Stimulus discriminability in free-operant and discrete-trial detection procedures.

Six pigeons were trained to discriminate different light intensities in four experimental procedures. Experiment 1 compared stimulus discriminability in a yes-no signal-detection task with discriminability measures obtained from two free-operant procedures. Discriminability estimates were significantly lower in the detection procedure. Experiment 2 showed this lowered discriminability to be a function of the delay between stimulus presentation and the availability of the choice-response keys in the standard detection task. In addition, reinforcement sensitivity was lowest when correct choice responses were intermittently, rather than continuously, reinforced.     

Methodological and theoretical issues in neuropsychological assessment

While there are philosophical and theoretical differences between neuropsychologists and behavior therapists, it is suggested that a significant reconciliation can be achieved in regard to assessment and treatment of the brain-damaged patient. While brain damage is often viewed as an irreversible disorder, there is evidence that substantial recovery of function can occur, particularly when adequate retraining is provided. There are practical and empirical considerations that suggest that neuropsychological tests are currently the best tools available for behavioral assessment of brain-damaged patients. However, the results of these tests can be used not only diagnostically but also to identify target behavioral deficits that may be rehabilitated through systematic retraining efforts. Such rehabilitation efforts can be optimally planned, implemented, and evaluated through an alliance between the neuropsychologist, who identifies the ability and deficit pattern, and the behavior therapist, who devises and evaluates the retraining program. It is possible that the successes of behavior therapy in numerous clinical and educational applications can be repeated in the cases of brain-damaged patients. However, such success would appear to be contingent on appreciation of the high degree of specificity often seen in neurological deficit patterns and the great complexities involved in the relationships between brain function and behavior.     

Considerations in the choice of interobserver reliability estimates

Two types of interobserver reliability values may be needed in treatment studies in which observers constitute the primary data-acquisition system: trial reliability and the reliability of the composite unit or score which is subsequently analyzed, e.g., daily or weekly session totals. Two approaches to determining interobserver reliability are described: percentage agreement and "correlational" measures of reliability. The interpretation of these estimates, factors affecting their magnitude, and the advantages and limitations of each approach are presented.     

Two simple classes of mastery scores based on the beta-binomial model

Two simple classes of mastery scores which are suitable for hand calculations are presented for beta-binomial test score distributions combined with linear and cubic referral success. The models provide a simple way to explore the consequences of selecting an arbitrary mastery score. Such assessment would be useful whenever the test user is not willing to posta priori a loss ratio, but wishes to look at the various consequences before aiming at a particular score.     

Fitting and testing carroll's weighted unfolding model for preferences

A quadratic programming algorithm is presented for fitting Carroll's weighted unfolding model for preferences to known multidimensional scale values. The algorithm can be applied directly to pairwise preferences; it permits nonnegativity constraints on subject weights; and it provides a means of testing various preference model hypotheses. While basically metric, it can be combined with Kruskal's monotone regression to fit ordinal data. Monte Carlo results show that (a) adequacy of true preference recovery depends on the number of data points and the amount of error, and (b) the proportion of data variance accounted for by the model sometimes only approximately reflects true recovery.This study is based on a doctoral dissertation submitted to the University of Illinois at Urbana-Champaign. The author wishes to thank the members of his dissertation committee—Lawrence E. Jones, Chairman, Charles Lewis, Stephen Golding, Ledyard Tucker, and Nancy Wiggins—for their helpful comments.     

Stopping rules for multi-category computerized classification testing

Computerized classification testing (CCT) aims to classify persons into one of two or more possible categories to make decisions such as mastery/non-mastery or meet most/meet all/exceed. A defining feature of CCT is its stopping criterion: the test terminates when there is enough confidence to make a decision. There is abundant research on CCT with a single cut-off, and two common stopping criteria are the sequential probability ratio test (SPRT) statistic and the generalized likelihood ratio statistic (GLR). However, there is a relative scarcity of research extending the SPRT to the multi-hypothesis case for when there is more than one cut-off. In this paper, we propose a new multi-category GLR (mGLR) statistic as well as a stochastically curtailed version of the CCT with three or more categories. A simulation study was conducted to show that the mGLR statistic outperformed the existing stopping rules by generating shorter average test length without sacrificing classification accuracy. Results also revealed that the stochastically curtailed mGLR successfully increased test efficiency in certain testing conditions.     

A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models

Using Louis’ formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation–maximization (EM) algorithm has converged. However, Louis’ formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis’ formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis’ formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis’ formula with previously existing methods. Results from these presentations suggest that Louis’ formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility.     

I can see myself helping: The effect of self-awareness on prosocial behaviour

Many studies indicate that increasing self-awareness leads to individuals reflecting on their values and ideals (Silvia & Duval, 2001). This self-reflection appears to increase prosocial behaviour (Berkowitz, 1987). However, previously studies typically manipulated self-awareness in situations in which the individual may have felt pressure from the researcher to help. Thus, experimenter pressure to behave prosocially confounds the self-awareness explanation provided in past research. We used a novel experimental paradigm to manipulate self-awareness and remove the researcher's presence to decrease the likelihood that the participant would conform to experimenter demand. Participants were 36 college students (M_age = 19.52; 25 women). The results indicated a strong probability that the experimental condition participants were more prosocial than control condition participants. These findings provide additional support for the hypothesis that self-reflection increases prosocial behaviour, even without experimenter demands. These findings and the importance of studying objective self-awareness in light of the coronavirus are discussed.     

Modeling Asymmetry in the Time–Distance Relation of Ordinal Personality Items

In analyzing responses and response times to personality questionnaire items, models have been proposed which include the so-called “inverted-U effect.” These models predict that response times to personality test items decrease as the latent trait value of a given person gets closer to the attractiveness of an item. Initial studies into these models have focused on dichotomous personality items, and more recently, models for Likert-type scale items have been proposed. In all these models, it is assumed that the inverted-U effect is symmetrical around 0, while, as will be explained in this article, there are substantive and statistical reasons to study this assumption. Therefore, in this article, a general inverted-U model is proposed which accommodates two sources of asymmetry between the response times and the attractiveness of the items. The viability of this model is demonstrated in a simulation study, and the model is applied to the responses and response times of the Temperament and Character Inventory–Revised, covering a broad range of personality dimensions.     

Personal Contact with Refugees is Key to Welcoming Them: An Analysis of Politicians' and Citizens' Attitudes Towards Refugee Integration

Politicians are influential both in directing policies about refugees and in framing public discourse about them. However, unlike other host country residents, politicians' attitudes towards refugees and integration are remarkably understudied. We therefore examine similarities and differences between politicians' attitudes towards refugee integration and those held by citizens. Based on the stereotype content model, we expect that political ideology informs stereotypes about refugees, which subsequently shape attitudes towards refugee integration. Based on the Contact Hypothesis, we further argue that personal contact with refugees reduces negative stereotypes about them—in particular for those endorsing a right-wing ideology. We draw on data collected via two surveys with 905 politicians and 8013 citizens in the Netherlands to show that (1) unlike those with a left-wing orientation, residents (i.e., both politicians and citizens) with a right-wing orientation hold more negative stereotypes about refugees, which in turn relate to more negative attitudes towards refugee integration; (2) personal contact with refugees is associated with less negative stereotypes among residents; and (3) politicians, compared to citizens, report less negative stereotypes and more positive attitudes towards refugee integration. The practical implication of fostering residents' contact with refugees as well as the implications for future research are discussed.