Escalating schedules of incentives increase physical activity with no differences between deposit and no-deposit groups: A systematic replication

Physical inactivity has increasingly affected public health in the United States during the COVID-19 pandemic as it is associated with chronic diseases such as arthritis, cancer, and heart disease. Contingency management has been shown to increase physical activity. Therefore, the present study sought to evaluate the effects of an escalating schedule of monetary reinforcement with a reset contingency on physical activity, as compared between 2 counterbalanced groups in which a monetary deposit of $25 was either required (deposit group) or not (no-deposit group). Twenty-five adults wore Fitbit accelerometers to monitor step counts. An ABA reversal design was used; in the 2 baseline phases, no programmed contingencies were in place for step counts. During intervention, step goals were set using a modified 70^th percentile schedule with a 7-day window: Reaching the first goal would result in $0.25, and incentives increased by $0.25 for each subsequent day in which the goal was met. Failure to reach a goal resulted in a reset of the monetary incentive value to $0.25. Ten out of 12 participants from the deposit group were determined to be responders to intervention, whereas 8 out of 13 participants from the no-deposit group were determined to be responders to intervention. Overall, there were no significant differences between the groups' step counts. However, the deposit group's intervention was cheaper to implement, which suggests that deposit contracts are a viable modification for physical activity interventions.     

Cultural consensus theory for multiple consensus truths

Cultural Consensus Theory (CCT) is a popular information pooling methodology used in the social and behavioral sciences. CCT consists of cognitive models designed to determine a consensus truth shared by a group of informants (respondents), and to better understand the cognitive characteristics of the informants (e.g. level knowledge, response biases). However prior to this paper, no CCT models have been developed that allow the possibility of the informant responses to come from a mixture of two or more consensus answer patterns. The major advance in the current paper is to endow one of the popular CCT models, the General Condorcet Model (GCM) for dichotomous responses, with the possibility of having several latent consensus answer patterns, each corresponding to a different, latent subgroup of informants. In addition, we augment the model to allow the possibility of questions having differential difficulty (cultural saliency). This is the first CCT finite-mixture model, and it is named the Multi-Culture GCM (MC-GCM). The model is developed axiomatically and a notable property is derived that can suggest the appropriate number of mixtures for a given data set. The model is extended in a hierarchical Bayesian framework and its application is successfully demonstrated on both simulated and real data, including a new experimental data set on political viewpoints.     

The Relationship between Creative Personality Composition,Innovative Team Climate,and Team Innovativeness: An Input — Process — Output Perspective

This study investigates the relationship between creative personality composition, innovative team climate, and team innovation based on an input‐process‐output model. We measured personality with the Creative Person Profile, team climate with the Team Climate Inventory, and team innovation through team‐member and supervisor reports of team innovativeness. The personality composition in each of 29 teams in a television production company was operationalized by mean scores for each creative personality variable, as well as the combination of different creative personality variables within a team. The team climate variable “vision” mediated the relationship between the mean level of associative orientation in teams and team innovation. The team climate variable “support of innovation” mediated the relationship between the joint variables of mean level of ambition x mean level of motivation and team innovation. The results indicated that when there are relationships between creative personality composition and team innovativeness, they are mediated by an innovative team climate.     

Schizotypal and dependent personality characteristics and managerial performance

The study of negatively connotated personality characteristics as predictors of work‐related performance is gaining momentum. While findings have generally suggested such characteristics to be negatively or curvilinearly related to performance, the current study was concerned with investigating any positive relationships that may exist between negatively connotated personality characteristics (as measured by the Hogan Development Survey) and work‐related performance in a sample of 103 Australasian financial services managers. The most notable findings from the study were that: (a) schizotypal personality characteristics positively predicted performance on a creativity competency; and (b) dependent personality characteristics positively predicted performance on a customer focus competency. Findings and limitations are discussed.     

Assessing individual differences in categorical data

In cognitive modeling, data are often categorical observations taken over participants and items. Usually subsets of these observations are pooled and analyzed by a cognitive model assuming the category counts come from a multinomial distribution with the same model parameters underlying all observations. It is well known that if there are individual differences in participants and/or items, a model analysis of the pooled data may be quite misleading, and in such cases it may be appropriate to augment the cognitive model with parametric random effects assumptions. On the other hand, if random effects are incorporated into a cognitive model that is not needed, the resulting model may be more flexible than the multinomial model that assumes no heterogeneity, and this may lead to overfitting. This article presents Monte Carlo statistical tests for directly detecting individual participant and/or item heterogeneity that depend only on the data structure itself. These tests are based on the fact that heterogeneity in participants and/or items results in overdispersion of certain category count statistics. It is argued that the methods developed in the article should be applied to any set of participant 3 item categorical data prior to cognitive model-based analyses.     

Cognitive psychometrics: assessing storage and retrieval deficits in special populations with multinomial processing tree models

This article demonstrates how multinomial processing tree models can be used as assessment tools to measure cognitive deficits in clinical populations. This is illustrated with a model developed by W. H. Batchelder and D. M. Riefer (1980) that separately measures storage and retrieval processes in memory. The validity of the model is tested in 2 experiments, which show that presentation rate affects the storage of items (Experiment 1) and part-list cuing hurts item retrieval (Experiment 2). Experiments 3 and 4 examine 2 clinical populations: schizophrenics and alcoholics with organic brain damage. The model reveals that each group exhibits deficits in storage and retrieval, with the retrieval deficits being stronger and occurring more consistently over trials. Also, the alcoholics with organic brain damage show no improvement in retrieval over trials, although their storage improves at the same rate as a control group.     

Parameter identification in multinomial processing tree models

Multinomial processing tree models form a popular class of statistical models for categorical data that have applications in various areas of psychological research. As in all statistical models, establishing which parameters are identified is necessary for model inference and selection on the basis of the likelihood function, and for the interpretation of the results. The required calculations to establish global identification can become intractable in complex models. We show how to establish local identification in multinomial processing tree models, based on formal methods independently proposed by Catchpole and Morgan (1997) and by Bekker, Merckens, and Wansbeek (1994). This approach is illustrated with multinomial processing tree models for the source-monitoring paradigm in memory research.