Motivation Science最新文献

People sometimes inflict harm with the intent to help the very target of their aggression. Across six studies (N=1,527), we examined the nature of such prosocial aggression. Many participants believed that altruistically-motivated aggression exists and exhibited a self-serving bias in which most believed their aggression was more altruistic than others' - beliefs that were linked to greater antisocial and prosocial traits. Translating these beliefs to behavior, participants were often prosocially-aggressive when given the chance - inflicting more harm when their aggression could also help (versus only hurt) the target. Such prosocial aggression also exhibited features of both aggression (i.e., it was positively correlated with dispositional aggressiveness) and altruism (i.e., it was preferentially doled out to people who had been previously kind to participants and even when it was personally costly to do so). We also independently varied the amount of harm and help that prosocial aggression provided, revealing that participants sought to maximize the help and minimize the harm done to people who had been kind to them but not towards those who had provoked them. Our findings argue against models that conceptualize harm- and help-based motives as opponent processes, showing that these motives readily coexist and dynamically interact to shape aggressive behavior - even towards the same target.

According to expectancy-value theories of motivation, individuals choose to pursue tasks that they expect to succeed at and find personally valuable. Historically, researchers have often suggested that these two factors interact to motivate behavior. However, expectancy × value interactions are rarely observed in empirical research and, when detected, they are often small in magnitude. Does this mean they can safely be ignored in models of motivation? In this paper we conduct two power analyses with simulated data to argue that expectancy × value interactions are likely far more important than a straightforward interpretation of effect sizes would suggest, and that downplaying them risks oversimplifying theory and recommendations for intervention. Specifically, Study 1 demonstrates that a realistic combination of three constraints (measurement error, skew, and correlation) can negatively bias expectancy × value interaction estimates by more than 50%. Study 2 shows that these interactions can create meaningful variability in motivation interventions and may contribute to a better understanding of treatment heterogeneity.

The Transtheoretical Model of Health Behavior Change (TTM) is a leading theoretical framework of motivation for healthful lifestyle modification and has been employed nationally and internationally within the civilian sector for decades. The TTM has demonstrated effectiveness in reducing the public health burden related to various chronic diseases that are largely preventable through successful health behavior change intervention. Because the VA healthcare system (VA) is committed to providing quality care to Veterans who, all too often, suffer from complex physical and psychological comorbidities, it is critical to reduce Veterans' unhealthy behaviors while also helping them adopt and sustain adaptive health behaviors. TTM interventions are typically delivered remotely via computer or mobile devices using Expert Systems (ES) programs (TTM-ES). As such, TTM-ES offers the VA an opportunity to access a larger number of Veterans and provide a variety of care choices that can fit into their personal life context. While the VA already utilizes numerous computer- based behavior change applications for a variety of psychological and physical health conditions, the TTM-ES is comprised of unique characteristics that keeps it at the forefront of effective health behavior change interventions. TTM-ES, now referred to as computer tailored interventions (CTIs), are individually tailored for each Veteran, based on initial and ongoing assessment of their degree of motivation for change, and utilizes evidence- based algorithms to provide the Veteran with feedback to synergistically move them toward health behavior modification. Further development, testing, implementation and dissemination of the TTM framework and TTM-CTIs for Veterans are discussed.