A comparison of procedures for unpairing conditioned reflexive motivating operations

This study compared the effectiveness of two procedures to reduce behavior evoked by a reflexive conditioned motivating operation (CMO‐R). Task demands were shown to evoke escape‐maintained problem behavior for 4 students with disabilities. Alternative communication responses were taught as an appropriate method to request escape and this treatment combined with extinction for problem behavior led to decreases in problem behavior for all students. A beeping timer was then arranged to temporally precede the task demand to create a CMO‐R that evoked communication responses. When data showed that the sound of the timer was functioning as a CMO‐R, two methods to reduce behavior evoked by a CMO‐R—extinction unpairing and noncontingent unpairing—were evaluated. Results indicated that noncontingent unpairing was an effective method to reduce the evocative effects of the CMO‐R. Extinction produced unsystematic effects across participants. Results are discussed in terms of abolishing CMOs and the implications of CMOs.     

Moral Emotions Toward Others at Work and Implications for Employee Behavior: a Qualitative Analysis Using Critical Incidents

We propose a theoretical framework for and, via critical incidents, inductively investigate (a) the situations that trigger moral emotions toward others at work and (b) worker responses to these situations. Critical incidents were collected from a heterogeneous sample of 423 workers. Participants described an incident that caused each of four moral emotions (gratitude, anger, admiration, and contempt) and their response. Incidents and responses were coded, and frequencies, differences across the moral emotions, and associations between incidents and responses were analyzed. Several morally relevant situations at work were found to trigger the moral emotions. Participants responded in ways that had implications for their performance and their own well-being. The incidents largely align with moral foundations theory (Haidt and Graham in Social Justice Research, 20, 98–116, 2007). Responses also show evidence for social exchange, social learning, and rational expectancy-based processes and behavior. The incidents specify several practices for managers and organizations to encourage and avoid. This was the first qualitative study of moral emotions at work and one of the few workplace studies to examine these emotions empirically. Several understudied factors and behavioral responses emerged.     

Collaboration facilitates abstract category learning

We examined the effects of collaboration (dyads vs. individuals) and category structure (coherent vs. incoherent) on learning and transfer. Working in dyads or individually, participants classified examples from either an abstract coherent category, the features of which are not fixed but relate in a meaningful way, or an incoherent category, the features of which do not relate meaningfully. All participants were then tested individually. We hypothesized that dyads would benefit more from classifying the coherent category structure because past work has shown that collaboration is more beneficial for tasks that build on shared prior knowledge and provide opportunities for explanation and abstraction. Results showed that dyads improved more than individuals during the classification task regardless of category coherence, but learning in a dyad improved inference-test performance only for participants who learned coherent categories. Although participants in the coherent categories performed better on a transfer test, there was no effect of collaboration.     

Category-length and category-strength effects using images of scenes

Global matching models have provided an important theoretical framework for recognition memory. Key predictions of this class of models are that (1) increasing the number of occurrences in a study list of some items affects the performance on other items (list-strength effect) and that (2) adding new items results in a deterioration of performance on the other items (list-length effect). Experimental confirmation of these predictions has been difficult, and the results have been inconsistent. A review of the existing literature, however, suggests that robust length and strength effects do occur when sufficiently similar hard-to-label items are used. In an effort to investigate this further, we had participants study lists containing one or more members of visual scene categories (bathrooms, beaches, etc.). Experiments 1 and 2 replicated and extended previous findings showing that the study of additional category members decreased accuracy, providing confirmation of the category-length effect. Experiment 3 showed that repeating some category members decreased the accuracy of nonrepeated members, providing evidence for a category-strength effect. Experiment 4 eliminated a potential challenge to these results. Taken together, these findings provide robust support for global matching models of recognition memory. The overall list lengths, the category sizes, and the number of repetitions used demonstrated that scene categories are well-suited to testing the fundamental assumptions of global matching models. These include (A) interference from memories for similar items and contexts, (B) nondestructive interference, and (C) that conjunctive information is made available through a matching operation.     

The natural mathematics of behavior analysis

Models that generate event records have very general scope regarding the dimensions of the target behavior that we measure. From a set of predicted event records, we can generate predictions for any dependent variable that we could compute from the event records of our subjects. In this sense, models that generate event records permit us a freely multivariate analysis. To explore this proposition, we conducted a multivariate examination of Catania's Operant Reserve on single VI schedules in transition using a Markov Chain Monte Carlo scheme for Approximate Bayesian Computation. Although we found systematic deviations between our implementation of Catania's Operant Reserve and our observed data (e.g., mismatches in the shape of the interresponse time distributions), the general approach that we have demonstrated represents an avenue for modelling behavior that transcends the typical constraints of algebraic models.     

Latent Growth Curve and Repeated Measures ANOVA Contrasts: What the Models are Telling You

The relationship between the latent growth curve and repeated measures ANOVA models is often misunderstood. Although a number of investigators have looked into the similarities and differences among these models, a cursory reading of the literature can give the impression that they are very different models. Here we show that each model represents a set of contrasts on the occasion means. We demonstrate that the fixed effects parameters of the estimated basis vector latent growth curve model are merely a transformation of the repeated measures ANOVA fixed effects parameters. We further show that differences in fit in models that estimate the same means structure can be due to the different error covariance structures implied by the model. We show these relationships both algebraically and through using data from a simulation.     

Detecting Clusters/Communities in Social Networks

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.