Using computer conferencing to formulate a computer simulation of transitive behavior

The activities of a group engaged in simulation modeling via computer conferencing are described, and the model which resulted from those activities is briefly outlined. The phenomena which were modeled were initially described in a series of studies by Trabasso and his colleagues. These studies centered upon exhibition of transitive choices by children and adults, and supported the hypothesis that the choices were guided by formation of “linear arrays“ of the choice objects.     

Caught between Scylla and Charybdis: How Economic Stressors and Occupational Risk Factors Influence Workers’ Occupational Health Reactions to COVID-19

Workers and their families bear much of the economic burden of COVID-19. Even though they have declined somewhat, unemployment rates are considerably higher than before the start of the pandemic. Many workers also face uncertainty about their future employment prospects and increasing financial strain. At the same time, the workplace is a common source of transmission of COVID-19 and many jobs previously seen as relatively safe are now viewed as potentially hazardous. Thus, many workers face dual threats of economic stress and COVID-19 exposure. This paper develops a model of workers’ responses to these dual threats, including risk perception and resource depletion as mediating factors that influence the relationship of economic stress and occupational risk factors with COVID-19 compliance-related attitudes, safe behavior at work, and physical and mental health outcomes. The paper also describes contextual moderators of these relationships at the individual, unit, and regional level. Directions for future research are discussed.     

“Doing Good” versus “Being Good”: The interplay between pride appeals and regulatory-focused messages in green advertising

Many consumers feel proud of making green choices, which is of crucial relevance to explaining environmentally responsible behaviors. However, compared to other self-conscious emotions, such as guilt and shame, little research attention has been paid to the role of pride in green consumerism. Through conducting two online experimental surveys, this research examined what features of a message induce the two facets of the emotion pride—authentic and hubristic—and how pride appeals interact with message frames having different regulatory foci. In Study 1, participants revealed more favorable eco-friendly attitudes and intentions when hubristic pride appeals were combined with promotion-focused messages (detailing the positive benefits of using the green product), and when authentic pride appeals were matched with prevention-focused messages (emphasizing the negative consequences averted by using the green product). Study 2 replicated and supported the proposed matching hypotheses while including a control condition. Findings of this research will add to a growing body of literature on pride as a discrete emotion and its carryover effects on persuasion while providing guidelines to help practitioners design green advertising campaigns.     

Idiographic network analysis of discrete mood states prior to treatment

Idiographic network models based on time‐series data have received recent attention for their ability to model relationships among symptoms and behaviours as they unfold in time within a single individual (cf. Epskamp, Borsboom, & Fried, 2018; Fisher, Medaglia, & Jeronimus, 2018). Rather than examine the correlational relationships between variables in a sample of individuals, an idiographic network examines correlations within a single person, averaged over many time points. Because the approach averages over time, the data must be stationary (i.e. relatively consistent over time). If individuals experience varying states over time—different mixtures of symptoms and behaviours in one moment or another—then averaging over categorically different moments may undermine model accuracy. Fisher and Bosley (2019) address these concerns via the application of Gaussian finite mixture modelling to identify latent classes of time points in intraindividual time‐series data from a sample of adults with major depressive disorder and/or generalised anxiety disorder (n = 45). The present paper outlines an extension of this work, wherein network analysis is used to model within‐class covariation of symptoms. To illustrate this approach, network models were constructed for each intraindividual class identified by Fisher and Bosley (137 networks across the 45 participants, mean classes/person = ~3, range = 2–4 classes/person). We examine the relative consistency in symptom organisation between each individual's multiple mood state networks and assess emergent group‐level patterns. We highlight opportunities for enhanced treatment personalisation and review nomothetic patterns relevant to transdiagnostic conceptualisations of psychopathology. We address opportunities for integrating this approach into clinical practice and outline potential shortcomings.