the child could learn the associations between snakes and unpleasant bites through direct experience, without developing excessive fear, but could later learn from others that snakes can have deadly venom, leading to a re-evaluation of the dangerousness of snake bites, and accordingly, a more exaggerated fear response to snakes. School psychology Many classroom and teaching strategies draw on principles of social learning to enhance students' knowledge acquisition and retention. For example, using the technique of guided participation, a teacher says a phrase and asks the class to repeat the phrase. Thus, students both imitate and reproduce the teacher's action, aiding retention. An extension of guided participation is reciprocal learning, in which both student and teacher share responsibility in leading discussions. Additionally, teachers can shape the classroom behavior of students by modelling appropriate behavior and visibly rewarding students for good behavior. By emphasizing the teacher's role as model and encouraging the students to adopt the position of observer, the teacher can make knowledge and practices explicit to students, enhancing their learning outcomes. Social learning algorithm for computer optimization In modern field of computational intelligence, the social learning theory is adopted to develop a new computer optimization algorithm, the social learning algorithm. Emulating the observational learning and reinforcement behaviors, a virtual society deployed in the algorithm seeks the strongest behavioral patterns with the best outcome. This corresponds to searching for the best solution in solving optimization problems. Compared with other bio-inspired global optimization algorithms that mimic natural evolution or animal behaviors, the social learning algorithm has its prominent advantages. First, since the self-improvement through learning is more direct and rapid than the evolution process, the social learning algorithm can improve the efficiency of the algorithms mimicking natural evolution. Second, compared with the interaction and learning behaviors in animal groups, the social learning process of human beings exhibits a higher level of intelligence. By emulating human learning behaviors, it is possible to arrive at more effective optimizers than existing swarm intelligence algorithms. Experimental results have demonstrated the effectiveness and efficiency of the social learning algorithm, which has in turn also verified through computer simulations the outcomes of the social learning behavior in human society. Another example is the social cognitive optimization, which is a population-based metaheuristic optimization algorithm. This algorithm is based on the social cognitive theory, simulating the process of individual learning of a set of agents with their own memory and