Stimulus generalization as a mechanism for learning to trust

Computational neuroscience approaches to social cognition

How do we form impressions of people and groups and use these representations to guide our actions? From its inception, social neuroscience has sought to illuminate such complex forms of social cognition, and recently these efforts have been invigorated by the use of computational modeling. Computational modeling provides a framework for delineating specific processes underlying social cognition and relating them to neural activity and behavior. We provide a primer on the computational modeling approach and describe how it has been used to elucidate psychological and neural mechanisms of impression formation, social learning, moral decision making, and intergroup bias.

Pervasive influence of idiosyncratic associative biases during facial emotion recognition

Facial morphology has been shown to influence perceptual judgments of emotion in a way that is shared across human observers. Here we demonstrate that these shared associations between facial morphology and emotion coexist with strong variations unique to each human observer. Interestingly, a large part of these idiosyncratic associations does not vary on short time scales, emerging from stable inter-individual differences in the way facial morphological features influence emotion recognition. Computational modelling of decision-making and neural recordings of electrical brain activity revealed that both shared and idiosyncratic face-emotion associations operate through a common biasing mechanism rather than an increased sensitivity to face-associated emotions. Together, these findings emphasize the underestimated influence of idiosyncrasies on core social judgments and identify their neuro-computational signatures.

Adding Multimedia Cues to Medical Providers' Online Biographies: Do Pictures, Video, and B-Roll Matter?

Developing engaging online healthcare provider biographies for prospective patients to consult is becoming a big business. Health-care systems are beginning to add more multichannel components-like videos-to these provider search tools at increasingly higher costs. Prior research finds that prospective patients desire videos offering additional footage (i.e., b-roll) displaying how a provider might interact with a patient during a consultation. Shooting and editing b-roll footage to add to providers' videos takes additional time and resources. Using predictions and questions generated from uncertainty reduction and cue summation theories, this study sought to determine whether the addition of multichannel media (e.g., a video with added b-roll) offers any greater effects in the form of reduced uncertainty, provider likability, patient satisfaction, or trust than biographies providing fewer multichannel cues. A between-subjects online experiment randomly exposed participants (n = 523) to one of four biographies with increasing levels of multichannel components (i.e., text only biography, text with picture, video, video with b-roll). Results revealed that the two video biographies generated more significant effects than text-only biographies. However, the biography with just the video of the provider (i.e., a-roll only) and the video including added b-roll were not significantly different from one another on any of the dependent variables measured. These findings indicate that there may be a limit to the number of multichannel components needed to be an effective biography to help patients select their next healthcare provider. Both theoretical and practical implications of these results are discussed, in addition to future directions for research.