Typical delay determines waiting time on periodic-food schedules: Static and dynamic tests

Pigeons and other animals soon learn to wait (pause) after food delivery on periodic-food schedules before resuming the food-rewarded response. Under most conditions the steady-state duration of the average waiting time, t, is a linear function of the typical interfood interval. We describe three experiments designed to explore the limits of this process. In all experiments, t was associated with one key color and the subsequent food delay, T, with another. In the first experiment, we compared the relation between t (waiting time) and T (food delay) under two conditions: when T was held constant, and when T was an inverse function of t. The pigeons could maximize the rate of food delivery under the first condition by setting t to a consistently short value; optimal behavior under the second condition required a linear relation with unit slope between t and T. Despite this difference in optimal policy, the pigeons in both cases showed the same linear relation, with slope less than one, between t and T. This result was confirmed in a second parametric experiment that added a third condition, in which T + t was held constant. Linear waiting appears to be an obligatory rule for pigeons. In a third experiment we arranged for a multiplicative relation between t and T (positive feedback), and produced either very short or very long waiting times as predicted by a quasi-dynamic model in which waiting time is strongly determined by the just-preceding food delay.     

Effects of alternative reinforcement sources: A reevaluation

The effects of two alternative sources of food delivery on the key-peck responding of pigeons were examined. Pecking was maintained by a variable-interval 3-min schedule. In the presence of this schedule in different conditions, either a variable-time 3-min schedule delivering food independently of responding or an equivalent schedule that required a minimum 2-s pause between a key peck and food delivery (a differential-reinforcement-of-other-behavior schedule) was added. The differential-reinforcement-of-other-behavior schedule reduced response rates more than did the variable-time schedule in most instances. The delay between a key peck and the next reinforcer consistently was longer under the differential-reinforcement-of-other-behavior schedule than under the variable-time schedule. Response rates and median delay between responses and reinforcers were negatively correlated. These results contradict earlier conclusions about the behavioral effects of alternative reinforcement. They suggest that an interpretation in terms of response–reinforcer contiguity is consistent with the data.     

Religion,Type A behavior,and health

In a study of air traffic controllers, religious differences are found in the way Type A behavior is associated with several health status indicators. Associations between the Jenkins Activity Survey (JAS) and physical illness incidence, health-promotive behavior, diastolic and systolic blood pressure, subjective distress and impulse control problems, and alcohol consumption are examined by religious attendance, religious affiliation, and change in affiliation. Findings confirm that Type A does not vary significantly by religion. However, there are several significant findings between Type A and various health indicators. Type A is associated with illness incidence, overall and more strongly in several religion, subgroups. Type A and alcohol consumption are related positively in Protestants and converts, and negatively in churchgoing Catholics. Type A is related to impulse control problems in churchgoing Protestants and to subjective distress in churchgoing Catholics. Finally, in individuals with weak or no religious ties, Type A is associated with lower blood pressure. This last finding suggests that in some people (for example, the irreligious or unchurched), the coronary-prone behavior pattern may have cardiovascular effects which are salutary in at least one respect.The authors wish to thank Dr. Kyriakos S. Markides and Laura A. Ray for their assistance with this study. Address requests for reprints to Dr. Levin at the Institute of Gerontology, 300 North Ingalls, Ann Arbor, MI 48109.     

A general model for the analysis of multilevel data

A general model is developed for the analysis of multivariate multilevel data structures. Special cases of the model include repeated measures designs, multiple matrix samples, multilevel latent variable models, multiple time series, and variance and covariance component models.We would like to acknowledge the helpful comments of Ruth Silver. We also wish to thank the referees for helping to clarify the paper. This work was partly carried out with research funds provided by the Economic and Social Research Council (U.K.).