Empirical support for a Type A belief system

Much of the research on Type A behavior has focused on overt manifestations of the pattern; consequently, the underlying psychological dimensions are poorly understood (Matthews, 1982). Price (1982) has proposed an elaborate, but empirically unvalidated, model suggesting that a specific set of beliefs underlies the Type A behavior pattern. A series of experiments was conducted to test the validity of Price's model and to develop a device for assessing these beliefs. The internal consistency and test-retest reliability of this measure were .94 and .84, respectively. Significant positive correlations were found with traditional measures of Type A behavior as well as various facets of anger and anxiety. Finally, subjects who endorsed a high degree of Type A beliefs set significantly higher performance standards and were less likely to achieve these standards than their low-scoring counterparts. Results are discussed with regard to the theoretical construct of Type A as well as their implications for future research and treatment.This research is an extension of a study presented at the 92nd meeting of the American Psychological Association, Toronto, Canada, August 1984Special thanks to to Drs. James A. Blumenthal, Martin T. Gipson, Abby C. King, and Ellie T. Sturgis for their helpful comments in the preparation of this article.     

Judgmental forecasting of univariate time series

To forecast numbers appearing in sequence, do people just take some sort of average of past items or do they use temporal pattern information that they have extracted from the sequence? In an experiment, subjects forecast successive numbers generated by a first-order auto regressive algorithm. Afterwards, they were asked to generate their own series of numbers to simulate the sequence they had been forecasting. It was found that (1) generation performance was good—subjects acquired an internal representation of the pattern in the sequence while forecasting it; (2) generation and forecasting performance were uncorrelated—this internal representation was not used for forecasting; (3) learnt ability to forecast a sequence did not transfer to another sequence that was of the same type but that subjects believed to come from another source; (4) subjects were good at estimating the probability that their forecasts would be correct but this ability declined with practice.     

A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/n” data

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze pick any/n choice data (e.g., consumers rendering buy/no buy decisions concerning a number of actual products). A maximum likelihood procedure is formulated to estimate a joint space of both individuals (represented as vectors) and stimuli (represented as points). The relevant psychometric literature concerning the spatial treatment of such binary choice data is reviewed. The nonlinear probit type model is described, as well as the conjugate gradient procedure used to estimate parameters. Results of Monte Carlo analyses investigating the performance of this methodology with synthetic choice data sets are presented. An application concerning consumer choices for eleven competitive brands of soft drinks is discussed. Finally, directions for future research are presented in terms of further applications and generalizing the model to accommodate three-way choice data.     

Paranormal believers are more prone to illusory agency detection than skeptics

It has been hypothesized that illusory agency detection is at the basis of belief in supernatural agents and paranormal beliefs. In the present study a biological motion perception task was used to study illusory agency detection in a group of skeptics and a group of paranormal believers. Participants were required to detect the presence or absence of a human agent in a point-light display. It was found that paranormal believers had a lower perceptual sensitivity than skeptics, which was due to a response bias to ‘yes’ for stimuli in which no agent was present. The relation between paranormal beliefs and illusory agency detection held only for stimuli with low to intermediate ambiguity, but for stimuli with a high number of visual distractors responses of believers and skeptics were at the same level. Furthermore, it was found that illusory agency detection was unrelated to traditional religious belief and belief in witchcraft, whereas paranormal beliefs (i.e. Psi, spiritualism, precognition, superstition) were strongly related to illusory agency detection. These findings qualify the relation between illusory pattern perception and supernatural and paranormal beliefs and suggest that paranormal beliefs are strongly related to agency detection biases.     

Bug distribution and statistical pattern classification

A model (called the rule space model) which permits measuring cognitive skill acquisition, diagnosing cognitive errors, detecting the weaknesses and strengths of knowledge possessed by individuals was introduced earlier. This study further discusses the theoretical foundation of the model by introducing bug distribution and hypothesis testing (Bayes' decision rules for minimum errors) for classifying subjects into their most plausible latent state of knowledge. The model is illustrated with the domain of fraction arithmetic and compared with the results obtained from a conventional artificial intelligence approach.The authors would like to acknowledge Mr. Robert Baillie for developing several computer programs used for this research.This research was sponsored by the Personnel and Training Research Program, Psychological Sciences Division, Office of Naval Research.Some of the analyses presented in this report were performed on the PLATO® system. The PLATO® system is a development of the University of Illinois and PLATO® is a service mark of the Control Data Corporation.