Psychological distance to reward: A human replication

Choice behavior in college students was examined in two experiments using the concurrent-chains procedure. In both experiments, the concurrent chains were presented on a microcomputer in the form of an air-defense game in which subjects used two radar systems to detect and subsequently destroy enemy aircraft. Access to one of two radar systems was controlled by a pair of independent concurrent variable-interval 60-s schedules with a 4-s changeover delay always in effect. In the terminal link, the appearance of an enemy aircraft was determined by a pair of differentially segmented fixed-time schedules (Experiment 1) or fixed-interval schedules (Experiment 2) of equal overall duration. In Experiment 1, the terminal-link duration was either 20 s or 40 s, and subjects preferred the unsegmented to the segmented intervals. In Experiment 2, the duration was either 10 s or 60 s, and the reinforcement contingencies required responding during the terminal link. Prior to the reinstatement of the initial link, subjects estimated the duration of the terminal-link schedule. Segmentation affected choice in the 60-s conditions but not in the 10-s ones. Preference for the unsegmented schedule was correlated with an overestimation of the durations for the segmented schedules. These results replicated those found in animal experiments and support the notion of increasing the psychological distance to reward by segmenting a time-based schedule of reinforcement.     

Statistics as psychometrics

In this paper, modern statistics is considered as a branch of psychometrics and the question of how the central problems of statistics can be resolved using psychometric methods is investigated. Theories and methods developed in the fields of test theory, scaling, and factor analysis are related to the principle problems of modern statistical theory and method. Topics surveyed include assessment of probabilities, assessment of utilities, assessment of exchangeability, preposterior analysis, adversary analysis, multiple comparisons, the selection of predictor variables, and full-rank ANOVA. Reference is made to some literature from the field of cognitive psychology to indicate some of the difficulties encountered in probability and utility assessment. Some methods for resolving these difficulties using the Computer-Assisted Data Analysis (CADA) Monitor are described, as is some recent experimental work on utility assessment.1980 Psychometric Society presidential address.I am indebted to Paul Slovic and David Libby for valuable consultation on the issues discussed in this paper and to Nancy Turner and Laura Novick for assistance in preparation.Research reported herein was supported under contract number N00014-77-C-0428 from the Office of Naval Research to The University of Iowa, Melvin R. Novick, principal investigator. Opinions expressed herein reflect those of the author and not those of sponsoring agencies.     

Effects of computer‐based training on procedural modifications to standard functional analyses

Few studies have evaluated methods for training decision‐making when functional analysis data are undifferentiated. The current study evaluated computer‐based training to teach 20 graduate students to arrange functional analysis conditions, analyze functional analysis data, and implement procedural modifications. Participants were exposed to training materials using interactive software during a 1‐day session. Following the training, mean scores on the posttest, novel cases probe, and maintenance probe increased for all participants. These results replicate previous findings during a 1‐day session and include a measure of participant acceptability of the training. Recommendations for future research on computer‐based training and functional analysis are discussed.     

Learning from exemplars: emulation,character formation and the complexities of ordinary life

In the field of character education role-modelling is advocated as an important pedagogical strategy. It is supposed that students learn from ‘significant others’ who exemplify important virtues and values. However, in these strategies it is not clear what and how students precisely can and should learn from exemplars and how the following of exemplars relates to the educational aim of ‘becoming a self.’ In this article, it is argued that modelling is only a relevant pedagogical strategy if moral exemplars are somehow related to life in its full extent, including its moral complexities and ambiguities. Understanding moral exemplarity demands not only the reappropriation of an Aristotelian conception of emulation but also an understanding of the typical modern relocation of moral exemplarity in the fullness of life, importantly originating from what Charles Taylor calls the Protestant ‘affirmation of ordinary life.’ In addition, a distinction is introduced between ‘role exemplarity’ and ‘existential exemplarity.’ Based on this distinction it is argued that emulation should not be limited to learning from role models, but should also include something that runs deeper, to the depth of our own subjectivity or self.     

“What Makes a Cooperative Driver?” Identifying parameters of implicit and explicit forms of communication in a lane change scenario

An automated vehicle needs to learn how human road users communicate with each other in order to avoid misunderstandings and prevent giving a negative outward image during interactions. The aim of the present work is to develop an autonomous driving system which communicates its intentions to change lanes based on implicit and explicit rules used by human drivers. To reach this goal, we aimed at gaining a deeper understanding of which aspects of lane change behaviour makes them cooperative from the perspective of other drivers. Therefore a vehicle used various lane change announcement strategies by varying combinations of driving parameters in a static driving simulator. (First study: Start indicator signal, Waittime, lane change duration; Second study: Longitudinal acceleration). It’s impact on the perception and behaviour of other road users was observed in two studies (N = 25 per study). The results showed that the earlier the merging vehicle was indicating its intentions, the more cooperative it was perceived. When turning on the indicator at a later time participants considered it as more cooperative to merge with a slower or faster lane change duration or to wait longer in the lane before starting to move to the other lane. An early longitudinal acceleration when starting to change lanes is perceived more cooperative. These findings can be used to model a lane change strategy based on human behaviour, which will eventually lead to more acceptable and safer interactions between automated and non-automated road users.     

Authentic leadership,followership, and psychological capital as antecedents of work engagement

The present study investigated authentic leadership, psychological capital, and followership behaviour influences on work engagement of employees. Respondents were 901 South African employees within the healthcare industry organisation (n = 647) and mining industry (n = 254). The employees completed questionnaires on authentic leadership, psychological capital, and followership behaviour, and work engagement. Results following structural equation modelling and mediation analysis suggest work engagement to be explained by the psychological capital of the employee rather than by authentic leadership qualities. Improving employee psychological capital has the potential to enhance the levels of work engagement of employees.     

学龄早期词语阅读习得对阅读理解能力的预测作用：初始水平和发展速率的贡献

本研究测查了183名儿童在一年级期间（学年初,学年中,学年末）的词语阅读能力（词语解码,阅读流畅性）及一年级末和二年级末的阅读理解能力,采用分层线性模型估计每名儿童在一年级期间词语阅读能力的初始水平和发展速率,以考察二者对阅读理解能力的独立预测作用。结果表明,词语识别的初始水平和增长速率都能独立预测一年级末和二年级末的阅读理解能力,阅读流畅性的初始水平和增长速率只能独立预测二年级末的阅读理解能力,但可以预测阅读理解能力在一年级到二年级的增长。     

Do complex models increase prediction of complex behaviours? Predicting driving ability in people with brain disorders

Prediction of complex behavioural tasks via relatively simple modelling techniques, such as logistic regression and discriminant analysis, often has limited success. We hypothesized that to more accurately model complex behaviour, more complex models, such as kernel-based methods, would be needed. To test this hypothesis, we assessed the value of six modelling approaches for predicting driving ability based on performance on computerized sensory–motor and cognitive tests (SMCTests?) in 501 people with brain disorders. The models included three models previously used to predict driving ability (discriminant analysis, DA; binary logistic regression, BLR; and nonlinear causal resource analysis, NCRA) and three kernel methods (support vector machine, SVM; product kernel density, PK; and kernel product density, KP). At the classification level, two kernel methods were substantially more accurate at classifying on-road pass or fail (SVM 99.6%, PK 99.8%) than the other models (DA 76%, BLR 78%, NCRA 74%, KP 81%). However, accuracy decreased substantially for all of the kernel models when cross-validation techniques were used to estimate prediction of on-road pass or fail in an independent referral group (SVM 73–76%, PK 72–73%, KP 71–72%) but decreased only slightly for DA (74–75%) and BLR (75–76%). Cross-validation of NCRA was not possible. In conclusion, while kernel-based models are successful at modelling complex data at a classification level, this is likely to be due to overfitting of the data, which does not lead to an improvement in accuracy in independent data over and above the accuracy of other less complex modelling techniques.