卡车驾驶员速度估计研究

用速度知觉测试仪,对251名男性卡车驾驶员在四种实验条件下的速度估计准确性进行了测量。结果发现：安全组卡车驾驶员的速度估计准确性优于事故组卡车驾驶员,两者在低速条件下差异不显著,高速条件下差异显著;事故组卡车驾驶员的高估次数高于安全组卡车驾驶员,低速条件下两者不显著.高速条件下差异显著;事故多发组与安全组卡车驾驶员的速度估计准确性在高速及低速长距离条件下差异显著;速度及距离对速度估计准确性有显著影响,且两者之间存在显著的交互作用;年龄对速度估计准确性无显著影响。     

Shrinkage estimation of linear combinations of true scores

This paper is concerned with combining observed scores from sections of tests. It is demonstrated that in the presence of population information a linear combination of true scores can be estimated more efficiently than by the same linear combination of the observed scores. Three criteria for optimality are discussed, but they yield the same solution which can be described and motivated as a multivariate shrinkage estimator.Input from Eric Bradlow, Charles Lewis, and Linda Zeger is acknowledged. Research for this paper was funded by the Program Research Council (ETS). Suggestions of the Editor and of anonymous referees were instrumental in several improvements of the paper.     

A marginalization model for the multidimensional unfolding analysis of ranking data

A marginalization model for the multidimensional unfolding analysis of ranking data is presented. A subject samples one of a number of random points that are multivariate normally distributed. The subject perceives the distances from the point to all the stimulus points fixed in the same multidimensional space. The distances are error perturbed in this perception process. He/she produces a ranking dependent on these error-perturbed distances. The marginal probability of a ranking is obtained according to this ranking model and by integrating out the subject (ideal point) parameters, assuming the above distribution. One advantage of the model is that the individual differences are captured using the posterior probabilities of subject points. Three sets of ranking data are analyzed by the model.     

Upward versus Downward Anchoring in Frequency Judgments of Social Facts

Frequency estimation of social facts in two methods of judgment elicitation was investigated. In the “narrow-range” condition, subjects answered questions in the format: “Out of 100 incidents, how many belong to category X?” In the “wide-range” condition, the frequency for the same event was assessed with respect to “Out of 10,000”. Judged frequencies in the wide-range condition were divided by 100, and were compared with the corresponding judgments in the narrow-range condition. Such comparisons were made for low-frequency and high-frequency events. Previous research has shown that, for low-frequency events, judged frequencies are proportionally greater in the narrow-range than in the wide-range condition. These results reflect cognitive processes of implicit anchoring, whereby judged frequencies lie close to small numbers within the response ranges provided. I call this process “downward anchoring,” and predicted that this tendency would be replicated in the present study. Moreover, I predicted that assessments about high-frequency events would evoke similar cognitive processes operating in the opposite direction. By such “upward anchoring,” judged frequencies would lie close to relatively larger numbers within the given response ranges. Consequently, I predicted that judged frequencies for high-frequency events would be proportionally greater in the wide-range condition than in the narrow-range condition. These predictions were confirmed.     

Calibration of sensory and cognitive judgments: A single model for both

In a recent issue of this journal, Winman and Juslin (34 , 135–148, 1993) present a model of the calibration of subjective probability judgments for sensory discrimination tasks. They claim that the model predicts a pervasive underconfidence bias observed in such tasks, and present evidence from a training experiment that they interpret as supporting the notion that different models are needed to describe judgment of confidence in sensory and in cognitive tasks. The model is actually part of the more comprehensive decision variable partition model of subjective probability calibration that was originally proposed in Ferrell and McGoey (Organizational Behavior and Human Performance, 26 , 32–53, 1980). The characteristics of the model are described and it is demonstrated that the model does not predict underconfidence, that it is fully compatible with the overconfidence frequently found in calibration studies with cognitive tasks, and that it well represents experimental results from such studies. It is concluded that only a single model is needed for both types of task.     

Human Signal-detection Performance: Effects Of Signal Presentation Probabilities And Reinforcer Distributions

University students participated in one of four standard two-choice signal-detection experiments in which signal presentation probability was varied and the reinforcement distribution was held constant and equal. In Experiments 1, 3 and 4, subjects' performance showed a systematic response bias for reporting the stimulus presented least often. Experiments 1 and 4 showed that this effect was reliable with extended training and monetary, rather than point, reinforcement. In Experiment 2, all correct responses were signaled in some way, and this produced the opposite relationship between signal presentation probability and response bias. Experiments 1 and 3 found that explicitly deducting money (intended as punishment) for equal numbers of incorrect responses on each alternative, or varying the obtained overall rate of reinforcement, produced no clear change in response bias. The bias, shown by humans, for reporting the stimulus presented least often remains a challenge for theories of stimulus detection.     

Changeover behavior and preference in concurrent schedules

Pigeons were trained on a multiple schedule of reinforcement in which separate concurrent schedules occurred in each of two components. Key pecking was reinforced with milo. During one component, a variable-interval 40-s schedule was concurrent with a variable-interval 20-s schedule; during the other component, a variable-interval 40-s schedule was concurrent with a variable-interval 80-s schedule. During probe tests, the stimuli correlated with the two variable-interval 40-s schedules were presented simultaneously to assess preference, measured by the relative response rates to the two stimuli. In Experiment 1, the concurrently available variable-interval 20-s schedule operated normally; that is, reinforcer availability was not signaled. Following this baseline training, relative response rate during the probes favored the variable-interval 40-s alternative that had been paired with the lower valued schedule (i.e., with the variable-interval 80-s schedule). In Experiment 2, a signal for reinforcer availability was added to the high-value alternative (i.e., to the variable-interval 20-s schedule), thus reducing the rate of key pecking maintained by that schedule but leaving the reinforcement rate unchanged. Following that baseline training, relative response rates during probes favored the variable-interval 40-s alternative that had been paired with the higher valued schedule. The reversal in the pattern of preference implies that the pattern of changeover behavior established during training, and not reinforcement rate, determined the preference patterns obtained on the probe tests.     

Robust estimation of ability in the Rasch model

Estimating ability parameters in latent trait models in general, and in the Rasch model in particular is almost always hampered by noise in the data. This noise can be caused by guessing, inattention to easy questions, and other factors which are unrelated to ability. In this study several alternative formulations which attempt to deal with these problems without a reparameterization are tested through a Monte Carlo simulation. It was found that although no one of the tested schemes is uniformly superior to all others, a modified jackknife stood out as the best one in general, it was also super efficient (more efficient than the asymptotically optimal estimator) for tests with forty or fewer items. It is proposed that this sort of jackknifing scheme for estimating ability be considered for practical work.This research was funded through a grant from the Law Enforcement Assistance Administration (78-NI-AX-0047) to the Bureau of Social Science Research, Howard Wainer, Principal Investigator. We would like to thank Ronald Mead, Anne Morgan and James Ramsay for kind, generous, and invaluable help at various stages of the project.     

A bayesian approach to confirmatory factor analysis

Confirmatory factor analysis is considered from a Bayesian viewpoint, in which prior information on parameter is incorporated in the analysis. An iterative algorithm is developed to obtain the Bayes estimates. A numerical example based on longitudinal data is presented. A simulation study is designed to compare the Bayesian approach with the maximum likelihood method.Computer facilities were provided by the Computer Services Center, The Chinese University of Hong Kong.     

Choice as a function of local versus molar reinforcement contingencies.

Rats were trained on a discrete-trial probability learning task. In Experiment 1, the molar reinforcement probabilities for the two response alternatives were equal, and the local contingencies of reinforcement differentially reinforced a win-stay, lose-shift response pattern. The win-stay portion was learned substantially more easily and appeared from the outset of training, suggesting that its occurrence did not depend upon discrimination of the local contingencies but rather only upon simple strengthening effects of individual reinforcements. Control by both types of local contingencies decreased with increases in the intertrial interval, although some control remained with intertrial intervals as long as 30 s. In Experiment 2, the local contingencies always favored win-shift and lose-shift response patterns but were asymmetrical for the two responses, causing the molar reinforcement rates for the two responses to differ. Some learning of the alternation pattern occurred with short intertrial intervals, although win-stay behavior occurred for some subjects. The local reinforcement contingencies were discriminated poorly with longer intertrial intervals. In the absence of control by the local contingencies, choice proportion was determined by the molar contingencies, as indicated by high exponent values for the generalized matching law with long intertrial intervals, and lower values with short intertrial intervals. The results show that when molar contingencies of reinforcement and local contingencies are in opposition, both may have independent roles. Control by molar contingencies cannot generally be explained by local contingencies.