The social facilitation of eating plays a significant role in influencing individuals’ eating decisions. However, how social eating cues are processed in health promotion messages is unclear. This study examined individuals’ food craving in response to social cues in images and emotional experiences, perceived threat, perceived efficacy, behavioral intentions, and motivational coactivation elicited by social eating cues in obesity prevention fear appeals. Results suggested that the presence of a group of people eating in an image facilitated food craving for the (...) presented foods. Moreover, fear appeals that presented obesity and its consequences with more social eating cues, versus individual eating cues, generated greater positive emotional responses, perceived threat severity, response and self-efficacy, and motivational coactivation indicating more attention and threat vigilance. However, these cues also generated fewer self-reported intentions to change unhealthy eating behaviors. Implications and suggestions for future research are discussed. (shrink)

This study examined whether the stability of highly relevant animate and inanimate information predicted encoding. Participants viewed audiovisual media and completed a change detection task of screenshots taken from the viewing session. The screenshots were either left as originally viewed or a factor was altered. The factors were all motivationally and story relevant. Half were part of an animal and half were part of other environmental information. This was crossed with whether the information was stable or fleeting in the scene. (...) Changes to animals were more recognized than inanimate information. Changes to fleeting inanimate information were better recognized than changes to stable inanimate information. These findings indicate potential for relevant change in environmental threat and opportunity is adaptively significant and likely to increase attention and encoding across animate and inanimate categories of information. (shrink)

Effective human–robot teaming increasingly requires humans to work with intelligent, autonomous machines. However, novel features of intelligent autonomous systems such as social agency and incomprehensibility may influence the human’s trust in the machine. The human operator’s mental model for machine functioning is critical for trust. People may consider an intelligent machine partner as either an advanced tool or as a human-like teammate. This article reports a study that explored the role of individual differences in the mental model in a simulated (...) environment. Multiple dispositional factors that may influence the dominant mental model were assessed. These included the Robot Threat Assessment, which measures the person’s propensity to apply tool and teammate models in security contexts. Participants were paired with an intelligent robot tasked with making threat assessments in an urban setting. A transparency manipulation was used to influence the dominant mental model. For half of the participants, threat assessment was described as physics-based ; the remainder received transparency information that described psychological cues. We expected that the physics-based transparency messages would guide the participant toward treating the robot as an advanced machine, while psychological messaging would encourage perceptions of the robot as acting like a human partner. We also manipulated situational danger cues present in the simulated environment. Participants rated their trust in the robot’s decision as well as threat and anxiety, for each of 24 urban scenes. They also completed the RoTA and additional individual-difference measures. Findings showed that trust assessments reflected the degree of congruence between the robot’s decision and situational danger cues, consistent with participants acting as Bayesian decision makers. Several scales, including the RoTA, were more predictive of trust when the robot was making psychology-based decisions, implying that trust reflected individual differences in the mental model of the robot as a teammate. These findings suggest scope for designing training that uncovers and mitigates the individual’s biases toward intelligent machines. (shrink)