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AbstractPeople achieve important life outcomes of health, financial security, and productivity by repeating operant behavior. To identify whether such operants reflect goal pursuit or habit, the present research introduces a new paradigm that yields objective measures of learning and controls for the motivations of goal pursuit. In two experiments, participants practiced a sequential task of making sushi and then completed a test of the strength of cue–response (habit) associations in memory. Finally, they repeated the sushi task without instructions while under cognitive load (designed to impede deliberation about goals). As predicted, greater task practice yielded stronger cue–response associations, which in turn promoted task success. Practice did not improve performance by enhancing goal intentions or other task motivations. We conclude that repetition facilitates performance by creating mental associations that automatically activate practiced, habitual responses upon perception of recurring context cues.

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Este texto, dedicado ao querido José Antonio Abib, fala de nossas memórias de quando o conhecemos no curso de graduação em psicologia da Universidade de Brasília, num tempo em que ele ainda era conhecido como José Antonio, fazia pesquisa experimental com animais não-humanos, mas já tinha a erudição e o interesse por questões conceituais e filosóficas que eventualmente o transformaram no Abib, pesquisador que publicou trabalhos marcantes e formou alunos brilhantes, contribuindo para transformar o Brasil no que é possivelmente o principal centro mundial da filosofia do behaviorismo.

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AbstractWe present the mathematical description of feedback functions of variable interval and variable differential reinforcement of low rates as functions of schedule size only. These results were obtained using an R script named Beak, which was built to simulate rates of behavior interacting with simple schedules of reinforcement. Using Beak, we have simulated data that allow an assessment of different reinforcement feedback functions. This was made with unparalleled precision, as simulations provide huge samples of data and, more importantly, simulated behavior is not changed by the reinforcement it produces. Therefore, we can vary response rates systematically. We've compared different reinforcement feedback functions for random interval schedules, using the following criteria: meaning, precision, parsimony, and generality. Our results indicate that the best feedback function for the random interval schedule was published by Baum (1981). We also propose that the model used by Killeen (1975) is a viable feedback function for the random differential reinforcement of low rates schedule. We argue that Beak paves the way for greater understanding of schedules of reinforcement, addressing still open questions about quantitative features of simple schedules. Also, Beak could guide future experiments that use schedules as theoretical and methodological tools.