Predicting behavior change from persuasive messages using neural representational similarity and social network analyses.

Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition.

Development and Validation of the Transgender Attitudes and Beliefs Scale.

In recent years, issues surrounding transgender have garnered media and legal attention, contributing to rapidly shifting views on gender in the U.S. Yet, there is a paucity of data-driven studies on the public's views of transgender identity. This study reports the development and validation of the Transgender Attitudes and Beliefs Scale (TABS). After constructing an initial 96-item pool from consulting experts and existing scales, Phase 1 of the study was launched, involving an exploratory factor analysis of 48 items. The initial factor analysis with 295 participants revealed three factors across 33 items-16 items on interpersonal comfort, 11 on sex/gender beliefs, and 6 on human value. The internal consistency of each factor was high-α = .97 for Factor 1, α = .95 for Factor 2, and α = .94 for Factor 3. A confirmatory factor analysis was conducted in the second phase with an independent sample consisting of 238 participants. The Attitudes Toward Transgender Individual Scale and the Genderism and Transphobia Scale were also included to test for convergent validity, and the Rosenberg Self-Esteem Scale and the short form of the Marlowe-Crowne Social Desirability Scale were utilized to test discriminant validity. Both of the data collection phases employed MTurk, a form of online sampling with increased diversity compared to college student samples and more generalizability to the general U.S. POPULATION: TABS represents an addition to the literature in its ability to capture a more nuanced conceptualization of transgender attitude not found in previous scales.

Simultaneous perceptual and response biases on sequential face attractiveness judgments.

Face attractiveness is a social characteristic that we often use to make first-pass judgments about the people around us. However, these judgments are highly influenced by our surrounding social world, and researchers still understand little about the mechanisms underlying these influences. In a series of 3 experiments, we use a novel sequential rating paradigm that enables us to measure biases in attractiveness judgments from the previous face and the previous rating. Our results reveal 2 simultaneous and opposing influences on face attractiveness judgments that arise from past experience of faces: a response bias in which attractiveness ratings shift toward a previously given rating and a stimulus bias in which attractiveness ratings shift away from the mean attractiveness of the previous face. Further, we provide evidence that the contrastive stimulus bias (but not the assimilative response bias) is strengthened by increasing the duration of the previous stimulus, suggesting an underlying perceptual mechanism. These results demonstrate that judgments of face attractiveness are influenced by information from our evaluative and perceptual history and that these influences have measurable behavioral effects over the course of just a few seconds.

Common and unique representations in pFC for face and place attractiveness.

Although previous neuroimaging research has identified overlapping correlates of subjective value across different reward types in the ventromedial pFC (vmPFC), it is not clear whether this "common currency" evaluative signal extends to the aesthetic domain. To examine this issue, we scanned human participants with fMRI while they made attractiveness judgments of faces and places-two stimulus categories that are associated with different underlying rewards, have very different visual properties, and are rarely compared with each other. We found overlapping signals for face and place attractiveness in the vmPFC, consistent with the idea that this region codes a signal for value that applies across disparate reward types and across both economic and aesthetic judgments. However, we also identified a subregion of vmPFC within which activity patterns for face and place attractiveness were distinguishable, suggesting that some category-specific attractiveness information is retained in this region. Finally, we observed two separate functional regions in lateral OFC: one region that exhibited a category-unique response to face attractiveness and another region that responded strongly to faces but was insensitive to their value. Our results suggest that vmPFC supports a common mechanism for reward evaluation while also retaining a degree of category-specific information, whereas lateral OFC may be involved in basic reward processing that is specific to only some stimulus categories.

Experimental manipulation of face-evoked activity in the fusiform gyrus of individuals with autism.

Previous research suggests hypoactivity in response to the visual perception of faces in the fusiform gyri and amygdalae of individuals with autism. However, critical questions remain regarding the mechanisms underlying these findings. In particular, to what degree is the hypoactivation accounted for by known differences in the visual scanpaths exhibited by individuals with and without autism in response to faces? Here, using functional magnetic resonance imaging, we report "normalization" of activity in the right fusiform gyrus, but not the amygdalae, when individuals with autism were compelled to perform visual scanpaths that involved fixating on the eyes of a fearful face. These findings hold important implications for our understanding of social brain dysfunction in autism, theories of the role of the fusiform gyri in face processing, and the design of more effective interventions for autism.

Methods for quantifying the informational structure of sensory and motor data.

Embodied agents (organisms and robots) are situated in specific environments sampled by their sensors and within which they carry out motor activity. Their control architectures or nervous systems attend to and process streams of sensory stimulation, and ultimately generate sequences of motor actions, which in turn affect the selection of information. Thus, sensory input and motor activity are continuously and dynamically coupled with the surrounding environment. In this article, we propose that the ability of embodied agents to actively structure their sensory input and to generate statistical regularities represents a major functional rationale for the dynamic coupling between sensory and motor systems. Statistical regularities in the multimodal sensory data relayed to the brain are critical for enabling appropriate developmental processes, perceptual categorization, adaptation, and learning. To characterize the informational structure of sensory and motor data, we introduce and illustrate a set of univariate and multivariate statistical measures (available in an accompanying Matlab toolbox). We show how such measures can be used to quantify the information structure in sensory and motor channels of a robot capable of saliency-based attentional behavior, and discuss their potential importance for understanding sensorimotor coordination in organisms and for robot design.