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.     

Discrimination learning in a foraging situation

Rats were allowed to forage in a simulated natural environment made up of eight food sources (patches) each containing a fixed number of pellets. Two of the eight contained an extra supply of peanuts. The peanut patches were signaled by an olfactory/visual cue located at the bottom of the ladder leading to the patch. In successive phases the number of sessions per day, height of the patches, and availability of peanuts were manipulated. Subjects showed evidence of discrimination learning under these conditions, although the degree of discriminatory behavior varied as a function of environmental manipulations. Assessment of behavior within foraging sessions showed that subjects systematically changed their patterns of utilization of patches across time. Sampling or exploration, as well as food reinforcement, seem implicated in these results.     

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.).     

Reconsideration of measurement of error in human motor learning

Human motor learning is often measured by error scores. The convention of using mean absolute error, mean constant error, and variable error shows lack of desirable parsimony and interpretability. This paper provides the background of error measurement and states criticisms of conventional methodology. A parsimonious model of error analysis is provided, along with operationalized interpretations and implications for motor learning. Teaching, interpreting, and using error scores in research may be simplified and facilitated with the model.     

Accuracy of estimations of body-frame size as a function of sex and actual frame size

Body-frame size is an important factor in determining an optimal body weight for a given height. Previous studies have indicated that many individuals incorrectly estimate their body-frame size, and, as a result, incorrectly assess their ideal weight. The present study investigated the accuracy of estimation of body-frame size as a function of sex and actual frame size. The subjects were 66 men and 52 women participating in a community adult fitness program. Data indicated that medium-framed individuals were the most accurate in their estimations of body-frame size. Also, women were twice as likely to be accurate as were men. These results are interpreted to mean that most people assume they are medium-framed and that there is a sex difference in the way body-frame size is estimated.