Approximate entropy and point correlation dimension of heart rate variability in healthy subjects

The contribution of nonlinear dynamics to heart rate variability in healthy humans was examined using surrogate data analysis. Several measures of heart rate variability were used and compared. Heart rates were recorded for three hours and original data sets of 8192 R-R intervals created. For each original data set (n=34), three surrogate data sets were made by shuffling the order of the R-R intervals while retaining their linear correlations. The difference in heart rate variability between the original and surrogate data sets reflects the amount of nonlinear structure in the original data set. Heart rate variability was analyzed by two different nonlinear methods, point correlation dimension and approximate entropy. Nonlinearity, though under 10 percent, could be detected with both types of heart rate variability measures. More importantly, not only were the correlations between these measures and the standard deviation of the R-R intervals weak, the correlation among the nonlinear measures themselves was also weak (generally less than 0.6). This suggests that in addition to standard linear measures of heart rate variability, the use of multiple nonlinear measures of heart rate variability might be useful in monitoring heart rate dynamics.     

Ratio requirement and reinforcer effects in concurrent fixed-interval fixed-ratio schedules

The fixed-ratio requirement was varied in concurrent fixed-interval fixed-ratio schedules. Fixed-interval responding was reinforced by food. In different phases, fixed-ratio responding was reinforced by food or water. There was a direct relation between the ratio requirement and interval response rates when both responses were reinforced with food, but essentially no relation when the reinforcers were different. The role of reinforcers in concurrent schedules merits detailed study.     

An objective test of goodness of fit of the continuous strength model in STM

The continuous strength model of recognition memory was evaluated in a task where Ss were tested for recognition of 10-number lists using a rating procedure. Maximum likelihood estimates of the parameters of the model were obtained by an iterative method on a high-speed computer, and a chi-square goodness-of-fit test was performed for individual Ss. For 15 of 20 Ss, the chi-square values were nonsignificant, p < .05, indicating that the model provided a good fit to the data. Although the model gave a good fit to the data, the Δm measure of sensitivity was highly correlated with a true recognition score computed by subtracting false alarmsfrom correct recognitions.     

An investigation of the relationships between problem solving strategies, representation and memory

There has been considerable debate in recent years about the status of “imagery” in problem solving. The present experiment attempts to show that while subjects may employ representational strategies when they first encounter a class of problems, they abandon such strategies as they gain experience with the problems. It does this by asking subjects to answer unexpected questions which are based upon the information which they have just used to solve a problem. The hypothesis, which is supported by the results, is that increasing experience with problems will be paralleled by a decreasing ability to answer unexpected questions. The experiment also shows that such effects are not attributable to a build-up in proactive interference.