United States. Despite the well documented dangers of smoking, nearly 20% of adults report regular use of tobacco. A majority desire to discontinue but the long-term cessation success rate remains near 4%. One challenge to reducing the prevalence of tobacco use is an incomplete understanding of the individual correlates that reinforce continued use. Evidence from research on nicotine and tobacco suggests that Tobacco Use Disorder is a complex, and multifactorial condition. Personality traits, comorbidities, habits and lifestyle, genetics, socioeconomic status, and mental and physical health all contribute to the risk for dependence and to the likelihood of quitting. This perspective review provides an overview of some common factors that contribute to liability risk for Tobacco Use Disorder and a framework for assessing individual tobacco users. The framework includes 5 areas that research suggests contribute to continued tobacco use: nicotine addiction, psychological influences, behavioral dependencies, neurobiological factors, and social reinforcement. Nicotine addiction includes drug-seeking behavior and the role of withdrawal avoidance. Psychological and emotional states contribute to a perceived reliance on tobacco. Behavioral dependence is reinforced by associative and non-associative learning mechanisms. Neurobiological factors include genetic variables, variations in neurotransmitters and receptors, pharmacogenetics, and interaction between psychiatric illnesses and nicotine use and dependence. Finally, social reinforcement of smoking behavior is explained by a network phenomenon and consistent visual cues to smoke. A comprehensive assessment of individual tobacco users will help better determine appropriate treatment options to achieve improved efficacy and outcomes.