who claimed that behavior is affected by positive reinforcement
Reinforcement Laboratory research on reinforcement is usually dated from the work of Edward Thorndike, known for his experiments with cats escaping from puzzle boxes.[10] A number of others continued this research, notably B.F. Skinner, who published his seminal work on the topic in The Behavior of Organisms, in 1938, and elaborated this research in many subsequent publications.[11] Notably Skinner argued that positive reinforcement is superior to punishment in shaping behavior.[12] Though punishment may seem just the opposite of reinforcement, Skinner claimed that they differ immensely, saying that positive reinforcement results in lasting behavioral modification (long-term) whereas punishment changes behavior only temporarily (short-term) and has many detrimental side-effects. A great many researchers subsequently expanded our understanding of reinforcement and challenged some of Skinner's conclusions. For example, Azrin and Holz defined punishment as a “consequence of behavior that reduces the future probability of that behavior,”[13] and some studies have shown that positive reinforcement and punishment are equally effective in modifying behavior. Research on the effects of positive reinforcement, negative reinforcement and punishment continue today as those concepts are fundamental to learning theory and apply to many practical applications of that theory.
Edward C. Tolman Tolman's theoretical model was described in his paper "The Determiners of Behavior at a Choice Point".[15] The three different variables that influence behavior are: independent, intervening, and dependent variables. The experimenter can manipulate the independent variables; these independent variables (e.g., stimuli provided) in turn influence the intervening variables (e.g., motor skill, appetite).[15] Independent variables are also factors of the subject that the experimenter specifically chooses for. The dependent variables (e.g., speed, number of errors) allows the psychologist to measure the strength of the intervening variables.[15]