A signal appeared for a certain time period. After the period elapsed, pigeons had to begin and complete a sequence of 15 responses in a time window ranging from the signal duration to 50% longer. Sessions involved as many as 10 different signal durations occurring in a random sequence. The times produced by pigeons often were in the same ranges as those that have been found with adult human subjects. The average times were described equally well as linear or power functions of signal duration. However, instead of the overestimation of durations usually found when animals have timed the duration of antecedent stimuli, the linear functions suggested that the pigeons underestimated the durations of their own behavior. The birds showing the strongest control when the conditions involved eight or 10 different duration requirements revealed the constant coefficients of variation that support Weber's law and scalar timing theory. Scalar timing in temporal differentiation appears to depend on non-ambiguous information about the duration required for reinforcement and on a high degree of sensitivity to the duration requirement.

Fixed-interval schedule performance is characterized by high levels of variability. Responding is absent at the onset of the interval and gradually increases in frequency until reinforcer delivery. Measures of behavior also vary drastically and unpredictably between successive intervals. Recent advances in the study of nonlinear dynamics have allowed researchers to study irregular and unpredictable behavior in a number of fields. This paper reviews several concepts and techniques from nonlinear dynamics and examines their utility in predicting the behavior of pigeons responding to a fixed-interval schedule of reinforcement. The analysis provided fairly accurate a priori accounts of response rates, accounting for 92.8% of the variance when predicting response rate 1 second in the future and 64% of the variance when predicting response rates for each second over the entire next interreinforcer interval. The nonlinear dynamics account suggests that even the "noisiest" behavior might be the product of purely deterministic mechanisms.

In this study goal orientation theory, a model used to explore achievement motivation that is commonly covered in introductory psychology textbooks, is used to improve student success. Some variants of the theory posit that mastery goals are associated with adaptive behaviours that can lead to increased success. As a result, the authors of this study developed an intervention designed to increase the pursuit of mastery goals. In previous research, they found that the intervention was associated with altered goals and improved grades. In the current study, they examined the consequences for retention and graduation rates. During the fall 2005 semester, the intervention was extended to 221 students enrolled in introductory psychology classes. A similar set of students who did not receive the intervention served as a comparison group. Five years later, the authors examined the students' transcripts for persistence in school. Students who were at risk for failure and who completed the intervention enrolled in courses at the university at nearly twice the rate of the comparison group in each of the subsequent years, and they graduated at nearly twice the rate of the comparison group. Thus, this intervention was associated with increased retention and graduation rates.

In order to illuminate a light signaling a correct response, adult humans had to space their button presses according to a range of time requirements. In some conditions, the spacing needed only to exceed a minimum duration; in others, it had to fall between lower and upper bounds. Mean interresponse times always exceeded the lower limit, and decreased the more stringent were the upper bounds. Variability of interresponse times increased with larger lower bounds, but was unaffected by the size of the upper bound. Feedback about the direction of errors in conditions involving both upper and lower bounds did not affect the means, but it did reduce variability. Predictions were derived from optimality theory, based on the assumption that the critical factor was minimization of the time between correct responses. Without upper bounds, the theory overestimated the mean interresponse times by about 10%; with upper bounds, the theoretical predictions corresponded closely to the actual data. The results did not appear to reflect a scalar timing process. Optimality theory, in contrast to Weber's law, correctly predicted the variety of curves relating sensitivity to duration requirements.

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